instruments for modelling black sea river basins: research
TRANSCRIPT
Mamuka Gvilava Instruments for Modelling Black Sea River Basins:
Research Proceedings for Guria Region of Georgia
Integrated Land-use Management Modelling of Black Sea Estuaries ( ILMM-BSE ) Project
Implemented with the financial assistance of European Union in the framework of Black Sea Basin Joint Operational Program 2007-2013
Instruments for Modelling Black Sea River Basins: Research Proceedings for Guria Region of Georgia
ILMM-BSE Project ENPI Partner from Georgia International Association CIVITAS GEORGICA
Georgia 2015 November
This Project is funded by the European Union http://europa.eu
EU Cross-Border Cooperation Black Sea Basin Joint Operational Programme 2007-2013 http://www.blacksea-cbc.net
Integrated Land-use Management Modelling of Black Sea Estuaries ( ILMM-BSE ) Project http://e-BlackSEa.net
The Project is implemented by the following Partners: ENPI Applicant: Bourgas Regional Tourism Association (Bulgaria) ENPI Partners: Bourgas Prof. Assen Zlatarov University (Bulgaria) Ukrainian Marine Environment Protection Association UkrMEPA (Ukraine) International Association Civitas Georgica (Georgia) IPA Lead Beneficiary: Hayrabolu Municipality (Turkey) IPA Partners: Namฤฑk Kemal University (Turkey) Turkish Marine Environment Protection Association TURMEPA (Turkey)
The contents of this publication is responsibility of authors engaged by ILMM-BSE Project and International Association Civitas Georgica and can in no way be taken to reflect the views of the European Union. ISBN: 978-9941-0-8381-5 For bibliographic purposes this publication may be cited as: Instruments for Modelling Black Sea River Basins: Research Proceedings for Guria Region of Georgia (2015), EU CBC Black Sea Basin JOP 2007-2013 funded Project Integrated Land-use Management Modelling of Black Sea Estuaries (ILMM-BSE), International Association CIVITAS GEORGICA, November 2015, Tbilisi, Georgia.
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CONTENTS
FOREWORD ..........................................................................................................................................vii
Three MoUs between Partner Universities, Local Authorities and CSOs ................................................... viii
Address of the Governor of Guria Region to ILMM-BSE Final Congress ....................................................... x
CHAPTER 1 WP4. P-S-R Indicators and Indices for Assessing Impacts Black Sea Coastal Areas ................. 11
BACKGROUND ............................................................................................................................................ 11
INTRODUCTION INTO INDICATORS AND INDICES ....................................................................................... 12
COASTAL ZONES ......................................................................................................................................... 15
Application of ICZM Progress Markers in Black Sea Region .................................................................... 16
Integral indices for coastal sustainability indicator sets ......................................................................... 18
Discussion ............................................................................................................................................... 20
RIVER BASINS / CATCHMETNS .................................................................................................................... 21
Flood Risk Sensitivity of Ergene River Basin ........................................................................................... 21
Catchment Erosion Model of Ergene River Basin ................................................................................... 24
DELTA, ESTUARINE AND MARINE AREAS .................................................................................................... 29
Developing Integrated GIS for Coastal Deltas and Associated Watersheds for Odessa Region .............. 29
Establishments of a Model Bank for Delta and Estuarine Areas of Odessa Region ................................ 32
Establishments of a Model Bank for Marine Areas of Odessa Region .................................................... 33
Modelling Black Sea River Mouths in Bulgaria under Climate Changes, See Level Rise & Disasters ....... 34
REFERENCES ............................................................................................................................................... 40
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CHAPTER 2 Proceedings of Black Sea Workshop on Catchment Observations, Modelling, Management . 45
Address of the Black Sea Commission Permanent Secretariat ................................................................... 47
Address of the Black Sea Commission Member from Georgia ................................................................... 49
ILMM-BSE Project Implemented in Bulgaria, Turkey, Georgia and Ukraine ............................................... 51
Instruments for Modelling Black Sea River Basins: Application Case of Guria Region in Georgia .............. 57
Earth Observation Marketing Tools and Business Opportunities for Environmental Management ........... 63
The Importance of Marine Aerosols for Climate Change Assessments ...................................................... 69
BGSIP Workshop: an Earth Observation Capacity Building Resource for the Black Sea Area ..................... 79
ILMM-BSE: Case of Ergene River Basin in Western Turkey ........................................................................ 85
Nutrient Pollution of the Bulgarian Black Sea Coastal Waters โ Problems and Prevention ....................... 89
CHAPTER 3 Proceedings of Students Scientific Workshop on Ecology of Black Sea River Basins............... 97
Address of the Rector of Batumi Shota Rustaveli State University ............................................................. 99
Emerald Network Habitats and Species of Kolkheti Lowland ................................................................... 101
Pollution Sources and Current Ecological State of Small Rivers of Adjara (Mejinistskali, Bartskhana) ..... 105
Ecotourism as the Key Factor for National Development......................................................................... 109
Current Data on Biodiversity of the River Natanebi Ichthyofauna and Water Pollution .......................... 113
Georgia-Turkey Transboundary Stripe Rare and Endangered Plants........................................................ 127
Use of Black Sea Coast Medical Flora against Some Chronic Diseases ..................................................... 135
Project of Global Importance: Sphagum as a Renewable Resource โ Establishing a Sphagnum Farm ..... 137
Medicinal Plants of Adjaristskali Valley .................................................................................................... 143
Protected Areas of Kolkheti...................................................................................................................... 151
State of the Ecology of Kintrishi River ...................................................................................................... 155
Anthropogenic Impacts on Habitats of Kolkheti Lowland Shorelines ....................................................... 159
Relic Kolkhic Forests of Kolkheti Lowland ................................................................................................ 165
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FOREWORD
Land is a scarce resource increasingly affected by the competition of mutually exclusive uses. Fertile land in rural areas becomes scarcer due to population growth, pollution, erosion and desertification, effects of climate change, urbanization etc. On the remaining land, local, national and international users with different socioeconomic status and power compete to achieve food security, economic growth, energy supply, nature conversation and other legitimate objectives.
Land use planning can help to find a balance among these competing and sometimes contradictory uses. Within the scope of European Integrated Land Use Management Modelling of Black Sea Estuaries (ILMM-BSE) project, land use change was modelled in Ergene basin and its delta in Turkey; Ropotamo and Veleka riversโ basins and their deltas in Bulgaria; Danube, Dniester and Dnieper deltas in Ukraine, Guria region in Georgia for their commonalities, from the
view point of their current conditions and characteristics.
Although the primary objective of the project was to model land use change, land use planning application to support sustainable development within given areas or specifically to ensure the protection of ecosystem services, biodiversity and high conservation values, mitigation of climate change and adaptation to it and food security subjects were studied.
Additionally, impact assessment and management tools for sustainable land use, new institutional legislation for land-use planning authorities, strategies for public and stakeholdersโ participation in the decision making process as well as the guidance for the development of decision-support systems were investigated. We indeed hope that network established during the project and culminated with triad of signed memoranda (see next page), will be successfully applied to solve common environmental problems.
Project Coordinator
Mrs. Sonya Enilova
Chairperson
Bourgas Regional Tourist Association
BULGARIA
Project Joint Research Coordinator
Prof. Dr. Fatih Konukcu
Academician
Namik Kemal University
TURKEY
viii
Three MoUs between Partner Universities, Local Authorities and CSOs
ix
FOREWORD
Integrated River Basin Management (IRBM), Integrated Coastal Zone Management (ICZM), other multistakeholder governance processes are gaining new impetus in Black Sea region, stimulated via recent signing by Georgia and others association agreements with the EU.
ILMM-BSE project comes timely in supporting the joint research coordination efforts and initiatives in four out of six Black Sea coastal countries, making emphasis on analyzing and modelling environmental impact of land uses and activities on riverine & estuarine ecology.
The Region of Guria and its main river basins (Supsa and Natanebi) were chosen as study areas in Georgia. ILMM-BSE thus followed-up the efforts supported by EuropeAid ECBSea, FP7 enviroGRIDS, PEGASO & IASON projects.
In addition to providing and testing a range of river catchment modelling and management tools, partially described in this publication, these efforts resulted in policy outcomes in Georgia and in the region, including joining
GEO โ The Group on Earth Observations โ by two remaining Black Sea countries (Georgia, Bulgaria) and by the Black Sea Commission Permanent Secretariat (at GEO X, XI and XII).
As a picture is worth a thousand words, this book starts with the deliverable produced by joint project partner efforts, illustrating with examples the value of indicators and integral indices within DPSRF context. This analytical framework is further implied when reporting the proceeding of two project workshops, held in Batumi, Georgia, contributed by both internationally renowned researchers, as well as the young Georgian scientists. But without great support of local partners from Guria, all this work would have not been feasible!
This colourful decision-making framework was apparently meant by Governor of Guria at the Project Final Congress (see next page), when citing greatest Georgian thinker in his speech, and whose very words we use as the epigram, explaining this publication.
Mr. Giorgi Meskhidze
President
International Association "Civitas Georgica"
GEORGIA
Dr. Mamuka Gvilava
Joint Research Coordinator for Civitas
ICZM National Focal Point
GEORGIA
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Address of the Governor of Guria Region to ILMM-BSE Final Congress
Koรง Holding Conference Hall, Istanbul, Turkey, 05 November 2015
Distinguished Rectors and Mayors from Partner Countries, all Participants of the Final Congress,
In my capacity of the Governor of Guria Region of Georgia, let me thank our hosts and organisers for inviting to the Final Congress of the European Black Sea Cross-Border Cooperation Project in this wonderful location of Istanbul!
Guria Region is situated in the south-west part of Georgia, along the Black Sea coast. Its population is around 140 thousand.
Our region consists of four Municipalities: Lanchkhuti, Ozurgeti, Chokhatauri and the City of Ozurgeti. Since 2014 City Ozurgeti obtained the administrative status of the self-governing town.
I am very pleased, that as an outcome of this European project, the Mayor of Ozurgeti will sign the Memorandum of Understanding with counterpart Municipalities from other participating Black Sea countries.
When addressing the Second Black Sea Stakeholder Conference, organized by the European Commission earlier this year in Sofia (Bulgaria), we have stated that the Guria actively captures all opportunities of participation in European programmes and initiatives and that Black Sea Cross-Border Cooperation Program is particularly attractive European instrument for regional integration. Let me reiterate, that we indeed look forward to participation of our governmental and non-governmental organisations, educational institutions, small and medium size enterprises in regional cooperation through Black Sea Cross-Border Cooperation and other European support mechanisms.
Guria is ready to engage with counterpart Regions from the Black Sea countries, thus striving to integrate with European values, strengthening the cooperation between these Regions and enhancing international links and visibility of Guria.
It is worth highlighting, that administrative borders of the Guria Region essentially coincide with ecological boundaries of our watersheds. With improved sanitation and waste management practices, where we would strongly benefit from greater support and sharing of European experience, Guria could indeed improve the protection of river basins, coastal zones and marine environment, by following the approaches compatible with Water Framework Directive, Marine Strategy Framework Directive and newly adopted Maritime Spatial Planning Directive.
Approximation with these governance arrangements are strongly present in EU-Georgia Association Agreement, signed in late 2014. Guria could indeed be the excellent test bed for comprehensive implementation of European compatible regulations. Outputs of this particular European project I trust also brings us a bit closer to the development and the establishment of European instruments of civic participation, education and research in support of governance.
We are therefore welcoming international community, European and regional partners to cooperate with us with more energy, and we are committed to provide all necessary means at our hands to make this cooperation and support mutually beneficial. I would like to especially call on sponsors of CBC program in the next phase to strongly support the Regions of the Black Sea, and of course โ the Guria Region in particular!
I am particularly pleased today that in addition to local authorities, the project participating civic organisations and universities are establishing partnership agreements. Such cooperation agreements can be a strong instrument for regional cooperation and for closing the gap between the civic movement, the science and the governance.
I am also pleased that the Georgian education establishment is represented here by the Batumi Shota Rustaveli State University and would like to use this opportunity and invite them to establish the similar Memorandum of Understanding with the Administration of the Governor of Guria Region!
Let me finish my address with excerpts from the 12th Century prominent Georgian poem The Knight in the Pantherโs Skin, where, I trust, the great Shota Rustaveli speaks about the holly link between the nature and the governance:
"โฆHe has us given the nature, infinite in its colours;
from Him is every monarch, and in His sole powerโฆ"
Concluding with these words, wish you all a very successful Final Congress. Thank you for the attention.
Mr. Gia Salukvadze
Governor of Guria Region, Georgia
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โโฆแฉแแแ, แแแชแแ, แแแแแชแ แฅแแแงแแแ, แแแแฅแแก แฃแแแแแแแ แคแแ แแแ, แแแกแแแ แแ แก แงแแแแ แฎแแแแฌแแคแ แกแแฎแแแ แแแก แแแแ แแแ...โ
แจแแแ แ แฃแกแแแแแแ, "แแแคแฎแแกแขแงแแแกแแแ"
"โฆHe has us given the nature, infinite in its colours; from Him is every ruler, and in His sole powerโฆ" Shota Rustaveli, The Knight in the Pantherโs Skin
CHAPTER 1
WP4. PโSโR Indicators and Integrated Indices for Assessing Impacts of Catchment Landโuses and Activities on Black Sea Coastal, Deltaic, Estuarine and Marine Areas
Mamuka Gvilava (Civitas Georgica), Fatih Konukcu (NKU), Valentin Nenov (BTU), Andriy Volkov (UkrMEPA/ODEKU), Husein Yemendzhiev (BTU), Selcuk Albut (NKU)
BACKGROUND
ILMMโBSE group of activities 2.1โ2.4 are designed to perform research work packages WP1โWP4 and generate related deliverables. Particularly, Work Package WP4 prescribes the (i) Development and evaluation of criteria and standards for implementation of integrated sustainable landโuse planning and management; (ii) Development of indices and index for assessing landโuse impacts on delta ecology; (iii) Erosion and desertification risks assessment for watersheds; (iv) Development of tools for predictions required for decisionโmaking; (v) Methodologies for qualitative and quantitative accounting of the multifunctional effects of land management and development strategies with regard to environmental protection, rural development, land use, landscape, tourism, recreation, agriculture and forestry activities; (vi) Assessment of transโboundary problems; (vii) Thresholds of sustainability; (viii) Guide for the development of decisionโsupport systems; (ix) Strategies for public and stakeholdersโ participation in the decision making process; (x) Institutional strengthening for landโuse planning authorities; (xi) New institutional legislation for landโuse planning authorities; (xii) Evaluation criteria for Natural Parks, Natural Assets, and World Heritage Sites in estuary watersheds; (xiii) Development of an integrated framework analysis; (xiv) Impact assessment and management tools for sustainable land use; (xv) Development of PโSโR of indicators for the use of decision makers.
Above tasks need to be considered in integrated methodological framework for decisionโmaking, while this particular deliverable deals with specific aspect of the framework concerned with pressureโstateโresponse type indicators (see (xv)), as well as cumulative indices/index for assessing landโuse impacts in river catchments and consequently on estuarine and delta ecology (see (ii)). These aspects essential would touch base for several aspects of the above listed components, such as (i), (v), (vii), (xiii) and (xiv).
This chapter first provides short introduction into general instrument of indicators and cumulative indices in support of the sustainable development, with special emphasis on river basins/catchments/watersheds, coastal zones, river deltas and estuarine systems and ultimately the marine environment of the Black Sea. Sets of progress and state of the environment monitoring indicators and related cumulative indices are defined for above components, based on and similar to European methodologies available for the coastal zones. Examples from various case study areas under ILMMโBSE domain are provided illustrating proposed approaches, and certain recommendations are prescribed how best to replicate these approaches elsewhere in countries and localities of the Black Sea region.
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INTRODUCTION INTO INDICATORS AND INDICES
There is a plethora of sources describing various aspects and methodologies related to decision making
instruments such as indicators and indices/index in multiple fields of applications.
Various sets of indicators are usually defined to measure specific symptomatic aspects of certain
phenomena of societal importance, so that these measurements are representative of wider more
complicated range of variable affecting or being affected by these phenomena. Rationale here is that due
to various restrictive factors (mostly related to constraints of available time, resources and expertise) not
all needed variables can be measured or inferred numerically, therefore a smaller subset of inherently
informative key variables are selected as indicators, those substantively representative of the wider sets of
variables.
Indicator variables can not be still reduced to comprehensible number of variables and further weighting
and cumulative aggregation is required for indicators to be useful for real life decision-making. These
constrains are largely due again to limiting factors such as squeezed timeframe available for decision-
making (time span for decision-making is inherently in short supply in democracies, defined at election
timeframes), as well as due to inability of human beings to consciously discriminate between too many
values derived even from selected key variables โ indicators, moreover that at the fundamental level final
decision-making, whenever sufficient information for decision-making is available, is performed in three
outcomes: positive, negative or โin progressโ. This defines the need to introduce indices (or even single
index), as a weighted scores or otherwise derived combination of calculations performed over selected
indicators.
As mentioned above, there is a large literature devoted to these subjects. For practical reasons it is
considered more valuable to direct reader to some encyclopaedic web resources, rather than diving into
rigorous scientific coverage of the field. Particularly useful are the following internet resources:
http://www.eoearth.org/view/article/151714 (Morse 2007). This reference provides examples of
development indicators and indices with practical explanations of various methodological aspects and
providing short description of textbook examples such as UNDPโs the Human Development Index (HDI), Corruption Perceptions Index (CPI) of the Transparency International and the Environmental Sustainability
Index (ESI) of the World Economic Forum. Latter can be represented into more informative pressure-state-
response (PSR) sub-components, capable of revealing finer details for both the developing (with weaker
response indicators) and developed (with stronger pressure and state indicators) countries.
http://www.eoearth.org/view/article/51cbee377896bb431f696317 by Bartelmus (2013) explains indicators
of sustainable development. Diagram from this resource, reproduced below, explains social, economic and
environmental triad, allocating various quantification and accounting tools invented for informed decision-
making, most comprehensive of which is the Drivers-Pressure-Sate-Response Framework (DPSRF), similar
to DPSIR (Drivers-Pressures-States-Impacts-Responses), regularly applied by the European Environmental
Agency (EEA) towards the European environment state and outlook reporting (see Figure 1 below,
reproduced from this reference).
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Figure 1. Various analytical instruments for measuring sustainable development
Aggregation of indicators into traffic light indices are introduced as well (red alert, yellow wait and see, and
green o.k.) in this reference. Excellent recent example of the county level index for land development
pressures for conterminous US is provided in Grekousis and Mountrakis, 2015 (see Figures 2 and 3 in that
reference), while nice example of direct population opinion sensing through crowdsourcing is provided at
the http://techpresident.com/news/24744/open-survey-data-transition-initiative-helps-interpret-state-new-york-city.
Before entering into our specific cases, it is worth mentioning that the availability of the proper Spatial Data
Infrastructure (shortly SDI), not necessarily comprehensively complete set, covering all potential variables
needed for the sustainable management of land and water resources, but of sufficient coverage and based
on sound principles for ultimate integration into indicators and indices used for meaningful decision-
making purposes, is the necessary aspect of these methodologies. Indeed, there are several best practice
examples of the application of SDI-based workflow into cumulative mapping of the environment
sustainability indices. Various methods and tools can be used for accomplishing such tasks, but three
attractive approaches are referred to below as an inspiration for combining modern SDI systems into
applications for deriving spatially explicit sustainability indices and indicators.
The first good example is the agricultural Land Evaluation and Site Assessment tool Enhanced with GIS
(ELESA), reported in Lee and Lee and Linebach (2008). The main advantage of this approach is the use of
the ESRI model builder for automating the weighted overlay of baseline SDI / GIS layers in a relatively short
reassessment time. This makes the approach acceptable for participatory planning and decision-making
applications because it can be optimized for use even during a stakeholder meeting or in the field. Despite
its agricultural origin, this tool can be adapted for watershed-based applications, as well as for processing
coastal management indicators (harmonization of indicator tools across river catchment basins and coastal
zones is advocated in the paper by Lehmann et al. 2009).
The second approach quoted here was reported by Steadman et al. (2004) and it is used by Minerals UK
(British Geological Survey) for establishing relationships between aggregates and environmental sensitivity
indices in the context of Strategic Impact Assessment (SIA). The approach is based on composite sensitivity
mapping of multiple thematic layers, ranging from conservation areas and cultural heritage to forestry and
Acronyms: DPSRF Driving-Force-Pressure- State-Response Framework FDES Framework for the Development Environment Statistics MFA Material Flow Accounts PSRF Pressure-State-Response Framework SAM Social Accounting Matrix SEEA System for integrated Environmental and Economic Accounting SNA System of National Accounts SSDS System of Social and Demographic Statistics
FDES (PSRF)
SSDS SAM
DPSRF
SNA
SEEA MFA
POPULATION
ENVIRONMENT
ECONOMY
14
agriculture. In this approach, similar to the ELESA methodology, polygonal GIS features are converted into
grid layers with cells assigned a value of 0 or 1. Grid layers are then assigned weighting scores based on
expert or stakeholder judgements, and the composite grid layer is generated and converted into a
graduated colour map depicting environmental sensitivity.
The third ecological example is oil spill sensitivity mapping of intertidal areas, reported at coastwiki
webpage at http://pegasoproject.eu/wiki/Oil_sensitivity, based on the system developed by Van Bernem et
al. (2007). In this approach complex GIS computational framework is exploited to derive the integral values
of the oil sensitivity calculated by combining sensitivities of benthos and bird areas based on their spatial
and seasonal variability. For the benthos only one index value is determined while for the birds, the index
value depends on the breeding and/or migration period. The final sensitivity map is assembled seamlessly
into GIS system digital map for the utility of Havariekommando authorities for contingency preparedness
towards oil spills in the sensitive and valuable coastal environment of Wadden Sea. More information on
the sensitivity raster of the German North Sea is available in Van Bernem et al. (2007).
As is evident from above descriptions and examples, indicators and cumulative indices are used in almost
all societal aspects of governance. From ILMM-BSE perspective, we are more concerned with
environmental sustainability variables with respect to Black Sea estuaries, deltas, catchments draining into
and marine areas affected by land based sources and riverine inputs, in particular those related to land
cover change as well as pollution loads. Respective concepts are therefore introduced and explained below
based on three (rather four) example cases from four Black Sea countries, considered for such systems as
the (i) coastal zones (Georgia), (ii) river basins / catchments (Turkey, Ukraine), (iii) estuarine (Ukraine) and
delta (Bulgaria) and (iv) marine areas (Ukraine). These various cases are described in the quoted order
subsequently further below in this deliverable.
As a last introductory note, distinction is made between the state of the environment and performance
indicators, briefly described at http://www.eoearth.org/view/article/152625 by Jakobsen (2008) article
(retrieved from the same web resource) after explaining in some further detail environmental indicators
(like those defined by the World Economic Forum, EEF), while more about this aspect is discussed in the
first presentation below concerning the coastal zones, explaining European progress markers/indicators
tool (particularly as applied in the Black Sea region) further in this section. It seems fairly straightforward to
extend the similar tool towards monitoring the implementation progress in upstream catchments, recipient
estuaries / deltas and marine areas. As integrated management principles are almost identical for these
environmental domains, simple modification of the tool is possible by substituting concepts of Integrated
Coastal zone Management (ICZM) respectively towards Integrated River Basin Management (IRBM), Delta
and Estuarine Management Planning (DEMP) as well as the Marine Spatial Planning (MSP) and/or
Integrated Maritime Policy (IMP), accompanying them with slightly modified texts where found appropriate
and needed. Similar to Black Sea Commissionโs ICZM Advisory Group (ICZM AG), Advisory Groups on Control of Pollution from Land Based Sources (LBS AG) as well as on the Pollution Monitoring and
Assessment (PMA AG) could provide regional umbrellas for assessing implementation progress governance
arrangements with regard to land based sources of pollution and their monitoring/assessments.
Finally, it is important to quote and consult with the recent monograph on marine indicators (UNEP, 2014),
compiled by UNEP for the Regional Seas in the context of international governance instruments such as the
Regional Seas conventions and action plans (including those adopted for the environmental protection of
the Black Sea). The quoted report is explaining in detail all sorts of sustainability indicators and their merits
within the DPSIR framework (see, for instance, Figures 1.1 and 1.2 from UNEP, 2014, and texts in-between).
15
COASTAL ZONES
Sub-section outlines the experience of the Black Sea countries with the application of European Union (EU)
Integrated Coastal Zone Management (ICZM) progress markers/indicators and presents the basic
instructions used by country representatives to adapt the use of EU ICZM progress indicators to their
particular needs, while providing some technical explanations and tips in the application of this toolset.
Then, the sub-section introduces and describes the software instrument developed to simplify data entry
and modification processes. In addition to the ICZM progress indicators, the sub-section applies spectrum-
type visualisation to coastal issues in order to derive coastal sustainability indicators for a small pilot area
along the Georgian coast, in Guria Region. Recommendations on the further application and use of both
instruments are made, and certain considerations in building an interface between ICZM progress reporting
and aggregated mapping of coastal sustainability indicators are suggested. Presentation in this sub-section
closely follows the recent reference Gvilava et al. (2015). Most relevant provisions are utilised hereby,
therefore the reader is referred to quoted manuscript to learn further details.
In May 2002, the European Parliament and the Council approved Recommendation 2002/413/CE
Concerning the Implementation of Integrated Coastal Zone Management (ICZM) in Europe (EC 2002). The
major requirement of the Recommendation was to outline the steps for member states to develop national
strategies for ICZM. Given the cross-border nature of many coastal processes, coordination and
cooperation with neighbouring countries and in the regional seas context were encouraged. It was
requested that the experience gained in the implementation of ICZM be reported back to the commission
within 45 months.
To facilitate the implementation of the Recommendation, a European ICZM Expert Group was set up, which
in turn, recognizing the importance of monitoring and benchmarking for sustainable development at the
coast, created an Indicators and Data Working Group (WG-ID). The WG-ID proposed that member states
and candidate countries employ two sets of indicators (Martรญ et al. 2007):
(i) ICZM progress indicators โ indicators that measure the progress of ICZM implementation; and
(ii) Coastal sustainability indicators โ a core set of indicators and measurements for monitoring sustainable
development of coastal zones.
Used together, the two sets were meant to reveal the degree to which ICZM implementation can be
correlated to more sustainable coastal development.
The national strategies on ICZM, requested by the European Recommendation, were the test beds for the
application of the ICZM progress and coastal sustainability indicators. Within the requested timeframe,
dozens of countries prepared reports on the implementation of ICZM national strategies, including
experiences with the use of indicators.
The Recommendation (EC 2002, Chapter VI.3) requested the European Commission to evaluate its
implementation. The main sources of information for this evaluation were the first national reports; state-
of-the-coast assessment by European Environmental Agency (EEA 2006). The results were documented in
the formal evaluation report of the European Commission (COM 2007).
In the evaluation, particular attention was paid to the use of indicators by the member states in their
national strategies and reports, recognizing that โalthough progress has been achieved towards a common assessment framework โฆ a methodology to link the efforts in ICZM to trends in sustainability is still lackingโ.
16
The results of the use of both types of indicators (ICZM progress and coastal sustainability) were well
documented by the WG-ID (2006). Their report highlights the importance of the cross-correlation of coastal
management efforts with the outcomes achieved in the sustainable development of coastal zones.
Antonidze (2010) also recommends a coherent system of indicators for an assessment of the state of Black
Sea coastal zones and implementation of ICZM.
The integration of management progress and sustainability indicators remains high on the agenda of the
European Commission, particularly in the context of a new Directive on Maritime Spatial Planning (MSP
2014) and the application of legally binding instruments, such as the Protocol on Integrated Coastal Zone
Management in the Mediterranean (Protocol 2008). This Protocol (2008), which was already ratified by the
European Commission, in its Article 27 calls on Parties, including European Community as a signatory and
ratifying Party, to โdefine coastal management indicators, taking into account existing ones and cooperate
in the use of such indicatorsโ.
Evaluating progress in complex disciplines such as ICZM is indeed a challenging task. The colour-coded set
of indicators proposed a decade ago by the ICZM Expert Group of the European Commission (WG-ID 2005;
Pickaver et al. 2004) is a recognized instrument, used frequently for monitoring the progress made in ICZM
implementation. An attempt to apply a similar monitoring and reporting methodology was conducted in
the Black Sea region with support of the EuropeAid-funded ECBSea project (Environmental Collaboration
for the Black Sea), whereby six coastal countries, Bulgaria, Georgia, Romania, Russian Federation, Turkey
and Ukraine, reported on their ICZM progress under the auspices of the Black Sea Commission (BSC). The
results were published in the State of the Environment of the Black Sea report (BSC 2008). The Advisory
Group on the Development of Common Methodologies for ICZM to the Commission on the Protection of
the Black Sea Against Pollution (ICZM AG for short) has further fine-tuned progress reporting to their needs,
expanding it to include an indexed reference system with the corresponding arguments in textual format to
underpin upgrading or downgrading colour-coded markers.
This sub-section also describes the local level effort of introducing spatially explicit mapping for measuring
those indicators that can be expressed in spatial terms by following the approaches suggested in the report
of the ICZM Expert Group of the European Commission and published by the European Topic Centre on
Terrestrial Environment (ETC-TE 2004). The illustrative example provided in this sub-section is based on the
experience of the above mentioned ECBSea project in Georgia.
Preparation of the document entitled the Integrated Plan for Sustainable Development of Tskaltsminda
Coastal Community (ECBSea 2009) was backed by the establishment of a small-scale Geographical
Information System (GIS). A range of thematic and planning maps produced for this purpose show how the
land is used today, highlight where the ecologically valuable areas are located, and propose different zones
for the future by integrating ecological sensitivities with economic development agendas and identifying
options that would benefit both the local people and the coastal environment. These GIS layers allowed to
test the spatial planning and indicator mapping methodologies developed for BSC ICZM AG (Yarmak 2004).
Application of ICZM Progress Markers in Black Sea Region
ICZM progress indicators developed for the European Union (EU) context have been applied to monitor the
progress of ICZM implementation in the Black Sea region (Lucius 2008), including in Georgia, as reported by
Bakuradze and Gvilava (2008). After this initial attempt in 2008, the BSC ICZM AG decided at its annual
meeting in 2010 to develop a concise user manual, a Guideline for Completing ICZM Progress Indicators โ
The Black Sea Region (draft version dated 2011.10.10).
17
This guideline is entirely based on and closely follows the approaches suggested by Pickaver et al. (2004)
and WG-ID (2005), updated to meet the needs of Black Sea coastal countries in completing periodic self-
assessments. In line with the original methodology tested in European countries, the ICZM progress
indicator table is grouped into 4 phases comprising 31 actions. Any progress in the implementation of ICZM
is indicated by filling colour-coded marker tables. Moreover, the guideline includes a section with
instructions and technical tips on how to fill in the progress indicator table and another section containing
notes explaining the meaning of the โphasesโ and โactionsโ, essentially repeating the provisions, as established at EU level (WG-ID 2005).
The guideline itself was proposed to be agreed upon (and amended from time to time) by the BSC ICZM AG
at its annual meetings, while reporting milestones for measuring progress with ICZM indicators were
proposed to correspond with ministerial meetings or international cooperative actions of Black Sea
countries within the framework of the Bucharest Convention. The reporting milestones to date include the
ministerial meetings convened for the adoption of Odessa Declaration (1993), signing of the Black Sea
Strategic Action Plan of 1996 (BS-SAP 1996) in Istanbul, adoption of the Sofia Declaration (2002) and signing
of the updated Black Sea Strategic Action Plan of 2009 (BS-SAP 2009).
Results of the ICZM progress assessments, covering approximately a 5-year period, are to be included in the
periodic reports on the implementation of the BS-SAP prepared by the Black Sea Commission and
submitted to the regular ministerial meetings. At the same time, operational update of the ICZM progress
indicators is meant to be performed annually and presented at ICZM AG meetings. Results of the
operational ICZM progress marker assessments should, therefore, be reported to the Black Sea Commission
on an annual basis as well.
The progress markers and respective endnoted textual arguments are addressed flexibly at four
administrative and spatial levels: international, national, sub-national and local. The international level
might include Black Sea regional, EU, regional seas or other applicable international scales. The sub-
national level might include coastal regions, large protected areas or similar units of sub-national
designation as determined by each country. Local level initiatives are to be considered in an ad hoc manner
as progress is monitored at local level and any initiatives at this stage of development are not accounted for
on a site-specific/geographic basis. However, in future, it is envisaged to integrate such initiatives with
spatially explicit progress indicators. The European Nomenclature of Territorial Units for Statistics (NUTS)1
and for Local Administrative Units (LAU)2 could indeed provide a common backbone for both types of
indicators.
It is considered the responsibility of the respective ICZM National Focal Points to complete and validate
with stakeholders the responses at national, sub-national and local levels. Progress at the international
level is to be observed and completed by the ICZM AG and endorsed at its annual meetings, reported to the
BSC annually and to ministerial meetings at least once in 5 years on average. The next reporting milestone
is a ministerial meeting, anticipated in 2015-2016.
The guidelines for filling the ICZM implementation progress markers contains full instructions for filling the
colour coded progress markers, as well as annotated description of all ICZM phases and actions as defined
in original sources quoted above and sample of the indicator table to fill in word processing format.
Moreover, software tool was developed to simplify indicator rating entry (see Figure 2). Repository of
developed toolset, scientific article describing it, as well as demonstration video are available as faceted
1 http://ec.europa.eu/eurostat/web/nuts/history 2 http://ec.europa.eu/eurostat/web/nuts/local-administrative-units
18
search items at the following link hosted by European FP7 IASON and EOPower projects at
http://www.iason-fp7.eu/index.php/en/knowledge-base-eng/toolkits-eng_and
http://eopower.grid.unep.ch/drupal_IASON/?q=node/22.
Figure 2. Main window of ICZM progress indicator software tool (sample view)
Integral indices for coastal sustainability indicator sets
In addition to progress reporting, the application of spatial indicators is another useful approach for
assessing progress at all levels of ICZM implementation. While progress markers are needed to assess
governance efforts, the next logical step is to introduce spatially explicit mapping tools for measuring those
indicators, which can be expressed in spatial terms.
Indeed, as suggested by ETC-TE (2004), visualisation of the indicators in a mapped form is an informative
way of presenting information on coastal issues and can be used for measuring spatial manifestation of the
progress achieved or deficiencies encountered in managing coastal environments. With more free and
open source spatial data and information being made accessible though internet data clearinghouses, as
well as with the advancement of user-friendly GIS tools, it is tempting to explore the possibilities with the
development of methodologies for spatial colour-coded indicator maps in addition to tabular progress
indicator sets.
A simplified combination of the above described methodologies was applied to our pilot coastal area,
exploiting the GIS dataset generated while preparing the Integrated Plan for Sustainable Development of
Tskaltsminda Coastal Community. The GIS layers available for use included vulnerability zones for flora and
fauna, habitat types, land use and cadastral layers, as well as functional zoning (see maps enclosed with
ECBSea 2009). These layers, describing the physical environment, as well as the current use and proposed
19
management regimes for the area, were first rasterised using a grid conversion tool, weighted based on
expert judgement and scored by specialists involved in GIS data generation. The results were combined into
a final layer that was interpreted as the indicator for the rate of coastal development pressures. The
adequacy of the end result was validated by expert judgement and by testing sensitivity against reasonable
values for weights applied to each parameter and layer. The process was automated in the model builder
environment; thus, reanalysis is easily feasible in case of a need to change the weight factors attributed.
The final step in the calculation and mapping of the results was to establish threshold values for the
combined indicator, where the level of land โdevelopmentโ could be rated as high, medium or low. Instead of using a graduated single colour ramp, traffic light colours were applied to distinguish among the levels of
development indicator values as red, yellow and green, with their obvious qualitative meanings. Built-up
areas, such as houses and buildings, transportation and other impermeable surfaces and dirt roads were
coloured in black and gray, respectively. The overall contrast of the map colours was subdued to improve
the cartographic appeal of the result. Although there were essentially no data available in the water
domain, for mere illustration purposes so that adequate graphical interpretation can be achieved for both
land and water in the coastal zone, again, expert assessments and local anecdotal knowledge were used to
characterize water quality with relevant indicators in cyan (high), blue (medium) and pink (low) colours
(indicating water quality). An excellent example of rigorous treatment of various water quality indices
integrated into traffic lightโvisualized pressure indicator for the water domain of the coastal zone can be
found in Konovalov et al. (2013).
Figure 3. Colour-coded indicator map for Tskaltsminda local coastal community pilot area (PEGASO project Spatial Data Infrastructure (SDI) Coastal Atlas tool can be used for web dissemination at http://pegasosdi.uab.es/geoportal/index.php/guria-coastal-region-case)
20
The final result of spatial indicator mapping for the Tskaltsminda coastal area in Guria Region of Georgia is
shown in Figure 3 above. The total areas occupied by each threshold value, which can easily be calculated
with GIS, could be treated as quantitative indicators, which can be monitored repetitively in time to
characterise the spatial development pressures at play in the given coastal area (Arobelidze 2010, personal
communication). Despite the fact that only a limited number of threshold values were used to codify the
pressure indicators (just three coding colours used for each environmental, land and water, domains, plus
built-up), the approach seems fairly compatible with experience from mire ecology, for instance, whereby
these very complex ecosystems are satisfactorily classified in only a limited number of subdivision
typologies (see quote from Joosten 1998).
Discussion
The application of progress indicators using software tool briefly described above is simple and robust for
interactive use by ICZM practitioners even in the presence of stakeholder forums invited for scrutinising
and validating the progress ratings. The toolset is believed to be of quite a generic nature for application
not only in the EU and Black Sea context, but to any regional sea, with potential even for replication from
ICZM into other policy contexts such as MSP and Integrated River Basin Management (IRBM), see further
below. Apart from data entry, the executable provides the user with much flexibility such as the option to
edit the texts defining the ICZM progress indicators, if so desired, as well as the possibility to attach the
visual identity attributes such as logos of the international, national, regional or local authorities wishing to
apply the tool. Editable attributes include entries to names of the regional sea, country, sub-national and
local coastal administrations, as well as entries of their preferred reporting milestones. A user can directly
manipulate records in the Microsoft Access database, while outputs can be generated in Microsoft Excel or
Adobe Portable Document Format for reporting the results. Both the executable and its source code are
shared openly, so that advanced users can adapt the tool to their particular needs and circumstances.
The application of the spatial indicator tools discussed in this sub-section was found to be feasible for
implementation in the Georgian and Black Sea context, acting as a useful instrument for measuring
development pressures both qualitatively and quantitatively. The spatial planning and indicator mapping
methodologies were thus applied to implement the approaches advocated for the Advisory Group to the
Black Sea Commission on the Development of Common Methodologies for ICZM (Yarmak 2004).
Exploring, refining and further developing the inherent methods for connecting the ICZM progress markers
(to monitor policy and management efforts) with spatially aggregated indices and indicators for monitoring
ICZM efforts and actual outcomes for the state of the coast could prove invaluable for European and
regional seas in the light of the need for monitoring progress with the recent entry into force of legally
binding instruments such as the Protocol on Integrated Coastal Zone Management in the Mediterranean
(http://www.pap-thecoastcentre.org/razno/PROTOCOL ENG IN FINAL FORMAT.pdf). Another purpose
would be to watch progress in the context of the newly adopted Directive (MSP 2014), concerned with
many countries and seas, including the Black Sea region.
Therefore, further work is indeed recommended to include the development and deployment of web-
based SDI tools with capabilities for nested visualisation of ICZM progress markers at all levels of
implementation (international, national, regional and local) and tight integration with coastal statistical
datasets. This would facilitate mapping of the state of the coastal zones at both large- and small-scale
resolutions, aggregated at the end into the colour-coded summary spatial indicators ranging in size from
national and sub-national to finest-area local units of administration and governance. Obviously, there can
be many possibilities for integrating and aggregating management progress markers and sustainability
21
indicators at various scales and levels of governance and administration. Furthermore, the results would
depend largely on the allocation of weights, scores and indices, as well as cross-correlating state of the
coast indexes with management progress indicators. However, modern spatial data processing
infrastructure can in principle cope with recalculating and reinterpreting current and past ratings as more
knowledge and experience becomes available to stakeholders. This can be achieved without the need to
introduce changes into the underlying datasets. The process is ultimately related to human intervention
and interpretation of governance outcomes rather than challenges of a technical nature, but good technical
instrumentation can indeed be of help to practitioners.
Similarly, there seems no technical constraint for seamlessly extrapolating the spectrum colour coding of
coastal sustainability indicators seaward (into marine and maritime domain) and landward (upstream into
river basins and catchments). Actually, there are excellent application examples of Cumulative Impact
Mapping for the Western Mediterranean sub-region (http://pegasosdi.uab.es/geoportal/index.php/atlas-
pegaso-regional-products/atlas-cumulative-impact-mapping). The methods used to this end are elaborated
in Micheli et al. (2013). In fact, a simplistic argument in support of such an extension of the tool is the
theoretical possibility of defining a coastal zone in its widest ecosystem-based interpretation (i.e., including
full marine and catchment areas into the coastal zone).
Summarizing this sub-section, the progress indicators elaborated in the EU context were applied for
monitoring ICZM implementation progress in the Black Sea region, including Georgia. This instrument was
further fine-tuned as a monitoring tool for the Black Sea countries by incorporating the listing of short
explanatory notes to index each change with time in the status of progress markers. Specific software tool
was developed to automate and simplify entry, manipulation and reporting of the data. In line with the
original methodology, this tool can be applied easily for use at the international, national, sub-national and
local levels. Progress marker tool can effortlessly be extended into fields of MSP and IRBM as well. In
addition, the potential for connecting progress reporting with spatially explicit indicators that measure
sustainability outcomes through application of ICZM at the local level was explored on an example of small
coastal community in Guria Region of Georgia.
RIVER BASINS / CATCHMETNS
Flood Risk Sensitivity of Ergene River Basin
Introduction
Ergene River Basin has an important place in Turkey due to its geographical location, topography, geological
structure, soil properties and incorporating several different climates. The basin has been facing many
problems related to land and water resources management, among which flooding is a significant issue.
Flood events occurring often in the basin cause serious damages.
The objective of this case study is to detect the area of high flood risk in Ergene River Basin to prevent or
reduce its damages.
Methodology
Among multicriterion decision analysis methods, Analytical Hierarchy Process (AHP) was used to determine
the flood-sensitive region in Ergene River Basin. AHP is a process that uses hierarchical decomposition to
deal with complex information in multicriterion decision making. It consists of three steps: i) developing the
hierarchy of attributes related, ii) identifying the relative importance of the attributes and iii) scoring the
alternativesโ relative performance on each element of the hierarchy.
22
There are many factors affecting river flow. Here, six criteria were used in the determining the risk of
flooding, namely runoff (Figure 4), elevation (Figure 5), slope (Figure 6), aspect (Figure 7), drainage density
(Figure 8) and size of sub basin (Figure 9). To obtain these criteria, an altitude map with 5m resolution, soil
map in the scale of 1/25000 and river layer map were used. Each criterion was formed into raster data with
10x10 resolution using the tool of GIS technology.
Figure 4. Runoff map of Ergene River Basin Figure 5. Digital elevation map of Ergene Basin
Figure 6. Slope map of Ergene River Basin Figure 7. Aspect map of Ergene River Basin
Figure 8. Drainage density map of Ergene Basin Figure 9. Size of subbasin map of Ergene Basin
23
Results
Matrix of pairwise comparisons with the Analytic Hierarchy Process was created (Table 1). As a result of
pairwise comparisons, weight ratio of each criterion was calculated (Table 2). First this ratio was multiplied
by the pixel values of each criterion. Then, maps were overlaid one on top of the other and finally flood risk
map was formed (Figure 10). The results showed that junction points of Ergene Riverโs branches, low lying areas with small slope are at high risk of flooding while areas with high elevation and slope have less risk.
Figure 10. Flood risk map of Ergene River Basin
Table 1. Matrix of pairwise comparisons with the Analytic Hierarchy Process
COMPARISONS Runoff Elevation Slope Aspect Drainage density Size of subbasin
Runoff 1.0 3.0 3.0 4.0 3.0 2.0
Elevation 0.33 1.0 0.5 2.0 1.0 0.5
Slope 0.33 2.0 1.0 3.0 1.0 0.5
Aspect 0.25 0.5 0.33 1.0 0.5 0.33
Drainage density 0.33 1.0 1.0 2.0 1.0 0.5
Size of sub basin 0.5 2.0 2.0 3.0 2.0 1.0
Table 2. Calculated weight ratio of each criterion
CRITERION Runoff Elevation Slope Aspect Drainage density Size of subbasin
WEIGHT 0.35 0.11 0.15 0.06 0.12 0.21
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Catchment Erosion Model of Ergene River Basin
Introduction
In the scope of the Integrated Land-use Management Modelling of Black Sea Estuaries (ILMM-BSE) for
Ergene Basin USLE/RUSLE (Universal Soil Loss Equation / Revised Universal Soil Loss Equation) methods
have been selected due to their database suitability and also availability of integration to Geographic
Information Systems (GIS), Remote Sensing (RS ) and geo-statistics (spatial statistics). By this way, the
current and potential erosion hazard areas maps have been created for whole basin.
By using USLE/RUSLE method, amount of soil lost from the unit surface area in a unit time (A, tonnes ha-1
yr-1) can be calculated quantitatively with the help of soil, topography, using climate and vegetation
databases. In addition, after determining the micro-basin based 'sediment delivery ratio' (SDR), the rate of
quantitatively defined actual erosion amount (tonnes ha-1 yr-1) reaching to rivers in the related basin has
been calculated.
As a result, USLE/RUSLE model is analysed in a GIS environment by considering micro-basin size with the
approach of the rate of transmission of sediment to develop the potential erosion map, the actual erosion
map and layers to show the amount of sediment transport reaching to rivers.
In parallel with the development of science and technology, in any country, region or basin basis, wide
range of methods for assessment of the danger of soil erosion are exist depending on the climate, soil,
topography and vegetation features. There are many mathematical models based on several physical
parameters related to natural elements; day by day the number increases.
USLE approach (Wischmeier & Smith, 1978; Renard et al., 1997), is just one of the models used to estimate soil loss in national, regional or basin scale and also it has been used widely in Turkey in order to assess erosion hazards recently (Erdogan et al., 2007; Ozcan et al., 2008).
Materials and Methods
The digital databases officially available for the whole country and used in the project while applying
USLE/RUSLE methodology for the evaluation of soil erosion risk (cellular [raster] and vector databases) are
given below:
โ Topographic Map (1:25.000) โ Digital Elevation Model (1:25.000) โ Forest Map (1:25.000) โ Soil Map (1:25.000) โ Land Use / Land Cover (CORINE, 2012) โ Drainage Data (DSฤฐ) โ Catchment and Dam Data (DSฤฐ) โ River Sediment Data (E.ฤฐ.E.ฤฐ, 2006) โ Turkey Rainfall Erosivity Data (Kaya, 2008; Erpul ve ark., 2009)
As shown above, for implementing USLE/RUSLE method across Turkey, soil, topography, climate and
vegetation databases prepared by various government agencies were used.
25
Methodology
As a project method, USLE/RUSLE erosion prediction technology was used (Wischmeier and Smith, 1978;
Renard et al., 1997). The 'process flowchart' to express the equality and also the databases where the
equation parameters coming from were explained respectively. The equation of USLE method is as follows:
A = R ยท K ยท L ยท S ยท C ยท P [1]
A: average soil loss (ton ha-1 yr-1),
R: rainfall erosivity factor (= EยทI30) (MJ mm ha-1 yr-1 hr-1),
K: soil erodibility factor (ton ha-1ยท ha MJ-1ยทh mm-1),
L and S: topographic (length-slope) factor,
C: crop and cover management factor,
P: prevention practices factor.
In equation [1], there is only the R and K variables have units, others are dimensionless. As a result, unit of
annual soil loss (A) "t ha-1 yr-1" is obtained with the multiplication of the R and K factors.
A1 = R ยท K ยท L ยท S [2]
In the equation above, A1 represents potential soil loss (tons ha-1 yr-1); and refers to any soil loss that can be
occurred when natural vegetation is destroyed.
A2 = R ยท K ยท L ยท S ยท C [3]
In equation [3], A2 represents actual soil loss (tons ha-1 yr-1); and indicates the soil losses that may occur
under the existing vegetation and product management in any terrain. At this stage, USLE approach (Eq.
[3]) provides comparative analysis of amount of soil loss from the unit area in the unit time (A2, tonnes ha-1
yr-1) and the amount of permissible soil loss (T, tonnes ha-1 yr-1). Additionally, it can be used as an important
tool in the task of planning for soil, topography, water and plant resources in a sustainable way.
A3 = R ยท K ยท L ยท S ยท C ยท P [4]
In equation [4], A3 represents soil losses that may occur under soil protected land management systems
(tonnes ha-1 yr-1).
A4 = R ยท K ยท L ยท S ยท C ยท P ยท (STO) [5]
In equation [5], A4 Sediment Delivery Ratio (Yearly Soil loss) compared with EฤฐEฤฐ measurements for different station on the basis of micro catchments.
Rainfall Erosivity Factor (USLE/RUSLE-R)
Rainfall erosivity factor values were obtained by applying geo-statistical methods on point data gathered
from rainfall stations within the scope of the master thesis conducted by Kaya (2008) as a part of the
project โDetermination of Rainfall Energy and Intensity at the National Scale by Using Long-term
Meteorological Dataโ (TUBITAK Project Number: CAYDAG-107Y155) (Erpul et al., 2009). Coordinates of
specified equality variables and sampling points and also USLE/RUSLE-R data were added into the ArcView
10.1 to create model map, performing calculations specified in the above referenced work (Figure 11).
26
Soil Erodibility Factor (USLE/RUSLE-K)
In order to determine the sensitivity of soil to erosion in Turkey, General Soil Map and Digital Soil Database
(Anonymous, 1982) were used. Soil features belonging to Great Soil Groups were rearranged for
USLE/RUSLE-K values and converted to a layer in GIS media. According to expert knowledge, along with
intermediate qualifications, lower and upper limits previously named as 'too low and too high values' were
converted into numerical values by means of Table 3 (Figure 12).
Table 3. USLE/RUSLE Soil Erosion Sensitivity Numerical Value
Sensitivity Value Very High High Medium Low Very Low USLE/RUSLE-K (t ha hour ha-1 MJ-1 mm-1)
> 0.092 0.066-0.092 0.033-0.066 0.017-0.033 < 0.017
The upper limits for the very low, low, medium and high classes of USLE/RUSLE-K were taken respectively
as 0.017, 0.033, 0.066 and 0.092. On the other hand, for very high-class, the K value was taken as 0.105.
Figure 11. R Factor Map for Ergene Catchment Figure 12. K Factor Map for Ergene Catchment
Topographic Length- and Slope-Factor (USLE/RUSLE-LS)
In the project, USLE/RUSLE-LS variable was obtained by using "Digital Elevation Model" (DEM) and the
calculation ability of "Hydrological Flow Accumulation, ArcView 10.1โ. Additionally, mathematical equation
was developed in GIS (Moore and Burch 1986a, 1986b) and in this way, USLE/RUSLE-LS value was not only
obtained by the steepness or length of slope, but also taking into account the expected flow on the soil
surface. So the slopes of the study area were calculated using DEM and slope length was taken as 15 m,
constant value for each pixel (Ogawa et al, 1997) (Figure 13).
Crop and Cover Management Factor (USLE/RUSLE-C)
In the scope of ILMM-BSE project, database produced in CORINE 2012 (Coordination of Information on the
Environment) for Ergene Basin were used to obtain USLE/RUSLE-C value.
CORINE Project is one of the important land management project under the European Global Monitoring
for the Environment and Security (GMES) program. By using the satellite images of 2006 and 2012, the
changes in land use have been detected with the help of GIS and RS to produce current land use maps in
2012. By this way, monitoring for environmental protection by looking at the changes in land cover would
27
be supplied according to the criteria of European Environment Agency. In the project CORINE Land Cover
(CLC) in 2012, computer-assisted visual interpretation of satellite imagery approach has been used as a
mapping methodology and also benefited from images produced by SPOT-4 and IRS-P6 satellite.
USLE/RUSLE-C levels (EEA, 2000) defined in CORINE land cover (2000) were used in this project for
vegetation cover and product management. Artificial areas (1), agricultural areas (2), forestry and semi-
natural areas (3), wetlands (4) and a total of 33 values of land cover types specified for the water bodies are
given in Table 4. C factor values for salt marsh, artificial areas and water structures were defined as "0" in
Table 4, and it means that soil loss does not occur from them. C values of agricultural areas ranges between
0.04 and 0.451, C values of semi-natural areas and forestry ranges between 0 and 0.36 (Figure 14).
Figure 13. LS Factor Map for Ergene Catchment Figure 14. C Factor Map for Ergene Catchment
Table 4. Completed CORINE Land Cover 2000 USLE/RUSLE-C Factors (EEA, 2000)
Code CORINE Land Cover C Factor 1 Artificial Surfaces 2 Agricultural Areas 2111 Non-irrigated arable land 0.4
2112 Non-irrigated arable land, green houses 0.4
2121 Irrigated arable land 0.2
2122 Irrigated arable land, green houses 0.2
213 Rice Fields 0.1
221 Vineyards 0.451
2221 Fruit trees and berry plantations, non-irrigated 0.296
2222 Fruit trees and berry plantations, irrigated 0.296
223 Olive Groves 0.296
231 Pastures 0.04
2421 Complex cultivation, non-irrigated 0.335
2422 Complex cultivation, irrigated 0.335
243 Land principally occupied by agriculture with significant areas of natural vegetation 0.04
3 Forests and Semi-Natural Areas 311 Broad leaved forest 0.003
312 Coniferous forest 0.001
28
313 Mixed forest 0.002
321 Shrub and/or herbaceous vegetation associations 0.005
323 Sclerophyllous vegetation 0.04
324 Transitional woodland shrub 0.04
331 Beaches, dunes and sand plains 0.36
3321 Bare rocks 0.36
3322 Bare rocks with very high salt content 0.36
333 Sparsely vegetated areas 0.36
334 Burnt Areas 0.36
335 Glaciers and perpetual snow 0
4 Wetlands 411 Inland marshes 0.001
421 Salt marshes 0.001
422 Salines 0
5 Water Bodies 0
Prevention Practices Factor (USLE/RUSLE-P)
In Ergene Basin, in the framework of this project conducted in sub-basins and micro-basins scale,
calculations were done assuming no soil or water conservation practices was taken except the reservoirs
existing in the basin. Areal data of the catchment of reservoirs taken officially from DSI (General Directorate
of State Hydraulic Works) was used to determine the USLE/RUSLE-P variable (Eq. [6]).
P = Sa / Sh [6]
In the equation [6], Sb represents the total area of the sub or micro watersheds with a dam at the outlet
(km2) and Sh represents the total basin area (km2). When information is updated reclamation works carried
out by various government agencies, may be added to the database P factor values for these basins.
Sediment Delivery Ratio (SDR)
In this study, USLE/RUSLE method was used to estimate the amount of soil loss (tons ha-1 yr-1) reaching the
outlet in the unit time from the unit area due to surface and rill erosion. The results of this method and also
hydrological DEM data were used to get SDR values (Figure 15).
Results and Discussion
Potential Soil Loss Map. As already stated, when natural vegetation is destroyed by any reason, it is
corresponding to the land cover loss. This map calculated from overlaying R, K, LS mapping units with GIS
software for Ergene catchment.
Actual Soil Loss (USLE/RUSLE-A2). This map calculated from overlaying of (R, K, LS ve C mapping units), with
GIS Software for Ergene River Catchment. These maps, show us soil loss might occur under product
management existing vegetation in watershed land.
Quantities of Sediment Reaching to the River Basin Systems (USLE/RUSLE-A4). The map for quantities of
sediment reaching to the river basin systems determined from 'Sediment Delivery Ratio' (SDR) in micro-
basin based is given in Figure 16. This map was obtained by using climate, soil, topography, vegetation
29
variables and also SDR layer given in Figure 15. Sediment Delivery Ratio (yearly soil loss) compared with EฤฐEฤฐ measurements for different station on the basis of micro catchments.
Figure 15. Ergene Sediment Delivery Ratio Map Figure 16. Sediment Reaching River Ergene Map
DELTA, ESTUARINE AND MARINE AREAS
Developing Integrated GIS for Coastal Deltas and Associated Watersheds for Odessa Region
Increasing of the information volume in all fields of human activity and actualization of environmental
issues nowadays become very important factor for understanding of the relationship between them. That is
also connected to development of society and to the needs of using modern information technologies in
the field of environmental management.
This research is rather important for the reason that informational systems arenโt well described in environmental science. But they are vital tool which can be used for designing decision support systems for
environmental management.
According to the Law of Ukraine "Basic Principles of Information Society Development in Ukraine in 2007-
2015" (ะะฐะบะพะฝ, 2007), the introduction of new information and communication technologies (ICT) in all
aspects and activities for state and local governments is one of the main priorities for state policy. That is
very important to create national, local and regional information systems in the field of environmental
protection that is also vital for sustainable use of natural resources, providing of public access to
environmental data and information which concerns the results of regional environmental audits and
environmental monitoring.
At the same time, in the main document that defines the environmental policy of Ukraine till 2020 (ะะฐะบะพะฝ,
2010), the emphasis is given to the informational component in the context of conservation and
improvement of the environment. One of the strategic goals of this document is to increase environmental
awareness, which is achieved by the establishment of a national environmental information system. Also
national information system should ensure an access to environmental information and include the
national system which gathers data of natural resources and registers of pollutants emission. The
appropriate Strategic Plans can help to improve the state system of environmental monitoring (SEM) and
the reference system of informational support of decision-making for the environmental issues.
The documents mentioned above emphasize the importance and relevance of ICT for the environmental
management as a universal tool for solving problems of conservation and improvement of the
30
environment, and at the same time determines the dominant role of information as one of the most
important resource of nowadays. State requirements which relate to information support of decision-
making (environmentally safe) implement the national environmental strategy, environmental policy, and
external requirements for compliance with international environmental commitments - these are necessary
conditions for formation and improvement of environmental management information systems
(ะะฐัััะฝะตะฝะบะพ ะธ ะดั., 2009).
The goal of any activity is its result, which is represented as the final product or an aggregate of relevant
conclusions and decisions. In the decision making process the most important component is the
information, which is directed to the general idea of conservation of the environment and provides
different ways of improving of the environmental conditions and assess the possible positive and negative
consequences of the decision. The structural elements of the management system for all levels of decision
making should be always available as information databases (ะะฐัััะฝะตะฝะบะพ ะธ ะดั., 2009). Decision making
process in the area of natural resources should be focused on understanding the concept of information in
that sphere. By definition which is formulated by Reimers (1992) Information in Nature Management is a
set of data which includes quantitative, qualitative and dynamic (past, present and future) aspects of
natural resources and systems, and also their relations with existing forms of economics and culture of
mankind. According to the Ukrainian Law "About information" (ะะฐะบะพะฝ, 1992), the environmental
information includes data concerning the components of the environment, including genetically modified
organisms and the interaction among them; factors which affect or may affect the components of the
environment (substances, energy, noise and radiation, and activities or measures, including administrative
agreements concerning environment, policies, legislation, plans and programs etc.); health and safety, life
conditions, cultural sites and buildings to the extent that they affect or may affect the conditions of
environmental components. That is also very important to identify those aspects that environmental data
depends on the person authorized to take appropriate action. Based on this informational support for
environmental issues we can keep process of gathering, assessment and analyzing primary environmental
data to make certain administrative decisions. This process should base on up-to-date data and provide
complex decisions. It is necessary that we should keep all details at each level of the assessment process
and understand the basic mechanisms of designing the proper informational platform, based on
information management software and various information systems which provide decision making
processes which are fully dependent on quality management (ะะฐัััะฝะตะฝะบะพ ะธ ะดั., 2009).
Levels of primary assessment and analysis are implemented by using special tools, software and hardware
to provide homogeneous, arranged and ranked data and other mathematical and statistical operations
which allow submitting the final product. This approach can be implemented using geographic information
systems (GIS), which became particularly popular in recent years as they allow to design data banks
combining spatial & attribute information and also capable to arrange analytical functions and capabilities.
Utilizing of GIS simplifies main goal: to design the required information platform for decision making.
The rapid development of ICT allows to collect and process big amount of data and also to give a
comprehensive assessment of the data and its usage in decision making. These problems have been
successfully solved by GIS software that besides the accumulation and displaying of spatially distributed
data allows integration of data for the area in question and effectively use this data to solve scientific and
applied problems related to the analysis, inventory, forecasting, expertise and management of the
environment (ะกะธะฒะฐะบ, 2007). It is necessary to mention that information platform is a set of prepared data
which has more convenient structure for analysis of cartographic material, designing various reports, smart
31
tables, graphics etc. It is a fundamentally convenient product which is presented as database and can be
used as a high level combination of diverse information.
In general, geographic information systems (GIS) - an integrated set of hardware, software and media,
providing input, storage, processing, analysis and display (presentation) of spatial coordinate data
(ะกะฒััะปะธัะฝะธะน, 2004). GIS structure can be represented as the following blocks (Figure 17). Analytic abilities
of GIS are presented in Figure 18.
Figure 17. Basic components of GIS Figure 18. GIS analytical mechanisms
Not only the person or team can make decisions. Today certain decisions can be made automatically
without direct human participation, the decisions can be given according some scenarios which are based
on characteristics of certain processes and phenomena which are appropriate to experience of decision-
making in the past. Type of a company or organization does not play a significant role on the decision-
making process that allows summarizing the general scheme of the process. General scheme of
environmental projects and solutions is shown in Figure 19.
In the context of this research it is also very important to consider the process of the informational support
for any applied activities (Figure 20) (ะะฐะปะดะถะธ ะธ ะดั., 2008). The primary goal is to figure out the main tasks
and designing the database structure. The last step in gathering the information should be developing the
approach to utilizing it for applied issues.
Figure 19. General structure of decisions making Figure 20. Structure of decision support systems
Case study for the establishment of the model bank based on above developed principles for land and sea
areas of the Odessa Region are presented further below.
32
Establishments of a Model Bank for Delta and Estuarine Areas of Odessa Region
This research provided an opportunity for implementation of the principles mentioned above that allows to
develop GIS which describes environmental conditions of water bodies of Odessa Region and areas nearby.
The initial data was taken from official statistical recourses of Odessa region. The data describe the level of
technogenic load on the environment of Odessa region.
There were designed the maps which describe spatial distribution of the technogenic load which is caused
by air, water pollution and also solid industrial wastes pollution (Figure 21-23).
The overall picture of the distribution of technogenic load on the environment in Odessa Region based on
the results of clustering analysis (described further below) is illustrated on Figure 24.
Figure 21. Spatial distribution of technogenic load on the air of Odessa Region
Figure 22. Spatial distribution of technogenic load on the water bodies of Odessa Region
Figure 23. Spatial distribution of technogenic load region caused by solid industrial wastes
Figure 24. Cluster analysis results for Odessa Region
33
Application of GIS also provides the possibility to use the methods of multivariate statistical analysis to
obtain the integral indexes and combine many layers of cartographic material. In this research we used
cluster analysis. Fundamentals of cluster analysis are shown below (Figure 25).
1. At the beginning 'CLUSTER PLUS' creates first cluster centre c1, ั1 = ั 1.
2. Next centre is the vector c2 which has the biggest distance to c1, ั2 = ั j2, i.e.
3. When 'CLUSTER PLUS' creates k cluster centres C(k) = {c1,..., ck} the next (k+1) centre is ั jk+1 which has the
biggest distance to the closest cluster centre c1,..., ck, i. e.
4. 'CLUSTER PLUS' stops creating new clusters when the condition is โtrueโ: Q(k+1) / Q(k) , (0,1)
Figure 25. Clustering scheme (two dimensions)
Establishments of a Model Bank for Marine Areas of Odessa Region
In addition to delta and estuarine areas presented above, a database was supplemented by layers, which
are responsible for spatial distribution of pollution for the nearby coast of the Black Sea. Map of the spatial
distribution of water pollution index is shown in Figures 26 and 27.
Figure 26. Overlay analysis of water pollution index Figure 27. WPI spatial distribution
Development of appropriate data banks allows the formation of the information systems that provide an
opportunity to resolve the problem quickly find the necessary information for a wide range of users. Also
presented approach to information allows one to develop decision support systems, aimed at identifying
optimal environmental policy in the region.
34
As conclusion we can figure out next features:
โ Zoning gives an overview of environmental conditions of the area in question and can be used as an
online reference/help system;
โ The research results are the basis for priorities in selection of management strategies for areas in
question;
โ This approach is a part of decision support systems concerning of development of the Odessa Region
for the long term.
Modelling Black Sea River Mouths in Bulgaria under Climate Changes, See Level Rise & Disasters
Foods are among the most dangerous natural phenomena causing severe damage to various branches of
the economy and in many cases lead to casualties. Flooding occurs when areas that are not normally under
water are inundated due to rising river levels and/or the level of groundwater due to rainfall and/or
snowmelt, due to breaking of embankments, the dam breaks, temporary blockage of the river bed etc.
(Nikolova and Nedkov, 2012). The risk of flooding is determined by the frequency (probability) of their
occurrence and exposure of the affected areas in terms of potential damage they may suffer. Damages in
turn depend on the degree of hazard of the corresponding flood, as well as the vulnerability of exposed
people and objects. Exposure to floods is assessed on one hand through flood hazard zoning and on the
other it is an important factor for vulnerability assessment. There are different systems (economic, social,
ecological etc.) that can be exposed to flood hazard in particular area. Furthermore their exposure is
different according to the flood risk zones where they belong to. Vulnerability depends on the degree of
flood hazard as well, but it also depends on many other factors such as the urbanization and buildings
density, the type of threatened infrastructure, population characteristics such as density, age structure,
mobility and health status, presence or absence of protective equipment in hazardous areas and early
warning systems etc.
The study area in this research includes basins of the rivers in South East Bulgaria which drain into Black sea
south of Burgas. It includes the river basins of Ropotamo, Dyavolska, Karaagach, Veleka, Rezovska, Silistar
as well as some small basins drained directly to the Black sea. It comprises an area of 184611 ha. The
biggest basins in the area are Veleka (79192 ha) and Ropotamo (24645 ha) therefore these two basins were
chosen as a main focus in this research. This area corresponds to the Project Unit XV South-Burgas rivers,
Veleka and Rezovska in the National Plan of Flood Risk Management. According to the preliminary flood
risk assessment in Black Sea region for water management (2012) there 135 floods registered for the
period 1979-2010 and almost half of them (64) are in the Basin of Veleka River.
The main objective of this work is to identify the flood vulnerability zones in the area of Veleka and
Ropotamo river valleys. The realization of this objective was accomplished through the following tasks:
โ Check and analysis of data availability;
โ Delineation of the floodplains in Veleka and Ropotamo river valleys;
โ Identification of the land use within the floodplains;
โ Flood vulnerability analyses of Veleka and Ropotamo floodplains.
35
Materials and methods
The necessary data for identification of flood vulnerability area include topographic maps, land cover data,
data for hydrological objects in the area, topography data, infrastructure data, and information for flood
events in the area. The analysis of data availability revealed that the objectives of the study could be
achieved by using 1:25000 topographic maps, 50 m DEM, and land cover data from CORINE project.
The area of Veleka river basin is located within 19 topographic map sheets at scale 1:25000, while
Ropotamo river basin covers 7 map sheets. All topographic maps have been scanned and georeferenced in
coordinate system UTM WGS1984 zone 35N. The river basins have been outlined using ArcGIS Hydrology
tools. The procedure includes generation of flow direction and flow accumulation grids, and model of river
flows which is used to define the outlets of the catchments (Tarboton, 1991).
The hydrological objects were digitized from the topographic maps using the Heads-up digitizing method
and the results were in form of vector GIS layers of rivers, water bodies and channels. The floodplains of
Veleka and Ropotamo rivers were delineated using two steps algorithm. At the first step 50 m DEM have
been used to derive slopes in the basins. Then, the slope layer was reclassified and flat surfaces were
extracted. The areas around the rivers were identified by intersection with rivers GIS layer. Thus the
potential floodplains were identified. At the second stage the results from the previous procedure were
compared with the topographic maps and the contours of the floodplain were checked and corrected. The
results from this procedure are vector polygon GIS layers that contain the floodplain area of Ropotamo and
Veleka rivers.
Land cover data were extracted from CORINE database which is available for three time series โ 1990, 2000
and 2006. The latest version of CORINE 2006 was used in the present study. The aim of the CORINE
program of the European Union is to compile information on the state of the environment with regard to
certain topics which have priority for all member states of the community (EEA 1994). CORINE includes 44
land cover classes altogether grouped in a three-level nomenclature into 1) artificial surfaces, 2) agricultural
areas, 3) forests and seminatural areas, 4) wetlands and 5) water bodies. These classes represent all land
cover types in Europe and they are clearly defined in the nomenclature provided by the project. The
CORINE data for Veleka and Ropotamo river basins were extracted from the main database and
transformed into separate vector polygon layers. Then, an overlay analysis was performed between the
floodplain and CORINE layers in order to identify the land cover classes within the floodplain. The results of
this procedure are vector polygon layers that contain all land cover classes within the floodplains of Veleka
and Ropotamo rivers.
The floodplains delineated from topographic maps represent the area exposed to floods. They are used as a
basis to assess the flood vulnerability in the studied areas. Each land cover class was assessed in order to
define its vulnerability against floods. Then, they were categorized using three-level scale including the
following classes: 1) High vulnerability; 2) Middle vulnerability; 3) Low vulnerability; 4) No vulnerability.
Land cover classes with high vulnerability are from the first level of the CORINE classification especially class
112 Discontinues urban fabric. The potential losses in such areas include all kinds of damages that could be
caused by flood e.g. destroyed buildings, cut transport network and communication, casualties etc. Middle
vulnerability is assigned to arable lands which may also badly suffer from flood that can destroy plants,
remove or inundate soil etc. Low vulnerability is assigned to other agriculture areas including pastures,
vineyards, agriculture with natural vegetation etc. Very low or no vulnerability was assigned to natural land
cover classes such as forest, natural grasslands, water bodies etc.
36
Flood vulnerability areas in Veleka river basin
Veleka river has its sources in Turkish territory of Strandzha Mountain. It is 147 km long and its basin covers
99500 ha, while in the Bulgarian part of the basin is 79192 ha. The river valley in its upper part has typical
mountainous character with narrow bottom and limited disconnected floodplain. The river banks are
covered predominantly by forests and there are no urban or agriculture areas, therefore there is no flood
risk and this part was not included in the analysis.
The floodplain of Veleka river (Figure 27) has an area of 2286.9 ha. The greatest part of it is occupied by
agricultural lands which comprise about 77% of the whole area. Most of them are represented by the
mixed class 243 Land principally occupied by agriculture with significant areas of natural vegetation (Table
5). Arable lands occupy 514 ha (22.5%) which are located mainly in the lower part of the river valley around
the largest floodplain areas near Kosti, Brodilovo and Sinemorets. Small patches of Complex cultivated
patterns (59 ha), Vineyards (16.5 ha) and Pastures (6.1 ha) are also presented in the Veleka floodplain. The
natural and seminatural land cover classes are presented by Broad-leaved forests with 392.3 ha (17.2%),
Transitional woodland-shrub with 85.1 ha and small patches of Mixed forests with 11.9 ha (0.5%). The
artificial surfaces cover limited areas but they are the most vulnerable to floods therefore should be
studied more precisely. Discontinuous urban fabric class covers 18 ha (0.8%), which are located in two
villages. Brodilovo has 9.6 ha located within the floodplain which is about 15% of the whole village while
Kosti has 8.3 ha which is about 10% of it area. There is also Sport and leisure facility class located in the
mouth of the river with 9 ha, which is used mainly for summer tourism. There are also limited urban lands
around Kachul locality which are represented by some small buildings and yards used mainly for recreation.
Table 5. Distribution of CORINE Land Cover classes within Veleka floodplain
CORINE class Area (ha) % 243 Agriculture with natural vegetation 1174.2 51.3%
211 Non-irrigated arable land 514.6 22.5%
311 Broad-leaved forest 392.3 17.2%
324 Transitional woodland-shrub 85.1 3.7%
242 Complex cultivation patterns 59.3 2.6%
112 Discontinuous urban fabric 18.0 0.8%
221 Vineyards 16.5 0.7%
313 Mixed forest 11.9 0.5%
142 Sport and leisure facilities 9.0 0.4%
231 Pastures 6.1 0.3%
37
Figure 27. Map of Veleka floodplain
The flood vulnerability analysis in Veleka river shows that the areas of high vulnerability cover 1.2% of the
floodplain area (Table 6). They are located in the lower part of the river valley (Figure 28) where floodplain
is wider and most suitable for agriculture. They represent the above mentioned villages and recreation
areas. The areas of middle vulnerability cover about quarter of the floodplain (25.1%) which are localized in
four areas. The first one is situated in the floodplain downstream of Brodilovo and covers about 415 ha
(Figure 5). This is the large agriculture area comprising almost 80% of all arable lands. The second one is
around the village of Kosti and covers 110 ha. They are presented by both arable land and complex
cultivation pattern classes. The third one is located south from Gramatikovo village and covers about 23 ha
of arable land. The fourth one is located north from Stoilovo village and covers about 21 ha of arable land.
The zone of low vulnerability covers almost half of the floodplain area. It is presented mainly by small
agricultural lands surrounded by natural vegetation and some small patches of pastures and vineyards
which are located all over the floodplain. The areas of no vulnerability cover 21.4 ha and represented
mainly by broad-leaved forests. They are located mainly in the upper part of the valley and around the
mouth of the river where large patches of riparian vegetation are present.
Table 6. Distribution of Veleka floodplain land cover according to their flood vulnerability
Vulnerability Area (ha) % High 27.0 1.2%
Middle 573.9 25.1%
Low 1196.7 52.3%
No 489.3 21.4%
38
Figure 28. Flood vulnerability of the lower part of Veleka river valley
Flood vulnerability areas in Ropotamo river basin
Ropotamo river has its sources in the northeastern slopes of Bosna ridge. It flows through narrow and deep
valley to northwest until Novo Panicharevo village. After this village the river turns to the east through wide
plain valley and flows into Black Sea forming large marsh and liman (Figure 29). It is 48.5 km long and its
basin comprises 24645 ha. Its main tributary is Rosenska river, which flows from Medni Rid ridge into south
until its infuse to Ropotamo river. The river valley in its upper part about 10 km from the sources has
narrow bottom and limited disconnected floodplain. The river banks are covered predominantly by forests
and there are no urban or agriculture areas, therefore there is no flood risk and this part was not included
in the analysis.
The floodplain of Ropotamo river has an area of 1466.7 ha. The greatest part of it is occupied by agricultural
lands which comprise about 67% of the whole area. Most of them are represented by class 243 Non-
irrigated arable lands (Table 7). They are located mainly in the lower part of the river valley to the east of
Yasna Polyana village. Land principally occupied by agriculture with significant areas of natural vegetation
have 436 ha (29.8%). Small patches of Complex cultivated patterns (2.4 ha) and Pastures (13.8 ha) are also
resented in the Ropotamo floodplain.
The artificial surfaces cover limited areas but they are the most vulnerable to floods therefore should be
studied more precisely. Discontinuous urban fabric class covers 19.2 ha (1.3%), which are located in two
villages. Novo Panicharevo has 18.1 ha located within the floodplain which is about 25% of the whole
village while Rosen has 1.1 ha. The natural and seminatural land cover classes are presented by Broad-
leaved forests with 221.7 ha (15.1%), Transitional woodland-shrub with 80.9 ha and small patches of Mixed
forests with 1.3 ha (0.5%). There are also Beaches and dunes that cover 4.2 ha, Water bodies with 33.3 ha
and Water courses with 85 ha which are located in the mouth of Ropotamo river.
39
Table 7. Distribution of CORINE Land Cover classes within Ropotamo floodplain
Figure 29. Map of Ropotamo floodplain
The flood vulnerability analysis in Ropotamo river shows that the zones of high vulnerability cover 1.3% of
the floodplain area (Table 8). They are located mainly in the middle part of the river valley where floodplain
is wider and most suitable for agriculture. Most of them are located in the village of Novo Panicharevo
which can be identified as the most important object of flood management in Ropotamo basin.
Table 8. Distribution of Ropotamo floodplain land cover according to their flood vulnerability
CORINE class Area (ha) % 211 Non-irrigated arable land 532.2 36.3%
243 Agriculture with natural vegetation 436.5 29.8%
311 Broad-leaved forest 221.7 15.1%
511 Water courses 85.0 5.8%
324 Transitional woodland-shrub 80.9 5.5%
411 Inland marshes 36.2 2.5%
512 Water bodies 33.3 2.3%
112 Discontinuous urban fabric 19.2 1.3%
231 Pastures 13.8 0.9%
331 Beaches, dunes, sands 4.2 0.3%
242 Complex cultivation patterns 2.4 0.2%
313 Mixed forest 1.3 0.1%
Vulnerability Area (ha) % High 19.2 1.3%
Middle 532.2 36.3%
Low 452.7 30.9%
No 462.6 31.5%
40
The zone of middle vulnerability cover about one third of the floodplain (25.1%) which are localized in three
areas. The first one is situated in the floodplain downstream of the infuse of Rosenska tributary and covers
of about 414 ha (Figure 30). This is the larges agriculture area comprising almost 80% of all arable lands.
The second one is upstream of Rosenska river and covers 38 ha. The third one is located to the east of Novo
Panicharevo village and covers about 78 ha of arable land. The zone of low vulnerability cover almost one
third of the floodplain area. It is presented mainly by small agricultural lands surrounded by natural
vegetation and some small patches of pastures and vineyards which are located all over the floodplain. The
areas of no vulnerability cover 462.6 ha and represented mainly by broad-leaved forests. They are located
mainly in the upper part of the valley and around the mouth of the river where large area of riparian
vegetation is located.
Figure 30. Flood vulnerability of the lower part of Ropotamo river valley
This subsection above describes the use of spatially explicit colour coded indicators/indices for flood
vulnerability characterisation. Quite similar approaches for essentially different environmental systems are
described in the next section (proceedings of ILMM-BSE Wrokshop-3 in Batumi, Georgia) in paper by Nenov
and Simeonova, concerned with the investigation of ecological status for the nutrients of the water bodies
along the Bulgarian Black Sea coastal waters, indexed/characterised according to the requirements of the
EU Water Framework Directive.
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ะะฐัะตัะบะพะฒะฝะธะน ะ.ะ., ะกัะผะฐะบัะฝ ะฎ.ะก., ะกะตัะณััะฝะบะพ ะ.ะ. (2011) ะะฐััะพััะฒะฐะฝะฝั ะณะตะพัะฝัะพัะผะฐััะนะฝะธั ัะตั ะฝะพะปะพะณัะน ะฒ ัะธััะตะผั ัะฟัะฐะฒะปัะฝะฝั ัะตะณัะพะฝะพะผ // ะงะตัะฝัะณัะฒััะบะธะนะฝะฐัะบะพะฒะธะนัะฐัะพะฟะธั. 2011. โ 2(2). ะก. 95-101.
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ะกะฒััะปะธัะฝะธะน ะ. (2004) ะะตะพัะฝัะพัะผะฐััะนะฝั ัะธััะตะผะธ ะฒ ะตะบะพะปะพะณัั (ะบะพะฝัะฟะตะบั ะปะตะบััะน). ะะดะตัะฐ: ะะะะะฃ, 2004. 768 ั.
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CHAPTER 2
Proceedings of the Black Sea Regional Workshop on Catchment Observations, Modelling and Management (30-31 October 2014, Batumi, Georgia)
46
47
Address of the Black Sea Commission Permanent Secretariat
Distinguished colleagues and friends, dear participants of the Black Sea Day
Workshop,
In my capacity of Executive Director of the Black Sea Commissionโ Permanent Secretariat, let me thank you for joining the Black Sea celebrations in Georgia, in
this wonderful city of Batumi! I do regret that I cannot join you today and wish
you a successful meeting!
I would like to mention that the Black Sea Day is one of the most outstanding and
important events of the Black Sea Commission recalling us the day then the first
Strategic Action Plan for the Rehabilitation and Protection of the Black Sea was
signed by all riparian countries of the Black Sea on the 31st of October back in
1996 in Istanbul. It continues to be our good tradition every year and let me
extend the warmest congratulations to each and every one of us, to every person
dealing with the preservation of the Black Sea, our precious common heritage.
By means of having such events in different coastal regions of the Black Sea we
try to attract the attention of scientists, politicians, decision-makers and just a
wider public to our day-by-day activities related to the protection of the Black
Sea. I hope your meeting will bring us to some new solutions and help to better
coordinate our efforts in the future.
I would like to thank my Georgian colleagues for organizing this event and mark
their continuous dedication and efforts to improve the environment of the Black
Sea and support of our activities.
Thank you very much for your kind attention and once again, heartily
congratulations to all of us!
Prof. Dr. Halil Ibrahim Sur, Executive Director
The Black Sea Commission
Permanent Secretariat
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49
Address of the Black Sea Commission Member from Georgia
Ladies and Gentlemen, distinguished guests,
Welcome to Batumi and 18th celebration of international Black Sea Day. Cooperation which started between Black Sea countries by signing the Bucharest Convention and Odessa Declaration deepened on 31st of October 1996, when first Black Sea Strategic Action Plan was endorsed. International Black Sea Day commemorates this date.
All six coastal states celebrate this day to raise public awareness for the protection of Black Sea environment and ongoing cooperation. We are indeed pleased and humbled to be greeted by the Black Sea Commission Permanent Secretariat Executive Director on this memorable event here in Batumi.
The Convention on the Protection of the Black Sea Against Pollution is the first International Environmental Agreement which Georgia signed as an independent country. Therefore the Convention and its following policy documents like the Black Sea Strategic Action Plan remain very important for Georgia. Several environmental specialists and other stakeholders gained first experience in negotiations of international agreements. We would like to underline in this regard the important role of the international community, GEF, UN organizations, EU and other key supporters in the development of this process. Many thanks to them.
Because of the economic and political crisis of that time, Georgia was unable to implement provisions of the Convention. From that time, Georgia attempts to develop stronger legal frameworks. As part of the environmental policy reform process, the legal framework on the environment and some other Black Sea relevant laws have been developed or updated. Black Sea issues were included in a separate chapter of the new National Environmental Action Plan. This chapter is based on priorities and findings of the Regional BS SAP.
The beautiful city of Batumi is an example of the outputs of this regional policy on environmental protection of the resources of the Black Sea. Quite recently, in 2012, a new system of sewage and water supply was completed in this city. Certainly the Government of Georgia is committed in recognizing environmental protection as one of the top priorities and is thriving to achieve much more for the protection of the Black Sea and its coastal zones.
This year, the international Black Sea Day celebration here in Georgia is organized in collaboration and in synergy with two European projects with quite different sources of funding โ IASON โ a so called 'uptake' type project supported by the European research 7th Framework Program (FP7) โ that is aiming at building
50
capacity on Earth Observation in the Black Sea and Mediterranean basins and catchments. IASON present its main results in relationship with international agreements on data sharing such as GEO/GEOSS at a global level, and INSPIRE at the European level. Some of the 'uptake' projects IASON is trying to peruse in the Black Sea region are FP7 enviroGRIDS and PEGASO. Until 2014 among the six Black Sea countries, only Georgia and Bulgaria still were not members of Group on Earth Observations โ GEO. It is indeed great pleasure to report, that thanks to efforts of enviroGRIDS and its continuation momentum under IASON Georgia accomplish these tasks and joined GEO as 90th of its member. It is also noteworthy that at least two countries in the Black Sea and Caucasus Region were indeed encouraged to take this step and at forthcoming GEO Plenary Armenia and Bulgaria are expected to be welcomed as member of this global network.
Another European project sponsoring this collaborative celebration of the Black Sea Day in Georgia is ILMM-BSE โ and initiative to apply toolsets of land use modelling in the catchments draining to estuaries and marine waters of the Black Sea. It is also noteworthy that Cross-Border Cooperation Program for the Black Sea is administered by Romania โ the European country adjoining the Black Sea. It is not surprising to expect that this project will try to deliver more at the regional and the local grassroots level.
These Projects are indeed relevant vehicles for strengthening the application and implementation of European instruments such as the Water Framework Directive, while helping the dissemination of good environmental and policy practice eastward. This is particularly important for Georgian environmental governance agenda in the context of the Association Agreements, established with the 28 Countries of the European Union.
A key lesson that we learned from our Regional and European cooperation for the protection of the Black Sea is that the network is stronger than its individual parts. Cooperation between countries, sharing the problems and best practices for their solving is a way how coastal states should act. We have difficult problems to face, and we will get through them if we face them together.
Thank you.
Nino Tskhadadze, The Black Sea Commission Member from Georgia
Ministry of Environment and Natural Resources
Protection of Georgia
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Integrated Land-Use Management Modelling of Black Sea Estuaries Project Implemented in Bulgaria, Turkey, Georgia and Ukraine
Sonya Enilova, Project Coordinator, Bourgas Region Tourism Association, [email protected]
Integrated Land-use Management Modelling of Black Sea Estuaries (ILMM-BSE) Project is financed by the Second call of the Joint Operational Programme 'Black Sea Basin 2007 โ 2013' under Priority 2, Measure 2.1. Its duration is 30 months and the total budget is โฌ 1 344 782.42, of which 90% is EU funding. Partners in the project are โ Applicant is Bourgas Regional Tourism Association (BRTA), Bulgaria; ENPI Partners are Bourgas Prof. Assen Zlatarov University, Bulgaria, Ukrainian Marine Environment Protection Association, Ukraine, International Association Civitas Georgica, Georgia; IPA Financial Beneficiary is Hayrabolu Municipality (HBM), Turkey, IPA Partners are Turkish Marine Environment Protection Association, Turkey and Namฤฑk Kemal University, Turkey.
ILMM-BSE Project Final Congress in Istanbul, Turkey, November 2015
The overall objective of the project is to develop, enhance, and evaluate, impact assessment and management tools for the sustainable land use of the watershed areas of coastal river mouths. The specific objectives are to create an integrated database system involving all relevant European research and application practices; to foster communication and collaboration on land management, in target deltas; to develop land-use models for target river mouths; to induce a cooperative institutional structure; to create cooperation and networking among scientists, land developers and decision makers in Black Sea basin; to develop an environmental education program. The target groups are academicians, researchers and experts of local universities and research institutions. The final beneficiaries are representatives and members of local NGOs, representatives and officials of local authorities and administrations. The project Integrated Land-use Management Modelling of Black Sea Estuaries (ILMM-BSE) is implemented by partners from four countries โ Bulgaria, Turkey, Ukraine and Georgia. The area of project covers Ergene basin and its delta in Turkey; Ropotamo and Veleka rivers'
52
basins and their river mouths in Bulgaria; Danube, Dniester and Dnieper river mouths in Ukraine, Guria region in Georgia. They have been selected as target river mouths, for the implementation of the activities of the action, for their commonalities, from the view point of their current conditions and characteristics. As a consequence of their high levels of biological productivity and their main topographical features, these coastal areas play an important and unique ecological role between the coastal zone and wetlands ecosystems, providing a collection of habitat types for many species maintaining high levels of biological diversity. Because of their location as an interface between the terrestrial and marine environments, and between mountains and coastal zones, they are subject to both continental and marine influences. Since early times, human settlement of these lands and utilization of their highly productive natural resources have created rural and urban landscapes reflecting cultures centred on trade, largely oriented towards the use of these special ecological systems. On the other hand, they are subjected to human exploitation โ through fisheries, aquaculture and tourism, coupled with associated urban, industrial, forestry or agricultural development โ inducing changes that affect their ecology. Accordingly, the development of an integrated framework analysis of these river mouthsโ lands take into account not only continental effects emanating from mountains on the one side and marine effects from coastal zones on the other side, but also cultural heritage assets inherited from ancient periods presenting apposition to the foreseeable effects of modern development.
First congress in Burgas, Bulgaria, in November 2013 Second congress in Odessa, Ukraine in September 2014
The concept of sustainable management of sensitive areas such as mountains, coastal zones as well as post-industrialised zones is neither well understood nor yet effectively applied. In consequence, various environmental problems are faced in these areas, including target territories of river mouths in participating countries, and these problems directly affect the utility of such areas and their surroundings, leading to important value loss in tourism, forestry, agriculture, fishery and the aquatic products sectors. Even if no protective measures are to be taken where no such adverse effects have hitherto been observed, similar effects may inevitable result as a consequence of rapid growth, rural development and other unsustainable development strategies. More than 30 percent of the areas of special protection designated under European Union directives for conservation are coastal. Many countries have developed a considerable body of protective legislation, which recognises of their value. In other words, the spatial-temporal variations in the ecosystems of the components of the territories of deltas should be evaluated within a very large, multi-dimensional, dynamic and complex framework. Once the need for sustainable management of sensitive areas has been identified, an integrated land-use management plan that will provide spatial and temporal guidance need to
53
be developed. Integrated means to achieve such goals and tasks need to be incorporated into as many existing programmes and entities that affect the system. The ideal result should be that each of the socio-economic entities, including individual citizens, considers their impacts and demands on these areas and their limited capacity to provide for these demands on a daily operational basis. In this regard, the joint action will undertake such an approach which is yet to be applied throughout the territories of European deltas, particularly when Associated Member Countryโs policies are considered.
PCU Meeting in Batumi, Georgia in 2014 Workshop in Batumi, Georgia in 2014
During the project all partners, beneficiaries and target groups met in Burgas In 2013 during the first congress of the project โIntegrated Land-use management modelling of Black Sea Estuariesโ (ILMM-BSE). The event was hosted by Burgas State University โProf. Dr Assen Zlatarovโ. The second congress was hosted by UKRMEPA in Odessa in 2014 and the third final congress of the project is in Istanbul, Turkey in November 2015 and is hosted by TURMEPA. Project partners from Turkey, Ukraine, Georgia and Bulgaria represented their organisations as well as the land-use management models in their countries. Academicians, researchers and experts from departments related to eco-system protection, biodiversity, environmental protection and land-use modelling, from local universities, NGOs, administrations and research institutions participated in the events.
Project Team Meeting in Odessa, Ukraine in February 2015
The formation and all meetings of partners in the Steering Committee, Project Coordination Unit, Financial Coordination Unit and Joint Research Unit are organised and hosted by Bourgas Regional Tourism Association. During three regular Steering Committee meetings, the outputs of the joint action and any difficulties encountered during implementation are discussed, decisions on the details of
54
implementing the project are made. During these meetings, participants produce additional material, complementing the project studies objectives.
Meeting in Tekirdag, Turkey in 2014 Training in Guria, Georgia in 2014
It is the responsibility of the Project Coordination Unit (PCU), to record all type of records emanating from various meetings, to present them for evaluations and reporting, by related experts and to distribute them in a transparent way. The PCU operates and continuously updates the website and the IMS, while directing and forwarding all questions and requests coming to these platforms to related working packages. Although creation of the database, GIS software and IMS was outsourced, the PCU coordinates the establishment of the structure and the framework of the system and all work packages and units of the project that produce and provide the requisite information for inclusion. A number of reports together with e-bulletins and press releases are published during the course of project by PCU and publications are posted on the website so that they can be easily downloaded by anybody. The PCU supplies all instruments to secure co-ordination between joint research activities. Virtual workshops and meetings on the IMS are used for co-ordination, in addition to report exchanges, under the supervision of the PCU, with the support of the DMC (Data Management Coordinator).
Environmental data and information training combined with beach cleanup event in Guria, Georgia in 2014
Financial Coordination Unit (FCU) is dealing with EU Commission issues, such as reporting, auditing, accounting, etc., in addition to recording the working days of researchers and payments made to them. The FCU undertakes the administrative management of the consortium for the successful completion of the programme. Joint Research Unit (JRU) supervises the implementation and coordination of joint research activities in each of the partnering countries and management of database, website and the IMS. The JRU supports the PCU in (i) carrying out all four work packages, within the context of
55
joint research programme, (ii) coordination and management of database, website and information management system. The JRU is also active in (i) spreading excellence and the dissemination of information and knowledge to public, (ii) organising public hearing meetings and prepared documents for public release, (iii) quality editing and publishing of reports, papers, etc., (iv) archiving all documents and materials produced by meetings, (v) coordination of organising congresses, workshops, training courses and executive / steering committees meetings, (vi) disseminating the results of all such meetings.
Training in Odessa, Ukraine Training course in Burgas, Bulgaria, organized by Burgas University 'Prof. Dr Assen Zlatarov'
The third group of activities in the project ILMM-BSE โ spreading excellence, is more inter-related with training activities, where one component in formal educational format, delivered at partnering universities, towards students. In this respect, an environmental education program was implemented in parallel in all partnering countries. This is formal education of young people in order for them to understand the central role of the natural environment and their future welfare. An environmental education program is developed by TURMEPA to ensure long-term sustainability of a participatory process.
Training course in Namik Kemal University, Turkey Workshop in Istanbul, Turkey in 2015
Training courses in Bulgaria were organised by the ENPI Partner University 'Prof. Dr Assen Zlatarov' - Burgas, training courses in Ukraine were organised by the ENPI Partner UKRMEPA, training courses in Georgia were organised by the ENPI Partner CIVITAS GEORGICA and training courses in Turkey were organised by the IPA Partner Namik Kemal University. During the project there were five workshops organized in different partner countries. The main aim
56
of the workshops was to discuss the joint research programme, which creates the required platform for researchers to meet each other and focus, discuss and evaluate working programme strategies and methodologies, in order to ensure that project targets are achieved, within required timeframe.
Visit of the mouth of Veleka River in Bulgaria with experts of local administration in Tsarevo Municipality
Public hearing in Georgia
After completing the review and evaluation of existing research and literature review, these events formed a platform for discussions for multifunctional approaches and needs for new tools and models for sustainable land-use planning and management, where representatives of work packages presented their views, all results and outputs of the studies were discussed and analyzed in detail and the shared conclusions obtained and disseminated, for the enhancement and development of new tools and models. The workshops were attended by all project partners as well as by representatives of all related institutions.
Public hearings in six Black sea municipalities in Burgas District, Bulgaria
Public hearings and press conferences were organized in all partner countries Bulgaria, Georgia, Ukraine and Turkey. All project activities and results were shared with the audience.
As a result of the implementation of project activities the following results were achieved: sharing knowledge, ensuring the lasting integration of information and data, networking experts and stakeholders throughout Black Sea basin, expanding the use of scientific tools to
promote sustainability in the use of territories of coastal river mouths and spread excellence
worldwide.
57
Instruments for Modelling Black Sea River Basins: Application Case of Guria Region in Georgia
Mamuka Gvilava a, *, Giorgi Meskhidze b
a ILMM-BSE Joint Research Coordinator for Civitas, ICZM National Focal Point for Georgia b President, International Association Civitas Georgica, Georgia
*Main author: [email protected] , +995 (599) 546616
Abstract Various tools and instruments, such as land cover change detection and hydrological modelling were employed to quantify changes in one of the Black Sea coastal regions of Georgia. These instruments, developed within several European supported projects (including Integrated Land Use Management Modelling of Black Sea Estuaries (ILMM-BSE)), were consistently applied to Guria Region. Local and global datasets allowed to build and to observe sustainable coastal development indicators, such as population (1989-2002) and land cover (2000-2010) changes, presenting them in a spatially explicit manner. DPRSF framework was employed to characterise governance and response action needs to address sustainability challenges in the catchments. Introduction Guria Region, with population around 140 thousand, is located along the Black Sea coast of Georgia spreading approx. 21.5 km from River Natanebi mouth to Supsa River mouth and further north to the edge of the port city of Poti. The region is composed of three administrative districts including Ozurgeti, Lanchkhuti and Chokhatauri Municipalities โ three most important settlements of the region, which are all non-coastal and located in the mountain foothill hinterland. Four small settlements are located along the Guria coast, from north to south: Grigoleti and Tskaltsminda (Lanchkhuti Municipality), Ureki and Shekvetili (Ozurgeti Municipality). Ozurgeti is the administrative centre of Guria. Figure 1 depicts Guria Region against the backdrop of the proposed boundaries for the coastal zone of Georgia.3
Figure. 1. Guria & Georgia Coastal Zone Figure 2. Datasets available for modelling 2 main catchments of Guria
3 http://sites.google.com/site/iczmgeo/Home/20050412-e-draft-ICZM-Law-GEORGIA.pdf
sites.google.com/site/iczmgeo/Home/20100322_Draft_ICZM_Strategy_Georgia_Eng.pdf
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Catchments of two main rivers, Supsa and Natanebi (with tributaries) essentially constitute the territory of the entire region, which is positive factor in terms of needs of the integrated management of catchment, coastal and maritime issues. Hydrological modelling of these two river basins therefore would provide important instrument to regional authorities to better deal with complex processes of land based sources of pollution, and monitor impacts of changes in land cover and land use in the catchment areas. Figure 2 above displays basins of these two key river systems (including their tributaries) discharging to the Black Sea in Georgian case area: Supsa (north) and Natanebi (south). Map shows topography, land cover and soils in river basins. Administrative boundaries of Guria Region are shown in red colour as well. Layers are overlaid against MODIS true colour image. These images show datasets, available for hydrological modelling of the river basins of the Guria Region. All these and other datasets not mentioned in this work are deployed on the Web-GIS portal developed under the EU Black Sea CBC Integrated Land Use Management Modelling of Black Sea Estuaries (ILMM-BSE) project. In synergy with enviroGRIDS (http://envirogrids.net) and PEGASO (http://pegasoproject.eu), as well as their uptake IASON (http://iason-fp7.eu) efforts, utilising instruments developed under these earlier projects, hydrological modelling and sustainable development indicator tools are applied to Guria Region and its main rivers, complemented by land cover change dynamics analyzed with ILMM-BSE methodology (http://e-BlackSea.net Web-GIS). This paper summarizes work done under ILMM-BSE project utilising the toolsets developed under these projects. Population dynamics and land cover change in Guria Region Coastal sustainability indicators (developed by PEGASO) are not yet fully feasible to apply for Guria Region, but some basic datasets were identified, best example of which is the population dynamics, as illustrated on Figures 3 and 4, where national census statistics was complemented by remote sensing (such as Landsat and NPP night lights) to visualise urban and rural dynamics, characterised mostly by the contraction of population. This indicator (and urban lights imagery) also illustrate, that coastal zone is indeed attracting lower density urban sprawl.
Figure 3. Population density of Guria Region according to national census (source: GeoStat, 2002)
Figure 4. Population change according to 1989 & 2002 census against backdrop of NPP night lights & Landsat
Another indicator was applied to illustrate and quantify changes in natural capital through land cover dynamics. For that purpose an opportunity was tapped with the newly opened access to 30 m global land cover dataset with 10 classes, available for years 2000 and 2010 (see GLC30,
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2000; GLC30, 2010 and Jun Chen, 2014). Land cover change dynamics for six main land classes present in Guria Region are displayed on Figures 5, 6 and summarised in Table 1. Colour coding for main land cover types โ artificial surfaces (developed), cultivated lands (semi-developed) and other natural land cover types (undeveloped), are in line with the traffic light methodology proposed in Chapter 1 and can serve as natural capital preservation indicator for Guria region.
Figure 5. Global 30 m Land Cover for Guria (2000) Figure 6. Global 30 m Land Cover for Guria (2010)
Table 1. Land use change of Guria Region between 2000 and 2010
Land Use 2000 2010 Land cover change (%) Area (%) Area (Hectare) Area (%) Area (Hectare)
Artificial surfaces 0.72 1,485 0.75 1,535 0.02
Cultivated lands 30.35 62,159 30.68 62,851 0.34
Forests 61.45 125,862 61.07 125,095 -0.37
Grasslands 6.86 14,052 6.80 13,923 -0.06
Wetlands 0.04 80 0.09 189 0.05
Water bodies 0.58 1,191 0.60 1,234 0.02
Source: http://www.globallandcover.org (GLC30, 2010); personal communication Chen Jun, NGCC (GLC30, 2000)
Hydrological modelling Main rivers of Guria Region are Supsa (length โ 108 km, catchment area โ 1130 km2, average multiannual discharge โ 46 m3/s), its tributaries Gubazeuli (47 km, 371 km2, 13.7 m3/s) and Bakhvistskali (42 km, 156 km2, 8.25 m3/s), as well as Natanebi (60 km, 657 km2, 33.5 m3/s) and its tributary Bzhuzhi (32 km, 259 km2, 14.3 m3/s). Natanebi river mouth is discharging into the Black Sea just 12 km south of Supsa river mouth. There used to be 8 hydrological gauge stations operated at all main rivers at various time intervals before 1992, but now only 1 hydrological and 1 meteorological posts are operation at Supsa near Chokhatauri (personal communication, Vakhtang Geladze, ILMM-BSE training on catchment hydrological modelling, 26 April 2014, Ureki, Georgia). The open source Soil and Water Assessment Tool (ArcSWAT, see Arnold et al. 1998) was applied to set-up the hydrological model for Guria Regionโs main river basins of Supsa and Natanebi. Global 30 m resolution land cover (GNCC), 30 m Global DEM, and FAO soils cover data (complemented with the national soils in 1/500,000 scale), combined with globally available climate datasets in ArcSWAT input format (see http://globalweather.tamu.edu) allowed to set-up and run the hydrological model for these catchments, but lack of hydrological discharge data for main river basins of Supsa and Natanebi (only one operating gauge station without open access to data) did not allow to calibrate and validate water quantity model.
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To compensate for the lack of discharge data, a thought experiment is proposed herby on how to apply remote sensing to address in situ discharge data scarcity. Indeed, recent advancements make it increasingly possible to calibrate river discharge data based on satellite observations of microwave measurements, using a global hydrology model (Brakenridge G.R., et al., 2012). Using methodology similar to one described in this reference, it is possible to fit discharge time series data with the extracted microwave signal measurements, acquired through the web portal http://www.gdacs.org/flooddetection. Figure 7 illustrates successful fitting of microwave measurements with the in situ discharge data for Rioni river case (enviroGRIDS, 2012).
Figure 7. Manual fitting of discharge time series (daily, monthly) with microwave satellite measurement data
Due to satisfactory visual fit of the in site measured and microwave satellite observation data, it is speculated, that instead of the use of global hydrological model to derive absolute values for river discharge time series from satellite observations, one could combine microwave satellite data (available in relative values), with absolute figures obtained via at-many-stations hydraulic geometry river width based methodology, described in Gleason and Smith (2014), in order to recalculate relative values of satellite measurement time series into absolute values for river discharge. Sentinel-2 satellite 10m resolution bands4 are expected to allow for such calculations for narrow width rivers such as Supsa and Natanebi, sensing their discharge data remotely. Responses in action As demonstrated above, using various available global and local datasets and the range of tools and instruments, modelling and quantification of land use/land cover and hydrological changes in the Black Sea catchments is feasible in case of Guria and other coastal regions of Georgia. Research work conducted within ILMM-BSE and other European projects enabled the capacity development to handle these complicated instruments in collaboration with Black Sea partners. Purpose of this concluding part of the paper is to characterise governance and management responses ongoing or needed to address many of the societal and environmental challenges ultimately affecting the Black Sea environment. Generalised Drive-Pressure-State-Response-
4 https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial
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Framework (DPRSF) for the specific case of land use change in the Black Sea river catchments of Georgia (considering the particular case of Guria Region) is depicted on Figure 8.
Figure 8. DPRSF for addressing land use / land cover change impacts in river catchments
Following further is the current status of necessary actions needed to address the governance and management challenges: Local land-use planning
โ Spatial planning legislation in largely in place on national level โ Municipal planning in process โ Inter-municipal approaches being established and tested
Land conservation and habitat restoration โ National parks and reserves in place โ Mechanisms are inadequate to conserve habitats and resources outside protected areas
Integrated coastal zone management โ Draft legislation and strategy exist โ Political will needed to implement
Integrated river basin management โ Baseline data on pilot area exists (Guria case) โ Georgia-EU association agreement (Roadmaps under implementation) โ Common approach for the Black Sea Region
Conclusions Population is contracting and land cover dynamics is moderate in the Black Sea river basins of the coastal Guria Region, but inadequacy of environmental regulations and weak enforcement impose increasing pressures on natural, social and economic subsystems. Introduction of integrated governance instruments such as ICZM and IRBM are necessity if requirements of EU-Georgia Association Agreement are to be implemented with success.
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Acknowledgements The authors would like to acknowledge the European Cross-Border Cooperation Black Sea Basin Joint Operational Programme 2007-2013 that supported the Integrated Land Use Management Modelling of Black Sea Estuaries (ILMM-BSE) project. Support by Chen Jun (GNCC) with access to GLC30 data for the year 2000 is sincerely acknowledged. Main author would also like to acknowledge EU FP7 enviroGRIDS, PEGASO and IASON projects for support and tools provided. Assistance by Tinatin Janelidze of GeoGraphic with land cover change quantification and by Vakhtang Geladze of (NEA) with hydrological baseline characterisation is kindly appreciated.
References Arnold, J. G., R. Srinivasan, R. S. Muttiah, and J. R. Williams. 1998. Large area hydrologic
modeling and assessment: Part I. Model development. J. American Water Resources Assoc. 34:73-89 (ArcSWAT is available at http://swat.tamu.edu/software/arcswat).
Brakenridge G.R., et al. (2012) Calibration of satellite measurements of river discharge using a global hydrology model. Journal of Hydrology, Volume 475, 19 December 2012, Pages 123-136. http://floodobservatory.colorado.edu/Publications/JourHydrology2012.pdf.
http://floodobservatory.colorado.edu/Publications/Chapman2012_poster_Cohen_et_al_2.pdf. http://floodobservatory.colorado.edu/CriticalAreas/forweb.pdf
EnviroGRIDS (2012), Remote Sensing Services, Deliverable D2.11, University of Geneva, 2012. http://envirogrids.net/index.php?option=com_jdownloads&Itemid=13&view=finish&cid=139&catid=11
Gleason, C.J., Smith, L.C. (2014) Toward global mapping of river discharge using satellite images and at-many-stations hydraulic geometry. Proceedings of the National Academies of Science, vol. 111, no. 13, 4788โ4791, http://dx.doi.org/10.1073/pnas.1317606111.
Jun Chen et al. (2014) Global Land Cover Mapping at 30 m Resolution: a POK-based Operational
Approach. ISPRS Journal of P&RS, http://dx.doi.org/10.1016/j.isprsjprs.2014.09.002. NGCC (2000) 30 m Global Land Cover 2000. National Geomatics Center of China (NGCC),
http://www.globallandcover.org, doi:10.11769/GlobeLand30.2000.db. NGCC (2010) 30 m Global Land Cover 2010. National Geomatics Center of China (NGCC),
http://www.globallandcover.org, doi:10.11769/GlobeLand30.2010.db.
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Earth Observation Marketing Tools and Business Opportunities for Environmental Management
Mark Noort a,*
a HCP international *Main author: [email protected], +31 (0) 629536467
Abstract There is a need for marketing of earth observation applications for environmental management. To address this more effectively, an impact assessment framework was developed that assesses the benefits and 'points still to be addressed' of possible solutions in three stages: step-by-step benefit framework, impact indicators and business environment. Based on an analysis of the environmental drivers, environmental challenges and policy priorities a number of business opportunities for earth observation applications are identified.
Introduction There is a need for marketing and promotion earth observation for environmental applications.
Partly, this is because the introduction of new (and innovative) technology takes some extra effort (Moore; 1991) and marketing and promotion derived from a carefully formulated customer value proposition is useful in itself (Barnes, Blake, Pinder; 2009). Additional marketing and promotion is needed because earth applications for environmental management deal with externalities that are not captured by current economic models. To target efforts better, a three stage impact assessment framework was developed (Noort; 2014), of which the first
stage consists of a step-by-step assessment of how the benefits of the earth observation application can be captured best (Figure 1).
Figure 1. Step-by-step assessment of the benefits of earth observation applications.
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Figure 2 gives an overview of where general environmental applications find a place in this framework. The information and analysis presented below is based on the marketing toolkits for environmental management, climate change and marine resources and environment that were developed in the framework of the EC FP7 GEONetCab, EOPOWER and IASON projects (Noort; 2014).
Figure 2. Where earth observation application categories for environmental management fit in the framework
The environmental setting Earth observation applications do not operate in a vacuum and are most beneficial when they form part of an effective and efficient organisational process. In relation to environmental management it is therefore important to look at a number of factors that influence
environmental decision making, such as drivers, challenges and policy priorities. Environmental drivers are, for example:
โ Economic growth; โ Population growth; โ Overexploitation of resources, such as in agriculture and fisheries; โ Lack of awareness, knowledge and consensus about what affects the environment and
what the consequences are.
Environmental challenges are, depending on viewpoint and perception: โ Freshwater scarcity, climate change, habitat change, invasive species, overexploitation
of oceans, nutrient overloading (UNEP; 2010); โ Cross-cutting issues, food โ biodiversity and land issues, freshwater and marine issues,
climate change issues, energy โ technology and waste issues (UNEP; 2012); โ Depletion of natural capital, climate change, biodiversity loss, emissions and waste
generation, pollution (EEA; 2010);
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โ Climate change, energy efficiency and renewable energy sources, management of ecosystems and biodiversity, forest loss, desertification and land degradation, water resources (de Ville, Kingham; 2011).
This leads to the following policy priorities: โ Better implementation and further strengthening of current environmental priorities;
โ Dedicated management of natural capital and ecosystem services; โ Coherent integration of environmental considerations across the many sectoral policy
domains; โ Transformation to a green economy; โ Compliance with international treaties environmental regulations.
Earth observation can support achieving these policy priorities, as will be shown in the next
section. Studies are available for some countries and international organisations that show the relevance of earth observation (CSA; 2012 and Secades et al.; 2014). Earth observation for environmental management Earth observation can particular contribute in the following areas:
โ Terrestrial, freshwater, marine and coastal ecosystems identification and monitoring;
โ Assessment of bio-geophysical variables;
โ Support to (national) park management; โ Biodiversity monitoring and modelling; โ Environmental accounting (including carbon accounts).
Earth observation is an excellent instrument for mapping and monitoring of land cover, land use, changes, classification and historical trends. It is a valuable tool for assessing the status of ecosystem goods and services, provided by the regulation, habitat, production, and information
functions of ecosystems. Evaluating ecosystem services in support of sustainable ecosystem management requires the use of (spatial) models. Some general models are available, such as the World Wildlife Fund (WWF) InVEST tool (Sharp et al.; 2015). For specific applications, new models will have to be developed. Earth observation facilitates measurement and assessment of individual bio-geophysical variables, such as vegetation, soil, radiation, water cycle and essential climate variables (ECVs). Bio-geophysical parameters provide the backbone for analysis and decision-making in
environmental management. Earth observation helps managers of national parks and protected areas improve park management. It provides valuable information on plant health, habitats, changes and relations between different factors that cannot be derived, or only at high cost, by in-situ analysis. Earth observation is instrumental in delineating optimum national park borders and environmental corridors. Earth observation helps predicting the impact of habitat loss and fragmentation on biodiversity
elements and ecosystems processes. It facilitates the inclusion of individual species or
functional types in ecosystem modelling and models (linked to carbon). Earth observation contributes to modelling of landscape dynamics, using geospatial data, to generate maps of suitable habitat over time for input into meta-population models.
Earth observation provides the basis for monitoring, reporting and verification for environmental accounting. The use of earth observation increases the precision of
quantification of carbon stocks and ecosystem type classification, result in more precise proxies for payment for ecosystem services (PES) schemes.
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Business opportunities To capitalise on the advantages listed above it is necessary to implement a more detailed assessment of the feasibility of each earth observation solution (in relation to other solutions). This is done by applying the indicators presented in table 1 (as second stage of the impact
assessment) and by a closer analysis of the business environment.
Table 1. Impact assessment indicators for earth observation applications
The rating of business environment is the third stage of the impact assessment and looks at circumstances that can differ by country or region, such as:
โ The willingness to pay (by clients);
โ The opportunities for embedding earth observation applications (in organizational processes);
โ Openness (transparency and ease of doing business, access to markets);
โ Institutions (is the institutional environment conducive to doing business, acceptance of new solutions?).
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With respect to environmental management, the way public sector information is dealt with is particularly relevant (Sawyer, de Vries; 2012). In summary, the main business opportunities for earth observation are in the fields of mapping and monitoring of ecosystems and biodiversity, protected area management, measurement
reporting and verification for environmental accounting. The main issues to be dealt with for particular applications are cost, data access, capacity and the business model. The use of support tools for earth observation marketing, such as success stories (what has already been implemented successfully elsewhere), demonstrators and roadshows (awareness raising) will increase the chance of success (Noort; 2013).
Conclusions Earth observation applications do not sell themselves, additional marketing is needed. This applies in particular to the field of environmental management, where not all the benefits can be captured in conventional economic models. Earth observation is valuable instrument for supporting environmental decision and policy making. The main business opportunities are in the fields of mapping and monitoring of
ecosystems and biodiversity, protected area management, measurement reporting and
verification for environmental accounting. The three stage impact assessment (step-by-step framework, impact indicators, rating the business environment) helps in the identification of business opportunities and targeting of marketing efforts. Acknowledgements The European Commission through its 7th Framework Programme supported the development of the impact assessment framework and marketing tools for earth observation as part of the GEONetCab, EOPOWER and IASON projects. References Barnes C., Blake H. and Pinder D. (2009) Creating and delivering your value proposition โ
managing customer experience for profit.
Canada Space Agency (2012) Space utilization earth observation โ Space applications linked to government priorities / departments.
Europe Environmental Agency (2010) The European environment โ state and outlook. Moore G.A. (1991) Crossing the chasm โ marketing and selling high-tech products to
mainstream customers. Noort M. (2014) Methodological framework for impact assessment of earth observation for
environmental applications. EOPOWER.
Noort M. (2014) Marketing toolkit: earth observation for environmental management.
EOPOWER. Noort M. (2014) Marketing toolkit: earth observation for climate change. EOPOWER. Noort M. (2014) Marketing toolkit: earth observation for marine resources and environment.
EOPOWER. Noort M. (2013) Marketing earth observation products and services, part #2. GEONetCab.
Sawyer G. and Vries M. de (2012) About GMES and data: geese and golden eggs - A study on the economic benefits of a free and open data policy for Sentinel satellite data.
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Secades C., O'Connor B., Brown C. and Walpole M. (2014) Earth observation for biodiversity monitoring: a review of current approaches and future opportunities for tracking progress towards the Aichi biodiversity targets. Secretariat of the Convention on Biological Diversity, Montrรฉal, Canada. Technical Series No. 72.
Sharp R., et al. (2015) InVEST +VERSION+ Userโs Guide. The Natural Capital Project. The Nature Conservancy and World Wildlife Fund.
UNEP (2012) 21 issues for the 21st century: result of the UNEP foresight process on emerging environmental issues.
UNEP et al. (2010) TEEB - The economics of ecosystems and biodiversity for business. Ville, G. de, and Kingham, R.A. (2011). Recent trends in EU external action in the fields of
climate, environment, development and security. IES.
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The Importance of Marine Aerosols for Climate Change Assessments
Nicholas Meskhidze a,* a Associate Professor at North Carolina State University
*Main author: [email protected], http://www4.ncsu.edu/~nmeskhi/Homepage.html
Introduction Everything, from an individual person to Earth as a whole, emits energy. In science, this energy is referred to as radiation. As Earth absorbs incoming sunlight, it warms up. In order for the planet to remain in thermodynamic equilibrium, the equal amount of energy received from the Sun must be emitted into space. The Earth's climate system constantly adjusts in a way that tends toward maintaining this balance between the energy that reaches the Earth from the Sun and the energy that goes from Earth back out to space. If the amount of energy emitted by the Earth is less than the incoming solar radiation, the temperature of the Earth will increase until a new thermodynamic balance is established. Such temperature increase has important ramifications for the Earthโs climate. Two components make up the Earth's outgoing energy: longwave (or thermal infrared radiation) that the Earth's surface and atmosphere emit; and shortwave (with wavelengths in the visible, near-ultraviolet, and near-infrared spectra) that the land, ocean, clouds, and particles (suspended in the air) reflect back to space. The balance between incoming sunlight and outgoing energy determines the planet's temperature and, ultimately, climate. Both natural and human-induced processes affect this balance, also known as the Earth's radiation budget. In what follows, I will discuss how aerosols play an important role in the Earthโs radiation budget.
The Impact of Aerosols on Climate An aerosol is fine solid particle or liquid droplet suspended in the air, produced by either natural processes or human activity. Aerosols in the atmosphere degrade air quality, adversely affect human health, reduce visibility and influence the Earthโs climate. But, of particular interest here is the role of aerosols on the Earthโs climate balance. Aerosols either reflect or absorb energy, depending on their size, chemical composition and altitude. The haze layer that is commonly seen in the summertime is one example of an aerosol that primarily reflects (scatters) sunlight. Soot emitted by diesel engines, as well as mineral dust suspended in the air, are some examples of aerosols that absorb sunlight. These absorption and scattering of incoming radiation are called direct aerosol radiative forcing and they act in a direct way to change the balance between incoming and outgoing energy. Aerosols can also affect the Earth's radiation budget indirectly by modifying the characteristics of clouds, which also play a major role in the Earthโs radiation budget. The study of clouds - where they occur and their characteristics โ is the key to the understanding of climate change. Low, thick clouds primarily reflect solar radiation and cool the surface of the Earth. High and thin clouds primarily transmit incoming solar radiation; at the same time, they trap some of the outgoing infrared radiation emitted by the Earth and radiate it back downward, thereby warming the surface of the Earth. Cloud particles almost always form around aerosols such as natural sea spray particles or human-made sulfate particles. The presence of additional aerosols can change the cloud particle size and the ability of the cloud to precipitate. Such changes ultimately affect the way clouds radiate energy and the length of time they stay intact. These effects are called indirect aerosol radiative forcing.
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As discussed above, aerosols have both natural and anthropogenic sources. In general, both of these sources can influence the climate directly, through absorption or scattering of radiation, or indirectly, through the changes to the reflectivity or lifetime of clouds. Considering that natural source of aerosols, such as sea spray, dust, volcanic eruptions, forest fires, etc. have been around for thousands of years, the phrase โclimate effectโ is typically reserved only for man-made aerosols. Therefore, it is a common practice to estimate aerosol effects on climate based on the differences between model simulations of present-day and of preindustrial aerosol emissions. In order to normalize all model predictions to the same background (starting) conditions, scientists agreed to use year 1750 as a proxy for the preindustrial conditions. The climate prediction calculations are conducted using complex 3-D Global Climate Models (GCMs). According to Intergovernmental Panel on Climate Change (IPCC) โ a scientific body under the sponsorship of the United Nations (UN) that reviews and assesses the most recent scientific, technical and socio-economic information produced worldwide relevant to the understanding of climate change โ aerosol direct effects (absorption or scattering of radiation) and indirect effects (changes to the reflectivity or lifetime of clouds) represent the largest source of uncertainty in current understanding of global radiative forcing [IPCC, 2013]. Figure 1 shows that unlike human-produced greenhouse gases, aerosols tend to have negative radiative forcing (i.e., cool the Earth); however, the uncertainty (shown by the error bars) remains very large.
Figure 1. Radiative forcing estimates in 2011 relative to 1750 and aggregated uncertainties for the main drivers of climate change. Values are global average radiative forcing, partitioned according to the emitted compounds or processes that result in a combination of drivers. The best estimates of the net radiative forcing are shown as black diamonds with corresponding uncertainty intervals; the numerical values are provided on the right of the figure, together with the confidence level in the net forcing (VH โ very high, H โ high, M โ medium, L โ low, VL โ very low). Figure adapted from IPCC, 2013: Summary for Policymakers.
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Sea Spray Aerosols Although natural aerosols do not affect climate directly, recent studies have shown that accurate representation of natural background aerosols, such as ones over the marine regions, is critical for better assessment of anthropogenic aerosol effects [Gantt et al., 2011; Carslaw et al., 2013]. The impact of sea spray aerosols on global climate remains one of the most uncertain components of the aerosolโradiationโclimate problem, but has received less attention than the impacts of terrestrial and anthropogenic aerosols. The last decade has produced a large body of information regarding the sources and composition of marine aerosols, resulting in a reassessment of the complex role that sea spray particles play in climate and various geophysical phenomena. As sea spray aerosol contributes substantially to the preindustrial, natural background which provides the baseline on top of which anthropogenic forcing should be quantified, and because the ocean covers over 70% of the Earthโs surface, the representation of sea spray aerosol in climate models strongly influences the predicted impact on climate of anthropogenic aerosols via direct and indirect effects. In addition, climate change affects atmospheric parameters, such as wind speed that has controlling effect on the production of sea spray aerosol. An international group of experts who convened at a marine aerosol workshop held in Raleigh, NC suggested that there is a great need for comprehensive observational data on marine aerosols that can be used for improvement/evaluations of climate models [Meskhidze et al., 2013]. Seawater-derived aerosol, themselves, can be separated in two broad classes: primary, i.e., derived from the mechanical process of bubble bursting, and secondary, derived through gas phase oxidation of dimethylsulfide and marine biogenic volatile organic compounds produced by oceanic biota or through photosensitized reactions involving the sea-surface microlayer. In the past, sea-salt was recognized as a major component of marine primary aerosols (i.e., sea spray); however, recent studies have shown that ocean-derived organic matter can contribute a considerable fraction to sub-micron primary marine aerosol mass [Gantt and Meskhidze, 2012]. Sea surface temperature and salinity were also suggested to influence sea spray emission [Mรฅrtensson et al., 2003]. Ocean-derived secondary aerosols, which are the outcome of gas-to-particle conversion processes, typically enhance concentrations of very small particles. Despite some controversy, today scientists agree that number of seawater-derived particles in the atmosphere is typically high above biologically active regions. Marine Aerosols in the Black Sea region and their Effect on the Climate One of such marine regions that are capable of producing large amounts of aerosol (both through primary and secondary mechanisms) is the Black Sea. The Black Sea is a sea between Southeastern Europe and Western Asia. It is bounded by Europe, Anatolia and the Caucasus, and drains through the Mediterranean into the Atlantic Ocean, via the Aegean Sea and various straits. The Black Sea has an area of 436,400 km2 (168,500 sq mi), a maximum depth of 2,212 m (7,257 ft), and a volume of 547,000 km3 (131,000 cu mi). Despite its importance, production of marine aerosols from the Black Sea and their effect on the climate remain poorly characterized.
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Enriched by nutrients carried in by the surrounding rivers, the waters of the Black Sea can maintain high biological productivity and are fertile territory for the growth of phytoplankton (Figure 2). Phytoplankton are the โprimary producersโ of the seas and oceans. These plant-like,
microscopic algae and bacteria use chlorophyll to make their own food from carbon dioxide (CO2), sunlight and dissolved nutrients. Many of Europeโs largest rivers dump fresh water into the Black Sea. The seaโs only source of salty water, on the other hand, is the narrow Bosporus Strait, which connects it to the Mediterranean Sea through the Sea of Marmara. The salty water is denser than the fresh water, and so it sinks to the bottom, leaving a layer of relatively fresh water on top. The density barrier between salt and fresh water is great enough that the two layers do not mix. As a result, when fresh water enters the sea from rivers, it only mixes with the relatively fresh water in the top 150 meters of the sea. This means that fertilizers and runoff carried in the river water remain concentrated in the top of the sea where they nourish the phytoplankton that grow on or near the surface. This also means that the Black Sea ecosystem is quite vulnerable to increased pollution from the surrounding rivers. The main phytoplankton groups present in the Black Sea are dinoflagellates, diatoms, coccolithophores and cyanobacteria. Generally, the annual cycle of phytoplankton development comprises significant diatom and dinoflagellate-dominated spring production, followed by a weaker mixed assemblage of community development below the seasonal thermocline during summer months and a surface-intensified autumn production. This pattern of productivity is also augmented by an Emiliania huxleyi bloom during the late spring and summer months. This natural-color image captured by the Moderate Resolution Imaging Spectroradiometer (MODIS) (see description in Box 1) on NASAโs Aqua satellite shows the Black Sea on June 20, 2012. Milky, light blue and turquoise-colored water in the middle and the eastern half of the sea is likely rich with blooming phytoplankton that trace the flow of water currents. Closer to the coast, the colors include more brown and green, perhaps a brew of sediment and organic matter washing out from rivers and streams, though it may also be a sign of phytoplankton. Puffs of spring clouds linger over parts of the coastline.
Figure 2. Phytoplankton Blooms in the Black Sea. NASA image by Jeff Schmaltz,
LANCE/EOSDIS Rapid Response. Caption by Michael Carlowicz.
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BOX 1. The Earth Observing System (EOS) is a program of NASA comprising a series of artificial satellite missions and scientific
instruments in Earth orbit designed for long-term global observations of the land surface, biosphere, atmosphere, and oceans of the Earth. The first satellite component of the
program was launched in 1997. The program is centerpiece of NASA's Earth Science Enterprise. Focused on measurements identified as important by U.S. and international scientists, EOS
satellites gazing down on our
planet from the unique vantage point of space enable research into how Earth's lands, oceans, air, ice, and life function together as a complex environmental system.
Along with in situ field measurements, laboratory experiments, and regional and global modeling, satellites help us to better understand the cause-and-effect relationships among Earth's lands, oceans and atmosphere. Improved understanding of the Earthโs biogeochemical interaction will enable us to make better predictions of future climate conditions. MODIS Aqua satellite was launched on May 4,
2002. It measures radiances in 36 spectral bands from 0.4 to 14.24 ฮผm and has a swath width of 2330 km. Aqua provides global
coverage every two
days from a polar-orbiting, sun-synchronous
platform at an altitude of 705 km.
Aqua is in an ascending orbit with an equatorial crossing of 1:30 pm local solar time. The spatial resolution at nadir has the following ranges: 250m (2 channels), 500m (5 channels), and 1 km
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(29 channels). The aerosol retrieval makes use of the first seven of these channels (0.47โ2.13ฮผ m) while additional wavelengths in other parts of the spectrum are used to identify cloud properties and ocean products [Esaias et al., 1998; Platnick et al., 2003; Remer et al., 2005]. All Aqua atmosphere products are archived into two categories: pixel-level retrievals (referred to as Levelโ2 products) and global gridded statistics at a latitude and longitude resolution of 1
(Levelโ3 products). The Level-3 products are temporally aggregated into daily, eight-day, and monthly files containing a comprehensive set of statistics and probability distributions (marginal and joint). Aqua ocean data consist of 36 Ocean Color and 4 sea surface temperature (SST) science parameters. There are an additional 38 parameters, such as wind speed, surface pressure, brightness temperatures, etc., that are used for quality control (QC). At Level 2, the 40 Ocean science parameters are grouped into 3 Ocean Color data types and one SST data type.
At Level 3, each of the 40 parameters is space-binned and time-averaged to a separate HDF-EOS grid file. Thus each Level 3 ocean parameter is available in daily, 8-day, monthly and yearly average, and at 4.63 km, 36 km and 1ยฐ spatial resolution. Each parameter's mean map has associated quality and statistics files where information for each pixel can be found [Esaias et al., 1998]. Less than 73 seconds behind Aqua flies Cloud-Aerosol Lidar and Infrared Pathfinder Satellite
Observations (CALIPSO) platform. The CALIPSO mission, launched on 28 April 2006, has been
able to provide the scientific community with vertically resolved measurements of both aerosol and cloud optical properties like depolarization ratio (a measure of particle sphericity), aerosol optical depth, and ice/water phase since June 2006. The CALIPSO payload includes a high-powered digital camera, an infrared radiometer, and the two-wavelength (532 and 1064 nm) near-nadir, polarization sensitive elastic backscatter lidar CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization). The level 1 data algorithms are responsible for the geolocation and
range determination of the satellite and produce profiles of attenuated backscatter coefficients. Data in this work were obtained from the 5 km level 2 operational products, version 3.01. Level 2 products have undergone various processing algorithms from the Selective Iterated BoundarY Locator (SIBYL), the Scene Classification Algorithm (SCA), and the Hybrid Extinction Retrieval Algorithm (HERA). First, SIBYL identifies layers, then the SCA identifies the type of feature (i.e., aerosol or cloud) and the subtype (i.e., aerosol type, ice/water phase), and finally the HERA generates extinction profiles for the feature. The theoretical basis of algorithm
can be found online at www-calipso.larc.nasa.gov/resources/project_documentation.php. The CALIPSO 5 km aerosol layer data include many operational products. Among them are the integrated attenuated backscatter and its uncertainty at 532 nm, the layer features such as number found in the column, their top and bottom altitudes and the feature classification flags.
[Images are courtesy of NASA]
Challenges related to the study of marine aerosol production The evaluation of background aerosols over the marine regions has been proven difficult both logistically (ship cost, etc.) and mechanically (marine aerosols frequently exist at very low
concentrations posing a measurement challenges for sensors). Retrieval of marine aerosols through passive remote sensing (e.g., MODIS Aqua sensor) has proven difficult, as aerosols are
often comprised of different natural (marine aerosol, dust) and anthropogenic components and are often located at different altitudes in a vertical column. Presence of clouds could further
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complicate the matter. CALIPSO sensor (see Box 1) is unique in its ability to concurrently retrieve aerosol chemical speciation and extinction profiles, and ocean sub-surface information. Such products are ideally suited for studying marine aerosols and could lead to new or significantly improved representation of marine aerosol radiative effects. Global 3-D extinction climatologies and dedicated case studies using CALIPSO clean marine aerosol products have
been successfully used for constraining aerosol radiative forcing over the oceans. However, to determine the aerosol subtypes (i.e., separate marine aerosol from other types of aerosol over the oceans), the CALIPSO algorithm uses volume depolarization ratio, integrated attenuated backscatter, the earth surface types (land/ocean), and altitude information. From a purely mathematical point of view, the separation of aerosol extinction and backscatter profiles from a single lidar measurement is intractable due to having one measurement and two unknowns.
To overcome this problem and obtain aerosol optical depth (AOD, that can be viewed as a proxy for aerosols suspended in the air), the CALIOP algorithm relies on a prescribed lidar ratio. The lidar ratio is an intrinsic aerosol property, i.e., a property that does not depend on the number density of the aerosol but rather on physical and chemical properties such as size
distribution, shape and composition. The lidar ratio at 532 nm of 20 6 sr (steradian) was selected by NASA scientists to represent marine aerosols. However, marine aerosol size
distribution and chemical composition can change significantly with ocean surface wind speed
(U10), temperature, salinity and chemical composition of surface seawater. For this reason, large disagreement exists in the literature regarding the value of maritime aerosol lidar ratio spanning the range from 17 to 39 sr (at 532 nm wavelength). Such uncertainty and the inability of the CALIOP-sensor to account for the possible variability in marine aerosol lidar ratio values over different parts of the open ocean causes over a factor of two uncertainty in the CALIOP-retrieved marine AOD values. Recently, my group has developed a new method to calculate lidar ratios of marine aerosol over cloud-free oceans using two independent sources: AOD from Synergized Optical Depth of Aerosols (SODA) and the integrated attenuated backscatter from CALIOP [Dawson et al., 2015]. The method itself is rather complex and is outside the scope of this article. However, I will say that this new method removes the dependence of the prescribed lidar ratio while still utilizing the active sensors to retrieve an AOD, thereby providing a means for independent evaluation of the lidar ratio. For example, instead of using one number for the lidar ratio (as it was done previously), Figure 3 created using our new method for calculating marine aerosol lidar ratio shows that the calculated aerosol lidar ratios decrease from ~22 sr for U10 > 15msโ1 to ~32 sr for 0 <U10 < 4 msโ1. Such changes in the lidar ratio are expected to have a corresponding effect on the marine AOD.
Figure 3. Probability density function of clean marine aerosol
lidar ratio for selected AMSR-E wind speed regimes. The ฮผ parameter shows the mean of each distribution.
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Figure 5. Image of cloud streets over the Black Sea captured by MODIS
sensor on Aqua satellite on January 8, 2015. NASA Earth Observatory image
courtesy Jeff Schmaltz. LANCE/EOSDIS MODIS Rapid Response eam, GSFC.
In addition to the wind speed, our initial data analysis suggests that the lidar ratio of marine aerosols can be sensitive to seawater biological productivity. Figure 4 shows CALIPSO aerosol extinction retrievals over the Black Sea. To highlight the contrast, in addition to biologically productive Black Sea in low right corner we show part of the low productivity (oligotrophic) Mediterranean Sea. Our studies in different parts of the oceans show that the retrievals with anomalous depolarization ratio (ฮด > 10%) seem to correlate with surface Chlorophyll-a concentration ([Chl-a]) detected by MODIS Aqua satellite. The finding, if confirmed by comprehensive analysis over different special location and time seasons would point to hypothesized air-sea interaction linking biological production and clean marine aerosol optical properties. Future Research directions/possible collaborative initiatives One can argue that for the clear-sky (no cloud) conditions like one shown on Figure 2, the climatic effects of marine aerosols likely to remain small. However, what happens when the domain is covered by the clouds like ones shown on Figure 5 captured by MODIS Aqua satellite on January 8, 2015? Figure 5 shows cloud streets, long parallel bands of cumulus clouds that form when cold air blows over warmer waters and a warmer air layer (temperature inversion) rests over the top of both. The comparatively warm water gives up heat and moisture to the cold air above, and columns of heated air called thermals naturally rise through the atmosphere. The temperature inversion acts like a lid. When the rising thermals hit it, they roll over and loop back on themselves, creating parallel cylinders of rotating air. As this happens, the moisture cools and condenses into flat-bottomed, fluffy-topped cumulus clouds that line up parallel to the direction of the prevailing winds. As the air rises, it also brings marine aerosols, affecting microphysical properties of overlying clouds.
Figure 4. 532 nm aerosol extinction for Level 2, 5km CALIPSO profile
data on top of the 8-day surface Chlorophyll-a concentration
composite from MODIS Aqua (Sept. 06-13, 2015). Each CALIPSO profile
that passes over the Black Sea region is shown.
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Ecosystem change โ caused by discharges from rivers, industry, agricultural pollution and domestic sewage โ affect the biological productivity of the Black Sea. Given current assessments of the worldโs future economic prospects, such changes will only intensify in future. Changes in seawater physicochemical and biological properties will undoubtedly cause the subsequent modifications in marine aerosol production, distribution and chemical composition influencing seasonal weather patterns and long-term climate variability of this region. Scientists in Georgia, in collaboration with the scientist in the US/Europe, can develop better research strategies to study the changes in marine aerosol production and their influence on seasonal weather patterns and long-term climate variability. This kind of collaborative research will enable Georgian scientists to utilize new measurements and remote sensing techniques, and using the Black Sea as a case study come up with some interesting breakthroughs in marine biology-aerosol-cloud-climate interaction field. Such research can also offer a more holistic picture of the Black Sea/Caucasus. References Carslaw, K. S., L. A. Lee, C. L. Reddington, K. J. Pringle, A. Rap, P. M. Forster, G. W. Mann, D. V.
Spracklen, M. T. Woodhouse, L. A. Regayre, and J. R. Pierce (2013a), Large contribution of natural aerosols to uncertainty in indirect forcing, Nature, 503, 67โ71, doi:
10.1038/nature12674. Dawson, K.W., N. Meskhidze, D. Josset, and S. Gassรณ (2015), A new study of sea spray optical
properties from multi-sensor spaceborne observations, Atmos. Chem. Phys., 15, 3241 - 3255, doi:10.5194/acpd-15-3241-2015.
Echalar, F., P. Artaxo, J.V. Martins, M. Yamasoe, F. Gerab, W. Maenhaut, and B. Holben (1998), Long-term monitoring of atmospheric aerosols in the Amazon Basin: Source identification and apportionment, J. Geophys Res., 103(D24), 31849โ31864, doi:
10.1029/98JD01749. Gantt, B. and N. Meskhidze (2012),The physical and chemical characteristics of marine organic
aerosols: a review, Atmos. Chem. Phys., 13, 3979-3996, 2013 doi:10.5194/acp-13-3979-2013, doi:10.5194/acp-13-3979-2013.
Gantt, B., J. Xu, N. Meskhidze, Y. Zhang, A. Nenes, S. J. Ghan, X. Liu, R. Easter, and R. Zaveri (2012), Global distribution and climate forcing of marine organic aerosol โ Part 2:
Effects on cloud properties and radiative forcing, Atmos. Chem. Phys., 12, 6555โ6563, doi:10.5194/acp-12-6555-2012.
IPCC, 2013: Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Mรฅrtensson, E. M., E. D. Nilsson, G. de Leeuw, L. H. Cohen, and H. -. Hansson (2003), Laboratory
simulations and parameterization of the primary marine aerosol production, J. Geophys. Res., 108, 4297, doi:10.1029/2002JD002263, doi: 10.1029/2002JD002263.
Meskhidze, N., M. D. Petters, K. Tsigaridis, T. Bates, C. O'Dowd, J. Reid, E. R. Lewis, B. Gantt, et
al. (2013), Production mechanisms, number concentration, size distribution, chemical composition, and optical properties of sea spray aerosols, Atmos. Sci. Lett., 14, 207-
213, doi:10.1002/asl2.441.
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Platnick, S., M.D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riedi, and R. A. Frey (2003), The MODIS cloud products: Algorithms and examples from Terra, IEEE Trans. Geosci. Remote Sens., 41(2), 459โ473, doi:10.1109/TGRS.2002.808301, 2003.
Remer, L. A., Y. J. Kaufman, D. Tanrรฉ, S. Mattoo, D. A. Chu, J. V. Martins, R.-R. Li, C. Ichoku, R. C. Levy, R. G. Kleidman, T. F. Eck, E. Vermote, and B. Holben (2005), The MODIS aerosol
algorithm, products, and validation, J. Atmos. Sci., 62(4), 947โ973, doi:10.1175/JAS3385.1.
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The Bringing GEOSS Services into Practice (BGSIP) Workshop: an Earth Observation Capacity Building Resource for the Black Sea Area
Lacroix Pierre a, b, *, Guigoz Yaniss a, b
a University of Geneva, Institute for Environmental Sciences, EnviroSPACE Lab., Uni Carl-Vogt, CH-1211 Geneva 4, Switzerland
b Global Resource Information Database (GRID) โ Geneva, International Environment House, 11 chemin des Anรฉmones, CH-1219 Chรขtelaine, Switzerland
* Main author: [email protected]
Abstract The โBringing GEOSS services into practiceโ workshop aims at teaching how to configure, use and deploy a set of open source software to set up a spatial data infrastructure. The workshop focuses on how to publish and share data and metadata using OGC and ISO standards and how to register services into the Global Earth Observation System of Systems (GEOSS). The related material is totally free, based on open source solutions, available in English and partly available
in six other languages. Since its creation in 2010 the workshop has been presented to more
than 500 people in the Black Sea area and beyond. In particular, it has been given during the Black Sea Day 2014, in Batumi, Georgia, organized jointly by European FP7 IASON (http://iason-fp7.eu) and CBC Black Sea ILMM-BSE (http://e-BlackSea.net) projects. Introduction Data discovery, access and integration are essential for conducting successful environmental research. To increase the capacity to access Earth Observation (EO) data the Group on Earth
Observations (GEO) (GEO, 2014) is leading the development of the Global Earth Observation System of Systems (GEOSS) (GEO secretariat, 2005), a voluntary effort that connects producers and users of EO data and resources. GEO actively promotes capacity building and education activities in order to reach a large adoption, acceptation and commitment on data sharing. More specifically, the GEO secretariat defined a capacity building strategy (GEO secretariat, 2006) and set up a specific task on capacity building T02 that has the following objectives:
โ โEnhancing coordination of national and international capacity-building efforts to produce and use EO and information;
โ Increasing the demand for day-to-day EO and information across societal benefit areas (SBAs);
โ Building national capacity in developing countries by enabling human, technical and institutional capacity for coordinating, accessing, using and sharing environmental data, information and services;
โ Developing cross-border education and training across societal benefit areas showing the short- and long-term benefits of Earth observation; and
โ Developing synergies, encourage cross-fertilization and address common challenges across capacity building initiatives.โ
The โBringing GEOSS services into practiceโ (BGSIP) workshop (Giuliani et al., 2014) adopts this approach by proposing an integrated set of teaching material and software to facilitate the
publication and use of environmental data through standardized discovery, view, download, and processing services. Trainees learn how to publish and share data and metadata using OGC (OGC, 2013) and ISO (ISO, 2015) standards, how to register services into GEOSS and how to set
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up a spatial data infrastructure (SDI) (Nebert, 2005). Beyond the primary goal of building technical capacity the ultimate objectives of BGSIP are to: (1) raise awareness on data sharing principles; (2) build capacity at different levels (people, institutions, infrastructure) to bring these principles into practice; and (3) build new synergies between national and regional actors for the benefit of national/regional โdata flowโ. Methods The workshop is based on free and open source software and the related material consists in: (1) a PDF tutorial; (2) a virtual machine containing all the necessary software and data; (3) a PowerPoint presentation in 7 languages, including English and Russian. All the material can be freely downloaded from a dedicated website (http://www.geossintopractice.org) and comes
along with further information (e.g. frequently asked questions, a story map and teaching videos). In order to keep track of people downloading it for measuring as much as possible the impact of the workshop, people are required to answer a few easy questions before being able to download the workshop material. These questions request the personโs name, email address, country, company name and type, position role and primary objective for downloading the material.
The programme of the workshop is structured in a sequence of questions that aim at teaching
the attendees how to use the whole chain of geospatial data from production to dissemination (Table 1). It is focused on OGC and ISO standards, e.g. Web Map Service (WMS) (Open Geospatial Consortium, 2006) for publishing maps, Web Feature Service (WFS) (Open Geospatial Consortium, 2005) and Web Coverage Service (WCS) (Open Geospatial Consortium, 2006) for accessing data, Web Processing Service (WPS) (Open Geospatial Consortium, 2007) for processing data and ISO 19115 (ISO, 2014)/19139 (ISO, 2007) for documenting data.
Table 1: Structure of the BGSIP workshop
Chapter Title
1 Concepts on SDI
2 How to store geospatial data?
3 How to publish geospatial data?
4 How to document and search geospatial data?
5 How to process geospatial data?
6 How to view geospatial data?
7 How to download geospatial data?
8 How to analyze geospatial data?
9 How to share geospatial data?
As language might be a barrier, the workshop presentation exists in 7 different languages: Arabic, Croatian, English, French, Russian, Serbian and Spanish. As the workshop has been
developed in a train-the-trainers approach, this multilingual presentation helps local trainers to modify and enrich it to build capacity in their own region or institution with the most suitable language.
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Results More than 500 people ranging from teachers or students to policy makers, scientists and people working in the private or public sector have been trained so far in ten countries (see the workshopโs agenda at http://www.unige.ch/tigers/fr/enseignements/geossinpractice/agenda), leading to dozens of download of the workshop material by workshopโs attendees. The workshop has been presented in various formats depending on the audience and the time available:
โ General presentations with live demonstrations (no hands-on) in case of international events (e.g., as a side event during the GEO-X conference
(http://www.earthobservations.org/me_se.php?id=7)); โ Theoretical presentations combined with hands-on exercises as was the case for the
2014 International Black Sea Day (Bourgas Regional Tourism Association, 2014) in Batumi (Figure 1). On that occasion the workshop was given in English during half a day to about 50 persons, targeting policy makers, researchers and stakeholders from the Black Sea area countries (Gvilava, 2014). Attendees came with their own laptops to follow the hands-on session.
Figure 1: BGSIP hands-on training workshop, Batumi, 30th October 2015
โ One-week block in the case of courses at University of Geneva. These courses are given once a year since 2013 to about 20 students who are further asked to put their knowledge into practice by developing and publishing a web application.
โ Workshop combined with project-specific activities such as integration of geospatial data into the SDI of the hosting institution (e.g. the ClimVar project:
http://www.globalclimateforum.org/index.php?id=127).
These examples show that the workshop addressed very different audiences. Still, it remains quite technical and requires some SDI expertise from participants. Besides, the hardware was
also a challenge in some cases due to laptops of the attendees: old versions of Windows not working with the workshopโs virtual machine, slow computers, keyboards using non Latin letters. Finally, the heterogeneous rhythm of progression of participants was also problematic.
To tackle these issues other formats of the workshop are planned, less technical and/or more thematic (e.g., in the field of disasters management, raw material or hydrology).
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Despite these problems the workshop has contributed to raise peopleโs awareness on geospatial data sharing principles. Participants that might have been slowed down by technical issues during the workshop can now practice on their own as all the material is available for free. Furthermore the fact that the workshop material has been downloaded about 1โ000 times (see Figure 2) demonstrates that it has definitely contributed to raise awareness on geospatial data issues in the Black Sea area and beyond.
Figure 2: Cumulative number of downloads of the workshop material (March 2014 to October 2015)
Inclusion of the โBringing GEOSS Services into Practiceโ workshop in a multi-project event, as was the case for the Batumi workshop, presents the advantage of building new synergies around the Earth Observation thematic between people from different horizons. In the Batumi workshop, awareness was raised on EO issues and technical aspects for several key regional actors (cf. participants list of (Gvilava, 2014)), which might have created new regional synergies and regional networks.
Finally, a questionnaire was sent to all the workshopโs past attendees. The expected feedback of this questionnaire relates to how they have put into practice the knowledge acquired with the workshop in their own institution. The results showed that a high percentage of the workshopโs past attendees have trained (or are planning to train) their colleagues. It also shows
that one third of the respondents have built up a SDI based on the knowledge acquired. It is therefore possible to assert that the workshop had impacts individual, institutional and technical levels, making it a successful capacity building resource. Conclusions In line with introductory objectives, awareness has been raised on Earth Observation and data sharing principles for the benefit of the countries and regions where the workshop is
presented. This is particularly true for Georgia where a long implication of national
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environmental key actors in several international projects including Earth Observation capacity building component such as the BGSIP workshop took place. These trainees have now the possibility to become trainers themselves in their own organization, country or region to build capacity. Coupling capacity building workshops to regional events such as the Black Sea Day can foster national/regional collaborations and new
synergies through convenience of thematic actors around a common thematic. The BGSIP workshop, promoted by the GEO secretariat, contributes to lower entry barriers to Earth Observation and data sharing solutions for both data users and providers. This is key to facilitate the development of local/regional technical skills, for the benefit of the whole Black Sea area.
Acknowledgements The authors would like to acknowledge the European Commission โโSeventh Framework Programโโ that funded EOPOWER (Grant Agreement no. 603500), IASON (Grant Agreement no. 603534), and enviroGRIDS (Grant Agreement no. 227640) projects. References Bourgas Regional Tourism Association (2014). Third Workshop of the ILMM-BSE project and
International Black Sea Day Celebration in Batumi, Georgia on 30 -31 October 2014. GEO (2014). "The Group on Earth Observations overview." (Retrieved from
http://www.earthobservations.org/index.php). GEO secretariat (2005). GEOSS 10-Year Implementation Plan: Table of Work Plan Targets: 1-22. GEO secretariat (2006). GEO Capacity building strategy: 13. Giuliani, G. et al. (2014) "Bringing GEOSS Services into Practice. GIS Open Source Workshop
Material." 189. https://itunes.apple.com/us/book/bringing-geoss-services-into/id806182409
Gvilava, M. (2014). IASON D3.4: Workshop II report and material. ISO (2007). ISO/TS 19139:2007: Geographic information -- Metadata -- XML schema
implementation. ISO (2014). ISO 19115-1:2014: Geographic information -- Metadata -- Part 1: Fundamentals. ISO (2015). "the International Organization for Standardization." (Retrieved from
http://www.iso.org/iso/home.html). Nebert, D. D. (2005). Developing Spatial Data Infrastructure: The SDI Cookbook. OGC. "The Open Geospatial Consortium." (Retrieved from http://www.opengeospatial.org). Open Geospatial Consortium (2005). Web Feature Service Implementation Specification. Open Geospatial Consortium (2006). OpenGIS Web Map Server Implementation Specification. Open Geospatial Consortium (2006). Web Coverage Service (WCS) Implementation
Specification.
Open Geospatial Consortium (2007). OpenGIS Web Processing Service.
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Integrated Land Use Management Modelling of Black Sea Estuaries: Case of Ergene River Basin in Western Turkey
Fatih Konukcu a, *, Selcuk Albut a, Bahadir Alturk b, Huzur Deveci b
a Prof. Dr., Namik Kemal University, Faculty of Agriculture, Biosystem Engineering Department, TR59030 Tekirdag-TURKEY
b Lecturer Namik Kemal University, Vocational School of Technical Sciences, TR59030 Tekirdag-TURKEY
*Main author: [email protected], +90 (282) 250 2261
Abstract Land use planning is a useful tool to find a balance among the competing and sometimes contradictory uses in order to achieve food security, economic growth, energy supply, nature conversation and other objectives. In this study, modelling land use change of Ergene River Basin in Western Turkey between the years of 1990 and 2012 was the primary objective, however, general data and elevation, soil, forest, protected areas maps of the Basin were also produced within the scope of ILMM-BSE Project (Integrated Land Use Management Modelling of Black Sea Estuaries) funded by EU and Turkish Ministry of EU Affairs. As a results, while the artificial area (including settlement area and industrial zone) and water bodies due to new reservoirs construction increased by 39.4 and 47.9%, respectively, wetlands and agricultural areas decreased dramatically.
Introduction Land is a scarce resource increasingly affected by the competition of mutually exclusive uses. Fertile land in rural areas becomes scarcer due to population growth, pollution, erosion and desertification, effects of climate change, urbanization etc. On the remaining land, local, national and international users with different socioeconomic status and power compete to achieve food security, economic growth, energy supply, nature conversation and other objectives. Land use planning can help to find a balance among these competing and sometimes contradictory uses (Wehrmann, 2010). In this study, modelling land use change of Ergene River Basin in Western Turkey between the years of 1990 and 2012 was the primary objective, however, general data and elevation, soil, forest, protected areas, erosion maps of the Basin were also produced. Methods Ergene River Basin, located in the European part of Turkey, is one of the 25 river basins in Turkey. Ergene River, 283 km in length, sourced in Istranca Mountain ranges close to the Bulgarian border, joins into the Maritsa River and Discharge into the Aegean Sea in the Saroz Golf. The basin area is about 11 000 km2 and the total population in the basin is 1 150 000. The climate of the basin is under the influence of the terrestrial climate with hot and dry summers and cold winters in the northern part while it is dominated by the Mediterranean climate with hot and dry summers and mild and rainy winters in the southern part. The annual average precipitation, temperature and relative humidity are about 600 mm, 13ยฐC and 70%, respectively (Action Plan, 2008). Major surface water resources are constituted of Maritsa and Ergene Rivers and their tributaries, which include 67 sub watersheds. The principle tributaries of Ergene River are Corlu Creek, Suluca Creek, Luleburgaz Creek, Babaeski (Seytan) Creek, Teke Creek,
86
Hayrabolu Creek and main stream (Ergene Action Plan, 2008). Total, surface and underground water potential of the basin, respectively, are 1.73 billon m3, 1.33 billion m3 and 0.4, billon m3. The hydrology map of the basin is presented in Figure1.
Figure 1. Hydrologic map of Ergene River Basin
In the modelling of land use changes, CORINE land cover maps (Figure 2) and ArcGIS based model developed within the scope of ILMM-BSE Project. 'ILMM-BSE - Integrated Land Use Management Modelling of Black Sea Estuaries' Project is financed by the Second call of the Joint Operational Programme โBlack Sea Basin 2007 โ 2013โ (http://e-blacksea.com).
Figure 2. CORINE land cover maps.
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Results The land use changes between 1990 and 2000, between 2000 and 2006, between 2006 and 2012 are shown in Figure 3, whereas the land use changes between 1990 and 2012 is summarised in Table 1.
Figure 3. Land use change maps of Ergene River Basin between 1990 and 2000, between 2000 and 2006,
between 2006 and 2012.
While the artificial area (including settlement area and industrial zone) and water bodies due to new reservoirs construction increased by 39.4 and 47.9%, respectively, wetlands and agricultural areas decreased dramatically. Maps of the Ergene River Basin for general data and elevation, soil, forest, protected areas, are given in Figure 4.
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Table 1. Land use change of Ergene River basin between 1990 and 2012.
Land Use 1990 2012 Land use change (%) Area (%) Area (Hectare) Area (%) Area (Hectare)
Artificial area 2.4 34764.26 3.3 48460.67 +39.4
Agricultural Area 79.7 1154121.93 78.8 1141081.66 -1.1
Forests and semi natural areas 17.1 246875.37 16.9 244509.39 -1.0
Wetlands 0.3 5053.15 0.2 3432.98 -32.1
Water bodies 0.5 6948.36 0.7 10275.21 +47.9
Figure 4. Maps of the Ergene River Basin for general data and elevation, soil, forest, protected areas
Conclusions Dramatic changes in agricultural areas to industrial area has been threatening not only natural resources but also food security since the basin has the most productive arable land of Turkey. Acknowledgements 'ILMM-BSE - Integrated Land Use Management Modelling of Black Sea Estuaries' Project is funded by EU and Turkish Ministry of EU Affairs. The contents of this publication are the sole responsibility of the authors and can in no way reflect the views of the European Union.
References Wehrmann B, (2010).Land Use Planning Concept, Tools and Applications. Deutsche Gesellschaft
fรผr Internationale Zusammenarbeit (GIZ) GmbH Division Agriculture, Fisheries and Food Sector Project Land Policy and Land Management Eschborn/Germany.
Action Plan to Protect Maritza-Ergene River Basin (2008). Turkish Ministry of Environment and Forestry, General Directorate of Environmental management.
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Nutrient Pollution of the Bulgarian Black Sea Coastal Waters โ Problems and Prevention
Valentin Nenov a, Anna Simeonova b
a Department of Water Treatment, Burgas University, 1 Y. Yakimov str., 8010, Burgas, e-mail: [email protected]
b Department of Navigation, Transport Management and Protection of Waterways, Technical University - Varna, 1 Studentska str., 9010 Varna, Bulgaria, e-mail: [email protected]
Abstract In the present study were assessed the sources of nutrient pollution, nutrient status and the
impact on the Bulgarian Black Sea coastal waters. Analyses of the nutrient contamination from point emitters were carried out โ WWTPs, sewerage systems, rivers runoff and their influence on the ecological status of the coastal waters for the period 2011-2013 was determined. The problems with the diffuse nutrients discharges were discussed. The following tendencies were outlined: point emitters could be assessed as significant source of nutrient pollution, failing to
meet the emission standards; the river discharges could not be considered as crucial for the
nutrients enrichment of the coastal waters; the diffuse sources control and assessment remain one of the main problem concerning nutrient contamination. Introduction The Bulgarian Black Sea coastal waters (BBSCW) have a great economic, social & ecologic value. The poor quality of the coastal waters has a negative influence on the marine ecosystems, on tourism and the whole coastal economy. Therefore prevention of further deterioration of the
BBSCW and their sustainable use is a priority of the Bulgarian water policy which can be achieved by effective management of the ecological and technological risks [14]. One of the key instruments of the BBSCW management is the implementation of the river basin management plan (RBMP) of the Black Sea River Basin District (BSRBD) [2], following the requirements of the Water Framework Directive 2000/60 EEC (WFD) [6] which first planning cycle was completed. Major problems of the coastal water management which need to be resolved during the next
planning cycle of the Black Sea RBMP are: reduction of pollution caused by untreated sewage water and waste water treatment plants; nutrient loads; toxic chemicals; illegal dumps; flooding prevention; protection of biodiversity; intrusion of invasive species; abrasion, etc. Nutrient pollution is still one of the main pollution problems of the Bulgarian coastal waters and has a range of negative effects on coastal system one of which is the eutrophication [18]. According to the requirements of Directive 91/271/ะEะก concerning urban wastewater treatment [7] and Order of the Minister of Environment and Water No. 970/28.07.2003, the
BBSCW have been determined as sensitive area since 2003, threatened by eutrophication, and a number of legal restrictions on nutrient loads were imposed. During the last year a reduction
of the nutrient levels have been observed but there are still measures to be resolved during the next planning cycle of the Black Sea RBMP. In the present study are assessed the nutrient status, sources of pollution and their impact on
the BBSCW as well as the weaknesses in the fulfilment of measures for nutrients reduction.
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Methods The Bulgarian Black Sea coastal waters are delineated in the one-mile coastal zone with total area 1434 km2. The length of the sea coast is 378 km. According to the requirements of the WFD 2000/60 โ 13 water bodies were differentiated along the BBSCW (Table 1), part of the BSRBD, and are managed by the Black Sea Basin Directorate (BSBD), responsible at River Basin
District level [2].
Table 1. Water bodies along the Bulgarian Black Sea coastal area
โ NAME OF THE WATER BODY TYPE CODE OF WB
1. from Durankulak to Shabla CW3 BG2BS000C001
2. from Nos Shabla to Kamen bryag CW2 BG2BS000C002
3. from Kamen bryag to Kaliakra CW1 BG2BS000C003
4. from Nos Kaliakra to resort โAlbenaโ CW5 BG2BS000C004
5. Varna Bay CW5 BG2BS000C005
6. from Nos Ilindg to point with coord. 27ยฐ53'43"/ 42ยฐ58'17" CW4 BG2BS000C006
7. from point 27ยฐ53'43"/ 42ยฐ58'17" to Nos Emine CW4 BG2BS000C007
8. Burgas Bay < 30m CW6 BG2BS000C008
9. Protected area โKoketraysโ CW4 BG2BS000C009
10. Burgas Bay > 30m CW6 BG2BS000C010
11. from Nos Akin to Nos Korakya CW4 BG2BS000C011
12. from Nos Korakya to river Rezovska mouth CW3 BG2BS000C012
13. from resort โAlbenaโ to Nos Ilindg CW5 BG2BS000C013
Results Eutrophication is a process of changing the water body status by nutrient enrichment and has a
wide range of negative effects on coastal systems [9]. Nutrients usually boost the primary productivity of the marine ecosystems that forms the base of the aquatic food web [1]. Human activities profoundly influence the global cycling of nutrients, especially movement of nutrients to estuaries and other coastal waters [14, 15]. The main sources of nutrient loads in the BBSCW are point discharges โ sewerage systems, waste water treatment plants (WWTPs),
rivers runoff and diffuse discharges from agriculture and livestock runoff, stormwater and urban runoff, leakage from wastes disposals along the coast, etc.
Point sources The point emitters of wastewater flows can sometimes be the major source of nutrients to the
coastal waters [5]. Large amount of eutrophication matter come into the coastal waters due to the row sewage water from different agglomerations as well as due to the lack of biological
treatment in the WWTPs.
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Along the Bulgarian Black Sea coast are functioning 8 WWTPs and six sewerage systems which effluents are discharged directly into the coastal waters [12, 13]. The wastewater treatment technology of 7 of the WWTPs includes only pre-treatment and aerobic biological treatment. Only one of the WWTPs โ Balchik WWPT โ Dobrich municipality applied nitrogen and phosphorus removal technology. The annual reports of the BSBD and the Regional Inspections
of Environment and Water (RIEW) [3, 12, 13] show that the nutrient discharges into the coastal waters from the seven WWTPs, which didnโt apply N and P removal, were above the limits imposed in their individual Wastewater Discharge Permit (WWDP) [11]. Three of the WWTPs โ Albena (Dobritch), Zlatni Pyasatsi (Varna) and Elenite (Nessebar) are trying to solve the problem by construction of deep underwater discharge but the rest doesnโt take any actions. Other point sources of nutrient inputs into the BBSCW are the rivers flowing directly into the
coastal waters, especially the river mouths [16, 17]. The Bulgarian rivers are recipient of different pollution sources from the catchment area such as small tributaries, wastewater discharges, heavy rains, urban runoff, wastes disposals, erosion, infiltration from agricultural areas, ground waters etc. The rivers flowing directly into the BBSCW are numbering 17. According to the data collected during the last years there were no deviations of the values of the physico-chemical indices monitored during the last years [4].
According to the classification system for assessment of the ecological status of the physico-
chemical quality elements (supporting the biological quality elements) [6, 10] most of the rivers at the area of inflow to the coastal waters during 2014 show good or very good status with reference to nutrient [4]. This means that the average values of N and P were with low concentrations under the threshold limits. Only at the Drashtela and Karaach river mouths (southern Black Sea coast) was registered moderate status. According to the results the river discharges could not be considered as crucial for the nutrients enrichment of the coastal
waters. Diffuse sources The diffuse sources of pollution show the highest percentage in the Black Sea RBD [8]. Agriculture and land use are one of the largest sources of P, N. Nutrients from these sources can reach the water either by direct leaching or runoff from farm fields. Some N and P are leached directly from agricultural fields to groundwater and surface waters. Due to the climate
changes extremely polluted storm water runoff form urban and rural areas enters into the coastal waters, enriching nutrients contamination. It has to be noted that monitoring data on phosphorus and nitrogen are lacking in many cases. It is reported that there is no methodology for the assessment of the diffuse sources along the Bulgarian Black Sea coast and it has been based on expert judgement and no numeric criteria were reported [8]. For that reason realistic risk assessment of the nutrient inputs and their influence on the coastal waters status could not be accomplished.
Nutrients impact on coastal waters The main objective of the WFD 2000/60 and the BSRBMP first cycle is all water bodies along the BBSCW to reach good status till 2015 [2, 6]. The ecological status of the nutrients,
phytoplankton and macroalgae in the 13 water bodies along the BBSCW for three years period (2011-2013) is presented in Tables 2 and 3, taking into consideration the last annual report of
BSBD for 2013 [3]. Usually the most influenced biological quality elements by the nutrient inputs are the phytoplankton and macroalgae.
92
Table 2. Ecological status for the physico-chemical quality elements of the water bodies along the Bulgarian Black
Sea coastal waters, according to the requirements of the WF Directive, 2011-2013
No Water body 2011 2012 2013
NO3, mg/l
PO4, mg/l
Total
N,P
NO3, mg/l
PO4, mg/l
Total
N,P
NO3, mg/l
PO4, mg/l
Total
N,P
1. BG2BS000C001 M M M M M M G M M
2. BG2BS000C002 M M M G V.G G V.G V.G V.G
3. BG2BS000C003 M M M G V.G G V.G V.G V.G
4. BG2BS000C004 M - M V.G M M V.G V.G V.G
5. BG2BS000C005 M - M V.G M M V.G V.G V.G.
6. BG2BS000C006 M - M G M M V.G G G
7. BG2BS000C007 M - M V.G M M V.G V.G V.G
8. BG2BS000C008 M - M V.G M M V.G V.G V.G
9. BG2BS000C009 M - M V.G V.G V.G. V.G V.G V.G
10 BG2BS000C010 M - M V.G M M G V.G G
11 BG2BS000C011 M - M V.G M M V.G G G
12 BG2BS000C012 M - M V.G M M G V.G G
13 BG2BS000C013 - M M V.G M M V.G G G
Legend M - Moderate G - Good V.G โ Very Good
The data showed very high levels of nutrients - N and P during 2011 and moderate status regarding all water bodies, according to the principal โone out - all outโ. Similar situation was observed during 2012 with reference to the total N, P status despite the reduction of N-NO3. Considerable improvement during 2013 for both N-NO3 and P-PO4 and the total nutrient status was recorded. Only the water body BG2BS000C001 from Durankulak to Shabla didnโt reach an improvement during the whole period.
With reference to the biological quality elements โ the status of phytoplankton was worse than the macrophyte almost through the whole period. A little improvement of the total status of phytoplankton and macrophyte was observed during 2013. The trend of the investigated
biological quality elements was similar to the trend of the nutrients status observed. This exhibited close relation between nutrients loads and the status of the phytoplankton communities and macrophyte. The worst was the situation again in the water body
BG2BS000C001 as well as in BG2BS000C005 โ Varna Bay. As a whole 11 of the water bodies were not in compliance with the requirements of the WFD for good ecological status.
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Table 3. Ecological status for the biological quality elements of the water bodies along the Bulgarian Black Sea
coastal waters, according to the requirements of the WF Directive, 2011-2013
No Water body 2011 2012 2013
Phyto-
plank-
ton
Macro-
phyte
Total
Phyto-
plank-
ton
Macro-
phyte
Total
Phyto-
plank-
ton
Macro-
phyte
Total
1. BG2BS000C001 M V.B V.B M B B M V.B V.B
2. BG2BS000C002 M G M M G M M V.G M
3. BG2BS000C003 M G M M G M G V.G G
4. BG2BS000C004 M M M M G M G G G
5. BG2BS000C005 M V.B V.B M B B M M B
6. BG2BS000C006 M - M M - M M - M
7. BG2BS000C007 M V.B V.B G V.G G M G M
8. BG2BS000C008 M M M M G M M G M
9. BG2BS000C009 M - M M - M G - M
10 BG2BS000C010 M - M M - M G - M
11 BG2BS000C011 M M M M V.G M M V.G M
12 BG2BS000C012 M V.G M M V.G M M V.G M
13 BG2BS000C013 M B B M M M G M M
Legend V.B- Very Bad B- Bad M โ Moderate G - Good V.G โ Very Good
Management and prevention Legislation. The coastal water management of the Bulgarian Black Sea follows strictly the EU environmental policy which has been developed in order to monitor, conserve and protect the marine environment. There are more than 200 EU directives, regulations and many other forms of legislation in the area of environmental policy. One of the EU Directives implemented in the
Bulgarian legislation which play an important role in the coastal water management and
especially for the nutrients reduction are the Urban Wastewater Treatment Directive (91/271/EEC) which dictated the level of sewage treatment; the Nitrates Directive (91/676/EEC) aimed at controlling diffuse pollution especially from agriculture and its adverse effects of
eutrophication. Other directives aimed at Risk Assessment and Risk Management are the Integrated Pollution Prevention and Control Directive (2008/1/EC, and the Environmental Impact Assessment Directive (EIA) (85/337/EEC). More recently this has continued with the
passing of the Flood Risk Management Directive (2007/60/EC) which aims to reduce and manage the risks that floods pose to the human health and environment.
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The main Bulgarian legal act which regulates the management of the coastal zone is the Bulgarian Law for Spatial Planning of the Black Sea Coast. In 2012 some important amendments related to the coastal zone have been adopted, namely, requirements to the municipal spatial and land-use plans to include regulations and conservation measures for the coastal water area as well.
River Basin Management Plan (RBMP). One of the key instruments for the coastal water management is the RBMP for the Black Sea River Basin District, adopted in 2010 by the Order of the Minister of Environment and Waters (No. 294/22.03.2010). The Plan is the main inter-sectoral strategic tool for water management in Bulgaria. It includes a set of measures (Programme of Measures) for water protection and restoration, most of which are related to the activities still to be implemented in the coastal zone thus setting the frame for integrated
coastal zone management in Bulgaria. 2015 is the year when a project of updated RBMP for the period 2016-2021 must be accomplished. References 1. Borysova, O., Kondakov, A., Paleari, S., Rautalahti-Miettinen, E., Stolberg, F. and D. Daler,
2005. Eutrophication in the Black Sea region; Impact assessment and Causal chain
analysis. University of Kalmar, Kalmar, Sweden, pp. 1-60.
2. BSBD, Plan for water management in the Black Sea River Basin District, 2010-2015ะณ., 2010, pp. 1-361, http://www.bsbd.org.
3. BSBD, Annual retort of the assessment of the water status in BSRBD for 2013, 2014, pp. 1-92, http://www.bsbd.org.
4. BSBD, Bulletin of the water quality in the Black Sea River Basin District for 2014, 2015, pp. 1-28, http://www.bsbd.org/UserFiles/File/2015/I_XII_buletin_2014.pdf.
5. Dineva S., Water Discharges into the Bulgarian Black Sea, International Symposium on Outfall Systems, May 15-18, 2011, Mar del Plata, Argentina, pp. 1-9.
6. EEC, Directive 2000/60/EC of the European Parliament and of the Council of 32 October 2000 establishing a framework for Community action in the field of water policy, Official Journal of the EU, 2000, OJ L 327/1/22.12. 2000, 2000, pp. 1-71.
7. EEC, Council Directive 91/271/EEC of 21 May 1991 concerning urban waste-water treatment Official Journal L 135, 30/05/1991 pp. 0040-0052.
8. EU Commission, Commission staff working document - member state Bulgaria, Report from the commission to the European Parliament and the Council on the implementation of the Water Framework Directive (2000/60/EC) River Basin Management Plans, Brussels, SWD, 2012, pp. 1-53.
9. Howarth R. et al., Nutrient Pollution of Coastal Rivers, Bays, and Seas, Issues in Ecology, 7, 2000, pp. 1-17.
10. MOEW, National Regulation No.ะ-4 / 14.09.2012 for characterization of surface waters,
2013, pp. 54, http://www.moew.government.bg.
11. MOEW, National Regulation No.6/2000 for emission threshold limits of harmful and toxic substances in the waste waters, 2000, pp. 55, http://www.moew.government.bg.
12. RIEW-Burgas, Report of the environmental status in 2014, 2015, pp. 1-234,
http://www.riosvbs.eu. 13. RIEWโVarna, Regional report of the environmental status in 2014, 2015, pp. 1-169,
http://www.riosv-varna.org.
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14. Simeonova A., 2010, Ecological aspects related to the Black Sea sustainable utilization in the context of the European water conservation policy, Journal of the Technical University โ Varna, Vol. I, pp.164-169 (in Bulgarian).
15. Simeonova A. K, Chuturkova R.Z, Todorov P.I., Pollution of Shokarski stormwater canal and its influence on the quality of the Varna Black Sea coastal area, Bulgaria, International
conference โAir and water components of the environmentโ, 23-24 march, Cluj Napoca, Romania, Aerul ลi Apa: Componente ale Mediului Journal, 2012, pp. 41-48.
16. Simeonova A., R. Chuturkova, V. Bojilova, J. Bekyarova, 2011, Quality of Varna Black Sea bathing water near the river Kamchiya mouth, Journal of Balkan Ecology, vol. 14, No. 3, pp. 295-300.
17. Simeonova A., J. Bekyarova, R. Chuturkova, 2010, Investigations of the river Kamchiya
impact over the Varna Black sea coastal status, Journal of Ecological engineering and environmental protection, No.1, pp. 25-30 (in Bulgarian).
18. Todorova V., Kosnulova T., Long term changes and recent state of Macrozoobenthic communities along the Bulgarian Black Sea coast Mediterranean Marine Science Vol. 1/1, 2000, pp. 123-131.
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CHAPTER 3
Proceedings of Students Scientific Workshop on Ecology of Black Sea River Basins (Batumi Shota Rustaveli State University, 05 October 2015, Batumi, Georgia)
98
99
Address of the Rector of Batumi Shota Rustaveli State University
แแแแฃแแแก แจแแแ แ แฃแกแแแแแแแก แกแแฎแแแแฌแแคแ แฃแแแแแ แกแแขแแขแแก แ แแฅแขแแ แแก แแแแแ แแแ
แแแแแกแแแแแแแ แกแขแฃแแแแขแแ แกแแแแชแแแแ แ แแแแคแแ แแแชแแแก แแแแแฌแแแแแแก.
แแแแฃแแแก แจแแแ แ แฃแกแแแแแแแก แกแแฎแแแแฌแแคแ แฃแแแแแ แกแแขแแขแ แแฅแขแแแแแ แจแ แแแแแก 80 แฌแแแก แแฃแแแแแก
แแฆแแแจแแแแก. แแ แกแแแฆแแกแแกแฌแแฃแแ แแแ แแฆแแแ แแแแแแจแแ แแแแ แแ แแแแแ แฆแแแแกแซแแแแ แแแแฅแแก แแแแแแแแแ.
แแ แฆแแแแกแซแแแแแแก แจแแ แแกแแ แฉแแแแ แแฆแแแแแแแแ แแแแคแแ แแแชแแ, แ แแแแแแช โแจแแแ แแฆแแแก แแแแแแ แแแ
แแฃแแแแแก แแแแแแแแแกโ แแแแจแแแแแแแแ แกแแแแแฎแแแก แแซแฆแแแแแ.
แกแแกแแแแแแแแ, แ แแ แแแแคแแ แแแชแแแจแ แแแแฃแแแก แจแแแ แ แฃแกแแแแแแแก แกแแฎแแแแฌแแคแ แฃแแแแแ แกแแขแแขแแก
แกแแแฃแแแแแกแแแขแงแแแแ แแ แฏแแแแแชแแแก แคแแแฃแแขแแขแแก แแ แแแแฃแแแก แแแขแแแแแฃแ แ แแแฆแแก แแฎแแแแแแ แแ
แแแชแแแแ แแแแแ แแ แแแ แแแแแแกแแก แแ แแแแแก แกแแฎแแแแฌแแคแ แฃแแแแแ แกแแขแแขแแแแก แแแแแแแแ แแแขแแก,
แแแแแกแขแ แแขแฃแ แแก แแ แแแฅแขแแ แแแขแฃแ แแก แกแแคแแฎแฃแ แแก แกแขแฃแแแแขแแแแช แแแแแฌแแแแแแแ.
แแแแคแแ แแแชแแแก แแ แแแแแแแแ แฎแแ แชแแแแแแแ แแแ แแแแแจแแ แแก แแ แแแฅแขแแก โแแแฌแแแกแแ แแแแแแแแก
แแแขแแแ แแ แแแฃแแ แแแแแแแ แแแ แแ แแแ แแแ แจแแแ แแฆแแแก แแกแขแฃแแ แแแแกแแแแแกโ แแฎแแ แแแญแแ แแ.
แแแแแแแแก แแแแแฎแกแแแแแ แแ แแแฅแขแแก แแแ แขแแแแ แก แกแแฅแแ แแแแแแแแ แกแแแ แแแจแแ แแกแ แแกแแชแแแชแแ โแแแแแขแแก
แแแแ แแแแแกโ แแ แแ แแแแแแแ แแแแแก แ แแแแแแแแแกแแแแก.
แฉแแแแ แฃแแแแแ แกแแขแแขแแกแแแแก แฃแแฆแ แแกแแ แแแแจแแแแแแแแแแ แแแแแแจแ แแแแแแ แแแ แแแแแจแแ แแก แจแแแ แแฆแแแก
แแ แแแแแแ แกแแแแแ แแชแแ แแ แแแ แแแแกแแแ, แ แแแแแก แแแแ แซแแแแแแช แแแ แแแแแแกแแแก แแแแ แแฅแขแแฃแ แแ
แแแแแแแแ 2015-2020 แแแ แแแแแกแแแแแก.
แแแแแ แแแแฅแแก, แ แแ แฉแแแแ แฃแแแแแ แกแแขแแขแแก แกแแแฃแแแแแกแแแขแงแแแแ แแ แกแฎแแ แแแ แแแแแก แแแแแชแแแแ แแ
แแฎแแแแแแ แแ แกแแแชแแแแแกแขแแแ แแ แแ แแแ แแแแก แคแแ แแแแแจแ แจแแซแแแแแ แฉแแแ แแแ แ แแแแแแแแฃแ
แแ แแแฅแขแแแจแ แกแฎแแแแแกแฎแแ แแแ แขแแแแ แแแแแ แแ แแแ แจแแแ แแฆแแแก แฅแแแงแแแแแแแ.
แแแแจแแแแแแแแแแ แแแแคแแ แแแชแแแก แแแแแขแแแ, แ แแแแแ แแแ แแแกแแฎแฃแ แแแ แฉแแแแ แแแแแแ แแแแแก แแ แจแแแ แแฆแแแก
แแแแแแแแฃแ แ แแแแแแแ แแแแแก แแแฃแแฏแแแแกแแแแก. แฃแแแแแ แกแแขแแขแแก แกแแแแชแแแแ แ แแแขแแแชแแแแ แแแแแแ
แแแแแก แฌแแแแแ แจแแแขแแแแก แจแแแ แแฆแแแก แแแ แแแแก แแแชแแแก แซแแแแกแฎแแแแแจแ แกแแแ แแแจแแ แแกแ แแ แแแแแแแแ แแ
แแแแแแ.
แกแขแฃแแแแขแแ แกแแแแชแแแแ แ แแแแคแแ แแแชแแแก แแ แแแแแแแแ แฉแแแแ แฃแแแแแ แกแแขแแขแแก แคแแขแแแแแแแแแแแกแ แแ
แแแแแ แแแแแคแแ แแแแแแแก แแแกแขแแขแฃแขแแก แแแแฎแแแแก แขแแ แคแแแ แแแแกแ แแ แฌแงแแแก แแแแกแแกแขแแแแแแก
แแแแกแแ แแแชแแแก แแแแงแแคแแแแแแ แแแแแ แแ แฌแแ แแแขแแแแ แแแแ แแแ แแแแ.
โแแแแแขแแก แแแแ แแแแแกแแแโ แแ แแแ แฉแแแแก แแแแ แฃแแ แฃแแแแแงแแคแแ แแฅแแ แแแแแแแแ แแแ แแแแฎแแแแแแแก
แฉแแแแงแแแแแแแ แกแขแฃแแแแขแแแแก แแแแ แแแแแกแแแแก แแแกแแแแแแแแแแ. แแแแแแ, แ แแ แแฅแแแ แแ แ แแฎแแแแ
แแแแ แแแแ แแแแ แแแแแกแแแแก แฌแแ แแแแแแแแก แแแฆแแ แขแแฅแแแแฃแ แแแแแแ, แแ แแแแ แจแแฅแแแแแ แกแแแแขแแ แแกแ
แกแแแแชแแแแ แ แแ แแแฃแฅแชแแแช.
โแแแแแขแแก แแแแ แแแแโ แแแแแ แแแแ, แ แแ แฃแแฎแแแแก แแแแแแแแจแ แแ แแแฃแแ แกแแฎแแ แแแแแกแชแแแก แแฅแแแแก
แแแจแ แแแแแก แฅแแ แแฃแ แแ แแแแแแกแฃแ แแแแแแ.
แแแแแแ แแ แแฅแแแแแแแแแแกแแแแก แแก แแแแแ แแแ แแแแ แกแแแแชแแแแ แ แแฃแแแแแแชแแ แแฅแแแแ. แงแแแแ
แแแแฎแกแแแแแแแก แแ แแแแแแแขแแ แก แแ แแแแแแแขแแ แแแแก แแฎแ แแแแ แแแแแแแแชแแแแ แกแแ แขแแคแแแแขแแแ
แฌแแ แแแขแแแฃแแ แแแแแฌแแแแแแแกแแแแแก.
แแแกแฃแ แแแแ แแแแคแแ แแแชแแแก แแแงแแคแแแ แแฃแจแแแแแก แแ แฌแแ แแแขแแแแแก แแแแแแแ แกแแแแชแแแแ แ แกแแฅแแแแแแแแแจแ.
แแ แแค. แแแ แแ แฎแแแแแจแ
แ แแฅแขแแ แ
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Emerald Network Habitats and Species of Kolkheti Lowland
Bulbuli Bolkvadze Batumi Shota Rustaveli State University
Email: [email protected]
Abstract Emerald Network habitats of Kolkheti Black Sea shoreline comprise the following: freshwater ponds and coastal sand dunes. Freshwater ponds represent significant habitat for the following globally IUCN Red List species: Trapa colchica (threatened species), Trapa natans (threatened species), Salvinia natans (LC), Marsilea quadrifolia (LC). These habitats are classified as
threatened by the IUCN Red list. Especially important are sand dunes, and respectively dune vegetation cover. But these habitats and respectively its species are under strong anthropogenic pressure, leading to their degradation and disappearance. Reasons are the implementation of infrastructure projects, such as ports, marine terminals, low level of public awareness, and lack of legislation safeguarding coastal habitats. All this necessitates the measures to be taken for in-situ and ex-situ conservation of coastal habitats.
แแแแฎแแแแก แแแแแแแแก โแแฃแ แแฃแฎแขแแก แฅแกแแแแกโ แฐแแแแขแแขแแแ แแ แกแแฎแแแแแแ
แแฃแแแฃแแ แแแแฅแแแซแ
แแแแฃแแแก แจแแแ แ แฃแกแแแแแแแก แกแแฎแแแแฌแแคแ แฃแแแแแ แกแแขแแขแ
แแแแฅแขแ แแแฃแแ แคแแกแขแ: [email protected]
แ แแแแฃแแ แแแแฎแแแแก แกแแแแแแ แ แแแแแก โแแฃแ แแฃแฎแขแแก แฅแกแแแแกโ แฐแแแแขแแขแแแแ: แแขแแแแ แฌแงแแแแแ แขแแแ แแแ
แแ แกแแแแแแ แ แฅแแแจแแแแ แแแฃแแแแ. แแขแแแแ แฌแงแแแแแ แขแแแ แแแ แแแแจแแแแแแแแ แฐแแแแขแแขแก
แฌแแ แแแแแแแแก แแกแแคแแแ แฌแแแแแ แแฃแกแฎแแก IUCN แกแแฎแแแแแแแกแแแแแก: Trapa colchica (threatened
species), Trapa natans (threatened species), Salvinia natans (LC), Marsilea quadrifolia (LC). แฐแแแแขแแขแแแ
แแกแแคแแแ แฌแแแแแ แแฃแกแฎแแก IUCN Red List แแแแ แจแแคแแกแแแฃแแแ, แกแแคแ แแฎแแก แฅแแแจ แแงแแคแ
แฐแแแแขแแขแแแ. แแแแกแแแฃแแ แแแฃแแแ แกแแแแแแ แ แฅแแแจแแแแ แแแฃแแแแ, แแ แจแแกแแแแแแกแแ แแแฃแแฃแ แ
แแชแแแแ แแฃแแ แกแแคแแ แ. แแแแ แแ แแก แฐแแแแขแแขแแแ แแ แจแแกแแแแแแกแแ แกแแฎแแแแแแ แฃแแแแแก
แแแแ แแแแแแแฃแ แแแแแฅแแแแแแแก แแแแแชแแแแ, แ แแช แแแ แแแแ แแแแชแแแกแ แแ แแแฅแ แแแแก แแฌแแแแก.
แซแแแแ แแแแแฅแแแแแแแก แแฌแแแแก แแกแแแ แแแคแ แแกแขแ แฃแฅแขแฃแ แฃแแ แแ แแแฅแขแแแแก แแ แแจแแแแแแแแแแแก
แแแแฎแแ แชแแแแแแ แ แแแแ แแชแแ: แกแแแฆแแแ แแแ แขแแแ, แขแแ แแแแแแแแ. แซแแแแแ แแแแแแแ
แแแ แแแแกแแแชแแแแ แจแแแแแแ แแแกแแฎแแแแแแจแ. แกแแฅแแ แแแแแแก แแแ แแแแกแแแชแแแ แแแแแแแแแแแแแแจแ
แฐแแแแขแแขแแแแก แแแชแแแก แจแแกแแฎแแ แแแแแแ แแ แแ แกแแแแแก. แแฃแชแแแแแแแแ แฐแแแแขแแขแแแแกแ แแ
แกแแฎแแแแแแ in-situ แแ ex-situ แแแแกแแ แแแชแแฃแแ แฆแแแแกแซแแแแแแ.
แจแแกแแแแแ แกแแฅแแ แแแแแ 1994 แฌแแแแแ แแแงแแแแแฃแแ แแ แแแแแ แแแ แแแแกแแแชแแแแ แแแแแแแชแแแก
แฌแแแ แ แฅแแแงแแแ แแแฎแแ, แ แแแแ แแชแแ: แแแแแ แแแแแคแแ แแแแแแแก แแแชแแแก แฉแแ แฉแ แแแแแแแชแแ,
แ แแแกแแ แแก แแแแแแแชแแ, CITES แแแแแแแชแแ, แแแแแแแชแแ แแแแ แแ แแแแแ แชแฎแแแแแแแแก แแแชแแแก
แจแแกแแฎแแ, แจแแแ แแฆแแแก แซแฃแซแฃแแฌแแแ แแแแก แแแชแแแก แแแแแแแชแแ, แแ แฐแฃแกแแก แแแแแแแชแแ, แแแ แแแก
แแแแแแแชแแ (แแแ แแแแก แแแแฃแ แ แแฃแแแแแกแ แแ แแฃแแแแ แแแ แฐแแแแขแแขแแแแก แแแชแแแก
แแแแแแแชแแแก) แแ แกแฎแแ. แแก แแแแแแแชแแแแ แแ แแ แกแแแแแ แแกแ แแแแฃแ แ แแฃแแแแแก แแแชแแแกแแแแแก,
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แแแแแแแแ แฃแแแแแกแแ แแแแแแแแแก แคแแฅแขแแ แ, แ แแช แฐแแแแขแแขแแแแกแ แแ แกแแฎแแแแแแ แแแแแ
แฃแคแ แ แแแข แแแแแแแฃแ แแแแก แแฌแแแแก. แแฃแ แแฃแฎแขแแก แฅแกแแแ (Emerald Network) แแแแแแ, แ แแช
แแแขแฃแ แ 2000, แแแแ แแ แแก แแแแชแแแก แ แแแแแแแแ แฅแแแงแแแแก: แแฃแ แฅแแแ, แแแ แแแแแ, แคแแแแแ,
แจแแแแแแ. แกแแฅแแ แแแแแแก แแแ แแแแกแ แแ แแฃแแแแ แแแ แ แแกแฃแ แกแแแแก แแแชแแแก แกแแแแแแกแขแ แแ
2007 แฌแแแก แฎแแแ แแแแฌแแ แ แแแแฃแแแแขแก แแฃแ แแฃแฎแขแแก แฅแกแแแแก แฐแแแแขแแขแแแแกแ แแ
แกแแฎแแแแแแ แแแชแแแก แจแแกแแฎแแ. แแแแแแแแแแ แ แแฅแแแแ แกแแฅแแ แแแแแแ แแแฆแ แแแแแแแฃแแแแ
แแแแชแแแก แแแแฃแ แ แกแแฎแแ แจแแแแ แฉแแแแแ แแ แแแแฃแแแแขแแก แกแแแจแ แแงแแคแ แฐแแแแขแแขแแแ แแ
แกแแฎแแแแแแ แแ แจแแกแแแแแแกแแ แแฆแแแแแแแก แแกแแแ.
แแแแแแ แแแแแแแก แแแแแแแ แฐแแแแขแแขแแแแก แแแแแแแก DAFOR แแแแแแ, แฎแแแ แกแแฎแแแแแแ
แแฆแ แแชแฎแแ แแแชแแแฃแ แฐแแแแขแแขแจแ แฎแแแแแแ แขแ แแแกแแฅแขแแแแก แแ แแแแแ แแขแแแแก แแแแแแแ, Domin-Krajina แจแแแแแก แแแแแงแแแแแแ [2,3].
แจแแแแแแแ แแแแฎแแแแก แแแแแแแแ Marsilea quadrifolia-แก แแแแ แชแแแแแแก แแ แแแแแ แแ
แแแแแแกแแแงแแคแแแแ แแฎแแแแ.
Salvinia natans แแแแ แชแแแแแแก แฐแแแแขแแขแ แแแแ แชแแแแแฃแแแ:
แแแแแแแแจแ - แแชแแ แ แแแแแก แแขแแแแ แฌแงแแแแแ แขแแแ แแแ แแแชแแแแแแ แแแขแ แ แแแแแแแแแก
แแงแ. แแ แขแแแ แแแแก แฃแแแขแแกแแแ แแแแแแแแก แแแแแกแฃแคแแแ แแแแแก แแแแแแแแ แแแแก แจแแแฌแแ แ.
แแแแแแ แแฎแแแแ แแ แ แแแขแแ แ (5 แ x 5แ) แขแแแ แแ แจแแแแ แฉแแแแแ.
แแ. แชแแแ - แแ แฎแแแจแ แซแแแแ แแชแแ แ แแแแฃแแแชแแฃแ แ แ แแชแฎแแแแแแแ แแ แแฅ แชแแแแแฃแแ
แแแแแแแแแ แแแ แแแฎแแแแแ.
แญแฃแ แแ - แงแฃแแแแแก แขแแ แแแแแแแก แกแแแฎแ แแแแ แแแแก แแแ แแก แแขแแแแ แฌแงแแแแแ แแ แฎแแแ;
แแแแแแแก แขแแ แคแแแ แแก แแแแแแแแ แ แแขแแแแ แฌแงแแแแ แแ แฎแแแกแ แแ แขแแแ แแแจแ แแแฎแแแแแ
แชแแแแแฃแแ แแแแแแแแแ แแแ แฌแงแแแก แแแแแแแแ แแ แแแ.
Salvinia natans แแฃแแแแแ แแแแแฎแแแแ แกแแฎแแแแแแแ: Trapa natans แแ Hydrocharis morsus- ranae.
แจแแแแแแแ แแ แ แแแแแแแแแชแแแแ Marsilea quadrifolia-แก แขแแแ แแก in-situ แแแแกแแ แแแชแแแก แแฃแชแแแแแแแแแก แจแแกแแฎแแ
แแแชแแแฃแแแ แแ แแแฅแขแจแ: แกแแแแแแ แแก แแแแ แแแ แแแแแแแแ แแแแก แแแขแแแ แแ แแแฃแแ แแแแแ
แฌแงแแแฌแแแแแแก แแแแแกแแแแแก (แแแ แแแแแจแแ แแก แแแแ แแแคแแแแแกแแแฃแแ แแ แแแฅแขแ:
โแแแแแแจแ แแแแแแ แจแแแ แแฆแแแก แแแ แแแแก แแแชแแแกแแแแแกโ), แกแแแแช แแแชแแแฃแแแ
แ แแแแแแแแแชแแ แแ แฐแแแแขแแขแแก แแแแกแแ แแแชแแแก แแฃแชแแแแแแแแแก แจแแกแแฎแแ. 2005 แฌแแแแแ
แ แแแแแแแแ แขแแแ แ แแงแ แแแแแแจแ, แ แแช แแแคแ แแกแขแ แฃแฅแขแฃแ แฃแ แแ แแแฅแขแแแก แจแแแฌแแ แ. แกแแแแแแแแก แแแแ แชแแแแแแก แฐแแแแขแแขแแแ แแแแฎแแแแก แแ แแแแฃแแ แแแ แแแก แขแแ แแขแแ แแแก
แคแแ แแแแแจแแ. 2014 แฌแแแก แแแแฎแแแแก แแแแแแแแ แแแแก แคแแแแแก แแแแ แงแฃแแแแแก
แขแแ แแแแแแแก แแแแแแแแ แ แขแแ แแขแแ แแแแ แแ แกแแแฃแแ แคแแแ แแกแขแฃแแ แกแแฎแแแแแแแก
แจแแคแแกแแแแก แแ แแแขแแแแแฃแ แ แแแแแฅแขแแแแก แแแแกแแ แแแชแแแก แแแแแแก แจแแแฃแจแแแแแแก
แคแแ แแแแแจแ แแแแแแแแ แ แแแแแแแแแชแแ แงแฃแแแแแก แขแแ แแแแแแแก แกแแแฎแ แแแแ แแแแแแ แ
แแขแแแแ แฌแงแแแแแ แแ แฎแแแแก (แกแแแแแแแแก แแแแแแแแขแแแแ) in-situ แแแแกแแ แแแชแแแก
แแฃแชแแแแแแแแแก แจแแกแแฎแแ [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11].
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แแแฎแแแ 1. Salvinia natans แแแฎแแแ 2. แแแแ แแแแแแแฃแ แ แคแแฅแขแแ แ
แแแขแแ แแขแฃแ แ 1. K. Smith, V. Barrios, W. Darwall, C. Numa (Editors), 2015, The Status and distribution of
freshwater biodiversity in the eastern Mediterranean, IUCN Red List., 129 p; 2. W. Darwall, S. Carrizo, C. Numa, V. Barrios, J. Freyhot, K. Smith, 2015. Freshwater key
biodiversity areas in the Mediterranean Basin Hotspot, IUCN Red List, 86 p; 3. Matchutadze I., B. Bolkvadze, J. jakeli, M. Tsinaridze, (2014), Kolkheti refugee-habitat and
biodiversity conservation, wise use, World Biodiversity Congress, Sri-Lanka, abstracts book, pp 78-79.
4. Matchutadze, B. Bolkvadze, T. Bakuradze, M. Gvilava, D. Baratashvili, 2013, Coastal Sand Dunes and Freshwater Ponds in Kolkheti โ Threats and Needs for Conservation, Nova Publisher, ISBN: 978-1-62808-092-6, Chapter 8.
5. Council Directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of
wild fauna and flora, O.J. L206, 22.07.92. 6. CORINE Biotopes - Technical Handbook, volume 1, p. 73-109, Corine/Biotopes/89-2.2, 19
May 1988. 7. CORINE Biotopes manual, Habitats of the European Community. EUR 12587/3, Office for
Official Publications of the European Communities, 1991. 8. EUR27. 2007 The Interpretation Manual of European Union Habitats. European Commission
DG Environment.
9. Relation between the Directive 92/43/EEC Annex I habitats and the CORINE habitat list 1991 (EUR 12587/3).
10. G. Nakhutsrishvili. 1999. The vegetation of Georgia (Caucasus). - Braun-Blanquetia 15:1-74. 11. M. Barbour, J. Burk, W. Pitts, M. Schwartz, 1999, Terrestrial Plant Ecology, 3rd Edition 373 p. 12. G. Nakhutsrishvili, I. Matchutadze, 2014, Floristically assessment and creation of
biodiversity monitoring program for flora surrounding of Kulevi terminal, 55 p.
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105
Pollution Sources and Current Ecological State of Small Rivers of Adjara (R. Mejinistskali and R. Bartskhana)
Mariam Gagoeva ([email protected]) and Rusudan Bezhanidze
Faculty of Natural Sciences and Health, 4th Grade Students of Ecology Speciality Batumi Shota Rustaveli State University, 3 Ninoshvili Street, Batumi, Georgia
Scientific Supervisor: Assist. Prof. Guguli Dumbadze
Abstract Ecological condition of the small rivers of Adjara โ Mejinistskali and Bartskhana is certainly not favourable. Anthropogenic factors impacting the rivers through pollution are apparent. The
following factors contributing to point and non-point sources of pollution were established: population, agriculture, catering objects, construction sites, car washing, small private cattle and chicken farms, and alike. In difference with sources polluting Mejinistskali River, Bartskhana River is in addition being polluted by 'Batumi Oil Terminal' Ltd. Few years ago significant source of pollution was edible oil producing company 'Batumi Oil', but this company
does not operate nowadays.
Hydro-chemical analysis of the rivers revealed high level of pollution with ammonium nitrogen, exceeding the maximal permissible limit.
แแญแแ แแก แแชแแ แ แแแแแแ แแแ (แแแฏแแแแกแฌแงแแแ แแ แแแ แชแฎแแแ) แแแแแแแแซแฃแ แแแแแ แฌแงแแ แแแแ แแ แแแแแแแแ แแแ แแแแแแแแฃแ แ
แแแแแแแ แแแแ
แแแ แแแ แแแแแแแ ([email protected]), แ แฃแกแฃแแแ แแแแแแแซแ
แแแแฃแแแก แจแแแ แ แฃแกแแแแแแแก แกแแฎแแแแฌแแคแ แฃแแแแแ แกแแขแแขแ
แกแแแฃแแแแแกแแแขแงแแแแ แแแชแแแแ แแแแแ แแ แฏแแแแแชแแแก แคแแแฃแแขแแขแแก
แแแแแแแแแก แกแแแชแแแแแแแก IV แแฃแ แกแแก แกแขแฃแแแแขแแแ
แกแแแแชแแแแ แ แฎแแแแซแฆแแแแแแ: แแกแแกแข. แแ แแค. แแฃแแฃแแ แแฃแแแแซแ
แ แแแแฃแแ แแญแแ แแก แแชแแ แ แแแแแแ แแแแแก โ แแแฏแแแแกแฌแงแแแกแ แแ แแแ แชแฎแแแแก แแแแแแแแฃแ แ
แแแแแแแ แแแแ แแ แช แแกแ แกแแฎแแ แแแแแแ. แแแแแแ แฉแแแก, แแแแ แแแแแแแฃแ แ แคแแฅแขแแ แแก
แแแแแแแ แแแแแแ แแแ แแแญแฃแญแงแแแแแแแแ. แแแแแแแแแแแ แแฅแแ แแแแแแซแฃแ แแแแก
แฌแแ แขแแแแแแแ แแ แแ แแฌแแ แขแแแแแแแ แฌแงแแ แแแแ: แแแกแแฎแแแแแ, แกแแคแแแก แแแฃแ แแแแแ,
แกแแแแแแแแแแ แแแ แแแแแแกแ แแ แกแแแจแแแแแแ แแแแแฅแขแแแ, แแแขแแกแแแ แแชแฎแแแแแ,
แแแชแฎแแแแแแแแแกแ แแ แแแคแ แแแแแแแแแแก แแแ แซแ แแชแแ แ แกแแแแแแแแ แแ แกแฎแแ. แแ.
แแแฏแแแแกแฌแงแแแก แแแแแแซแฃแ แแแแก แฌแงแแ แแแแแกแแแแ แแแแกแฎแแแแแแแ, แแ. แแแ แชแฎแแแแก แแกแแแ
แแแแแซแฃแ แแแก แจแแก โแแแแฃแแแก แแแแแแแขแแ แแแแแแโ. แ แแแแแแแแ แฌแแแก แฌแแ แฅแแแแฃแ แ
แแแแแแซแฃแ แแแแก แแแแจแแแแแแแแแ แฌแงแแ แ แแงแ แแแแแก แกแแฌแแ แแ แจแแก โแแแแฃแแ แแแแโ, แ แแช
แแฆแแแกแแแแแก แแแฉแแ แแแฃแแแ.
แแแแแแ แแแ แฐแแแ แแฅแแแแฃแ แ แแแแแแแแก แจแแแแแแ แแแแแแแแแแ แแแแแแฃแแแก แแแแขแแก
แแแแชแแแขแ แแชแแแก แญแแ แแ แ แแแแแแแแ แแฆแแ แฃแแแ แแแกแแจแแแ แแแแชแแแขแ แแชแแแกแแแ
แจแแแแ แแแแ.
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แแแแแแแก แแฅแขแฃแแแแแ แงแแแแแกแแแแแก แชแแแแแแแ, แ แแ แแฆแแแกแแแแแก แจแแแ แแฆแแ แซแแแแ แ แแแแ แแแแแแแฃแแ
แแแขแแแ แแแแก แฅแแแจ แแแงแแคแแแ. แแแก แแแแแแแซแฃแ แแแแ แฌแงแแ แแแแก แจแแ แแก แแ แ-แแ แแ
แงแแแแแแ แแแแจแแแแแแแแแแ แแแกแจแ แจแแแแแแแ แแแแแแ แแแแแก แแแแ แแฆแแแจแ แแแแ
แ แแแแแแแแแ แแงแแ แ แแแ แฉแแแแแแกแ แแ แกแฎแแแแแกแฎแแ แฌแแ แแแจแแแแก แแฎแแแแแ
แแแแแแแ แแแแแแก แแแฎแแแแ แ, แ แแแแ แแชแแ: แแแแแแแฃแ แ แแแแแแแ แแแแแ, แแแแแแแ แแ แแแกแ
แแ แแแฃแฅแขแแแ, แแแกแขแแชแแแแแ, แคแแแแแแแ, แคแแกแแแ แแ แ. แจ.).
แแฅแแแแ แแแแแแแแแแ แ, แแแแแแ แแแ แแฃแแแแแก แแ แกแแแฃแแ แแแแแแแ แแแแแก แแแแแแ แแ
แแแแแแแ, แแแแแแซแฃแ แแแแกแแแแ แแแชแแ, แแแคแ แแฎแแแแแ แแแแแกแแแแแ แแฆแแแก
แแแคแ แแฎแแแแแแกแ แแ แแแชแแแก แแแจแแแแก แแ แแแขแแ แแแแจแแแแแแแแแแ.
แแแแแแแก แแแแแแ แจแแ แแฆแแแจแ แฉแแแแแแแ แ, แฅแแแแฅ แแแแฃแแจแ แแแแแแแแ, แแชแแ แ แแแแแแ แแแแแก โ
แแแฏแแแแกแฌแงแแแกแ แแ แแแ แชแฎแแแแก แแแแแแแแซแฃแ แแแแแ แฌแงแแ แแแแแกแ แแ แแแแแแซแฃแ แแแแก
แฎแแ แแกแฎแแก แแแแแแ แแ แแแแแแแ.
แแแแแแ แแแแ แฌแแ แแแแแแ 2014 แฌแแแก แกแแฎแแแแฌแแคแ แแ แแแขแแแแก แแแแแฃแ แกแแก โแแแแแแแแ
แแแกแฌแแแแแแ แแแแแฌแแแแแแแโ แแแแแ แฏแแแแฃแแ แแ แแแฅแขแแก SC/66/9-240/14 โแแแ แแแแก
แฅแแแแฃแ แ แแ แ แแแแแชแแฃแแ แแแแแแซแฃแ แแแ แฅแแแแฅ แแแแฃแแจแโ แคแแ แแแแแจแ.
แแแแแแแก แแแแแฅแขแ แแแแแแแก แแแแแฅแขแ แแญแแ แแก แแชแแ แ แแแแแแ แแแแแ. แแแฏแแแแกแฌแงแแแ แกแแแแแแก แแฆแแแก
แแแฎแแแ แแก แแแแแแแแ, แแแแแแแแแ แแแแฎแแแแแแ 9 แแ-แแ แแ แแ แแแแก แแฆแแแก
แแแ แแแแ แขแกแ แแ แฅแแแแฅ แแแแฃแแก แจแแ แแก. แแแขแแแกแแฃแ แ แฌแแแแแแแก แแ แแก แแฎแแกแแแแแแก
แแแแแแแ.
แแแแแแ แ แแแ แชแฎแแแ แแฌแงแแแ แแฎแแแจแแแแก แกแแแ แแแฃแแแก แขแแ แแขแแ แแแแ, แแฎแแแจแแแแก
แแแฆแแแแแก แฉแ แแแแ-แแแกแแแแแแแก แคแแ แแแแแ, แแแกแ แกแแแ แซแ 8.6 แแ-แแ. แแแแฉแแแ
แจแแแแแแแแแ: แแฎแแแจแแแแก แฌแงแแแ, แแแแจแแก แฆแแแ, แคแแ แแแก แฌแงแแแ แแ แกแฎแแ แแแขแแ แ
แฆแแแแแแแก แกแแฎแแ.
แแแแแแแก แแแแแแแแ แแแแแแ แฉแแขแแ แแ แแแ แจแ แฃแขแฃแแ แแแแแแแ, แแ. แแแ แชแฎแแแแกแ แแ แแแฏแแแแกแฌแงแแแก
แแแแแแแแซแฃแ แแแแ แฌแงแแ แแแแแก แแฆแ แแชแฎแแแก แแแแแแ, แฃแจแฃแแแ แแแแแแ แแแแแ, แฎแแแ
แแแกแจแ แแแแแแแแซแฃแ แแแแ แฅแแแแฃแ แแแแแแแ แแแแแ แแแแชแแแขแ แแชแแแแแก แแแแแแแแก แแแแแแ
แฉแแขแแ แแ แฌแงแแแก แกแแแฏแแแแก แฐแแแ แแฅแแแแฃแ แ แแแแแแแ.
แแแแแแแก แจแแแแแแแ แแแแแแ แแแแแ แแแแแแแแแแ แแแแแแ แ แแแฏแแแแกแฌแงแแแก แแแแแแแแซแฃแ แแแแแ แฌแงแแ แแแแ:
แแแกแแฎแแแแแ, แกแแคแแแก แแแฃแ แแแแแ, แกแแแแแแแแแแ แแแ แแแแแแกแ แแ แกแแแจแแแแแแ
แแแแแฅแขแแแ, แแแขแแกแแแ แแชแฎแแแแแ, แแแชแฎแแแแแแแแแกแ แแ แแแคแ แแแแแแแแแแก แแแ แซแ แแชแแ แ
แกแแแแแแแแ.
แแ. แแแฏแแแแกแฌแงแแแก แแแแแแซแฃแ แแแแก แฌแงแแ แแแแแกแแแแ แแแแกแฎแแแแแแแ, แแ. แแแ แชแฎแแแแก
แแแแแซแฃแ แแแก แจแแก โแแแแฃแแแก แแแแแแแขแแ แแแแแแโ, แแกแแแ, แ แแแแแแแแ แฌแแแก แฌแแ แฅแแแแฃแ แ
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แแแแแแซแฃแ แแแแก แแแแจแแแแแแแแแ แฌแงแแ แ แแงแ แแแแแก แกแแฌแแ แแ แจแแก โแแแแฃแแ แแแแโ, แ แแช
แแฆแแแกแแแแแก แแแฉแแ แแแฃแแแ.
แแแแแแ แ แแแฏแแแแกแฌแงแแแ แแแแซแฃแ แแแแ: แแงแแ แ แแฃแแแชแแแแแฃแ แ แแแ แฉแแแแแแ,
แแแกแแฎแแแแแแกแแแแ แฉแแแแแแแ แ แฌแงแแแแแ แแ แคแแแแแฃแ แ แแแกแแแแ, แแแกแขแแชแแแแแแ,
แแแชแฎแแแแแแแแแกแ แแ แแแคแ แแแแแแแแแแก แแฅแกแแ แแแแแขแแแแ, แแ แแแ แแแแฃแแ แกแแกแฃแฅแแแแ,
แชแแขแ แฃแกแแแแแแ แแแงแแคแแ, แกแแแจแแแแแแ แแแ แฉแแแแแแแ แแ แกแฎแแ. แแ. แแแ แชแฎแแแแก
แจแแแแฎแแแแแจแ แแ แแแแแแแแซแฃแ แแแแแแก แแแแขแแแ แกแแแ แแฌแแแแ แแฎแแแแแ แแแ แฉแแแแแ,
แแแแแแแ แแ แแแแแแแแ แแแฃแฅแขแแแ.
แแแ แแแแ, แแแแแแ แแแแแก โ แแแฏแแแแกแฌแงแแแกแ แแ แแแ แชแฎแแแแก แแแแแแแแฃแ แ แแแแแแแ แแแแ
แแ แช แแกแ แกแแฎแแ แแแแแแ. แแแแแแแแแแแ แแฅแแ แแแแแแซแฃแ แแแแก แฌแแ แขแแแแแแแ แแ
แแ แแฌแแ แขแแแแแแแ แฌแงแแ แแแแ, แแแแแแ แฉแแแก, แแแแ แแแแแแแฃแ แ แคแแฅแขแแ แแก แแแแแแแ
แแแแแแ แแแ แแแญแฃแญแงแแแแแแแแ. แแงแแ แ แแฃแแแชแแแแแฃแ แ แแแ แฉแแแแแแ แแแแแแซแฃแ แแแ แฃแแแ
แแฌแงแแแ แแแแแแ แแแ แแแแแ แแแแ แแแกแแฎแแแแแแก แแแกแแฎแแแแแกแแแ แแ แแแ, แแก
แแแแแแแแแแแแ แซแแแแ แแแแ แแแแแแ แแแ แแแแแ แแแแ, แแฆแแแก แจแแกแแ แแแแแแ
แแแแแแแชแแแแแแกแแก, แแแแแแ แฉแแแก แแแแ แแคแแแแชแแ.
แแแแแแ แแแแแก แแแ แชแฎแแแแกแ แแ แแแฏแแแแกแฌแงแแแแก แฐแแแ แแฅแแแแฃแ แ แแแแแแแแก แจแแแแแแ
แแแแแแแแแแ แแแแแแฃแแแก แแแแขแแก แแแแชแแแขแ แแชแแแก แญแแ แแ แ แแแแแแแแ แแฆแแ แฃแแแ
แแแกแแจแแแ แแแแชแแแขแ แแชแแแกแแแ แจแแแแ แแแแ. แแแ แซแแ, แแ. แแแ แชแฎแแแแจแ โ 0.511 แแ/แ
(แแแ-แแ 1.3-แฏแแ แแแขแ), แฎแแแ แแ. แแแฏแแแแกแฌแงแแแจแ โ 0.863 แแ/แ (2.2 แแแ). แกแแแแแแ
แแแ แแแแจแ แแแแแ แฉแแแ แแแแกแแแฆแแ แฃแแ แแแแแแแแแขแแแแก แแแแชแแแขแ แแชแแแแ
แแแแแแ แแแแจแ แแแ แแแก แคแแ แแแแแจแ แแงแ.
แกแแญแแ แแ แแ แแแแแแแ แซแแแแกแฎแแแแ, แกแแแแแแแแแแแก แแแแแจแแแแแแแก, แแแแแแแแฃแ แ
แชแแแแแก แแแแแก แแแแฆแแแแ, แกแแฎแแแแฌแแคแแก แแ แฃแแแ, แ แแแ แแแแแชแแแ แแ
แแแแฃแคแ แแฎแแแแแ แฉแแแแก แกแแแแแแ แแก โ แแขแแแแ แฌแงแแแก, แแแแฃแคแ แแฎแแแแแ แแฆแแแก,
แแแแแกแแแแแ, แกแฃแคแแ แแแแแแ แ แฎแแ แกแฃแคแแ แแฆแแแก แแแจแแแแก.
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Ecotourism as the Key Factor for National Development
Nino Jijavadze Batumi Botanical Garden
Email: [email protected]
Abstract Wilderness nature in Kolkheti region provides ample opportunities to develop ecotourism as the important component of tourism. Factors contributing are the following. Diversity of habitats: the sea, coastal dunes, coastal freshwater ponds, living sphagnum peatlands, forests, fields, lakes and river mouths. Diversity of species: mammals โ 51 species, birds โ 300 species,
reptiles/amphibians โ 28 species, fish โ 40 species, vegetation 1848 species. Habitats are those of Emerald Network and NATURE-2000. With the aim of conservation and wise use the following protected areas are established in Kolkheti: Kintrishi Protected Landscape, Kobuleti Protected Areas, Mtirala National Park, Machakhela Trans-boundary Protected Area. Nature monuments are also of global importance: Goderdzi fossilized forest, stoneman-column (Kvakatsa-Sveti), high conservation value forests of regional importance. Values of protected areas are: educational, natural-museum, natural-scientific, historical-cultural, fitness-
recreational, religious, ethical, ecological, nature protection, in-situ conservation.
แแแแขแฃแ แแแแ, แ แแแแ แช แฅแแแงแแแก แแแแแแแแ แแแแก แแแแแแ แ แคแแฅแขแแ แ
แแแแ แฏแแฏแแแแซแ
แแแแฃแแแก แแแขแแแแแฃแ แ แแแฆแ
แแแแฅแขแ แแแฃแแ แคแแกแขแ: [email protected]
แ แแแแฃแแ แแแแฎแแแจแ แ แแแแแแจแ แแ แกแแแฃแแ แแแแฃแ แ แแฃแแแแ แกแแฃแแแแแกแ แกแแจแฃแแแแแแก แแซแแแแ
แขแฃแ แแแแแก แแกแแแ แฃแแแแจแแแแแแแแแแกแ แแแ แแแก แแแแแแแแ แแแแกแแแแแก, แ แแแแ แแชแแ
แแแแขแฃแ แแแแ. แแกแแแแ: แฐแแแแขแแขแแแแก แแ แแแแแคแแ แแแแแแ: แแฆแแ, แกแแแแแแ แ แแแฃแแแแ,
แกแแแแแแ แ แแขแแแแ แฌแงแแแแแ แขแแแ แแแ, แชแแชแฎแแแ แกแคแแแแฃแแแแแ แขแแ แคแแแ แแแ, แขแงแแแแ,
แแแแ แแแ แแแแแแแแ, แขแแแแ แแ แแแแแแ แแแ แจแแกแแ แแแแแแ, แแแแแแแแ. แกแแฎแแแแแแ
แแ แแแแแคแแ แแแแแแ: แซแฃแซแฃแแฌแแแ แแแ โ 51 แกแแฎแแแแ, แคแ แแแแแแแแ โ 300 แกแแฎแแแแ,
แ แแแขแแแแแแ/แแแคแแแแแแ โ 28 แกแแฎแแแแ, แแแแแแแ โ 40 แกแแฎแแแแ, แแชแแแแ แแแแ โ 1848
แกแแฎแแแแ. แแฃแ แแฃแฎแขแแก แฅแกแแแแกแ แแ NATURE-2000 แแก แกแแฎแแแแแแ แแ แฐแแแแขแแขแแแ.
แแแแแ แแแแแคแแ แแแแแแแก แแแแกแแ แแแชแแแกแ แแ แแแแแแ แฃแแ แแแแแงแแแแแแก แแแแแแ
แแแแฎแแแจแ แฉแแแแงแแแแแแแฃแแแ แจแแแแแแ แแแชแฃแแ แขแแ แแขแแ แแแแ: แแแแขแ แแจแแก
แแแชแฃแแ แแแแแจแแคแขแ, แฅแแแฃแแแแแก แแแชแฃแแ แขแแ แแขแแ แแแแ, แแขแแ แแแแก แแ แแแแฃแแ
แแแ แแ, แแแญแแฎแแแแก แขแ แแแกแกแแกแแแฆแแ แ แแแชแฃแแ แขแแ แแขแแ แแ. แแแแแแแฃแ แ
แฆแแ แแแฃแแแแแก แแฃแแแแแก แชแแชแฎแแแ แซแแแแแแ: แแแแแ แซแแก แแแแแ แฎแ แขแงแ, แฅแแแแแชแ-แกแแแขแ,
แ แแแแแแฃแแ แฆแแ แแแฃแแแแแก: แแแฆแแแแแแกแแ แแแชแแฃแแ แฆแแ แแแฃแแแแแก แขแงแแก แแแ แแแแแ.
แแแชแฃแแ แขแแ แแขแแ แแแแแก แฆแแ แแแฃแแแแแแ: แแฆแแแ แแแแแแแแ; แแฃแแแแ แแ-
แกแแแฃแแแฃแแ;, แแฃแแแแ แแ-แกแแแแชแแแแ แ, แแกแขแแ แแฃแ-แแฃแแขแฃแ แฃแแ, แกแแแแแแแแแแแแแแ,
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แแแแแฏแแแกแแฆแแแแ-แ แแแ แแแชแแฃแแ, แ แแแแแแฃแ แ, แแแแแฃแ แ, แแกแแแขแแแฃแ แ, แแแแแแแแฃแ แ
(แแแ แแแแกแแแชแแแแ), แกแแแแแ แซแแแ, in situ แแแแกแแ แแแชแแ.
แจแแกแแแแแ แกแแฅแแ แแแแแแก แแแชแฃแแ แขแแ แแขแแ แแแแแก แแแขแแแแ แแแแ แแแงแแแแแแแ แชแฎแ แแแจแ 1.
แชแฎแ แแแ 1. แกแแฅแแ แแแแแแก แแแชแฃแแ แขแแ แแขแแ แแแแแก แแแขแแแแ แแแแ
แแแชแฃแแ แขแแ แแขแแ แแ แแแแแแ แแแขแแแแ แแ (IUCN)
แกแแฎแแแแฌแแคแ แแแแ แซแแแ แแแแชแ แ แแแชแแ, แกแแแแแแแแแแแแแแ แแ
แแ แแแแแแแฃแแแชแแฃแ แ แแแแแแแแ แกแแแชแแแแฃแ แ แแแแแ แแแแ I
แแ แแแแฃแแ แแแ แแ แแแแกแแกแขแแแแแแก แแแแกแแ แแแชแแ, แแแแแแแแแ, แขแฃแ แแแแแก
แแแแแแแแ แแแ II
แแฃแแแแแก แซแแแแ แแฃแแแแแก แแแแแกแแแฃแ แแแแแแก แแแแกแแ แแแชแแ (แแชแแ แ แแแแแก
แแแแกแแแฃแแ แแแฃแแ แขแแ แแขแแ แแแก แแแชแแ) III
แแฆแแแแแแแ แแฃแแแแแก แแแชแแ แแ แจแแแแ แฉแฃแแแแ แแฅแขแแฃแ แ แแแ แแแแก แแแแ IV
แแแชแฃแแ แแแแแจแแคแขแ แแแแแจแแคแขแแก แแ/แแ แแฆแแแก แแแแแขแแ แแแก แแแแกแแ แแแชแแ แแ
แขแฃแ แแแแแก แแแแแแแแ แแแ V
แแ แแแแแแฎแ แแแ
แแแแแงแแแแแแก แขแแ แแขแแ แแ แแฃแแแแ แแแ แ แแกแฃแ แกแแแแก แแแแ แแแ แแแแแงแแแแแ VI
แแแชแฃแแ แขแแ แแขแแ แแแแแก แแแขแแแแ แแแแแก แแ แแขแแ แแฃแแแแ:
โ แขแแ แแขแแ แแแก แแแแ;
โ แแฃแแแแ แแแแแ;
โ แแจแแแแแแแ/แฃแแแแแแฃแ แแแ;
โ แขแแแแฃแ แแแ;
โ แกแแแฃแแแแแ;
โ แแกแขแแ แแฃแแแแ.
แกแแฅแแ แแแแแแก แแแชแฃแแ แขแแ แแขแแ แแแแแก แกแแแ แแ แคแแ แแแแแ 368 941 แฐแ, แ แแช
แกแแฅแแ แแแแแแก แแแแแแแ แคแแ แแแแแก แแแแฎแแแแแแ 7%-แก แจแแแแแแแก, แ แแแแแแช
แแแแ แแแแแแแฃแแแ แแแชแฃแแ แขแแ แแขแแ แแแแแก แแแแแแแกแขแ แแชแแแแแก แฅแกแแแจแ แแ แแแ แแแแก
แกแแฅแแ แแแแแแก แแแ แแแแกแ แแ แแฃแแแแ แแแ แ แแกแฃแ แกแแแแก แแแชแแแกแ แกแแแแแแกแขแ แแก แแแชแฃแแ
แขแแ แแขแแ แแแแแก แกแแแแแแขแ.
แกแฃแ แแแ 1. แกแแแแแแแแแแแแแแแแ แขแฃแ แแแแ แกแฃแ แแแ 2. แคแ แแแแแแแแแ แแแแแแ แแแแ
111
แจแแแแแ แแแแฎแแแแก โแชแแชแฎแแแ แกแคแแแแฃแแแแแ แขแแ แคแแแ แแแ แแ แ แแแแฅแขแฃแ แ แแแแฎแฃแ แ แขแงแแแแโ
แแกแแคแแแแก แฃแแแแแแฃแ แ แฐแแแแขแแขแแแแ แแ แกแแฎแแแแแแแ แฌแแ แแแแแแแแ UNESCO-แจแ
แ แแแแ แช แแกแแคแแแ แแฃแแแแ แแแ แแแแแแแแ แแแแแก แฃแแแแ. แแก แแแแแ แฃแคแ แ แจแแฃแฌแงแแแก
แฎแแแก แขแฃแ แแแแแก แแแแแแแแ แแแแก. แแแแขแฃแ แแแแ โ แ แแแแ แช แฅแแแงแแแก แแแแแแแแแแก
แแแแแแแแ แแแแก แแแแจแแแแแแแแแ แคแแฅแขแแ แ. แกแแฃแแแแแกแ แแแ แแแแขแแ แแแแขแฃแ แแแแแก
แแกแแแ แกแแฎแแแแแก แแแแแแแแ แแแแกแแแแแก, แ แแแแ แแชแแ: แกแแชแฎแแแแกแแ, แกแแแแจแฅแ แ,
แแฃแแขแฃแ แฃแแ แขแฃแ แแแ, แแแกแแแแแแ แแแชแฃแ แขแแ แแขแแ แแแแ, แกแแแแชแแแแ แ แขแฃแ แแแแ,
แกแแแแแแแแแแแแแแ แขแฃแ แแแ, แคแ แแแแแแแแแ แแแแแแ แแแแ, แกแแแแงแแแ แฃแแ แแแแแญแแ แ,
แกแแแแแกแแ แขแฃแ แแแ โ แแแขแแ แแแ แแ แแแแขแแแแแแ แแแกแแแ แแแแ, แขแฃแ แแแ
แคแแขแแแแงแแแ แฃแแแแแแแก, แแแ แแขแฃแ แแแแ.
แกแฃแ แแแ 3. แกแแแแชแแแแ แ แขแฃแ แแแแ
แแแกแแแแแแ แแแชแฃแแ แขแแ แแขแแ แแแแแก แแแแแแแแ แแ แแฃแชแแแแแแแแ แแฃแคแแ แฃแแ, แขแ แแแแชแแฃแแ แแ
แขแฃแ แแกแขแฃแแ แแแแแแแก แแแแ แกแแแ, แแแชแฃแ แขแแ แแขแแ แแแแแ แแ แแแก แแแแฎแแแ แ แแแแแจแ,
แแคแแฅแขแฃแ แแ แแแ แแแแ แขแฃแ แแแแก, แจแแกแแแแแแกแ แขแฃแ แแกแขแฃแแ แแแคแ แแกแขแ แฃแฅแขแฃแ แแก
แฉแแแแงแแแแแแแแก แแแแจแแแแแแแแแ แ แแแ แจแแฃแซแแแ แจแแแกแ แฃแแแก แจแแแแแแ แแ แแชแแกแแแแก
แแแแแแแแ แแแแจแ:
โ แแแกแแฎแแแแแแก แแแกแแฅแแแแ แแ แกแแชแแแแฃแ -แแแแแแแแแฃแ แ แแแแแแแ แแแแแก
แแแฃแแฏแแแแกแแแ;
โ แแชแแ แ แแ แกแแจแฃแแแ แแแแแแกแแก แแแแแแแแ แแแ;
โ แแแแแ แแแแแคแแ แแแแแแแก แแแชแแแก แฎแแแจแแฌแงแแแ.
แแแขแแ แแขแฃแ แ แแแญแฃแขแแซแ แ. 2005. แแแแฎแแแแก แขแแ แคแแแ แแแ, 40 แแ.
แแแญแฃแขแแซแ แ. 2008. แ แแแแฅแขแฃแ แ แแแแฎแฃแ แ แขแงแ: โแฌแแ แกแฃแแ, แแฌแแงแ, แแแแแแแแโ. 40 แแ.
แแแญแฃแขแแซแ แ. 2009. แแแแฎแแแแก แแแแแแแแก แชแแชแฎแแแ แกแคแแแแฃแแแแแ แขแแ แคแแแ แแแแก
แแชแแแแ แแฃแแ แกแแคแแ แ, 133 แแ.
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Matchutadze I., Goradze I., Tsinaridze M., E. Jakeli, 2010, Inventory of High Conservative Value Forests (among them old) in Adjara Mountainous Forest Eco-Systems, Turkish-Japanese International conference, Vol. 1. Trabzon, pp. 17-33.
Matchutadze I., Kurkhuli T., Tsinaridze M., 2010, Why is the Relict Forest of Kolkheti lowland so Valuable and Significant? Turkish-Japanese International conference, Vol. 3. Trabzon,
pp. 55-60.
แแแชแฃแแ แขแแ แแขแแ แแแแแก แกแแแแแแขแแก แแแ แแแแ แแ: http://apa.gov.ge.
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Current Data on Biodiversity of the River Natanebi Ichthyofauna and Water Pollution
Tatia Kuljanishvili a,*, Marine Bozhadze a, Giorgi Epitashvili a, Bella Japoshvili a
a MSc student, Ilia State University, Institute of Zoology a Assistant researcher, Ilia State University, Institute of Zoology
a MSc, Ilia State University, Institute of Zoology a PhD, Ilia State University, Institute of Zoology
* Corresponding author: [email protected], +995 (598) 913988
Abstract Biological diversity, as for water ecosystems, as for terrestrial habitat depends on freshwater resources. Biodiversity of inland waters is critically important to eradicate poverty and to achieve different goals, fishery supports food for millions of humans. Study of river ichthyofauna, control of water quality and analysis of chemical parameters is essential to maintain freshwater ecosystems . In this paper we report the results of a study of
ichthyofauna in the river Natanebi and chemical analysis of water parameters, based on the
materials collected on 3 deferent seasons, in 2012. Introduction Presently more than 30000 different fish species are described, 40% of this number are identified as freshwater species. To take into account the size of freshwater and marine habitat, freshwater fish species thousand times exceed to saltwater species. Climate regulation, mitigation of floods, water purification and recycling of nutrients and waste materials depends
on the water ecosystems. Biodiversity of Inland waters is essential to develop millennium plans and aims (millenniumassessment.org; Japoshvili, 2012). The river Natanebi is known to be an important spawning area for Black Sea salmonids and sturgeons (Ninua & Guchmanidze, 2013). Nowadays the river is under the anthropogenic pressure, the main threats for important trade fish species. In Natanebi municipality there are three gravel excavation quarries (Losaberidze, 2013). Gravel excavations cause degradation of
whole river channel, an exhaustion of river surface and reducing spawning areas for anadromous fish species (Packer et al., 2005). Chemical and physical parameters such as water temperature, conductivity, dissolved oxygen and mineralization has very big impact for normal being of fishes (Yudkin, 1970). Pollution and habitat change causes a change of chemical parameters of water. At present, up-to-date information on river ecosystem biodiversity is largely unknown for most of the rivers in Georgia, except a few works (Japoshvili et al., 2013; Ninua, Japoshvili and
Botchorishvili, 2013; Ninua and Guchmanidze, 2013). In 1975 the Natanebi river ichthyofauna was studied by P. Kheladze (Kheladze, 1976). Our aim was to study ichthyofauna of river
Natanebi and compare it with literature data, which is not updated during the four decades. Methods The samples were collected during 2012 (June, August and November) in order to describe fish
fauna end to detect water pollution level. Fish specimens were obtained from three different sites with different anthropogenic pressure. The first site was near the upstream of the river (undisturbed area), the second near to a village with a fish farm and grazing area (central
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basin), and the third near to gravel excavation site and dams (downstream). We preferred the first site as a reference site to compare it with two others (Subramanian & Sivaramakrishnan, 2007). For fish sampling hand net and fishing rod were used. Samples were fixed in 70% ethanol. Identification of fish specimen was performed in the field, and also in the laboratory using the identification key. Morphological study was done with measuring characters like:
total length; standard length; head length; eye diameter; body depth; caudal peduncle depth. Simultaneously with collecting fish specimens water samples were collected, altogether 27 water samples were obtained. For each samples 21 water parameters were analyzed.. Water temperature; pH; turbidity; conductivity; dissolved oxygen was defined in the field. Therefore multifunctional measuring device EXTECH โ ExStik EC 500 and ExStik DO600 were used. The rests of parameters were defined in laboratory: ammonium ion; nitrites; nitrates; chlorides;
sulfates; hydro-carbonates; calcium; magnesium; sodium; potassium; iron; hardness; mineralization; permanganate oxidation; bi-chromatic oxidation; BOD5; TOC. To analyze main water ions one litre water specimens was taken from each point and before the transportation at the laboratory they were saved in frozen container. To determine the main ions, such as Na+, K+, Ca2+, Mg2+, Cl-, SO4
2-, HCO3-, ISO standard methods were used (Benashvili, 2012).
Results During the study 12 fish species were obtained (147 individuals). Those were: Colchic bitterling (Rodeus sericeus amarus (=Rhodeus colchicus)); colchic minnow (Phoxinus colchicus); colchic nase (Chondrostoma colchicum); Caucasian river goby (Gobius cephalarges constructor (=Neogobius (Ponticola) constructor)); spined loach (Cobitis taenia); south minnow (Alburnoides bipunctatus fasciatus (=Alburnoides fasciatus)); Caucasian gudgeon (Gobio gobio lepidolaemus (=Gobio lepidolaemus caucasica)); stone morocco (Pseudorasbora parva); colchic barb (Barbus tauricus); Batumi shamaya (Chalcalburnus chalcoides derjugini); trout (Salmo fario (=Salmo trutta fario)) and chub (Leuciscus cephalus (=Squalius cephalus)) (Picture 1. a, b, c).
a b c Picture 1. a. Spined loach (Cobitis taenia), b. Colchic bitterling (Rhodeus colchicus), c. Colchic barb (Barbus tauricus).
In our materials most abundant was Caucasian river goby, followed by colchic bitterling, south
minnow and caucasian gudgeon; then stone morocco, Batumi shamaya, colchic minnow, colchic nase, chub and spined loach. Very few amount of trout and colchic barb were caught. Morphometric measurements of different fish species is given in table 1.
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Table 1. Mean morphometric characters of caught fish
Spice\Measurement
Total
length
(mm)
Standard
length
(mm)
Head
length
(mm)
Eye
diameter
(mm)
Body depth
(mm)
Caudal
perduncle
depth (mm)
Rhodeus colchicus 55.05 45.25 10.56 2.97 17.79 5.94
Phoxinus colchicus 60.76 50.2 11.75 2.72 12.3 5.92
Chondrostoma colchicum 61.09 49.61 12.5 3.23 12.4 5.46
Neogobius constructor 76.21 63.62 17.86 3.37 12.75 6.18
Cobitis taenia 77.64 68.26 12.7 2.3 11.41 7.03
Alburnoides fasciatus 84.56 70.26 15.14 3.81 20.17 7.92
Gobio caucasicus 30.59 26.12 6.76 1.82 5.59 2.71
Pseudorasbora parva 67.92 56.62 13.53 2.82 14.15 6.44
Barbus tauricus 210.32 180.79 32.11 4.78 42.72 18.21
Chalcalburnus chalcoides 161.78 134.66 24.58 6.62 31.07 12.01
Salmo trutta fario 179.97 150.76 41.57 7.79 39.12 15.96
Squalius cephalus 212.72 175.92 45.92 7.70 43.05 18.31
Two new species has been found in our materials, those were: stone morocco (Pseudorasbora parva) and colchic minnow (Phoxinus colchicus). Fish species, such as northern pike (Esox lucius); colchic khramulya (Capoeta sieboldi)); vimba bream (Vimba vimba tenella (=Vimba vimba)); common carp (Cyprinus carpio); catfish (Silurus glanis); mosquito fish (Gambusia affinis holbrooki (=Gambusia holbrooki)); golden gray mullet (Mugil auratus (=Liza aurata)); river perch (Perca fluviatilis) and monkey goby (Gobius fluviatilis (=Neogobius fluviatilis)) were not detected in our materials, but mentioned in Kheladzeโs paper. Additional study and materials are needed to prove, that above mentioned fish species disappeared from Natanebi River. However it is obvious, that their quantity has decreased significantly, as they are absent in our catch data.
Common bitterling mentioned in Kheladzeโs paper was described incorrectly. Bitterling which inhabits in Natanebi River, was described as a new species โ colchic bitterling (Rhodeus colchicus) by Bogutskaya and Komlev in 2001.
Water chemical analysis showed, that water mineralization is low (80-103 mg/l), dissolved oxygen is within the accepted range (6-8.1 mg/l), permanganate and bi-chromatic oxidation is high, but it doesnโt exceeds limited permissible norms. Natanebi river water is sodium-
hydrocarbon type.
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Table 2. R. Natanebi water chemical parameters
Season April June November
Parameter\Site I II III I II III I II III
Water temperature oC 12 16 22 14.5 20.5 21 6 14 9
pH 7.9 7.9 7.7 6.9 7.1 7.8 8.3 6.9 6.9
Turbidity cm 24 24 24 30 30 30 17 17 17
conductivity 120,2 120 122,5 156,2 149,9 150,2 147,8 140,5 149,2
Dissolved oxygen mg/l 8 8,2 8,1 6,4 6,4 7,2 6 6,1 6,5
(NH4+) mg/l 0,2 0,2 0,2 0,2 0,2 0,2 0,15 0,15 0,15
(NO2-) mg/l 0,1 0,1 0,15 0,001 0,001 0,001 0,001 0,001 0,001
(NO3-) mg/l 0,2 0,2 0,2 0,2 0,2 0,2 0,1 0,1 0,1
(Cl-) mg/l 8,2 8 8,1 8,2 8,1 8,1 8 8 8,1
(SO42-) mg/l 10 11 12 10 12 11 5,5 6 6
(HCO3-) mg/l 40,2 40 40 61,24 61 61 61 61 48,8
(Ca2+) mg/l 8,4 8,4 8,4 9,4 9,4 9,4 9,1 8,9 8,8
(Mg2+) mg/l 2,6 2,6 2,6 2,16 2,36 2,36 2,76 2,76 2,76
( Na+, K+) mg/l 10,58 10,58 10,58 10,4 10 10,3 10 10 10
hardness 0,65 0,65 0,65 0,65 0,66 0,66 0,68 0,68 0,67
(Fe+2
,+3
) mg/l 0,2 0,2 0,21 0,2 0,2 0,1 0,15 0,1 0,1
Mineralization mg/l 79,78 79,58 81,68 101,4 102,86 102,16 96,36 96,66 84,46
Permanganatic-ox mg/l 2,4 2,5 2,3 3,2 3,68 3,84 2,4 2,8 2,8
Bichromatic ox (COD) mg/l 10 10,2 10 15 19 21 12,1 12,2 12
(BOD5) mg/l 1,1 2,1 2,2 1,2 2,1 2,4 1 2,1 2,5
TOC mg/l 3,75 4,69 5,02 5,63 7,13 7,88 3,45 4,5 4,5
Conclusions Our study indicates, that the ichthyofauna of river Natanebi changed considerably during the
last forty years. . Chemical analysis of water has shown that the second site is the most polluted
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[15 mg/l, 19 mg/l, 21 mg/l,], where village and grazing area is located. Downstream of river is less polluted which may be a result of water filtration capacity. To detect the changes in fish species composition along the river channel and to show how it relates to pollution intensity in Natanebi River, additional work is needed. Also monitoring program should be applied in order to detect long term trend of freshwater ecosystem changes
in the river. Acknowledgements We would like to thank Levam Mumladze, Zhanetta Shubitidze and Giorgi Nozadze for their help during the expeditions. The work was supported under the project of Institute of Zoology โBiodiversity of Guria Regionโ. References Benashvili N. (2012). โIssues of Georgian black sea sector ecogeochemistryโ, Dissertation for
doctor of ecology, St. Andrew the First-Called Georgian University Of the Patriarchate of Georgia, 164 p.
Losaberidze. D., Kandelaki K., Abuladze M., Kapanadze N., Tchitchinadze D., Chitadze M., Tordinava T., Chkheidze P. and Mazmishvili G. (2013). Sentences about Telavi, Mtskheta, Gori, Akhaltsikhe, Zugdidi, Ambrolauri, Ozurgeti and Tsalenjikha Municipalities administration-territorial optimization. 199 p.
Ninua N. & Guchmanidze A. (2013). Sturgeons of Georgia, Georgian National Museum. 120 p. Ninua N., Japoshvili B. and Botchorishvili V. (2013). Fishes of Georgia. Tsigni+Eri. 180 p. Kheladze P. (1976) the study of riv. Natanebi ichtiofauna. Proceedings of the State University,
vol. #178, pp 183-189. Japoshvili B. (2012) NBSAP #10 Thematic Direction: Biodiversity of Georgian Inland Waters
Situation Analysis. Report. Ministry of Environment and Natural Resources Protection of Georgia, 75 p.
Packer D. B., Griffin K. and McGlynn K. E. (2005) National Marine Fisheries Service National Gravel Extraction Guidance. U.S. Dep. Commerce, NOAA Tech. Memo. NMFS-F/SPO-70, 27 p.
Subramanian K. A., Sivaramakrishnan K. G. (2007) Aquatic Insects for Biomonitoring Freshwater Ecosystems - A Methodology Manual, Ashoka Trust for Research in Ecology and Environment (ATREE), 31 p.
Judkin I. I. (1970) Ichthyology. Publishing house โPishevaya promishlennostโ. 380 p. http://www.millenniumassessment.org (18.09.2015) Millennium Ecosystem Assessment.
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แฃแแฎแแแกแ แแแแแชแแแแแ แแแแแแ แ แแแขแแแแแแก แแฅแแแแคแแฃแแแก แแแแแ แแแแแคแแ แแแแแแแก แแ แฌแงแแแก แฅแแแแฃแ แ แแแแแแซแฃแ แแแแก แจแแกแแฎแแ
แแแแแ แงแฃแแฏแแแแจแแแแ a,*, แแแ แแแ แแแแแซแ a, แแแแ แแ แแแแขแแจแแแแ a, แแแแ แฏแแคแแจแแแแ a
a แแแแแกแขแ แแแขแ, แแแแแก แกแแฎแแแแฌแแคแ แฃแแแแแ แกแแขแแขแ, แแแแแแแแแก แแแกแขแแขแฃแขแ a แแแแแแแแ แ, แแแแแก แกแแฎแแแแฌแแคแ แฃแแแแแ แกแแขแแขแ, แแแแแแแแแก แแแกแขแแขแฃแขแ a แแแแแกแขแ แ, แแแแแก แกแแฎแแแแฌแแคแ แฃแแแแแ แกแแขแแขแ, แแแแแแแแแก แแแกแขแแขแฃแขแ
a แแแแแแแแแก แแแชแแแแ แแแแแ แแแแแแแแขแ, แแแแแก แกแแฎแแแแฌแแคแ แฃแแแแแ แกแแขแแขแ,
แแแแแแแแแก แแแกแขแแขแฃแขแ
*แแแแแแ แ แแแขแแ แ: [email protected], +995 (598) 913988
แ แแแแฃแแ แแแแแแแฌแแแ แแ แกแแแฃแแ แแขแแแแ แ แฌแงแแแก แ แแกแฃแ แกแ แฃแแ แฃแแแแแงแแคแก แแแแแแแแฃแ
แแ แแแแแคแแ แแแแแแแก แแ แแแฎแแแแ แฌแงแแแจแ, แแ แแแแ แแแกแแ แแแแแแแแแแฃแ แฎแแแแแแแก
แฐแแแแขแแขแจแแช. แจแแแ แฌแงแแแแแก แแแแแ แแแแแคแแ แแแแแแ แแ แแขแแแฃแแแ แแแแจแแแแแแแแแแ
แกแแฆแแ แแแแก แแฆแแแกแแคแฎแแ แแแแ แแ แกแฎแแแแแกแฎแแ แแแแแแแแก แแแกแแฆแฌแแแแ, แแแ แแแแแ แ
แแแแแงแแแแแ แแแฃ แแแแแแแแแแ แกแแแแแแแ แฃแแ แฃแแแแแงแแคแก แแแแแแแแแแ แแแแแแแแก.
แกแฌแแ แแ แแแแขแแ, แแแแแแ แแแแแก แแฅแแแแคแแฃแแแก แแแแแแ, แฌแงแแแก แฎแแ แแกแฎแแก แแแแขแ แแแ
แแ แฅแแแแฃแ แ แแแ แแแแขแ แแแแก แแแแแขแแ แแแแ แกแแญแแ แ แแ แแ แกแแแแแแ. แฌแแ แแแแแแแแ
แแแจแ แแแจแ แแแชแแแฃแแแ แแแแแแ แ แแแขแแแแแแก แแแแแแแแก แกแแฎแแแแ แแแ แจแแแแแแแแแแแแก
แชแแแแแแแ แแ แฌแงแแแก แฅแแแแฃแ แ แแแ แแแแขแ แแแแก แแแแแแแ 2012 แฌแแแก แกแแ แกแแแแแแ
แแแแแแแแฃแแ แแแกแแแแแแก แกแแคแฃแซแแแแแ.
แจแแกแแแแแ แแฆแแแกแแแแแก แแฆแฌแแ แแแ 30 000-แแ แแแขแ แแแแแแก แกแแฎแแแแแแแ 40% แแขแแแแ แ แฌแงแแแก
แแแแแแแ แแ. แแฃ แแแแแแแแแแกแฌแแแแแ แกแแแฆแแแ-แแขแแแแ แ แฌแงแแแแแก แจแแคแแ แแแแแก แแแแแแ
แฎแแแแ, แ แแ แฐแแแแขแแข แแแแแแแแแแฃแแ แแขแแแแ แ แฌแงแแแก แกแแฎแแแแแแ แแ แแแแแคแแ แแแแแแ
1000-แฏแแ แฃแคแ แ แแแฆแแแแ. แจแแแ แฌแงแแแแแก แแแแแ แแแแแคแแ แแแแแแ แแ แแขแแแฃแแแ
แแแแจแแแแแแแแแแ แกแแฆแแ แแแแก แแฆแแแกแแคแฎแแ แแแแ แแ แแแแแแแแแ แแแแแแแแ แแแแก
แแแแแแแแก แแแกแแฆแฌแแแแ, แแแ แแแแแ แ แแแแแงแแแแแ แแแฃ แแแแแแแแแแ แกแแแแแแแ
แฃแแ แฃแแแแแงแแคแก แแแแแแแแแแ แแแแแแแแก. แแแแกแแแแแแ, แคแแ แแ แแแแกแแกแขแแแฃแ แ
แแแแกแแฎแฃแ แแแ แแแแแก แแแแจแ แแฃแแแกแฎแแแแก แแแแแแขแแก แ แแแฃแแแชแแแก, แฌแงแแแแแแแแแแแก
แแแขแแแแชแแแก, แฌแงแแแก แแแกแฃแคแแแแแแแก, แกแแแแแแ แแแแแแแ แแแแแแก แแ แแแแแแก แแแแแแฃแจแแแแแแก.
แแแแกแฌแแแฃแแแก แแแแแแแแ แแแแก แแแแแแก แแแแแแแแกแ แแ แแแแชแแแแแแก แแแแ แแแฌแแแแก
แแแฆแฌแแแแจแ แจแแแ แฌแงแแแแแก แแแแแ แแแแแคแแ แแแแแแแก แแแแจแแแแแแแแแ แแแแแแ แฃแญแแ แแแก
(millenniumassessment.org, แฏแแคแแจแแแแ, 2012).
แแแแแแ แ แแแขแแแแแ แจแแแ แแฆแแแก แแ แแแฃแแแก แแ แแฃแแฎแแกแแแแ แแแแก แกแแฅแแแ แแแ แแ แ-
แแ แแ แแแแจแแแแแแแแแ แแแแแแ แแ (แแแแฃแ แแ แแฃแฉแแแแแซแ, 2013), แแฃแแชแ แแแแแแแแ แแแ
แแแ แแแแจแ แแแแแแ แ แซแแแแ แ แแแแ แแแแแแแฃแ แ แกแขแ แแกแแก แฅแแแจแแ, แ แแช แแแแจแแแแแแแแ
แกแแคแ แแฎแแก แฃแฅแแแแก แซแแแ แคแแกแ แกแแ แแฌแแ แแแแแแแแก แแฆแฌแแ แแแแแแก แแ แแแแแแแ
แแ แกแแแแแแก. แแแขแแแแแแก แแฃแแแชแแแแแแขแแขแจแ แแแแแแแแแแ แกแแจแแ แแแกแแแแก (แฎแ แแจแ). แแฅ
แคแฃแแฅแชแแแแแ แแแก 3 แแแ แแแ แ (แแแกแแแแ แแซแ, 2013). แฅแแแจแ-แฎแ แแจแแก แแแแแแแแ แแ
120
แแแแแแแแแแแแแช แแ แฎแแแแ, แกแแแแช แกแแแญแแฃแ แแแ แแแแจแแช แแแ แซแแแฃแแ แแงแ. แแ
แงแแแแแแแแ แแแแแแฌแแแ แแแแแแ แ แแแขแแแแแแก แแแแแแแขแแก แชแแแแแแแ แแ
แแแกแแฎแแแแแแกแแแแ แกแแงแแแ แคแแ แแแแแแแก แแแขแแชแแแ (alion.ge). แแฆแแก แแแแแแแแแแแจแ
แแฃแจแแแก แแแแฎแแแแแแ 250 แแฃแแฃแ แ แแแขแ แ แฎแ แแจแ แแแแฅแแ (gurianews.com).
แซแแแแ แ แแแแ แแแแแแแฃแ แ แแแแแแแแแ, แแแแแแ แแก แแแแแแแขแแก แจแแชแแแ แแ แกแแงแแแ
แแแฌแแแแก แแแขแแชแแแ แแแแแกแแแแแ แฅแแแแก แแแแแแแแกแแแแก แแ แแกแแกแฃแ แแแ แกแแแแแแแ แ
แแแ แแแแแก. แแกแแแ แแแแ แแแแชแแ แแฌแแแแก แแ แฎแแก แแแแแแ แแแแแคแแขแแแก, แแจแแจแแแแแก
แแแแแแ แแก แกแฃแแกแขแ แแขแก, แ แแแแแแช แฎแ แแจแแก แฅแแแจแแ, แแแชแแ แแแก แแแแแ แแแฃแแ แแแแแแแแก
แกแแฅแแแ แแแ แแแแแแแแก แแ แกแฎแแ แชแฎแแแแแแแแกแแแแแก แจแแกแแคแแ แแก แฐแแแแขแแขแก (Packer et al.,
2005). แฌแงแแแแแแแแก, แแแ แคแแแแขแ แแแก, แฐแแแ แแฅแแแแฃแ แ แแแฉแแแแแแแแแแก แแ
แขแแแแแ แแขแฃแ แแก แชแแแแแแแแแก แแแแงแแแแ แ แซแแแ แคแแกแ แแขแแแแ แ แฌแงแแแก แแแแแแแแก
แแแแฃแแแชแแแก แแแแ แแแแชแแแกแแแ (ะะธะฟะพัะฝ, 2010). แฌแงแแแก แฅแแแแฃแ แแ แคแแแแแฃแ
แแแ แแแแขแ แแแก, แ แแแแ แแแแชแแ: แฌแงแแแก แขแแแแแ แแขแฃแ แ, แแแแฅแขแ แแแแแขแแ แแแ, แแแกแจแ
แแแฎแกแแแแ แแแแแแแแ แแ แแแ แแแแแแแแ, แแแแ แแแแจแแแแแแแ แแฅแแก แแแแแแแแก แแแ แแแแฃแ
แชแฎแแแแแฅแแแแแแแจแ (ะฎะดะบะธะฝ, 1970). แแแแแแซแฃแ แแแ แแ แฐแแแแขแแขแแก แชแแแแแแแ แแฌแแแแก
แฌแงแแแก แฅแแแแฃแ แ แแแ แแแแขแ แแแแก แชแแแแแแแแก, แ แแช แแแแแก แแฎแ แแ แแแแแแแแก แแฎแแแแก
แแแแแแแแ.
แแฆแแกแแฆแแแแแ แกแแฅแแ แแแแแแจแ แแแแ แ แแแแแแ แแกแแแแก แฌแงแแแก แแแแกแแกแขแแแแก
แแแแแ แแแแแคแแ แแแแแแแกแแแ แแแแแแจแแ แแแแ แแแแแฎแแแแฃแแ แแแคแแ แแแชแแ แแ แแ แกแแแแแก,
แแฃ แแ แฉแแแแแแแ แแ แแแฃแ แแแจแ แแแแแก (แฏแแคแแจแแแแ แแ แกแฎแ. 2013; แแแแฃแ, แฏแแคแแจแแแแ
แแ แแแญแแ แแจแแแแ, 2013; แแแแฃแ แแ แแฃแฉแแแแแซแ, 2013 ). แแแแแแ แ แแแขแแแแแแก แแฅแแแแคแแฃแแ
แแแแแแแแแฃแแ แแฅแแ แ. แฎแแแแซแแก แแแแ 1975 แฌแแแก (แฎแแแแซแ, 1976). แกแฌแแ แแ แแแแขแแ
แฌแแ แแแแแแแแ แแแจแ แแแจแ แฉแแแ แแแแแแ แแแแแกแแฎแแ แจแแแแแกแฌแแแแ แแแแแแ แ แแแขแแแแแแก
แแฅแแแแคแแฃแแ, แจแแแแแแแ แแแแแ แแก แแแขแแ แแขแฃแ แฃแ แฌแงแแ แแแแแแ, แ แแแแแแช แฃแแแ แแแฎแ
แแแแฃแแ แฌแแแแ แแ แแแแแฎแแแแฃแแ, แแแแแแแแแแแแแแแแ แฌแงแแแก แแแแจแแแแแแแแแ
แฅแแแแฃแ แ แแแ แแแแขแ แแแ.
แแแแแแแแแแแ แแแกแแแ แจแแแ แแแแ 2012 แฌแแแก, แแแแแกแจแ, แแแแแกแขแแกแ แแ แแแแแแแ แจแ, แแแแกแแแแแก, แ แแ
แแฆแแแแฌแแ แ แแแแแแแแก แคแแฃแแ แแ แแแแแแแแแแ แแแแแแซแฃแ แแแแก แแแแ. แแแแแแแ
แแแแแแแแฃแ แแฅแแ แกแแแ แแแแกแฎแแแแแแฃแแ แแแแ แแแแแแแฃแแ แแแแแฅแแแแแแแก แแแแแแแแแ
(แกแแแขแแแแ). แแแ แแแแ แกแแแขแ แจแแ แฉแแฃแ แแฅแแ แแแแแแ แแก แแแแ แแแแแแแกแแแ แแฎแแแก,
แ แแแแแแแแแช แแแแ แแแแแแแฃแ แแแแแฅแแแแแแแก แแแแแแ แแ แฅแแแแ. แแแแ แ แกแแแขแ โ
แแแแแแ แแก แชแแแขแ แแแฃแ แ แแแฌแแแ โ แแแแแแ แแแแแ แกแแคแแแแแ แแฎแแแก, แกแแแแช
แแแแแแแกแแแฃแแแ แกแแแแแแแ แแแฃแ แแแแแ แแ แแ แแก แกแแซแแแ แแแ, แฎแแแ แแแกแแแ โ แแแแแแ แแก
แฅแแแแ แแแแแแ, แแแจแฎแแแก แแ แฎแ แแจแแก แแแแฆแแแแก แแแแแแแก. แแแ แแแแ แกแแแขแ แแแแแฉแแแแ
แกแแแแแขแ แแแ แกแแแขแแ, แ แแแแแแช แจแแแแแแแจแ แจแแแแแแ แแ แแแแแ แฉแแ แแ แกแแแขแก
(Subramanian & Sivaramakrishnan, 2007). แแแแแแก แแแกแแญแแ แแ แแแแแงแแแแแฃแ แแฅแแ แแแแแกแ
แแ แฎแแแแก แแแแ. แแแแฃแจแแแแก แแแคแแฅแกแแ แแแ แฎแแแแแแ 70%-แแแ แแแแแแแจแ. แกแแฎแแแแแก
แแแแแขแแคแแแแชแแ แซแแ แแแแแแ แฎแแแแแแ แกแแแแแ แแแ แแแแแจแ, แฎแแแ แแแแแแ แแแก
แแแแแ แแขแแ แแแจแ, แกแแ แแแแแแก แแแแแงแแแแแแ. แฉแแขแแ แแ แแแแแแแแฃแแ แกแฎแแแแแกแฎแแ
แกแแฎแแแแแก แแแแแแก แแแ แคแแแแขแ แแฃแแ แแแแแแ. แแแแแแแแ แแฅแแ แงแแแแ แแแแแแแแแก
แแแแแแแ แกแแแ แซแ; แกแแ แแฌแแ แกแแแ แซแ; แแแแแก แกแแแ แซแ; แแแแแแก แแแแแแขแ แ; แกแฎแแฃแแแก
แแแฅแกแแแแแฃแ แ แแ แแแแแแแแฃแ แ แกแแแแฆแแ. แแฅแแแแแแแแฃแ แ แกแแแฏแแแแก แแแแแแแแแก
121
แแแ แแแแแฃแ แแ, แแฆแแแฃแ แแฅแแ แฌแงแแแก แกแแแฏแแแ, 27 แแแแฃแจแ. แแแแแแฃแแ แแแแฃแจแแกแแแแแก
แแแแแแแแแแแฃแแ แแ แแแแกแแแฆแแ แฃแแ แแฅแแ 21 แฅแแแแฃแ แ แแแ แแแแขแ แ. แแแแแแแ แแแฎแแ
แจแแแแแแ แแแ แแแแขแ แแก แแแแแแแ: แฌแงแแแก แขแแแแแ แแขแฃแ แ; pH; แกแแแฆแแ แแแ; แแแแแแขแแ แแแ;
แฌแงแแแจแ แแแฎแกแแแแ แแแแแแแแ, แ แแกแแแแกแแช แแแแแงแแแแแฃแ แแฅแแ แแฃแแขแแคแฃแแฅแชแแฃแ แ
แกแแแแแ EXTECH โ ExStik EC 500 แแ ExStik DO600. แแแแแ แฉแแแ แแแ แแแแขแ แแแ แแแแแกแแแฆแแ แ
แแแแแ แแขแแ แแแจแ: แแแแแแฃแแแก แแแแ; แแแขแ แแขแแแ; แแแขแ แแขแแแ; แฅแแแ แแแแแ;
แกแฃแแคแแขแแแ; แฐแแแ แแแแ แแแแแขแแแ; แแแแชแแฃแแ; แแแแแแฃแแ; แแแขแ แแฃแแ, แแแแแฃแแ; แ แแแแ;
แกแแฎแแกแขแ; แแแแแ แแแแแแชแแ; แแแ แแแแแแแแขแฃแแ แแแแแแแแแแ; แแแฅแ แแแแขแฃแแ แแแแแแแแแแ;
แแแแแแแแแก แแแแฅแแแแฃแ แ แแแฎแแแ แแแ (แ.แ.แ.) แแ TOC. แแแแแแ แแก แฌแงแแแก แซแแ แแแแแ
แแแแแแแก แแแแแแแแกแแแแแก แแแแแแฃแ แฌแแ แขแแแแ, แกแแแแแแแแก แแแฎแแแแแ, แแฆแแแฃแแ
แแฅแแ 1 แ. แแแชแฃแแแแแก แแแแฃแจแแแ, แ แแแแแแแช แแแแแ แแขแแ แแแจแ แขแ แแแกแแแ แขแแ แแแแแแ
แแแแฎแแแแแ แกแแแชแแแแฃแ แงแแแฃแแแแ แแแแขแแแแแ แแแจแ. แซแแ แแแแแ แแแแแแแก Na+, K+, Ca2+,
Mg2+, Cl-, SO42-, HCO3
- แจแแแชแแแแแแแก แแแแกแแแฆแแ แแกแแแแแก แแแแแงแแแแแฃแแ แแงแ ISOโแก
แกแขแแแแแ แขแฃแแ แแแแแแแแ: Ca2+, Mg2+-แแก แแแแกแแแฆแแ แ แแแฎแแ แขแแขแ แแแแขแ แฃแแ
แแแแแแ EDTA-แก แแแแแงแแแแแแ (แแแแแจแแแแ, 2012), แแแแแแแขแแ แแแแ แแแแแงแแแแแฃแ แแฅแแ
แแ แแแฅแ แแ-แจแแแ แแ แแฃแ แแฅแกแแแ. แฐแแแ แแแแ แแแแแขแแกแ แแ แแแ แแแแแขแแก แแแแแแแก
แแแแกแแกแแแฆแแ แแ แแแแแงแแแแแฃแ แแฅแแ แขแแขแ แแแแขแ แฃแแ แแแแแแ. แฅแแแ แแแแแแก
แแแแกแแแฆแแ แแกแแแแแก โ แแแ แแก แแแแแแ (แแแแแจแแแแ, 2012).
แจแแแแแแแ แฉแแแแ แแแแแแแก แแแ แแแแจแ แกแฃแ แแแแแแแแฃแ แแฅแแ 12 แกแแฎแแแแแก 147 แแแแแแแแ, แแกแแแแ:
แแแแฎแฃแ แ แขแแคแแแ (Rodeus sericeus amarus (=Rhodeus colchicus)); แแแแฎแฃแ แ แแแแ แฉแฎแแ
(Phoxinus colchicus); แแแแฎแฃแ แ แขแแแ (Chondrostoma colchicum); แแแแแแ แแก แแแแแแกแแฃแ แ
แฆแแ แฏแ (Gobius cephalarges constructor (=Neogobius (Ponticola) constructor));
แฉแแแฃแแแแ แแแ แแแแแแแ (Cobitis taenia); แกแแแฎแ แแแฃแแ แแแ แแฃแแ, แคแ แแขแ (Alburnodise bipunctatus fasciatus (=Alburnoides fasciatus)); แแแแแแกแแฃแ แ แชแแแแ แ (Gobio gobio lepidolaemus (=Gobio lepidolaemus caucasica)); แคแกแแแแแ แแแแแ แ (Pseudorasbora parva);
แแแแฎแฃแ แ แฌแแแ แ (Barbus tauricus); แแแแฃแแแก แจแแแแแ (Chalcalburnus chalcoides derjugini); แแแแแแฎแ (Salmo fario (=Salmo trutta fario)) แแ แแแแแแกแแฃแ แ แฅแแจแแแ (Leuciscus cephalus (=Squalius cephalus)) (แกแฃแ แแแ 1. แ, แ, แ).
แ แ แ
แกแฃแ แแแ 1. แ. แฉแแแฃแแแแ แแแ แแแแแแแ (Cobitis taenia), แ. แแแแฎแฃแ แ แขแแคแแแ (Rhodeus colchicus),
แ. แแแแฎแฃแ แ แฌแแแ แ (Barbus tauricus).
แฉแแแแก แญแแ แแแจแ แงแแแแแแ แแแแ แ แแแแแแแแแ แฌแแ แแแแแแแแแ แแงแ แแแแแแ แแก
แแแแแแกแแฃแ แ แฆแแ แฏแ, แแแก แแแกแแแแแ แแแแฎแฃแ แ แขแแคแแแ, แคแ แแขแ แแ แชแแแแ แ; แญแแ แแแจแ
แจแแแแแ แแแแแแแ, แแแแฅแแแก แแแแแแแ แ แแ แแชแแแขแฃแแ แจแแแชแแแแแแแ, แฌแแ แแแแแแแแแ
122
แแงแ แคแกแแแแแ แแแแแ แ, แแแแฃแแแก แจแแแแแ, แแแแฎแฃแ แ แแแแ แฉแฎแแ, แแแแฎแฃแ แ แขแแแ,
แแแแแแกแแฃแ แ แฅแแจแแแ แแ แฉแแแฃแแแแ แแแ แแแแแแแ. แงแแแแแแ แแชแแ แ แ แแแแแแแแแ
แแ แแแฃแแ แแแแแแแแแแ แจแแแแฎแแ แแแแแแฎแ แแ แแแแฎแฃแ แ แฌแแแ แ. แแแแแแแแฃแแ
แแแแแแแแก แกแฎแแฃแแแก แแแแแแแแแแแก แแแแแชแแแแแ แแแชแแแฃแแแ แชแฎแ แแแ 1-แจแ.
แชแฎแ แแแ 1. แแแแแแแแแแแก แกแแจแฃแแแ แแแแแชแแแแแ แแแแแแฃแแ แกแแฎแแแแแกแแแแก
แกแแฎแแแแ\แแแแแแแแ
แแแแแแแ
แกแแแ แซแ
(แแ)
แกแแ แแฌแแ
แกแแแ แซแ
(แแ)
แแแแแก
แกแแแ แซแ
(แแ)
แแแแแแก
แแแแแแขแ แ
(แแ)
แแแฅแกแแแแแฃแ แ
แกแแแแฆแแ (แแ)
แแแแแแแแฃแ แ
แกแแแแฆแแ (แแ)
Rhodeus colchicus 55.05 45.25 10.56 2.97 17.79 5.94
Phoxinus colchicus 60.76 50.2 11.75 2.72 12.3 5.92
Chondrostoma colchicum 61.09 49.61 12.5 3.23 12.4 5.46
Neogobius constructor 76.21 63.62 17.86 3.37 12.75 6.18
Cobitis taenia 77.64 68.26 12.7 2.3 11.41 7.03
Alburnoides fasciatus 84.56 70.26 15.14 3.81 20.17 7.92
Gobio caucasicus 30.59 26.12 6.76 1.82 5.59 2.71
Pseudorasbora parva 67.92 56.62 13.53 2.82 14.15 6.44
Barbus tauricus 210.32 180.79 32.11 4.78 42.72 18.21
Chalcalburnus chalcoides 161.78 134.66 24.58 6.62 31.07 12.01
Salmo trutta fario 179.97 150.76 41.57 7.79 39.12 15.96
Squalius cephalus 212.72 175.92 45.92 7.70 43.05 18.31
แแแแแแ แ แแแขแแแแแแ 1975 แฌแแแก แฉแแขแแ แแแฃแ แแแแแแแแจแ, แ แแแแแแแช แแแแแแแแ แแแแแ
แแแแแคแฎแฃแแแก, แแแคแฎแฃแแแก, แจแแแแแแแแแกแ แแ แแแฌแแแแแ แแ แแแแแ แแก แแแแแแแแแแแจแ
แจแแแ แแแแแฃแแ แแฅแแ 19 แกแแฎแแแแแก แแแแแ (แฎแแแแซแ, 1976).
แฉแแแแ แแแแแแแแแก แแ แฎแแแแซแแก แแแแแชแแแแแแก แจแแแแ แแแแก แจแแแแแแ แแแแแแแแแแ แแฎแแแ
แกแแฎแแแแแแ, แ แแแแ แแแแชแแ แคแกแแแแแ แแแแแ แ (Pseudorasbora parva) แแ แแแแฎแฃแ แ
แแแแ แฉแฎแแ (Phoxinus colchicus), แ แแแแแแแช 1975 แฌแแแก แฉแแขแแ แแแฃแ แญแแ แแแจแ แแ
แแแคแแฅแกแแ แแแฃแแ. แแกแแแ แแฆแแแฉแแแ แแกแแแ แกแแฎแแแแแแ, แ แแแแแแแช แฌแแ แกแฃแแจแ
แแแคแแฅแกแแ แแ แแ แฉแแแ แญแแ แแแจแ แแฆแแ แจแแแแฎแแแ, แแกแแแแ: แฅแแ แแงแแแแแ (Esox lucius);
แแแแฎแฃแ แ แฎแ แแแฃแแ (Varicorhinus sieboldi (=Capoeta sieboldi)); แแแแแ (Vimba vimba tenella
(=Vimba vimba)); แแแแ แ, แแแญแ (Cyprinus carpio); แฉแแแฃแแแแ แแแ แแแฅแ (Silurus glanis);
แฉแแแฃแแแแ แแแ แแแแแฃแแแ (Gambusia affinis holbrooki (=Gambusia holbrooki)); แกแแแแแแ
(Mugil auratus (=Liza aurata)); แแแแแแ แแก แฅแแ แญแแแ (Perca fluviatilis) แแ แแแฅแแแจแแ แฆแแ แฏแ
(Gobius fluviatilis (=Neogobius fluviatilis)). แแก แแ แแซแแแแ แแแแก แแขแแแชแแแแก แกแแคแฃแซแแแแก, แ แแ
แงแแแแ แแก แกแแฎแแแแ แฃแแแ แแฆแแ แแแแแแ แแแก แแแขแแแแแจแ, แแแแก แแแกแแแแกแขแฃแ แแแแแ
แกแแญแแ แแ แแแแแขแแแแแ แแแแแแแแแก แฌแแ แแแแแ แแ แแแกแแแแก แแแแแแแแ, แแฃแแชแ แแ แแขแแแแ
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แจแแแซแแแแ แแแฅแแแก แ แแ แแแแ แ แแแแแแแแ แกแแแ แซแแแแแแ แแ แแก แจแแแชแแ แแแฃแแ, แ แแกแแช
แแแแกแขแฃแ แแแก แแแแ แแ แแ แกแแแแแ แฉแแแแก แแแกแแแแจแ.
แ แแช แจแแแฎแแแ แฉแแแฃแแแแ แแ แขแแคแแแแก, แ แแแแแแช แแแชแแแฃแแ แแงแ แฎแแแแซแแก แแแจแ แแแจแ,
แแฆแแแฉแแแ, แ แแ แขแแคแแแแก แแก แคแแ แแ แแ แแกแฌแแ แแ แแงแ แแแ แแแแฃแแ. แแแแแแ แ แแแขแแแแแจแ
แแแแแแแ แ แขแแคแแแ 2001 แฌแแแก, แแแแฃแชแแแแแกแ แแ แแแแแแแแก แแแแ แแฆแฌแแ แแ แแฅแแ
แ แแแแ แช แแแชแแแแ แแแแกแแแแก แแฎแแแ แกแแฎแแแแ แแ แแแก แแฌแแแ แแแแฎแฃแ แ แขแแคแแแ (Rhodeus colchicus Bogutskaya & Komlev, 2001).
แฌแงแแแก แฅแแแแฃแ แ แแแแแแแแก แจแแแแแแแแ แแฉแแแแ, แ แแ แฌแงแแแก แแแแแ แแแแแแชแแ แแแแแแแ
(80-103 แแ/แ), แแแฎแกแแแแ แแแแแแแแแก แจแแแชแแแแแแ แแแ แแแก แคแแ แแแแแจแแ (6-8.1 แแ/แ),
แแแแแแแแแแ, แ แแแแ แช แแแ แแแแแแแแขแฃแแ, แแกแแแ แแแฅแ แแแแขแฃแแ, แจแแแแ แแแแ แแแฆแแแแ
แแแแ แ แแแแฃแจแแก แฌแงแแแแจแ, แ แแช แแแแแฌแแแฃแแแ แแ แแฃแแฅแขแแก แแแแแแแแแแแ แแแแแ,
แแฃแแชแ, แแฆแแ แฃแแแ แแแกแแจแแแ แแแแชแแแขแ แแชแแแแก แแแแแช แแ แแฆแแแแขแแแ. แซแแ แแแแแ
แแแแแแแก แแแแแแแแแแ แฉแแแก, แ แแ แฌแงแแแ แแแขแ แแฃแ-แฐแแแ แแแแ แแแแแขแฃแแ แขแแแแกแแ.
แแ แกแแแแแก แแแแแชแแแแแ, แ แแแแแก แแแแแฎแแแแแช แแแขแแแแแแก แฌแงแแแ แแ แแก แแแแชแแฃแ-
แฐแแแ แแแแ แแแแแขแฃแแ (แแแแแจแแแแ, 2012), แ แแช แแแแแแแ แแชแแ แแ แแแแแ แแแแแแแฃแแ
แแแแแแ แแแแแกแแแแกแแ แแแแแฎแแกแแแแแแแแ, แแฃแแชแ, แฌแงแแแแชแแ แแแแก แแ แแก แแแแชแแฃแแแก
แแแแแแก แญแแ แแแแก แแแขแ แแฃแแแก แแแแแแแก แแแแชแแแขแ แแชแแ, แ แแช แแแแแฌแแแฃแแแ แแฆแแแก แฌแงแแแก
แจแแ แแแแ.
แชแฎแ แแแ 2. แฌแงแแแก แฅแแแแฃแ แ แแแแแแแแก แจแแแแแแแ
แกแแแแแ แแแ แแแ แแแแแกแ แแแแแแแ แ
แแแ แแแแขแ แ\แกแแแขแ I II III I II III I II III
แฌแงแแแก แขแแแแแ แแขแฃแ แ oC 12 16 22 14.5 20.5 21 6 14 9
pH 7.9 7.9 7.7 6.9 7.1 7.8 8.3 6.9 6.9
แกแแแฆแแ แแแ แกแ 24 24 24 30 30 30 17 17 17
แแแแแแขแแ แแแ แแจ/แกแ 120,2 120 122,5 156,2 149,9 150,2 147,8 140,5 149,2
แฌแงแแแจแ แแแฎแกแแแแ แแแแแแแแ
แแ/แ 8 8,2 8,1 6,4 6,4 7,2 6 6,1 6,5
แแแแแแฃแแ (NH4+) แแ/แ 0,2 0,2 0,2 0,2 0,2 0,2 0,15 0,15 0,15
แแแขแ แแขแแแ (NO2-) แแ/แ 0,1 0,1 0,15 0,001 0,001 0,001 0,001 0,001 0,001
แแแขแ แแขแแแ (NO3-) แแ/แ 0,2 0,2 0,2 0,2 0,2 0,2 0,1 0,1 0,1
แฅแแแ แแแแแ (Cl-) แแ/แ 8,2 8 8,1 8,2 8,1 8,1 8 8 8,1
แกแฃแแคแแขแแแ (SO42-) แแ/แ 10 11 12 10 12 11 5,5 6 6
แฐแแแ แแแแ แแแแแขแแแ (HCO3-)
แแ/แ 40,2 40 40 61,24 61 61 61 61 48,8
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แแแแชแแฃแแ (Ca2+) แแ/แ 8,4 8,4 8,4 9,4 9,4 9,4 9,1 8,9 8,8
แแแแแแฃแแ (Mg2+) แแ/แ 2,6 2,6 2,6 2,16 2,36 2,36 2,76 2,76 2,76
แแแขแ แแฃแแ, แแแแแฃแแ ( Na+, K+)
แแ/แ 10,58 10,58 10,58 10,4 10 10,3 10 10 10
แกแแฎแแกแขแ แแ-แแแ/แ 0,65 0,65 0,65 0,65 0,66 0,66 0,68 0,68 0,67
แ แแแแ (Fe+2,+3) แแ/แ 0,2 0,2 0,21 0,2 0,2 0,1 0,15 0,1 0,1
แแแแแ แแแแแแชแแ แแ/แ 79,78 79,58 81,68 101,4 102,86 102,16 96,36 96,66 84,46
แแแ แแแแแแแแขแฃแแ แแแแแแแแแแ
แแ/แ 2,4 2,5 2,3 3,2 3,68 3,84 2,4 2,8 2,8
แแแแฅแ แแแแขแฃแแ แแแแแแแแแแ
(COD) แแ/แ 10 10,2 10 15 19 21 12,1 12,2 12
แ.แ.แ (BOD5) แแ/แ 1,1 2,1 2,2 1,2 2,1 2,4 1 2,1 2,5
TOC แแ/แ 3,75 4,69 5,02 5,63 7,13 7,88 3,45 4,5 4,5
แแแกแแแแแแ แแแแแแแ แแฉแแแแ, แ แแ แแแแแแ แ แแแขแแแแแจแ แแฅแขแแแคแแฃแแแก แจแแแแแแแแแแแ แจแแชแแแแแแ
แแ แแแกแจแ แซแแแแแ แแจแแแแแแ แแ แกแแแ แแแ แแ แแแฎแแแแแ แแก แกแแ แแฌแแ แแแแจแแแแแแแแก
แซแแแ แคแแกแ แแแแแแแ, แ แแแแแแแช แแแ แ แแแฎแแแแแแแแแ. แฅแแแแฃแ แ แแแแแแแแก แจแแแแแแแแ
แแฉแแแแ แ แแ แแแแแแ แแก แงแแแแแแ แแแแแแซแฃแ แแแฃแแ แฃแแแแ, แกแแแแแ แกแแแแแแ แแ แแก
แกแแคแแแแแ แแฎแแแก [15 แแ/แ, 19 แแ/แ, 21 แแ/แ], แกแแแแช แแแแแแแแแฃแแแ แกแแแแแแแ
แแแฃแ แแแแแ แแ แแกแแแฆแแ แแแ แกแแซแแแแ แ, แจแแแแ แแแแ แแแแแแแแแแ แแแแแแซแฃแ แแแฃแแ
แแแแแแ แแก แฅแแแแ แแแแแแ, แฎแแแ แแแแ แแแแแแแก แแแแแชแแแแแ แแ แแก แคแแแฃแ แ.
แแแแกแแแแแก แ แแ แแแแแแแแแแ แแฅแแแแคแแฃแแแก แแฃแกแขแ แจแแแแแแแแแแแ แแ แแแฉแแแแแ แแฃ
แ แแแแ แแแแแแแแก แแฎแแแแก แแแแแแซแฃแ แแแ แแแแแแ แ แแแขแแแแแจแ แแชแฎแแแ แแ แคแแ แแแแแ
แกแแญแแ แแ แแแแแแแก แแแแ แซแแแแแ, แแแขแ แกแแแฏแแก แแแแแแแแ/แแแแแแแแแแแ แแ แฎแแแแ แซแแแแ
แแแแแขแแ แแแแ.
แแแแแแแ
แแแแแแแแก แแฃแฎแแแ แแแแแ แแฃแแแแซแแก, แแแแแขแ แจแฃแแแแแซแแก แแ แแแแ แแ แแแแแซแแก แแแ
แแแแ แแแฌแแฃแแ แแแฎแแแ แแแแกแแแก แแแแแ แแ แแแกแแแแก แจแแแ แแแแแแกแแแแก. แแแแแแ
แจแแกแ แฃแแแ แแแแแแแแแก แแแกแขแแขแฃแขแแก แแ แแแฅแขแแก โแแฃแ แแแก แ แแแแแแแก
แแแแแ แแแแแคแแ แแแแแแโ แคแแ แแแแแจแ.
แแแขแแ แแขแฃแ แ แแแแแจแแแแ แ. (2012) โแจแแแ แแฆแแแก แกแแฅแแ แแแแแแก แกแแฅแขแแ แแก แแแแแแแฅแแแแแก แกแแแแแฎแแแโ,
แแแแแแแแแก แแแฅแขแแ แแก แฎแแ แแกแฎแแก แแแกแแแแแแแแแ แฌแแ แแแแแแแแแ
แกแแแแกแแ แขแแชแแ แแแจแ แแแ, แกแแฅแแ แแแแแแก แกแแแแขแ แแแ แฅแแก แฌแแแแแ แแแแ แแ
แแแ แแแแฌแแแแแฃแแแก แกแแฎแแแแแแก แฅแแ แแฃแแ แฃแแแแแ แกแแขแแขแ, 164 แแ.
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แแแกแแแแ แแซแ แ., แแแแแแแแแ แ., แแแฃแแแซแ แ., แแแแแแแซแ แ., แญแแญแแแแซแ แ., แฉแแขแแซแ แ.,
แแแ แแแแแแ แ., แฉแฎแแแซแ แ. แแ แแแแแแจแแแแ แ. (2013) แฌแแแแแแแแแแแ แแแแแแแก,
แแชแฎแแแแก แแแ แแก, แแฎแแแชแแฎแแก, แแฃแแแแแแก, แแแแ แแแแฃแ แแก, แแแฃแ แแแแแกแ แแ
แฌแแแแแฏแแฎแแก แแฃแแแชแแแแแแขแแขแแแแก แแแแแแแกแขแ แแชแแฃแ-แขแแ แแขแแ แแฃแแ
แแแขแแแแแแชแแแก แจแแกแแฎแแ. 199 แแ.
แแแแฃแ แ. แแ แแฃแฉแแแแแซแ แ. (2013) แกแแฅแแ แแแแแแก แแฃแแฎแแกแแแแ แแ. แกแแฅแแ แแแแแแก
แแ แแแแฃแแ แแฃแแแฃแแ. 120 แแ.
แแแแฃแ แ., แฏแแคแแจแแแแ แ. แแ แแแญแแ แแจแแแแ แ. (2013) แกแแฅแแ แแแแแแก แแแแแแแ.
แฌแแแแ+แแ แ. 180 แแ.
แฎแแแแซแ แ. (1976) แแ. แแแขแแแแแแก แแฅแแแแคแแฃแแแก แจแแกแฌแแแแแกแแแแก. แแแแแแกแแก แจแ แแแแก
แฌแแแแแ แแ แแแแแกแแแ แกแแฎแแแแฌแแคแ แฃแแแแแ แกแแขแแขแแก แจแ แแแแแ, แขแแแ #178, แแ
183-189.
แฏแแคแแจแแแแ แ. (2012) NBSAP-แแก #10 แแแแแขแฃแ แ แแแแแ แแฃแแแแ: โแกแแฅแแ แแแแแแก แจแแแ
แฌแงแแแแแก แแแแแ แแแแแคแแ แแแแแแโ แกแแขแฃแแชแแแก แแแแแแแ. แแแแแ แแจแ.
แกแแฅแแ แแแแแแก แแแ แแแแกแ แแ แแฃแแแแ แแแ แ แแกแฃแ แกแแแแก แแแชแแแก แกแแแแแแกแขแ แ. 75
แแ.
Packer D. B., Griffin K. and McGlynn K. E. (2005) National Marine Fisheries Service National
Gravel Extraction Guidance. U.S. Dep. Commerce, NOAA Tech. Memo. NMFS-F/SPO-70, 27 p.
Subramanian K. A., Sivaramakrishnan K. G. (2007) Aquatic Insects for Biomonitoring Freshwater Ecosystems โ A Methodology Manual, Ashoka Trust for Research in Ecology and Environment (ATREE), 31 p.
ะะธะฟะพัะฝ ะก. ะฅ. (2010) ะัะพะฑะตะฝะฝะพััะธ ะคะพัะผะธัะพะฒะฐะฝะธั ะั ัะธะพัะฐัะฝั ะะพะดะพั ัะฐะฝะธะปะธั ะัะผะฝะตะฝะธะธ. Annals of Agrarian Science. Vol. 8 No.4 Pg74-77
ะฎะดะบะธะฝ ะ. ะ. (1970) ะั ัะธะพะปะพะณะธั. ะะธัะตะฒะฐั ะัะพะผััะปะตะฝะฝะพััั, 380 ัั. http://www.gurianews.com/_/left_wide/18797_74_ka/mosaxleoba_qviSis_karieris_generlebis
_winaaRmdeg.html (10.09.2015) แแแแ แแแแจแแแแ แ. โแแแกแแฎแแแแแ โแฅแแแจแแก
แแแ แแแ แแก แแแแแ แแแแแกโ แฌแแแแแฆแแแแโ, แแฃแ แแ News.
http://www.alion.ge/public/117--.html (10.09.2015) แแแแแแ แแแแแแ. โแแแแแแ. แแ.
แแแขแแแแแแ แฎแแแ แแฆแแแแแโ, แแแแแแ.
http://www.millenniumassessment.org (18.09.2015) Millennium Ecosystem Assessment.
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Georgia-Turkey Transboundary Stripe Rare and Endangered Plants
Elza Makaradze, Natela Varshanidze Researcher at Batumi Shota Rustaveli State University
Main author: [email protected], +995 593 66 30 30
Abstract In this work there is explained Adjara-Turkey transboundary stripe plants, which are protected in โGeorgia Red Listโ (2006): Buxus colchica; Castanea sativa; Celtis australis; Juglans regia; Laurus nobilis.; Osmanthus decorus; Pterocarya pterocarpa; Quercus hartwissiana; Staphylea colchica; Ulmus glabra, and Georgianโ Redbook (1982) are protected 16 species bioecology:
Adianthum capillis veneris, Marsilea quadrifolia, Helleborus caucasicus, Euphorbia paralias, Nymphaea colchica, Ficaria grandiflora Robert (F.popovii A.Khokhr.) Epimedium colchicum, Hippophae rhamnoides, Trapa colchica, Trapa Maleevi, Glaucium flavum, Cyclamen adzharicum, Galanthus rizechensis; G. Woronowii, Leucojum aestivum, Jris lazica. Scientific research species, to identify their endangered category is used IUCN recommendations. Scientific research 26 species are united into 20 families and 18 genera. Rare species are with high numbers in Amarilidaceae 23 species, Fagaceae, Trapaceae 2-2 species, IUCN endangered
CR category has 12 species, EN 8 species, VU 6 species. Introduction Adjara floristical region is known with its geographical location and subtropical climate, and it is main important touristic-recreational region in Caucasus. Adjara is also famous with its plant biodiversity and is one of the best ecoregion in Caucasus, which gives us good opportunities to
develop tourism. In Georgia-Turkey transboundary stripe, there are unique biological diversity and touristic-recreational resources, to protect the nature and develop tourism is one of the main reason. To do this with success, it is necessary to establish modern, effective protected areas system. Nowadays, in Adjara-Turkey transboundary area are protected 10 species of wild arboretrum plants: Buxus colchica; Castanea sativa; Celtis australis; Juglans regia; Laurus nobilis.; Osmanthus decorus; Pterocarya pterocarpa; Quercus hartwissiana; Staphylea colchica; Ulmus glabra. Georgian โRedbookโ (1982) are protected 15 species: Adianthum capillis veneris, Marsilea quadrifolia, Helleborus caucasicus, Euphorbia paralias, Nymphaea colchica, Epimedium colchicum, Hippophae rhamnoides, Trapa colchica, Trapa Maleevi, Glaucium flavum, Cyclamen adzharicum, Galanthus rizechensis; G. Woronowii, Leucojum aestivum, Jris lazica. They represent ancient flora of Colchis, some of them are relic and endemic. They grow in humid, warm conditions, and they repeat ancient periodโs rhythm, grow in forest or marshland, and they try to avoid sunshine. Among them there are a lot of arborous, medical, scented, feed
or dye plants. Biotope is changed by anthropogenic factor, which cause decrease of population. Thatโs why it is so important to study their bioecology.
Research methods The research had taken place with traditional expedition. The expedition-excursion method โ
collecting plants, and identification was with helping of โKey of Plant Identification of Georgiaโ and โThe flora of Georgiaโ (Ketskhoveli, Kharadze, Gagnidze). Taxonomy of species is exactly
from modern nomenclature (Gagnidze, 2005). We calculated populations area for giving them a
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rare status 10x10 km2, endangered categories correlation 1-2 CR Critically; 3-9 EN (Endangered);10-49 VU (Vulnerable.) Results In Adjara-Turkey transboundary zone, there are โGeorgia Red Listโ (2006) and Georgia โRedbookโ (1982) protected plants, see their taxonomy, systematic and bioecology in Table 1.
Table 1. Adjara-Turkey transboundary zone rare and endangered species
Species Family IUCN Biotope
Latin name Georgian name 1 2 3 4 5
1. Buxus colchica แแแแฎแฃแ แ แแแ BUXACEAE CR Subforest
2 Castanea sativa แฌแแแแ FAGACEAE CR Deciduous forest
3. Juglans regia แแแแแแ, แแแแแแแก แฎแ IUGLANDACEAE VU Deciduous forest
4. Celtis australis; แกแแแฎแ แแแแก แแแแแ CELTACEAE CR Subforest
5. Laurus nobilis. แแแแแแจแแแแแ แแแคแแ LAURACEAE EN Evergreen forest
6. Pterocarya pterocarpa แแแคแแแ IUGLANDACEAE CR Deciduous forest
7 Osmanthus decorus แฌแงแแแแแแ OLEACEAE VU Subforest
8 Quercus dshorochensis แญแแ แแฎแแก แแฃแฎแ FAGACEAE VU Deciduous forest
9. Staphylea colchica แแแแฎแฃแ แ แฏแแแฏแแแ STAPHYLEACEAE VU Subforest
10. Ulmus glabra แจแแจแแแแ แแแแแแฃแแ ULMACEAE CR Deciduous forest
11. Adianthum capillis veneris แแแแแ แแก แแแ ADIANTACEAE CR Moist rock
12. Marsilea quadrifolia แแแฎแคแแแแแ แแแ แกแแแแ MARSILEACEAE CR Reeded clubrush lake
13. Helleborus caucasicus แแแแแแกแแฃแ แ แฎแแ แแกแซแแ แ HELLEBORACEAE EN Forest slope
14. Euphorbia paralias แแฆแแแกแแแ แแก แ แซแแแแ EUPHORBIACEAE CR Seaside sandy
15. Cyclamen adzharicum Pobed แแญแแ แฃแแ แงแแฉแแแแ แแ PRIMULACEAE VU Seafront hill
16. Nymphaea colchica แแแแฎแฃแ แ แแฃแแคแแ แ NYMPHAEACEAE CR Pool
17. Epimedium colchicum แแแแฎแฃแ แ แฉแแขแแฌแแแแ BERBERIDACEAE EN Colchic subforest
18. Hippophae rhamnoides แฅแแชแแ RHAMNACEAE EN Riverside sandy
19. Trapa colchica แแแแฎแฃแ แ แฌแงแแแก แแแแแแ TRAPACEAE CR Pool
20. T. Maleevi แแแแแแแแก แฌแงแแแก แแแแแแ TRAPACEAE CR Pool
21.Ficaria grandiflora Robert (F.popovii A.Khokhr.)
แแแแงแแแแแแ แฉแแฌแงแแแแแ
แแแแ
RANUNCULACEAE VU Lowland
22 Glaucium flavum แงแแแแแแ แงแแงแแฉแฃแ แ PAPAVERACEAE EN Seafront sandy
23. Galanthus rizechensis Stern แ แแแแก แแแแ แงแแแแแแ AMARYLLIDACEAE CR Seafront slope
24. G. Woronowii แแแ แแแแแแก แแแแ แงแแแแแแ AMARYLLIDACEAE EN Seafront slope
25. Leucojum aestivum แชแฎแแแแกแแแแแ AMARYLLIDACEAE EN Seafront marshes
26. Jris lazica แญแแแฃแ แ แแแแแแฎแ IRIDACEAE EN Dry seafront marshes
As we see from schedule 1, in Adjara-Turkey transboundary zone there are 26 plants species. The lifeform spectrum looks like: 5 species wooden trees, 4 species of bush, 17 species herbaceous are united into 20 families and 18 genera. A high number of the rare species are
Amarilidaceae 23 species, Fagaceae, Trapaceae 2-2 species. Conclusions The 26 plants which are grown in Adjara-Turkey transboundary stripe are divided from lifeform spectrum: 5 species wooden tree, 4 species shrubs, 17 species perennial herbaceous plants are united into 20 families and 18 genera. Rich families with rare species are Amarilidaceae 23
species, Fagaceae, Trapaceae 2-2 species. IUCN endangered CR category have 12 species EN 8
species, VU 6 species.
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Gratitude We are very thankful to organisers, which worked a lot and gave us the great chance to take part in the workshop. Reference แแแแแแซแ แ ., แแแแแแแซแ แ. (2000). แแแแแแแแ แแแ แคแแแ แ โแแญแแ แโ, แแแแฃแแ, 271 แแ.
แแแแแแแซแ แ. (2002.). แแญแแ แแก แแแแแแขแฃแ แ แคแแแ แแก แแแแแแ แคแแแแแแฃแ แ แแแแแแแ,
แแแแแแชแแแแแแ, โแแแแฃแแแก แฃแแแแแ แกแแขแแขแโ, แแแแฃแแ.
แแแชแฎแแแแแ แ., แฎแแ แแซแ แ., แแแแแแซแ แ ., โแกแแฅแแ แแแแแแก แคแแแ แโ, แข.1-13, แแแแแแกแ.
แกแแฅแแ แแแแแแก แแชแแแแ แแแแแก แกแแ แแแแแ, (1964, 1969). แแแชแแแแ แแแ, 1971-2003.
ะะผะธััะธะตะฒะฐ ะ.ะ. ะะฟัะตะดะตะปะธัะตะปั ัะฐััะตะฝะธะน ะะดะถะฐัะธะธ.ยซะะตัะฝะธะตัะตะฑะฐยป, ั.1, ะขะฑะธะปะธัะธ, 327 ัั.,
1990. Manvelidze Z. K., Memiadze N. M., Kharazishvili D. and Varshanidze N., Diversity of floral area
of Adjara (List of wildgrown plants species), Annals of Agrarian Science, 2008, vol. 6, no 2, pp. 93-164.
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131
แแญแแ แ-แแฃแ แฅแแแแก แขแ แแแกแกแแกแแแฆแแ แ แแแแแก แแจแแแแแ แแ แฅแ แแแแแ แกแแฎแแแแแแ
แแแแ แแแแแ แแซแ, แแแแแแ แแแ แจแแแแซแ
แแแแฃแแแก แจแแแ แ แฃแกแแแแแแแก แกแแฎแแแฌแแคแ แฃแแแแแ แกแแขแแขแแก แกแแแฃแแแแแกแแแขแงแแแแ
แแแชแแแแ แแแแแ แแ แฏแแแแแชแแแก แคแแแฃแแขแแขแแก แแแฅแขแแ แแแขแ
แแแแ แแแแแ แแซแ [email protected] +995 593 66 30 30
แ แแแแฃแแ แแแจแ แแแจแ แแแแฎแแแฃแแแ แแญแแ แ-แแฃแ แฅแแแแก แขแ แแแกแกแแกแแแฆแแ แ แแ แแแแจแ
แแแแ แชแแแแแฃแแ แกแแฅแแ แแแแแแก โแฌแแแแแ แแฃแกแฎแแโ (2006) แแแชแฃแแ แกแแฎแแแแแแแก Buxus colchica; Castanea sativa; Celtis australis; Juglans regia; Laurus nobilis.; Osmanthus decorus;
Pterocarya pterocarpa; Quercus hartwissiana; Staphylea colchica; Ulmus glabra, แแ
แกแแฅแแ แแแแแแก โแฌแแแแแ แฌแแแแแโ (1982) แแแชแฃแแ 16 แกแแฎแแแแแก: Adianthum capillis veneris, Marsilea quadrifolia, Helleborus caucasicus, Euphorbia paralias, Nymphaea colchica, Ficaria grandiflora Robert (F.popovii A.Khokhr.) Epimedium colchicum, Hippophae rhamnoides, Trapa colchica, Trapa Maleevi, Glaucium flavum, Cyclamen adzharicum, Galanthus rizechensis; G. Woronowii, Leucojum aestivum, Jris lazica, แแแแแแแแแแแ, แกแแแแแแแ แกแแฎแแแแแแแก
แแแแแจแแแแแแก แกแแคแ แแฎแแก แกแแแแแแก แแแขแแแแ แแแแแก แแแกแแแแแแแ แแแแแงแแแแแฃแแแ
แแฃแแแแแก แแแชแแแก แกแแแ แแแจแแ แแกแ แแแแจแแ แแก (IUCN) แ แแแแแแแแแชแแแแ. แกแแแแแแแ 26
แกแแฎแแแแ แแแแแฌแแแแแฃแแแ 20 แแฏแแฎแจแ แแ 18 แแแแ แจแ. แแจแแแแแ แกแแฎแแแแแแแ แแแแแแ แ
แแฏแแฎแแแแ: Amarilidaceae 23 แกแแฎแแแแ, Fagaceae, Trapaceae 2-2 แกแแฎแแแแแแ. IUCN แกแแคแ แแฎแแก
CR แแแขแแแแ แแ แแแแแญแแแฃแแ แแฅแแก 12 แกแแฎแแแแแก EN 8 แกแแฎแแแแแก, VU 6 แกแแฎแแแแแก.
แจแแกแแแแแ แแญแแ แแก แคแแแ แแกแขแฃแแ แ แแแแแ แแแแแกแ แแแแแ แแคแแฃแแ แแแแแแ แแแแแแ แแ
แกแฃแแขแ แแแแแฃแแ แฐแแแแ แแแแจแแแแแแแแแ แขแฃแ แแกแขแฃแ-แ แแแ แแแชแแฃแแ แ แแแแแแ แแแแ
แแแแแแกแแแจแ, แแญแแ แ แแกแแแ แแแแแแ แฉแแแ แแชแแแแ แแฃแแ แกแแคแแ แแก แกแแฎแแแแ แแแ
แแ แแแแแคแแ แแแแแแแ. แแแ แงแแแแแแ แแแแแแ แ แคแแแ แแกแขแฃแแ แ แแแแแแ แแแแแก
แแแแแแกแแแก แแแแ แแแแแแจแ, แ แแช แแแ แ แกแแคแฃแซแแแแก แแซแแแแ แขแฃแ แแแแแก
แแแแแแแแ แแแแกแแแแแก. แกแแฅแแ แแแแแ-แแฃแ แฅแแแแก แขแ แแแกแกแแกแแแฆแแ แ แแ แแจแ, แฃแแแแแแฃแ แ
แแแแแแแแฃแ แ แแ แแแแแคแแ แแแแแแแกแ แแ แขแฃแ แแกแขแฃแ-แ แแแ แแแชแแฃแแ แ แแกแฃแ แกแแแแก
แแแแแแแแกแฌแแแแแแ, แแฃแแแแแก แขแแ แแขแแ แแฃแแ แแแชแแ แแ แจแแกแแแแแแกแแ แแฅ
แแแแ แชแแแแแฃแแ แแชแแแแ แแแ แแจแแแแแ แกแแฎแแแแแแแก แแแชแแ แแ แ-แแ แ แฃแแแแแ แแก
แแ แแแ แแขแแขแแ แฃแแแ แฉแแแแแแแแก. แแฆแแแกแแแแแก แแญแแ แ-แแฃแ แฅแแแแก แขแ แแแกแกแแกแแแฆแแ แ
แแ แแแแจแ แแแชแฃแแแ แแญแแ แแก แคแแแ แแกแขแฃแ แ แแแแแจแ แแแแฃแ แแ แแแแแ แแ 10 แแแ แฅแแแแแ
แกแแฎแแแแ: Buxus colchica; Castanea sativa; Celtis australis; Juglans regia; Laurus nobilis.;
Osmanthus decorus; Pterocarya pterocarpa; Quercus hartwissiana; Staphylea colchica; Ulmus glabra. แฎแแแ แกแแฅแแ แแแแแแก โแฌแแแแแ แฌแแแแแโ (1982) แแแชแฃแแแ 15 แกแแฎแแแแ:
Adianthum capillis veneris, Marsilea quadrifolia, Helleborus caucasicus, Euphorbia paralias, Nymphaea colchica, Epimedium colchicum, Hippophae rhamnoides, Trapa colchica, Trapa Maleevi, Glaucium flavum, Cyclamen adzharicum, Galanthus rizechensis; G. Woronowii, Leucojum aestivum, Jris lazica. แแกแแแ แแแแฎแแแแก แคแแแ แแก แฃแซแแแแแกแ แฌแแ แแแแแแแแแแแแ
แแ แแแ, แแแแแแ แแ แกแแฎแแแแ แ แแแแฅแขแ แแ แแแแแแแ. แแญแแ แแก แแแขแแ, แแแแ แแแ แแแแแจแ
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แฌแแแ แแแแแแแแฃแ แแแแฅแแจแ แแแแแแฃแจแแแแแฃแ แแแแแแแแ แแแแก แ แแขแแก แแแแแ แแแแ,
แขแงแแแแจแ แแ แญแแแแแแจแ แแแ แแแแแแ, แฎแ-แแชแแแแ แแแแแก แแแแแแแก แแคแแ แแแแแ แแ แแแแก
แกแฎแแแแแแก แแแ แแแแแ แแแแแฅแแแแแแแก แแแฃแ แแแแ. แแแ แจแแ แแก แแแแ แ แแแ แฅแแแก แแแแชแแแ,
แกแแแแฃแ แแแแ, แแ แแแแขแฃแแ, แแแแแ แแขแแฃแแ, แกแแแแแแ, แแ แกแแฆแแแแแ แแชแแแแ แแแแแ,
แแแแ แแแแแแแฃแ แ แคแแฅแขแแ แแแแ แแชแแแแแแ แแแแขแแแ, แ แแช แแแแฃแแแชแแแแ แจแแแชแแ แแแแก
แแ แแแฅแ แแแแก แแแแแแฌแแแแก. แแแแแแแ แกแแญแแ แแ แแ แกแแฎแแแแแแ แแแแ แแแแแแ, แแฃแแขแฃแ แแจแ
แแแแแ แแแ แแ แแแชแแ. แ แแกแแแแกแแช แแแแจแแแแแแแแแแ แแแแ แแแแแแแแแแแแก แจแแกแฌแแแแ.
แแแแแแแแแแแ แกแแแแแ แแแแแแแแ แแแแแแฎแแ แชแแแแแ แขแ แแแแชแแฃแแ แแแ แจแ แฃแขแฃแแ, แแฅแกแแแแแชแแแก
แแแแแแแ. แฐแแ แแแ แแฃแแแก แจแแแ แแแแแ แแ แแแกแ แแแแแ แฃแแ แแแแฃแจแแแแแ, แ แแแแแ-
แแแแแขแแคแแชแแ แแแ แแแแแแฎแแ แชแแแแแ แแญแแ แแก, แกแแฅแแ แแแแแแก แแชแแแแ แแแ แกแแ แแแแแแแแก
แแ โแกแแฅแแ แแแแแแก แคแแแ แแกโ แแแฎแแแ แแแแ (แแแชแฎแแแแแ, แฎแแ แแซแ, แแแแแแซแ, 1971-2003;
แกแแฅแแ แแแแแแก แแชแแแแ แแแแแก แกแแ แแแแแ, 1964, 1969; ะะผะธััะธะตะฒะฐ, 1959, 1990 I, II).
แแจแแแแแแแแก แกแขแแขแฃแกแแก แแแแแแแแก แแแแแแ แแแแแแแแแแแ แแแแฃแแแชแแแแแก
แแแแ แชแแแแแแก แกแแฎแจแแ แ โ 10x10 แแ2 UTM-แแแแแก แแแแแ แแขแแแแก แ แแชแฎแแแก แแ แแแแแจแแแแแแก
แกแแคแ แแฎแแก แแแขแแแแ แแแแแก แจแแแแแแ แแแแแคแแ แแแแแ: 1-2 โ แแแแแจแแแแแแก แแ แแขแแแฃแ
แกแแคแ แแฎแแจแ แแงแแคแ โ CR (Critically); 3-9 โ แแแแแจแแแแแแก แกแแคแ แแฎแแจแ แแงแแคแ โ EN
(Endangered); 10-49 โ แแแฌแงแแแแแ โ VU (Vulnerable).
แจแแแแแแแ แแญแแ แ-แแฃแ แฅแแแแก แขแ แแแกแกแแกแแแฆแแ แ แแแแแจแ แกแแฅแแ แแแแแแก โแฌแแแแแ แแฃแกแฎแแโ (2006)
แแ แกแแฅแแ แแแแแแก โแฌแแแแแ แฌแแแแแโ (1982) แแแชแฃแแ แกแแฎแแแแแแแก แขแแฅแกแแแแแแ,
แกแแกแขแแแแขแแแ แแ แแแแแแแแแแแ แแแชแแแฃแแแ แชแฎแ แแแจแ 1.
แชแฎแ แแแ 1. แแญแแ แ-แแฃแ แฅแแแแก แขแ แแแกแกแแกแแแฆแแ แ แแแแแก แแจแแแแแ แแ แฅแ แแแแแ แกแแฎแแแแแแ
แกแแฎแแแแแก แแแกแแฎแแแแแ แแฏแแฎแ IUCN แแแแขแแแ แแแแแแฃแ แ แฅแแ แแฃแแ
1 2 3 4 5
1. Buxus colchica แแแแฎแฃแ แ แแแ BUXACEAE CR แฅแแแขแงแ
2 Castanea sativa แฌแแแแ FAGACEAE CR แคแแแแแแแแ แขแงแ
3. Juglans regia แแแแแแ, แแแแแแแก แฎแ IUGLANDACEAE VU แคแแแแแแแแ แขแงแ
4. Celtis australis; แกแแแฎแ แแแแก แแแแแ CELTACEAE CR แฅแแแขแงแ
5. Laurus nobilis. แแแแแแจแแแแแ แแแคแแ LAURACEAE EN แแแ แแแแฌแแแแ แฅแแแขแงแ
6. Pterocarya pterocarpa แแแคแแแ IUGLANDACEAE CR แคแแแแแแแแ แขแงแ
7 Osmanthus decorus แฌแงแแแแแแ OLEACEAE VU แฅแแแขแงแ
8 Quercus dshorochensis แญแแ แแฎแแก แแฃแฎแ FAGACEAE VU แคแแแแแแแแ แขแงแ
9. Staphylea colchica แแแแฎแฃแ แ แฏแแแฏแแแ STAPHYLEACEAE VU แฅแแแขแงแ
10. Ulmus glabra แจแแจแแแแ แแแแแแฃแแ ULMACEAE CR แคแแแแแแแแ แขแงแ
11. Adianthum capillis veneris แแแแแ แแก แแแ ADIANTACEAE CR แขแแแแแแ แแแแ
12. Marsilea quadrifolia แแแฎแคแแแแแ แแแ แกแแแแ MARSILEACEAE CR แแแแแแ-แแแฅแแจแแแแ
แขแแ
13. Helleborus caucasicus แแแแแแกแแฃแ แ แฎแแ แแกแซแแ แ HELLEBORACEAE EN แขแงแแก แคแแ แแแแแแ
14. Euphorbia paralias แแฆแแแกแแแ แแก แ แซแแแแ EUPHORBIACEAE CR แแฆแแแกแแแ แ แฅแแแจแแแ แ
15. Cyclamen adzharicum Pobed แแญแแ แฃแแ แงแแฉแแแแ แแ PRIMULACEAE VU แแฆแแแกแแแ แ แแแ แแ-
แแแ แชแแแแ
16. Nymphaea colchica แแแแฎแฃแ แ แแฃแแคแแ แ NYMPHAEACEAE CR แขแแแ แ
17. Epimedium colchicum แแแแฎแฃแ แ แฉแแขแแฌแแแแ BERBERIDACEAE EN แแแแฎแฃแ แ แฅแแแขแงแ
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18. Hippophae rhamnoides แฅแแชแแ RHAMNACEAE EN แแแแแแ แแกแแแ แ
แฅแแแจแแแ แ
19. Trapa colchica, แแแแฎแฃแ แ แฌแงแแแก แแแแแแ TRAPACEAE CR แขแแแ แ
20. T. Maleevi แแแแแแแแก แฌแงแแแก แแแแแแ TRAPACEAE CR แขแแแ แ
21.Ficaria grandiflora Robert (F.popovii A.Khokhr.)
แแแแงแแแแแแ แฉแแฌแงแแแแแ
แแแแ
RANUNCULACEAE VU แแแแแแแแแ
22 Glaucium flavum แงแแแแแแ แงแแงแแฉแฃแ แ PAPAVERACEAE EN แแฆแแแกแแแ แ
แฅแแแจแแแ แแแ
23. Galanthus rizechensis Stern แ แแแแก แแแแ แงแแแแแแ AMARYLLIDACEAE CR แแฆแแแกแแแ แ
แคแแ แแแแแแ
24. G. Woronowii แแแ แแแแแแก
แแแแ แงแแแแแแ
AMARYLLIDACEAE EN แแฆแแแกแแแ แ
แคแแ แแแแแแ
25. Leucojum aestivum แชแฎแแแแกแแแแแ AMARYLLIDACEAE EN แแฆแแแกแแแ แ แญแแแแแแ
26. Jris lazica แญแแแฃแ แ แแแแแแฎแ IRIDACEAE EN แแจแ แแแ แแฆแแแกแแแ แ
แคแแ แแแแแแ
แ แแแแ แช 1-แแ แชแฎแ แแแแแแ แฉแแแก แแญแแ แ แแฃแ แฅแแแแก แขแ แแแกแกแแกแแแฆแแ แ แแแแแจแ
แแแแ แชแแแแแฃแแแ 26 แกแแฎแแแแแก แแชแแแแ แ, แกแแกแแชแแชแฎแแ แคแแ แแแแแก แแแฎแแแแแ 5 แกแแฎแแแแ
แฎแ-แแชแแแแ แแ, 4 แกแแฎแแแแ แฎแ แแ แแแแแแ แแฃแฉแฅแแ, 17 แกแแฎแแแแ แแ แแแแแฌแแแแแแ แแแแแฎแแแแแ
แแชแแแแ แแ. 20 แแฏแแฎแจแ แแ 18 แแแแ แจแ. แแจแแแแแ แกแแฎแแแแแแแ แแแแแแ แ แแฏแแฎแแแแ:
Amarilidaceae 23 แกแแฎแแแแ, Fagaceae, Trapaceae 2-2 แกแแฎแแแแแแ.
แแแกแแแแแแ แแญแแ แ แแฃแ แฅแแแแก แขแ แแแกแกแแกแแแฆแแ แ แแแแแจแ แแแแ แชแแแแแฃแแแ 26 แกแแฎแแแแแก แแชแแแแ แ,
แกแแกแแชแแชแฎแแ แคแแ แแแแแก แแแฎแแแแแ 5 แกแแฎแแแแ แฎแ-แแชแแแแ แแ, 4 แกแแฎแแแแ แฎแ แแ แแแแแแ
แแฃแฉแฅแแ, 17 แกแแฎแแแแ แแ แแแแแฌแแแแแแ แแแแแฎแแแแแ แแชแแแแ แแ. 20 แแฏแแฎแจแ แแ 18 แแแแ แจแ.
แแจแแแแแ แกแแฎแแแแแแแ แแแแแแ แ แแฏแแฎแแแแ: Amarilidaceae 23 แกแแฎแแแแ, Fagaceae,
Trapaceae 2-2 แกแแฎแแแแแแ. IUCN แกแแคแ แแฎแแก CR แแแขแแแแ แแ แแแแแญแแแฃแแ แแฅแแก 12 แกแแฎแแแแแก,
EN 8 แกแแฎแแแแ, VU 6 แกแแฎแแแแ.
แแแแแแแ แแแ แฆแ แแ แแแแแแแ แแแแก แแแแแแฎแแขแแแ แแ แแแแแแแขแแ แแแแก แแแแแ แ แแแฌแแฃแแ แจแ แแแแกแแแแก,
แแแแแแแแแแกแแแแแก, แแแแคแแ แแแชแแแก แแ แแแแแแแแแกแแแแแก.
แแแขแแ แแขแฃแ แ แแแแแแซแ แ ., แแแแแแแซแ แ. (2000). แแแแแแแแ แแแ แคแแแ แ โแแญแแ แโ, แแแแฃแแ, 271 แแ.
แแแแแแแซแ แ. (2002.). แแญแแ แแก แแแแแแขแฃแ แ แคแแแ แแก แแแแแแ แคแแแแแแฃแ แ แแแแแแแ,
แแแแแแชแแแแแแ, โแแแแฃแแแก แฃแแแแแ แกแแขแแขแโ, แแแแฃแแ.
แแแชแฎแแแแแ แ., แฎแแ แแซแ แ., แแแแแแซแ แ ., โแกแแฅแแ แแแแแแก แคแแแ แโ, แข. 1-13, แแแแแแกแ.
แกแแฅแแ แแแแแแก แแชแแแแ แแแแแก แกแแ แแแแแ, (1964, 1969). แแแชแแแแ แแแ, 1971-2003.
ะะผะธััะธะตะฒะฐ ะ. ะ. ะะฟัะตะดะตะปะธัะตะปั ัะฐััะตะฝะธะน ะะดะถะฐัะธะธ. ยซะะตัะฝะธะตัะตะฑะฐยป, ั.1, ะขะฑะธะปะธัะธ, 327 ัั., 1990.
Manvelidze Z. K., Memiadze N. M., Kharazishvili D. and Varshanidze N., Diversity of floral area of Adjara (List of wildgrown plants species), Annals of Agrarian Science, 2008, vol. 6, no 2, pp. 93-164.
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135
Use of Black Sea Coast Medical Flora against Some Chronic Diseases
Kristine Makharadze Biology Student
Faculty of Natural Sciences and Health Batumi Shota Rustaveli State University Main author: [email protected]
The nature has a huge source of material wealth. In case to survive, human use everything from the nature. The nature is polluted and there are ecological problems, obviously human organism has toxins from environment, which have a huge affects to their health. Thatโs why it is very important to clean organism and for this are good medical plants and the tincture which they make. The tincture is well-known from the past and is recommended from national medicine as a treatment for different kind of diseases. Especially it is important to use medical plants as the treatment for chronic diseases. The aim of my topic is exactly medical plants which are used against to chronic diseases and which have area near the Black Sea, we want to study bioecology and their place in medicine. It is very important to study that many people do not know medical plants great value, the methods how to prepare and use the tincture. Adjara
is very important floristic region. Now I want to discuss some species such as: dandelion, cress, camomile, colchic plush, wasp, horsetail and so on. The subject of study were plants which are spread near the Black Sea, and which are possible to use for the treatment for chronic diseases such as chronic colitis, cholangitis, diarrhea, eczema, ulcers, gastritis, constipation and so on.
แจแแแ แแฆแแแก แกแแแแแแ แแก แคแแแ แแก แกแแแแฃแ แแแแ แแชแแแแ แแแแ แแแแแแ แแ แฅแ แแแแแฃแแ แแแแแแแแแแแแก แฌแแแแแฆแแแแ
แฅแ แแกแขแแแ แแแฎแแ แแซแ
แแแแแแแแแก แกแขแฃแแแแขแ
แกแแแฃแแแแแกแแแขแงแแแแ แแแชแแแแ แแแแแ แแ แฏแแแแแชแแแก แคแแแฃแแขแแขแ
แแแแฃแแแก แจแแแ แ แฃแกแแแแแแแก แกแแฎแแแแฌแแคแ แฃแแแแแ แกแแขแแขแ
แแแแฅแขแ แแแฃแแ แคแแกแขแ: [email protected]
แแฃแแแแแก แฃแแแแแกแ แฆแแ แแแฃแแแแ แแฅแแก, แ แแแแ แช แแแขแแ แแแแฃแ แ แแแแแแแแก
แแแ แแแแฌแงแแ แแก. แกแแแ แกแแแแ แแแแแแแแ แแฃแแแแแแแ แแฆแแแก แงแแแแแคแแ แก. แแแ แแแแก
แแแแแแซแฃแ แแแแกแ แแ แแแ แแฃแแแแฃแแ แแแแแแแแฃแ แ แแแ แแแแแแก แแแแ, แแฃแแแแ แแแแ,
แแแแแแแแแก แแ แแแแแแแจแ แแแแกแคแแ แแก แแแแแฅแแแแแแแ แแแ แแแแฃแแ แ แแแแแแแแแ
แฎแแแแแ แขแแฅแกแแแฃแ แ แแแแแแแ แแแแแ, แ แแช แฃแแ แงแแคแแ แแแแแแแแก แแฎแแแแก
แฏแแแแ แแแแแแแแ. แแแแขแแ แแแแ แแแแจแแแแแแแ แแแแญแแแ แแ แแแแแแแแก แแแฌแแแแแแก,
แแแแกแแแแก แงแแแแแแ แแแ แแแ แกแแแแฃแ แแแแ แแชแแแแ แแแแแก แแ แแแแแแ แแแฆแแแฃแแ แแแงแแแแแแก
แแแแแงแแแแแ, แ แแช แชแแแแแแแ แฃแซแแแแแกแ แแ แแแแแ แแ แ แแแแแแแแแแฃแแแ แฎแแแฎแฃแ
แแแแแชแแแแจแ แกแฎแแแแแกแฎแแ แแแแแแแแแแแแก แกแแแแฃแ แแแแแ แแ แแ แแคแแแแฅแขแแแแกแแแแก.
แแแแกแแแฃแแ แแแแ แแแแจแแแแแแแแแแ แแชแแแแ แแแแแก แแแแแงแแแแแ แฅแ แแแแแฃแแ
แแแแแแแแแแแแก แกแแแแฃแ แแแแแ. แฉแแแแ แแแจแ แแแแก แแแแแแแ แกแฌแแ แแ แฅแ แแแแแฃแแ
แแแแแแแแแแแแก แฌแแแแแฆแแแแ แแแแแงแแแแแฃแแ แจแแแ แแฆแแแก แกแแแแแแ แแแ แแแแ แชแแแแแฃแแ
136
แกแแแแฃแ แแแแ แแชแแแแ แแแแแก แแแแแแแแแแแแก แแแแฎแแแแ แแ แแแแแชแแแแจแ แแแแ
แแแแแงแแแแแแก แแแแแแแแแก แแแชแแแแ. แแแกแแแแแแแกแฌแแแแแแแแ แแก แคแแฅแขแแช, แ แแ
แแแแฎแแแ แแแแแแ แฃแแ แแแแแกแแแ แฏแแ แแแแแ แแ แแ แแก แแแชแแแแแแ แแแฃแแ แกแแแแฃแ แแแแ
แแชแแแแ แแแแแก แแแแกแแแแแแก, แแแแ แแแแแแแแแแก แแ แแแฎแแแ แแแแก แกแฌแแ แ แแแแแแแแแก
แจแแกแแฎแแ. แแญแแ แแก แกแแแแแแ แ แแแแ แแแแแแ แฉแแแ แคแแแ แแกแขแฃแแ แแ แแแแแคแแ แแแแแแแ.
แแแฏแแ แแ แแฎแแแแ แแแแแแ แ แแแแแแแแ แแแแแแแฎแแแแแแ แงแฃแ แแแฆแแแแก. แแกแแแแ:
แแแแฃแแฌแแแ แ, แแแกแขแแแก แฌแแแแแ, แแแแ แแแ, แแแแฎแฃแ แ แกแฃแ แ, แแ แแแแแ, แแจแแจแ,
แฅแ แแกแขแแกแแกแฎแแ, แจแแแขแ, แฉแแแฃแแแแ แแแ แแกแแกแแแแ, แฌแแแแแ แกแแแงแฃแ แ แแ แฎแแ แแกแจแฃแแแ.
แแแแแแแก แแแแแฅแขแก แฌแแ แแแแแแแแแ แจแแแ แแฆแแแก แกแแแแแแ แแแ แแแแ แชแแแแแฃแ
แแชแแแแ แแแแ, แ แแแแแแ แแแแแงแแแแแ แจแแกแแซแแแแแแแ แแแแแแ แแ แฅแ แแแแแฃแแ
แแแแแแแแแแก แกแแแแฃแ แแแแแ, แแแ แซแแ:
โ แแฌแแแแ แแ แฅแ แแแแแฃแแ แแแแแขแ,
โ แแแฆแแแแกแ แแ แกแแแแฆแแแ แแแแแแก แฅแ แแแแแฃแแ แแแแแแแแแแแ,
โ แแฃแญแแแฌแแแแแก แฅแ แแแแแฃแแ แแจแแแแแแ (แแแแ แแ),
โ แฅแ แแแแแฃแแ แแแแแแแก, แฌแงแแฃแแแแแก, แแแกแขแ แแขแแก แแ แแก,
โ แฅแ แแแแแฃแแ แจแแแ แฃแแแแแก,
โ แฅแ แแแแแฃแแ แ แแแแแขแฃแแ แแแแแแแแแก,
โ แฅแ แแแแแฃแแ แแ แแแฅแแขแแก
แแชแแแแ แแแ แ แแแ แแฃแแแแแกแ แแ แแแแแแแแแก แชแฎแแแ แแแแจแ แแแแฃแกแแแฆแแ แแแแ แแแแแ
แแ แแฎแแ แแแแ แฌแแฃแแแแแแ. แแแแขแแ แแฃแชแแแแแแแแ แกแแแแฃแ แแแแ แแชแแแแ แแแแแก
แกแแกแแ แแแแแ แแแแกแแแแแแกแ แแ แแแแ แแแแแงแแแแแแก แกแฌแแ แ แแแแแแแแแก แชแแแแ.
137
Ecologically and Economically Feasible Project of Global Importance: Sphagum as a Renewable Resource โ Establishing a Sphagnum Farm
Manuchar Mamuladze ([email protected]), Merab Tsinaridze, Natela Tetemadze, Alexandre
Tsertsvadze, Nino Jijavadze, Ketevan Memarne, Izolda Matchutadze Batumi Shota Rustaveli State University
Abstract Sphagnum peat is irreplaceable growing habitat for cultivating orchids and green salads and demand for it in the Europe is very high. Due to warm, mild, ideally humid climate of Kolkheti refugium, sphagnum here is characterised with highest rate growth of 32 cm per annum! This
rate is the highest globally. Scientific research carried out in Kobuleti and Grigoleti demonstrate that Kolkheti can indeed host the ecologically and economically feasible project of global importance: Sphagnum as a Renewable Resource โ Establishing a Sphagnum Farm. Such a project is promising economic development prospects to the region.
แแแแแแแแฃแ แแ แแ แแแแแแแแแฃแ แแ แแแแแแแแแแ แแ แแแฅแขแแก, แแกแแคแแแ แคแแแแแแแแก: โแกแคแแแแฃแแ, แ แแแแ แช แแแแแฎแแแแแแ แ แแกแฃแ แกแ โ แกแคแแแแฃแแแก แแแแแขแแชแแแก แจแแฅแแแโ
แแแแฃแฉแแ แแแแฃแแแซแ ([email protected]), แแแ แแ แชแแแแ แแซแ, แแแแแแ แขแแขแแแแซแ,
แแแแฅแกแแแแ แ แชแแ แชแแแซแ, แแแแ แฏแแฏแแแแซแ, แฅแแแแแแ แแแแแ แแ, แแแแแแ แแแญแฃแขแแซแ
แแแแฃแแแก แจแแแ แ แฃแกแแแแแแแก แกแแฎแแแแฌแแคแ แฃแแแแแ แกแแขแแขแ
แ แแแแฃแแ แกแคแแแแฃแแแก แขแแ แคแ แจแแฃแชแแแแ แฐแแแแขแแขแก แฌแแ แแแแแแแแก แฏแแแแแ แแแแกแ แแ แแฌแแแแ
แกแแแแแแก แแฃแแขแแแแ แแแแกแแแแแก แแ แแแกแแ แแแ แแแแจแ แซแแแแแ แแแฆแแแ แแแแฎแแแแแ.
แแแแฎแแแแก แ แแคแฃแแแฃแแแก แแแแแ, แ แแแแ, แแแแแแฃแ แ แแแขแแ แแแแแแขแแแแ
แแแแแแแแแแ แ แกแคแแแแฃแแแก แกแแฎแแแแแแ แแ แแแก แงแแแแแแ แแแฆแแแ แขแแแแแ แฎแแกแแแแแแแแแ
แฉแแแแจแ, 32 แกแ แฌแแแแฌแแแจแ! แแก แงแแแแแแ แแแฆแแแ แแแฉแแแแแแแแแ แกแคแแแแฃแแแก แแ แแแกแ
แแกแแคแแแแจแ. แฅแแแฃแแแแกแ แแ แแ แแแแแแแจแ แฉแแขแแ แแแฃแแ แแแชแแแแ แฃแแ แแแแแแแแ
แกแแคแฃแซแแแแก แแซแแแแ แ แแ แแแแฎแแแ แแแฎแแแก แแกแแคแแแ แคแแแแแแแแก โ แแแแแแแแฃแ แแ แแ
แแแแแแแแแฃแ แแ แแแแแแแแแแ แแ แแแฅแขแแก โแกแคแแแแฃแแแก แแแจแแแแแแกแ แแ แแแกแ แแแแแขแแชแแแก
แจแแฅแแแแกแแแแแกโ, แ แแช แจแแกแแแแแแกแแ แ แแแแแแก แแแ แแแแแแแก แแแฃแขแแแก.
แจแแกแแแแแ แแแแฎแแแแก แแแแแแแแก แแแ แแแแแชแแฃแ แ แขแแแแก แขแแ แคแแแ แแแแแแ (แแกแแแแ 2 แแ แแแแแแ)
แกแขแ แแขแแแ แแคแแฃแแ แญแ แแแแแแแแ แแฆแแแฃแแ 14C แ แแแแแแฃแแแแแแฃแ แ แแแแแ แแฆแแแแ
แแ แแแแแ, แ แแ 4 แ แกแแกแฅแแก แกแคแแแแฃแแแก แขแแ แคแแก แฉแแแแงแแแแแแแแก 1000 แฌแแแแฌแแแ
แแแกแญแแ แแ, แ. แ. แฌแแแแฌแแแจแ แฎแแแแ 4 แแ แขแแ แคแแก แแแฃแแฃแแแชแแ. แแแแแแจแ แแ แขแแ แคแแก
แแแแ 6 แ แกแแกแฅแแก แคแแแแก (แกแคแแแแฃแแแกแ แแ แกแคแแแแฃแแแแ แจแแ แแฃแแ แขแแ แคแแกแ) แแแแแแแแ
แแ แฌแแแแฌแแแจแ 3 แแ, แแแแแแแแกแ แแ แแแแแแแจแ แฌแแแแฌแแแจแ 1.2 แแ (Nejschtadt 1965). แแก
แแแฉแแแแแแแแแ แแแฆแแแแ แแแ แแแแฃแ แ แแ แแแแแแ แ แกแแ แขแงแแแก แขแแ แคแแแ แแแแแ
แจแแแแ แแแแ. แแแจแแกแแแแแ, แแกแแคแแแแจแ แแแแฎแแแแก แขแแ แคแแแ แแแ แงแแแแแแ แแแฆแแแ
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แแแฃแแฃแแแชแแแก แฃแแแ แแ แฎแแกแแแแแแแแแ. แแกแแแ แแแฆแแแแ แแ แแแก แขแแแแ แกแคแแแแฃแแแก
แกแแฎแแแแแแแกแ แแแแฎแแแจแ.
แแแแแแแก แแแแแแ แแฃแแแแ แแแ แแ แแแก แ แแขแแแก แแแแกแแแฆแแ แแกแแแแแก แแฅแขแแแแแ แจแ แแแแแแจแแ ,,แแกแแแแ 2โ - แแก
แขแแ แคแแแ แแ แแแแ แชแแแแแฃแแ แกแคแแแแฃแแแก แแแฎแแแ แกแแฎแแแแ: Sphagnum imbricatum, Sphagnum papilosum, Sphagnum rubellum, Sphagnum palustre. แงแแแแแแแแฃแ แแ
แฌแแ แแแแแแ แแแแแแแแแ. แ แแแแแแ แแชแแแก แฃแแแ แแก แแแแกแแแฆแแ แแกแแแแแก แ แแแแแแ แแชแแแก
แคแแ แแแแแแแ แฌแแแก แแแกแแฌแงแแกแจแ แแแแญแ แ แกแคแแแแฃแแ แแ แแแแแแจแแ แแแแแแแแแแแแก
แฎแแแฃแแแ. แแแแแแแแแ แแฅแแช แงแแแแแแแแฃแ แแ แฎแแแแแแ. แแฆแแแแแแก แฃแแแ แแก แแแแกแแแฆแแ แแกแแแแแก แแแแฌแงแ แแแจแแแแแแก แคแแ แแแแ. แแแจแแแแแแกแแแแแก
แแแแแแงแแแแแแแ แแ แ แกแแฎแแแแแก แกแคแแแแฃแแ: Sphagnum palustre แแ Sphagnum papillosum.
แกแฃแ . 1. Sphagnum imbricatum & Sphagnum papillossu แกแฃแ . 2. แฎแแแฃแแแ แแแแแจแแฃแแ แกแคแแแแฃแแ
แจแแแแแแแ แแ แแแฅแขแแก แแแแแแแแฃแ แ แฆแแ แแแฃแแแแ แแแแฎแแแแก แแแแแแแแ, แฌแแแแแก แฌแแ แแแแแแ แแชแแฃแแ แแ แแชแแกแแแแกแ แแ แขแแ แคแแก
แแแแแแแแแก แแแแ แแแแ แฆแแ แแแแฎแแแแก แขแแ แคแแแ แแแแกแแแแแก แแแแแฎแแกแแแแแแแแ,
แแแแกแแแฃแแ แแแฃแแ แฐแแแ แแแแแแฃแ แ แ แแแแแ, แแแแ แฆแแ แกแคแแแแฃแแแแแ แขแแ แคแแแ แแก
แฌแงแแแจแแแแแแแแแแแ แคแฃแแฅแชแแ. แแก แแแแแแแแ แกแแกแแคแแ-แกแแแแฃแ แแแ แแแแแจแแฃแแแแแก
แแแแแแ แแ แแกแแแแก แแแแแฃแงแแแแแแแ. แแแแ แแแแ แแแฃแแ แขแแ แคแแแ แแแ แแแงแแแแแแ
แกแแแ แแแแแ แแ แฎแแแแก แแแแ แแแแแ แแฅแชแแแ แแแญแฃแญแงแแแแแแแก แฌแงแแ แแ, แแแแแแ แแ
แแแฎแจแแ แแ แแแแแแก แแแแกแแ แแขแแแกแคแแ แแจแ. แแ แแแแ แแแฃแ แแ แขแแ แคแแแ แ แขแงแแก แแแฉแแฎแแแก
แจแแแแแแ แฉแแแแงแแแแแแ แแแแ แแแ แแแแแแแแ, แกแแแแช แแแแแแแฃแ แ แกแแฎแแแแแแ
แแแแ แแแแแ. แฉแแแแงแแแแแแ แแแแแแ แฎแแ แแกแฎแแก แกแแซแแแ แแแ. แกแแแ แแแแแแ แแแงแแแแแแแ
แแ แฎแแแแ แแแแแแฌแแแ แแก, แ แแ แแแแ แฆแแ แแแแกแแกแขแแแแก แจแแแฅแแแแแ แกแแฎแแแแแก แกแคแแแแฃแแแก
แแแแแฎแแกแแแแแแแแ แฃแแแแแแฃแ แ แแแแกแแแ แฌแงแแแก แจแแฌแแแแกแ. แซแแแแ แ แแแแแกแแแ แฃแแ
แฌแแแแแแแกแแก แแขแแแ แแแ แแแแแแแแ แ แขแแ แแขแแ แแแแ แแ แแแแ แฅแแแฃแแแแแช. แฌแแ แกแฃแแแ
แแแแแแแแฃแ แ แแแแฅแแ, แแแก แจแแแแแ, แ แแช แแฎแแแจแแแแฆแแฃแ แ แขแแ แแกแ แฉแแแแงแแแแแแ,
แแกแแแแแก แขแแ แคแแแ แแแ แแ แกแแแ แแแ แแแแฎแแแแก แงแแแแ แขแแ แคแแแ แ แแฆแแแก แแแแแแ
แแแแแแ. แขแแ แคแแก แกแขแ แแขแแแ แแคแแฃแแแ แญแ แแแแแแ แแแแฉแแแแ, แ แแ แแแแแแ แแ แฃแแแแแ
แแฆแแแก แแแแ 1.7 แแแขแ แแ แแแแแ แแแแแแ แแแแแ. แกแฌแแ แแ แแแแขแแแแ, แ แแ แกแแกแแคแแ-
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แกแแแแฃแ แแแ แแแแแจแแฃแแแแแก แแแแแแ แแแแแฃแกแแแแแแ แ แแแฎแแ แแ แแแแ แแแฃแแ
แขแแ แคแแแ แแแ, แแแแแแแแ แฎแจแแ แแ แแขแแแ แแแแแ. แแแแแช แแแแฎแแแแก แขแแ แคแแแ แแแ
แกแคแแแแฃแแแแแแ, แกแ แฃแแแแ แแแฃแฎแ แฌแแแแแ แแ แแแแแฃแกแแแแแแ แแ แ แแแแ แช แกแแกแฃแฅแ.
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แกแฃแ . 3. แ แแแแแแ แแชแแแก แคแแ แแแแ แกแฃแ . 4. แกแคแแแแฃแแแก แแแจแแแแแแก แคแแ แแแแ
แขแแ แคแแแ แแแ แแแฎแจแแ แแแแแก แกแแแแแแ แแ แแ แแแแแ แแแแแแ แแแแแแ แแแฌแงแแแแแแแแแก
แฌแแ แแแแแแแแแ แแแแแแแฌแแแ. แฃแแแแแกแแ แแแแ แฌแแแแแ แแแแแแฃแ แ, แ แแแแแแฃแแ แแ
แแแแแแแฃแ แ แแแแแแขแแก แ แแแฃแแแชแแแจแ. โแขแแ แคแ, แขแแ แคแแแ แ แแ แแแแแแขแโ แแกแแแ
แกแแฎแแ แจแแแแแ แแก 2012 แฌแแแก แแแแแแขแแก แชแแแแแแแแก แฉแแ แฉแ แแแแแแแชแแแจแ แแ แแ แ
แ แแแแ แช โแขแแ แคแ, แ แแแแ แช แกแแกแฃแฅแโ.
แแ แ แแ แแก แกแแฃแแฃแแแก แฌแแ แแ แแแแ แแแฃแแ แแ แแ แแ แแแฅแขแแก แคแแ แแแแแจแ แแฆแแแแแแแ
แขแแ แคแแแ แ แแแแแแแแ แกแคแแแแฃแแแก แแแแกแแแแแแ แแแแแแแแแแ แ, แ แแ แแแก แแแก แฌแแแแกแแแ
แจแแแแ แแแแ 25 แฏแแ แแแขแ แฌแงแแแก แจแแฌแแแแก แฃแแแ แ แแแแฉแแแ, แแแแแ แฃแคแ แ แแแแชแแแก
แแแแแแแแ แ แขแแ แแขแแ แแแแก แแแขแแแ แแแกแแแแ.
แแ แแแฅแขแแก แกแแชแแแ-แแแแแแแแแฃแ แ แฆแแ แแแฃแแแแ แกแคแแแแฃแแแก แแแแแขแแชแแแก แจแแฅแแแแ แแแกแแฅแแแแแ แแแแแแแแ แแแ แแแกแแฎแแแแแ, แแฅแแแแ
แจแแแแกแแแแแ, แ แแช แแแแแแแแแฃแ แกแแ แแแแแแก แแแฃแขแแแก แ แแแแแแก. แแแแแแแแ แแแแ
แแแงแแแแแแแแแแก แแกแแแ แแแ แแแแ, แ แแแแ แแชแแ แแ แแฌแแแแแ แงแแแแแแแแแแแ แฅแแแแฅแแแแก
แแแแฌแแแแแแแกแแแแแก.
แชแแชแฎแแแ แกแคแแแแฃแแ โ แขแแ แคแแก แฎแแแกแ แแแแแ แแแแกแแแแแแ แแแแแแ แฉแแแ แ แแแแ แช แแแแ
แขแแ แคแ แแ แฌแแ แแแแแแแแก แฃแแแขแแ แแแขแแแ แกแฃแแกแขแ แแขแก แแ แฅแแแแแแแกแแแแแก.
แแแแแแแแแแ แ แแฅแแแแ, แ แแ แแแแแก แแแ แแแแจแ แแแแฌแฃแ แฃแแแ แขแแ แคแแก แแแ แแแ แแ
แขแแ แคแ แแฆแแแแแแก แแ แแฅแแแแแแแแ แแแ, แขแแ แคแแ แแแแฎแแแแ แแแแ แแแแแ. แกแคแแแแฃแแแก
แแแกแแแแแก แแฆแแแแก แจแแแแแ แแแคแแกแแแแฃแแ แกแแฎแแ แแแแงแแแแแ แแแ แแแแจแ.
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แแ แแแฅแขแแก แแแชแแแแ แฃแแ แฆแแ แแแฃแแแแ แจแแแฅแแแแแ แแแแแ แแ แแ แฌแงแแ แ แแแแจแแแแแแแแแ แแแชแแแแ แฃแแ แแแแแแแแแก (แกแแแแแแกแขแ แ,
แกแแแแแแแแแ แ แแ แกแแแแฅแขแแ แ) แฉแแขแแ แแแแกแแแแแก.
แแ แแแฅแขแแก แกแแแแฃแ แแแแ แฆแแ แแแฃแแแแ แแแขแแกแแแขแแแฃแ แ, แแแแแแแก แกแแฌแแแแแฆแแแแแ. แแแแจแแแแแแแแแแ แกแแแแฃแ แแแแ แขแแแแฎแ
แขแแ แคแแแแ แแแแแแแแแ, แ แแแแแกแแช แกแแแแฃแ แแแแ (แแแขแแกแแแขแแแฃแ แ), แคแแ แแแชแแแขแฃแแ
แฆแแ แแแฃแแแแแก.
แแ แแแฅแขแแก แแแแกแแ แแแชแแฃแแ แฆแแ แแแฃแแแแ แแ แฆแแแแกแซแแแแแ แแแแแ แฃแคแ แ แแแแชแ แแ แแฅแแแแ แแแชแฃแแ แแกแแคแแแแจแ แฃแแแแแแฃแ แ
แแกแแแแแก แขแแ แคแแแ แแแ, แฅแแแฃแแแแแก แแแชแฃแแ แขแแ แแขแแ แแแแ. แแฆแแแแแแแ
แกแคแแแแฃแแแแแ แขแแ แคแแแ แ แฐแแแแขแแขแ แจแแแกแ แฃแแแแก แแฃแคแแ แฃแ แ แแแก แแแชแฃแแ
แขแแ แแขแแ แแแแแกแแแแแก
แแ แแแฅแขแแก แฆแแ แแแฃแแแแ แ แแแแ แช แแแแ แแแแ แแแฃแแ แฐแแแแขแแขแแแแก แแฆแแแแแแกแ แแ แแแแแแขแแก แชแแแแแแแแก แจแแแแ แแแแแแแแ แแแแแแ แกแแคแฃแซแแแแ แฉแแแงแ แแแ แฐแแแแขแแขแแแแก แแฆแแแแแแก แแ แแแฅแขแแแก, แ แแแแแจแแช แแแแ
แแแกแแฎแแแแแ แฉแแแ แแแแแ. แขแแ แคแแแ แแแ, แแแแแแแแแแ แ แแฅแแแแ, แ แแ แแแแจแ
แแแฎแจแแ แแแแแก แแแแ แ แแแแแแแแแ แแ แแแแแแ แแแแแแ แแแฌแงแแแแแแแแแก แฌแแ แแแแแแแแก,
แแแ แ แแแก แแแแแจแแแแ แแแแแแแฃแ แ, แ แแแแแแฃแแ แแ แแแแแแฃแ แ แแแแแแขแแก
แ แแแฃแแแชแแแจแ. แฎแแแ แจแแแฌแงแแแ แแแแแแขแแก แ แแแฃแแแชแแแก, แแแแแแแแ แแแ แฆแแแฃแแ
แขแแ แคแแแ แ แกแแแแฃแ แ แแแแแแแก แแแแกแแแก แฌแงแแ แแก แฌแแ แแแแแแแแก.
แแแขแแ แแขแฃแ แ แกแแฅแแ แแแแแแก แแแชแแแแ แแแแแ แแแแแแแแ, แแแแฃแแแก แแแขแแแแแฃแ แ แแแฆแ. แแแแฃแแ 2003 แฌ. แ.
แแแญแฃแขแแซแ โ แญแแ แแฎแแก แแแแขแแก แซแแ แแแแแ แคแแขแแชแแแแแแแ. 158 แแ.
แแแญแฃแขแแซแ แ. โ โแแแแฎแแแแก แขแแ แคแแแ แแแโ. 2002 แฌ.
แแแญแฃแขแแซแ แ. โ แแแแฎแแแแก แแแแแแแแก แชแแชแฎแแแ แกแคแแแแฃแแแแแ แขแแ แคแแแ แแก
แแชแแแแ แแฃแแ แกแแคแแ แ. แแแแฃแแ 2008 แฌ. แแ 32.
แแฃแ แแแ แแแแแแแซแ โ แแญแแ แแก แแแแแแขแฃแ แ แคแแแ แแก แแแแแแ แคแแแแแแฃแ แ แแแแแแแ.
แแแแฃแแ โ 2002. แแ. 214.
แแแแแแแซแ แ. โ แงแแแแแแ แแแแแจแ โแแแแแแชแแแแแแ แแญแแ แโ. แแแแฃแแ 2003 แฌ. แแ. 235.
แแแแแแซแ แ ., แแแแแแแซแ แ. โ แแแแแแแแ แแแ แคแแแ แ. โ แแแแแแชแแแแแแ แแญแแ แโ แแแแฃแแ
2000 แฌ. แแ. 274.
แ แแแแ แแแแแแซแ โ แแชแแแแ แแแ แแแแแ แแคแแ, แแแแแแกแ 1996. (203 แแ).
แ แแแแ แแแแแแซแ, 2005., แกแแฅแแ แแแแแแก แคแแแ แแก แแแแกแแแฅแขแ แแแแแแแแแขแฃแ แฃแแ แแฃแกแฎแ,
แแแแแแกแ., 247 แแ.
Mamuladze, M. Tsinaridze, 2009, IMCG in Georgia, IMCG Newsletter, N 47, pp. 12-14. http://www.imcg.net.
แแแแฃแแแซแ แ., 2011, แแญแแ แ, แแแแ แแแ แแแแแแแแ แแแ, โแกแคแแแแฃแแ, แ แแแแ แช แแแแแฎแแแแแแ
แ แแกแฃแ แกแโ, แ แฃแกแแแแแแแก แฃแแแแแ แกแแขแแขแแก แกแขแฃแแแแขแฃแ แ แแแแคแแ แแแชแแ, แแ.
57โ60.
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Kaffke, A., Couwenberg, J., Joosten, H., Matchutadze, I. & Schulz, J. 2000. Ispani II: the worldโs first percolation bog. In: Quรจbec 2000 Millennium Wetland Event, Program with Abstracts, p. 487.
Matchutadze I., Kaffke A., 2002, Calluna vulgaris (Linnaeus) Hull, the first record for Georgia.
Kaffke A., Matchutadze I., Couvenberg J., Joosten H. 2002., Early 20th century Russian peat
scientists as possible vectors for the establishment of Calluna vulgaris in Georgian
sphagnum bogs, Souseura-Finnish peatland Society, Helsinki pp. 61-66.
Goradze R., Matchutadze I., Goradze I., 2002, Georgia, Directory of Azov-Black Sea Coastal
Wetlands, Wetlands International. Kyiv. pp. 46-75.
Matchutadze I., Skhiladze N., 2003., Mires of Kolkheti lowland, International conference of wetlands conservation, Biodiversity and Wise Use, Armenia, Sevan
Joosten H., Kaffke A., Matchutadze I., 2003, Kolkheti wetlands ecosystem IMCG Newsletter, pp. 19-23.
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Medicinal Plants of Adjaristskali Valley
Nino Manvelidze, Natella Varshanidze, Nazi Turmanidze Batumi Shota Rustaveli State University, Faculty of Natural Sciences and Health
Main author: Nino Manvelidze [email protected], +995 514 34 00 04
Summary The paper deals with spread of medical plant diversity, systematic structure, and medicine for use in the river. Adjaristskali valley. An estimated, 142 species of medicinal plants are
widespread in the river Adjaristskali in different biotopes (แแแ แจแแแแซแ 2011, 2013, 2014), (Varshanidze 2011, 2013, 2014), they are distributed in 56 families and 112 genus. Medicinal
species occur in the families: Asteraceae โ 14 species, Lamiaceae โ 14, Rosaceae โ 12, Hypericaeae โ 6, Polypodiaceae โ 6, Scrophulariaceae โ 3, Fabaceae โ 4, Fagaceae โ 4, Solanaceae โ 4. Polygonaceae โ 4. Introduction Today, the medical practice has been successfully used for preparations made from medicinal
plants, their advantage over synthetic drugs reflected in the fact that they do not cause side effects, allergies, chronic toxicity, and do not demonstrate any teratogenic, mutagenic effect, which is typical of synthetic drugs. Today, a third of the medicines are produced from medicinal plants. Therefore, the study of medicinal plants in modern biology is one of the urgent problems. Methodology Field studies carried out the traditional route, the expedition method. Medicinal Plant Research conducted the poll of local residents. Herbarium collection and the processing, treatment,
implemented in Adjara, with the help of Georgia plant identification guides, and the "Flora"
(แแแชแฎแแแแแ, แฎแแ แแซแ, แแแแแแซแ, 1971-2003; แกแแฅแแ แแแแแแก แแชแแแแ แแแแแก แกแแ แแแแแ, 1964,
1969; ะะผะธััะธะตะฒะฐ, 1990 I, II). (Ketskhoveli., Kharadze., Gagnidze, 1971-2003; of plant identification guides, Guidebooks of Georgian Plants 1964, 1969; ะะผะธััะธะตะฒะฐ, 1990 I, II).
Results Our studies of the river. Adjaristskali valley 142 species of medicinal plants are widespread in various biotopes. They are distributed in 56 families and 112 genus. Most numerous families are as follows: Asteraceae โ 14 species, Lamiaceae โ 14, Rosaceae โ 12, Hypericaeae โ 6, Polypodiaceae โ 6, Scrophulariaceae โ 4, Fabaceae โ 4, Fagaceae โ 4, Solanaceae โ 4. Polygonaceae โ 4. Here Acharistskali valley medicinal plant systematic structure, according to
the tax as Cherepanov (Czerepanov, 1995).
PTERIDOPHYTA Equisetaceae: Equisetum arvense L., E. majus Gars. Hypolepidaceae: Pteridium aquilinum (L.) Kuhn. Polypodiaceae: Asplenium trichomonas L. Asplenium septentrionale (L.) Hof.,
Dryopteris filix-mas (L.) Schott, D. austriaca (Jacq.) Woynar, D. oreades
Fomin. Polypodium vulgare L. Pteridaceae: Pteris cretica L.
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GYMNOSPERMAE Pinaceae: Abies nordmanniana (Stev.) Spach, Picea orientalis (L.) Link., Pinus kochiana Klotzsch ex C. Koch
ANGIOSPERMAE Dicotyledonae Apiaceae: Carum cavri L., Cervaria caucasica (Bieb.) M. Pimen. Sanicula europaea L., Apocynaceae: Vinca minor L. Araliaceae: Hedera colchica (C. Koch.) C.
Koch., H. helix L. Asclepiadaceae: Periploca graeca L. Asteraceae: Achillea millefolium L., Arctium lappa L., Artemisia vulgaris L., A. absinthium L., Bidens tripartita L., Cichorium intybus L., Cicerbita pontica
(Boiss.) Grossh., D Dichrocephala bicolor (Roth) Schlecht., Matricaria chamomilla L., Pyrethrum parthenifolium Willd., Pyrethrum roseum (Adam) Bieb. Solidago virgaurea L., Taraxacum officinale Wigg., Tussilago farfara L. Berberidaceae: Berberis vulgaris L.
Betulaceae: Alnus barbata C.A.Mey. Buxaceae: Buxus colchica Pojark. Cannabaceae: Humulus lupulus L. Caryophyllaceae: Herniaria glabra L., Saponaria officinalis L. Corylaceae: Corylus avellana L. Crassulaceae:Hylotelephium caucasicum
(Grossh.) H. Ohba, S. stoloniferum S. G. Gmel. Cruciferae: Capsella bursa-pastoris (L.) Medik. Ebenaceae: Diospyros lotus L. Ericaceae: Rhododendron ponticum L., Vaccinium arctostaphylos L.
Fabaceae: Galega officinalis L., Melilotus officinalis (L.) Pall., Ononis arvensis L.,Trifolium pratense L. Fagaceae: Castanea sativa Mill. Fagus orientalis Lipsky., Quercus dshorochensis C. Koch., Q. hartwissiana Stev.
Gentianaceae: Centaurium erythraea Rafn., Gentiana cruciata L.
Hypericaeae: Hypericum androsaemum L., H. grossheimii Kem.-Nat., H.orientale., H.perforatum L., H.polygonifolium Rupr., H. xylosteifolium (Spach) N. Robson. Juglandaceae: Juglans regia L.
Lamiaceae: Calamintha grandiflora (L). Moench., C.nepeta (L.) Savi, C. Officinalis Moench., Clinopodium umbrosum (Bieb.) C. Koch., C. Vulgare L., Glechoma hederacea L., Lamium album L., Leonurus quinquelobatus Gilib., Melissa officinalis L.,
Mentha longifolia (L.) Huds., Mentha pulegium L. Origanum vulgare L., Stachys officinalis (L.) Trevis. Trachistemon orientalis (L.) G. Don fil. Lauraceae: Laurus nobilis L. Malvaceae: Althaea officinalis L., Malva sylvestris L.
Oleaceae: Fraxinus excelsior L Papaveraceae: Chelidonium majus L., Glaucium flavum Grantz Plantaginaceae: Plantago lanceolata L., P. major L. Polygonaceae: Poligonum aviculare L.,
Persicaria hydropiper (L.) Spach, P. maculata (Rafin.) A.&D. Love, Rumex crispus L. Primulaceae: Cyclamen adzharicum Pobed., Lysimachia verticillaris Spreng. Primula sibthorpii Hoffmgg. Punicaceae: Punica granatum L.
Ranunculaceae: Helleborus caucasicus A. Br., Clematis vitalba L. Rhamnaceae: Frangula alnus Mill., Rhamnus microcarpa Boiss. Rosaceae:Cydonia oblonga Mill., Geum urbanum L., Fragaria vesca L., Laurocerasus officinalis M. Roem., Malus orientalis Uglitzk., Potentilla erecta (L.) Raeusch., Poterium polyganum W. Et K.Rosa canina L., R. Pomifera Herrm. Rubus caesius L., R. buschii Grossh. ex Sinjkova, Sorbus boissieri Schneid. Rubiaceae:Asperula odorata L. Salicaceae: Salix alba L., S. caprea L.
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Sambucaceae: Sambucus ebulus L., S. nigra L. Scrophulariaceae:Digitalis ferruginea L., D. purpurea L., Verbascum thapsus L., Veronica officinalis L.
Solanaceae: Datura stramonium L., Hyoscyamus niger L., Solanum nigrum L. Tiliaceae: Tilia begoniifolia Stev. Urticaceae: Urtica dioica L. Viburnaceae: Viburnum opulus L. Violaceae: Viola arvensis Murr.
Viscaceae: Viscum album L. Vitaceae: Vitis vinifera L.
Zygophyllaceae. Tribulus terrestris L. MONOCOTYLEDONAE Alliaceae: Allium ursnum L. Ruscus ponticus Woronow., Ruscus colchicus P. F.
Yeo. Amaryllidaceae: Galanthus woronowii Losinsk., Leucojum aestivum L. Asparagaceae: Asparagus litoralis Stev. Convallariaceae:Convallaria majalis L., Cyperaceae: Cyperus badius Desf. Juncaceae: Juncus bufonius L. Poaceace: Elytrigia repens (L.) Nevski
Conclusions Our study has identified: 1. In the various biotopes of river Adjaristskali spread 142 species of medicinal plants;
2. They are distributed in 56 families and 112 genus; 3. Most numerous families are as follows: Asteraceae โ 14 species, Lamiaceae โ 14, Rosaceae โ 12, Hypericaeae โ 6, Polypodiaceae โ 6, Scrophulariaceae โ 4, Fabaceae โ 4, Fagaceaeโ4, Solanaceae โ 4. Polygonaceae โ 4. References Varshanidze N. Turmanidze N. (2011). Taxonomic diversity of Adjarian medicinal plants.
International Conference on Biodiversity Conservation in Georgia, Tbilisi. 110-114 pp. Varshanidze N. (2013). Medicinal plant species diversity in Adjara. Varshanidze N. Asanidze N. Turmanidze N. (2014) Medicinal plant species diversity in Adjara
and bio-ecology. (Monograph.). Tbilisi. โUniversalโ 268 pp. Ketskhoveli N., Kharadze A., Gagnidze R. (1971-2003). Flora of Georgia, 1-13, Tbilisi, Science. The plant identification guides of Georgia (1964) V. 1 Tbilisi. Science. 458 pp. The plant identification guides of Georgia (1969) V 1. Tbilisi. Science. 440 pp.
ะะผะธััะธะตะฒะฐ ะ.ะ. ะะฟัะตะดะตะปะธัะตะปั ัะฐััะตะฝะธะน ะะดะถะฐัะธะธ. ะขะฑะธะปะธัะธ, โะะตัะฝะธะตัะตะฑะฐโ, ั. I, 1990. 327 ััั.
ะะผะธััะธะตะฒะฐ ะ.ะ. ะะฟัะตะดะตะปะธัะตะปั ัะฐััะตะฝะธะน ะะดะถะฐัะธะธ. ะขะฑะธะปะธัะธ, โะะตัะฝะธะตัะตะฑะฐโ, ั. I, 1990.; ั. II, 1990. 278 ััั.
Czerepanov S. (1995). Vascular plants of Russia and Adjacent states (the former USSR). Cambridge University press, 516 pp.
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147
แแญแแ แแกแฌแงแแแก แฎแแแแแก แกแแแแฃแ แแแแ แแชแแแแ แแแแ
แแแแ แแแแแแแแซแ, แแแแแแ แแแ แจแแแแซแ, แแแแ แแฃแ แแแแแซแ
แแแแฃแแแก แจแแแ แ แฃแกแแแแแแแก แกแแฎแแแฌแแคแ แฃแแแแแ แกแแขแแขแ
แกแแแฃแแแแแกแแแขแงแแแแ แแแชแแแแ แแแแแ แแ แฏแแแแแชแแแก แคแแแฃแแขแแขแ
แแแแแแ แ แแแขแแ แ: [email protected], +995 514 34 00 04
แ แแแแฃแแ แแแจแ แแแจแ แแแแฎแแแฃแแแ แแ. แแญแแ แแกแฌแงแแแก แฎแแแแแจแ แแแแ แชแแแแแฃแแ แกแแแแฃแ แแแแ
แแชแแแแ แแแแแก แแ แแแแแคแแ แแแแแแ, แกแแกแขแแแแขแแแฃแ แ แกแขแ แฃแฅแขแฃแ แ, แแ แแแแแชแแแแจแ
แแแแแงแแแแแแก แแแแแกแแแฃแ แแแแแ. แแแแแแแแแแ, แแ. แแญแแ แแกแฌแงแแแก แกแฎแแแแแกแฎแแ แแแแขแแแจแ
แแแแ แชแแแแแฃแแแ 142 แกแแฎแแแแแก แกแแแแฃแ แแแแ แแชแแแแ แ (แแแ แจแแแแซแ 2011, 2013, 2014), แแกแแแ แแแแแฌแแแแแฃแแแ 56 แแฏแแฎแจแ แแ 112 แแแแ แจแ. แกแแแแฃแ แแแแ แกแแฎแแแแแแ
แกแแแ แแแแแ แแแแแแ แฉแแแ แแฏแแฎแแแ: Asteraceae โ 14 แกแแฎแแแแ, Lamiaceae โ 14, Rosaceae โ 12, Hypericaeae โ 6, Polypodiaceae โ 6, Scrophulariaceae โ 3, Fabaceae โ 4, Fagaceae โ 4, Solanaceae โ 4. Polygonaceae โ 4.
แจแแกแแแแแ แกแแแฆแแแกแแ แกแแแแแแชแแแ แแ แแฅแขแแแแจแ แฌแแ แแแขแแแแ แแแแแแงแแแแแแแ แกแแแแฃแ แแแแ
แแชแแแแ แแแแแกแแแแ แแแแแแแแแฃแแ แแ แแแแ แแขแแแ, แแแแ แฃแแแ แแขแแกแแแ แกแแแแแแฃแ
แแ แแแแ แแขแแแแแ แจแแแแ แแแแ แแแแแแฎแแขแแแ แแแแจแ, แ แแ แแกแแแ แแ แแฌแแแแแ แแแแ แแแ
แแแฅแแแแแแแแก, แแแแ แแแแแก, แฅแ แแแแแฃแ แขแแฅแกแแแแแแแก แแ แแ แแแแแแแ แขแแ แแขแแแแแฃแ,
แแฃแขแแแแแฃแ แแ แแแแชแแ แแแแแฃแ แแแฅแแแแแแแก, แ แแช แแแแแฎแแกแแแแแแแแแ แกแแแแแแฃแ แ
แแ แแแแ แแขแแแแกแแแแแก. แแฆแแก แแ แกแแแฃแ แกแแแแฃแ แแแแ แแ แแแแ แแขแแ แแแกแแแแแ
แแชแแแแ แแแแแกแแแแ แแแแแแแแ. แแแแขแแ แกแแแแฃแ แแแแ แแชแแแแ แแแแแก แจแแกแฌแแแแ
แแแแแแแแ แแแ แแแแแแแแแก แแ แโแแ แแ แแฅแขแฃแแแฃแ แ แแ แแแแแแแ.
แแแแแแแแแแแ แกแแแแแ แแแแแแแแ แแแแแแฎแแ แชแแแแแ แขแ แแแแชแแฃแแ แแแ แจแ แฃแขแฃแแ, แแฅแกแแแแแชแแแก
แแแแแแแ. แกแแแแฃแ แแแแ แแชแแแแ แแแ แจแแกแฌแแแแ แแแฌแแ แแแแ แแแแแแแแ แแแ
แแแกแแฎแแแแแแก แแแแแแแแฎแแแ. แฐแแ แแแ แแฃแแแก แจแแแ แแแแแ แแ แแแกแ แแแแแ แฃแแ
แแแแฃแจแแแแแ, แ แแแแแ-แแแแแขแแคแแชแแ แแแ แแแแแแฎแแ แชแแแแแ แแญแแ แแก, แกแแฅแแ แแแแแแก
แแชแแแแ แแแ แกแแ แแแแแแแแก แแ โแกแแฅแแ แแแแแแก แคแแแ แแกโ แแแฎแแแ แแแแ (แแแชแฎแแแแแ.,
แฎแแ แแซแ., แแแแแแซแ, 1971-2003; แกแแฅแแ แแแแแแก แแชแแแแ แแแแแก แกแแ แแแแแ, 1964, 1969;
ะะผะธััะธะตะฒะฐ, 1990 I, II).
แจแแแแแแแ แฉแแแแ แแแแแแแแแแแแแ แแญแแ แแกแฌแงแแแก แฎแแแแแก แกแฎแแแแแกแฎแแ แแแแขแแแจแ แแแแ แชแแแแแฃแแแ
142 แกแแฎแแแแแก แกแแแแฃแ แแแแ แแชแแแแ แ. แแกแแแ แแแแแฌแแแแแฃแแแ 56 แแฏแแฎแจแ แแ 112 แแแแ แจแ.
แงแแแแแแ แแ แแแแแ แแชแฎแแแแแ แฌแแ แแแแแแแแแแ แจแแแแแแ แแฏแแฎแแแ : Asteraceae โ 14 แกแแฎแแแแ, Lamiaceae โ 14, Rosaceae โ 12, Hypericaeae โ 6, Polypodiaceae โ 6, Scrophulariaceae โ 4, Fabaceae โ 4, Fagaceae โ 4, Solanaceae โ 4. Polygonaceae โ 4.
แแแแแงแแแก แแญแแ แแกแฌแงแแแก แฎแแแแแก แกแแแแฃแ แแแแ แแชแแแแ แแแ แกแแกแขแแแแขแแแฃแ แ
แกแขแ แฃแฅแขแฃแ แ, แขแแฅแกแแแแแ แแแชแแแฃแแแ แฉแแ แแแแแแแแก (Czerepanov 1995) แแแฎแแแแแ.
148
PTERIDOPHYTA Equisetaceae: Equisetum arvense L., E. majus Gars. Hypolepidaceae: Pteridium aquilinum (L.) Kuhn. Polypodiaceae: Asplenium trichomonas L. Asplenium septentrionale (L.) Hof., Dryopteris filix-mas (L.) Schott, D. austriaca (Jacq.) Woynar, D. oreades Fomin. Polypodium vulgare L. Pteridaceae: Pteris cretica L.
GYMNOSPERMAE Pinaceae: Abies nordmanniana (Stev.) Spach, Picea orientalis (L.) Link., Pinus kochiana Klotzsch ex C. Koch
ANGIOSPERMAE Dicotyledonae Apiaceae: Carum cavri L., Cervaria caucasica (Bieb.) M. Pimen. Sanicula europaea L., Apocynaceae: Vinca minor L. Araliaceae: Hedera colchica (C. Koch.) C. Koch., H. helix L.
Asclepiadaceae: Periploca graeca L. Asteraceae: Achillea millefolium L., Arctium lappa L., Artemisia vulgaris L., A. absinthium L., Bidens tripartita L., Cichorium intybus L., Cicerbita pontica (Boiss.) Grossh., D Dichrocephala bicolor (Roth) Schlecht., Matricaria chamomilla L.,
Pyrethrum parthenifolium Willd., Pyrethrum roseum (Adam) Bieb. Solidago virgaurea L., Taraxacum officinale Wigg., Tussilago farfara L. Berberidaceae: Berberis vulgaris L. Betulaceae: Alnus barbata C.A.Mey. Buxaceae: Buxus colchica Pojark.
Cannabaceae: Humulus lupulus L.
Caryophyllaceae: Herniaria glabra L., Saponaria officinalis L. Corylaceae: Corylus avellana L.
Crassulaceae:Hylotelephium caucasicum (Grossh.) H. Ohba, S. stoloniferum S. G.
Gmel.
Cruciferae: Capsella bursa-pastoris (L.) Medik. Ebenaceae: Diospyros lotus L. Ericaceae: Rhododendron ponticum L., Vaccinium arctostaphylos L.
Fabaceae: Galega officinalis L., Melilotus officinalis (L.) Pall., Ononis arvensis L.,Trifolium pratense L. Fagaceae: Castanea sativa Mill. Fagus orientalis Lipsky., Quercus dshorochensis C. Koch., Q. hartwissiana Stev.
Gentianaceae: Centaurium erythraea Rafn., Gentiana cruciata L. Hypericaeae: Hypericum androsaemum L., H. grossheimii Kem.-Nat., H.orientale., H.perforatum L., H.polygonifolium Rupr., H. xylosteifolium (Spach) N. Robson.
Juglandaceae: Juglans regia L.
Lamiaceae: Calamintha grandiflora (L). Moench., C.nepeta (L.) Savi, C. Officinalis Moench., Clinopodium umbrosum (Bieb.) C. Koch., C. Vulgare L., Glechoma hederacea L., Lamium album L., Leonurus quinquelobatus Gilib., Melissa officinalis L.,
Mentha longifolia (L.) Huds., Mentha pulegium L. Origanum vulgare L., Stachys officinalis (L.) Trevis. Trachistemon orientalis (L.) G. Don fil. Lauraceae: Laurus nobilis L. Malvaceae: Althaea officinalis L., Malva sylvestris L.
Oleaceae: Fraxinus excelsior L Papaveraceae: Chelidonium majus L., Glaucium flavum Grantz Plantaginaceae: Plantago lanceolata L., P. major L. Polygonaceae: Poligonum aviculare L., Persicaria hydropiper (L.) Spach, P. maculata (Rafin.) A.&D. Love, Rumex crispus L. Primulaceae: Cyclamen adzharicum Pobed., Lysimachia verticillaris Spreng. Primula sibthorpii Hoffmgg. Punicaceae: Punica granatum L.
Ranunculaceae: Helleborus caucasicus A. Br., Clematis vitalba L.
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Rhamnaceae: Frangula alnus Mill., Rhamnus microcarpa Boiss. Rosaceae:Cydonia oblonga Mill., Geum urbanum L., Fragaria vesca L., Laurocerasus officinalis M. Roem., Malus orientalis Uglitzk., Potentilla erecta (L.) Raeusch., Poterium polyganum W. Et K.Rosa canina L., R. Pomifera Herrm. Rubus caesius L., R. buschii Grossh. ex Sinjkova, Sorbus boissieri Schneid. Rubiaceae:Asperula odorata L. Salicaceae: Salix alba L., S. caprea L. Sambucaceae: Sambucus ebulus L., S. nigra L. Scrophulariaceae:Digitalis ferruginea L., D. purpurea L., Verbascum thapsus L., Veronica officinalis L.
Solanaceae: Datura stramonium L.,
Hyoscyamus niger L., Solanum nigrum L.
Tiliaceae: Tilia begoniifolia Stev. Urticaceae: Urtica dioica L. Viburnaceae: Viburnum opulus L. Violaceae: Viola arvensis Murr. Viscaceae: Viscum album L.
Vitaceae: Vitis vinifera L. Zygophyllaceae. Tribulus terrestris L. MONOCOTYLEDONAE Alliaceae: Allium ursnum L. Ruscus ponticus Woronow., Ruscus colchicus P. F.
Yeo. Amaryllidaceae: Galanthus woronowii Losinsk., Leucojum aestivum L. Asparagaceae: Asparagus litoralis Stev. Convallariaceae:Convallaria majalis L., Cyperaceae: Cyperus badius Desf. Juncaceae: Juncus bufonius L. Poaceace: Elytrigia repens (L.) Nevski
แแแกแแแแแแ แฉแแแแก แแแแ แฉแแขแแ แแแฃแแ แแแแแแแก แจแแแแแแ แแแแแแแแแแ:
1. แแ. แแญแแ แแกแฌแงแแแก แกแฎแแแแแกแฎแแ แแแแขแแแจแ แแแแ แชแแแแแฃแแ 142 แกแแฎแแแแแก
แกแแแแฃแ แแแแ แแชแแแแ แ
2. แแกแแแ แแแแแฌแแแแแฃแแแ 56 แแฏแแฎแจแ แแ 112 แแแแ แจแ.
3. แกแแแแฃแ แแแแ แกแแฎแแแแแแ แกแแแ แแแแแ แแแแแแ แฉแแ แแฏแแฎแแแ: Asteraceae โ 14 แกแแฎแแแแ,
Lamiaceae โ 14, Rosaceae โ 12, Hypericaeae โ 6, Polypodiaceae โ 6, Scrophulariaceae โ 3, Fabaceae โ 4, Fagaceae โ 4, Solanaceae โ 4. Polygonaceae โ 4.
แแแแแแแ แแแ แฆแ แแ แแแแแแแ แแแแก แแแแแแฎแแขแแแ แแแแคแแ แแแชแแแก แแ แแแแแแแขแแ แแแแก แแแแแ แ,
แแแแคแแ แแแชแแแก แแแฌแงแแแแกแแแแแก แแ แแแแ แแจแ แแแแแแกแแแแแก.
แแแขแแ แแขแฃแ แ แแแ แจแแแแซแ แ. แแฃแ แแแแแซแ แ. (2011). แแญแแ แแก แกแแแแฃแ แแแแ แแชแแแแ แแแ แขแแฅแกแแแแแแฃแ แ
แแ แแแแแคแแ แแแแแแ. แกแแแ แแแจแแ แแกแ แแแแคแแ แแแชแแ: แกแแฅแแ แแแแแแก
แแแแแ แแแแแคแแ แแแแแแ แแแแแแกแ. แแ. 110-114.
แแแ แจแแแแซแ แ. (2013). แแญแแ แแจแ แแแแ แชแแแแแฃแแ แกแแแแฃแ แแแแ แแชแแแแ แแแแแก แกแแฎแแแแ แแแ
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แฃแแแแแ แกแแขแแขแโ. แแแแฃแแ. 267 แแ.
แแแ แจแแแแซแ. แ. แแกแแแแซแ แ. แแฃแ แแแแแซแ แ. (2014) แแญแแ แแจแ แแแแ แชแแแแแฃแแ แกแแแแฃแ แแแแ
แแชแแแแ แแแแ แแ แแแแ แแแแแแแแแแแ. (แแแแแแ แแคแแ). แแแแแแกแ. ,,แฃแแแแแ แกแแแโ. 268 แแ.
150
แแแชแฎแแแแแ แ., แฎแแ แแซแ แ., แแแแแแซแ แ .`(1971-2003). แกแแฅแแ แแแแแแก แคแแแ แ~. 1-13.
แแแแแแกแ, แแแชแแแแ แแแ.
แกแแฅแแ แแแแแแก แแชแแแแ แแแแแก แกแแ แแแแแ (1964) แข. 1. แแแแแแกแ. แแแชแแแแ แแแ. 458 แแ.
แกแแฅแแ แแแแแแก แแชแแแแ แแแแแก แกแแ แแแแแ (1969 ) แข. 1. แแแแแแกแ. แแแชแแแแ แแแ. 440 แแ.
ะะผะธััะธะตะฒะฐ ะ.ะ. ะะฟัะตะดะตะปะธัะตะปั ัะฐััะตะฝะธะน ะะดะถะฐัะธะธ. ะขะฑะธะปะธัะธ, โะะตัะฝะธะตัะตะฑะฐโ, ั. I, 1990. 327 ััั.
ะะผะธััะธะตะฒะฐ ะ.ะ. ะะฟัะตะดะตะปะธัะตะปั ัะฐััะตะฝะธะน ะะดะถะฐัะธะธ. ะขะฑะธะปะธัะธ, โะะตัะฝะธะตัะตะฑะฐโ, ั. I, 1990.; ั. II, 1990. 278 ััั.
Czerepanov S. (1995). Vascular plants of Russia and Adjacent states (the former USSR). Cambridge University Press, 516 pp.
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Protected Areas of Kolkheti
Ketevan Memarne MSc Student of Biology Department
Faculty of Natural Sciences and Health Physiopathology and Biodiversity Institute
Batumi Shota Rustaveli State University Email: [email protected]
Abstract The beauty of Kolkheti nature and particularly of south Kolkheti is inimitable and unique.
Elements of Kolkhic flora are gathered here and similar is almost nowhere to find. Floral elements present here are living nature monuments, as Kolkheti represents the refugium of ancient flora trapped here ever since glacial period. For centuries changing environmental conditions lead to disappearance of many species. In established ancient plant communities species are interconnected and dependent on each other as organs in the living organism. Loss of some species from the community has knock-on effect on other species and, affects the entire community. To address this problem humans are inevitably engaged in taking care of
rare and disappearing species. In all countries globally protected areas and nature reserves are established to safeguard unique ecosystems, providing these areas with strict protection zones, designate nature monuments, compile and maintain 'red lists' of species.
แแแแฎแแแแก แแแชแฃแแ แขแแ แแขแแ แแแแ
แฅแแแแแแ แแแแแ แแ
แแแแฃแแแก แจแแแ แ แฃแกแแแแแแแก แกแแฎแแแแฌแแคแ แฃแแแแแ แกแแขแแขแ
แกแแแฃแแแแแกแแแขแงแแแแ แแ แฏแแแแแชแแแก แคแแแฃแแขแแขแ
แแแแแแแแแก แแแแแ แขแแแแแขแแก แแแแแกแขแ แแแขแ
แคแแขแแแแแแแแแแแกแ แแ แแแแแ แแแแแคแแ แแแแแแแก แแแกแขแแขแฃแขแ
แแแแฅแขแ แแแฃแแ แคแแกแขแ: [email protected]
แ แแแแฃแแ
แแแแฃแแแแ แแแแแ แแ แฃแแแแแแฃแ แแ แแแแฎแแแแกแ แแ แแแแกแแแฃแแ แแแแ แแ แกแแแฎแ แแแ
แแแแฎแแแแก แแฃแแแแ. แแฅ แแแแ แแแฃแงแ แแ แแแแฎแแแแก แคแแแ แแก แแแแแแแขแแแก, แ แแแแแแ
แแกแแแแกแ แกแฎแแแแแ แซแแแแแ แแฃ แแแแซแแแแแแ. แแ แกแแแฃแแ แคแแแ แแก แแแแแแแขแแแ แแฃแแแแแก
แชแแชแฎแแแ แซแแแแแแแ, แแแแแแแแ แแแแฎแแแ แแแแงแแแแแ แแแแก แแแแฅแแจแ แฃแซแแแแแกแ แคแแแ แแก
แ แแคแฃแแแฃแแก แฌแแ แแแแแแแแ. แกแแฃแแฃแแแแแแก แแแแซแแแแ, แแแ แแแ แแแ แแแแแแก
แชแแแแแแแแแแแ, แแ แแแแแ แกแแฎแแแแแก แแแฆแฃแแแ แแแแแแฌแแแ. แแแแแแแแแ แฉแแแแงแแแแแแแฃแ
แแแแแกแแแแแแแแแแแจแ แกแแฎแแแแแแ แแกแ แฃแแแแจแแ แแแแแแ แแ แแแแแแแก, แ แแแแ แช
แแ แแแแแแแจแ แแ แแแแแแแ แแ แกแแแแแแแแแแแแแ แ แแแแแแแ แกแแฎแแแแแก แแแแแแ แแแ แแแแแก
แแงแแแแแก แแแก แแ แแฌแแแแก แแแแแแแแ แแแแก. แงแแแแแแแ แแแแ แแแซแฃแแ แแแแ แฎแแแแ
แแแชแแแ แแแแ, แ แแแ แแแ แฃแแ แแจแแแแแ แแ แฅแ แแแแแ แกแแฎแแแแแแแก แแแแแ แฉแแแแกแแแแแก.
แแกแแคแแแแก แงแแแแ แฅแแแงแแแแจแ แฃแแแแแแฃแ แ แแแแกแแกแขแแแแแแก แจแแแแ แฉแฃแแแแแกแ แแ
แแแแแ แฉแแแแกแแแแแก แจแแฅแแแแแแ แแแชแฃแแ แขแแ แแขแแ แแแแ, แแแแ แซแแแแแ (แแฃแแแแแก แแแแชแ แ
แแแชแแแก แแแแ), แแฃแแแแแก แชแแชแฎแแแ แซแแแแแแ, แแ แกแแแแแก โแฌแแแแแ แฌแแแแโ.
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แจแแกแแแแแ แแฆแแแกแแแแแก แแญแแ แแจแ แแแฎแ แแแชแฃแแ แขแแ แแขแแ แแแ:
แแแแขแ แแจแแก แกแแฎแแแแฌแแคแ แแแแ แซแแแ: แแแแ แกแแแแก แแแ แแแแ แแ แฃแแแแแแฃแ แแแ:
แแแแขแ แแจแแก แกแแฎแแแแฌแแคแ แแแแ แซแแแ (แแฃแแแแแก แแแชแแแก แกแแแ แแแจแแ แแกแ แแแแจแแ แแก IUCN
แแแ แแแแ แแแขแแแแ แแ) แจแแแฅแแแ 1959 แฌแแแก แแ แแฆแแแแ แคแฃแแฅแชแแแแแ แแแก. แแแกแ แคแแ แแแแ
แจแแแแแแแก 16 000 แฐแแก. แแแแ แซแแแแก แแแแแแแกแขแ แแชแแ แฎแแแแซแฆแแแแแแแแก 2002 แฌแแแก 22
แแแแแแ แก แงแแคแแแ แกแแฅแแ แแแแแแก แแแชแฃแแ แขแแ แแขแแ แแแแแก, แแแแ แซแแแแแแกแ แแ
แกแแแแแแแแ แแ แแแฃแ แแแแแแก แกแแฎแแแแฌแแคแ แแแแแ แขแแแแแขแแก แแแแ แแแฆแแแฃแแ
แแแแฃแแแแแ [4]. แฅแแแฃแแแแแก แแแชแฃแแ แขแแ แแขแแ แแแแ: แฅแแแฃแแแแแก แแแแ แซแแแ แแ แฅแแแฃแแแแแก แแฆแแแแแแแ: แแแแ แกแแแแก แแแ แแแแ แแ แฃแแแแแแฃแ แแแ: 1996 แฌแแแแแ แแกแแแแ II แชแแชแฎแแแ
แกแคแแแแฃแแแแแ แขแแ แคแแแ แ แจแแขแแแแแแ แ แแแกแแ แแก แแแแแแแชแแแ แแแชแฃแแ แขแแ แแขแแ แแแก
แกแแแจแ, แ แแแแ แช แแแแจแแแแแแแแแ แฐแแแแขแแขแ แแแแ แแ แแแแแ, แแแแฃแแแ แ แแ แแแแแแแ แ
แคแ แแแแแแแแแกแแแแแก. 1999 แฌแแแแแ แแแฆแแแฃแแ แแฅแแ แแแแแแ แแแแฎแแแแก แญแแ แแขแแแแแแ
แแแชแฃแแ แขแแ แแขแแ แแแแแก แจแแกแแฎแแ, 2002 แฌแแแแแ แแ แคแฃแแฅแชแแแแแ แแแ แแแแฌแงแ
แฅแแแฃแแแแแก แแแแ แซแแแแ (แแกแแแแ II) แแ แฅแแแฃแแแแแก แแฆแแแแแแแแ (แแกแแแแ I). แแแกแ
แคแแ แแแแ 750 แฐแแฅแขแแ แแ. แแกแแแแ II แแแแแ แแ แแก แแกแแคแแแแก แแแ แแแแ แแแ แแแแแชแแฃแ แ
แขแแ แคแแแ แแ. แแ แแแแแแ: แแแแแแแก แแแฎแแแแแ แแแแแแแแกแฌแแแแแฃแแ แแงแ แแกแแแ แแแชแฃแแ
แขแแ แแขแแ แแแก แแแกแแแ แแแขแแแแ แแ: โแแ แแแแแแฎแ แแแ แแแแแงแแแแแแก แขแแ แแขแแ แแโ, แ แแแแแแช แแ แฉแแแแงแแแแแแแฃแแ แแ แแฃแชแแแแแแแแ แฉแแแแงแแแแแแแก. แแขแแ แแแแก แแ แแแแฃแแ แแแ แแ: แแแแ แกแแแแก แแแ แแแแ แแ แฃแแแแแแฃแ แแแ: แฉแแแแงแแแแแแ
2007 แฌแแแก. แแขแแ แแแแก แแ แแแแฃแแ แแแ แแ, แคแแ แแแแ 16000 แฐแ, แแแแชแแแก แกแแ แ แแแแแก:
แฅแแแฃแแแแแก, แฎแแแแแฉแแฃแ แแก, แฅแแแแก แ แแแแแแแก. แกแแแแช แแแแแแแแแก แกแแฃแฎแแ แจแแแแแจแแแแ
[3]. แแฅแแ แแกแแคแแแแจแ แชแแแแแแ แแ แแแแ โแจแฅแแ แแแแโ, แแแแแแฃแ แ แแแแแแ
แแ แคแแแแแแแแแ. แแแ แแก แแคแแแแแกแแแก แแแ แแแแแแก แแแแแ แแแ แแ แแแ แแแแแแก แแแแแแแแ แแแแก
แคแแแแ. แแฆแแแกแแแแแก แแแแแแแแ แแแแก แแแแฎแแแ แ แแ แแแฅแขแแก แแแแฎแแ แชแแแแแแ, แ แแแแแแช
แแฃแแแกแฎแแแแก แแแแแแแแ แ แแแกแแฎแแแแฃแแ แแฃแแฅแขแแแแก แ แแกแฃแ แกแแแแ แกแแ แแแแแแแแก.
แแแญแแฎแแแแก แแ แแแแฃแแ แแแ แแ: แแ แแแแฃแแ แแแ แแ แแญแแ แแจแ, แแแแแแ แ แแแญแแฎแแแแกแฌแงแแแก
แฎแแแแแจแ แแแแแแ แแแแก. แคแแ แแแแ 8733 แฐแ (2012). แแแแ แกแแ 2012 แฌแแแก แฃแแแแแแฃแ แ
แแแแแแแแฃแ แ แแ แแแแแจแแคแขแฃแ แ แแแแแ แแแแแคแแ แแแแแแแก แจแแแแ แฉแฃแแแแแก, แแแแฎแฃแ แ
แขแงแแแแแก แแแแกแแกแขแแแแก แแ แซแแแแแแแแแ แแแชแแแก, แแแแแแแแฃแ แ แฃแกแแคแ แแฎแแแแแกแ แแ
แแฃแแแแ แแ แแแ แแแแจแ แขแฃแ แแกแขแฃแแ แแ แ แแแ แแแชแแฃแแ แกแแฅแแแแแแแแก แแแแแแแแ แแแแก
แฃแแ แฃแแแแแงแแคแแก แแแแแแ [4].
แแแชแฃแ แขแแ แแขแแ แแแแจแ แแ แกแแแฃแแ แแ แแแแแแแแ แแแแแแแแ แแแแ แซแแแจแ แแแแชแ แ แ แแแแแแก แแแแ แแ แ แแ แแแกแแฎแแแแแแก แกแแแฎแแแแแ แแแชแฃแ
แขแแ แแขแแ แแแจแ, แแแกแแฎแแแแแแก แแแแแแแแฃแ แ แแแขแแ แแกแ, แแแแก แแแแ, แ แแ แแ แแ
แกแแแแชแแแแ แแแแแแแแ แแแ แแแกแแฎแแแแแแก แแแขแแ แแแขแแฃแแ แ แแกแฃแ แกแแแแ แแแแแ แแแแแ
แแ แแแกแแฅแแแแ แแแแก แแฉแแแก แซแแ แแแแแ แแ แแแแแแแแ. แ แ แฃแแแ แแแแแแแแก
แแแแแ แแแแแคแแ แแแแแแแก แแแแกแแ แแแชแแแก แแแแแแ, แแแแ แซแแแแก แแแ แแแแฃแ แ
แคแฃแแฅแชแแแแแ แแแแกแแแแแก แแฃแกแข แแแชแแแแ แฃแ แแแแแแแแแ แแแงแ แแแแแแ แฉแแแแงแแแแแแแก
แแแชแฃแแ แขแแ แแขแแ แแแก แแกแแแ แแแขแแแแ แแ, แ แแแแ แแชแแ แแ แแแแฃแแ แแแ แแ, แ แแแแแจแแช
แแแแแแแแฌแแแแแฃแแ แแฅแแแแ แแฃแแแแแก แแแแชแ แ แแแชแแแก (แแแแ แซแแแ) แแแฃ แแแ แแแฃแแ แแแแ
153
(แ แแแแแแช แแฅแแแแแ แงแแแแแแ แแแขแแ แฎแแแฃแฎแแแแแแ แแ แแแแแ แแแแแคแแ แแแแแแแ
แแแแแ แฉแแฃแ แฃแแแแจแ), แแฃแแแแแก แซแแแแ, แแ แแแแแแฎแ แแแ แแแฃ แขแ แแแแชแแฃแแ
แแแแแงแแแแแแกแ แแ แแแแแขแแ แแ แแแแแแ. แขแ แแแแชแแฃแแ แแแแแงแแแแแแก แแแแแจแ
แฉแแแแงแแแแแแแแ แแแคแ แแกแขแ แฃแฅแขแฃแ แ, แ แแช แแแแขแฃแ แแแแแก แแแแแแแแ แแแแก แกแแฃแแแแแกแ
แแแ แแแ แแฅแแแแ [1,2]. แจแแกแแแแแแกแแ แแแแแแแแแแ แแ แแแฅแขแแแ แแแชแฃแแ แขแแ แแขแแ แแแกแแแ
แแชแฎแแแ แแแ แแแกแแฎแแแแแแก แกแแชแแแ-แแแแแแแแฃแ แ แแแ แแแแแแก แแแฃแแฏแแแแกแแแแก แแแแแแ.
แแแฎแแแแ แขแ แแแแชแแฃแแ แแแ แแแแแก แแฆแแแแแ. แซแแ แแแแแ แแ แแแแแแฃแ แ แกแแแแแฎแแแแ: โ แฃแแแแแแฃแ แ แแแแแ แแแแแคแแ แแแแแแแก แแแฆแแ แแแแแ; โ แแแกแแฎแแแแฃแ แแฃแแฅแขแแแแแ แกแแแฎแแแแ;
โ แแแฌแแแกแแ แแแแแแแแก แกแแแแแฎแ;
โ แแแแแแแแ แแแ แแแกแแฎแแแแแแก แแแแแแแแฃแ แ แแแขแแ แแกแแแ แแแชแฃแแ
แขแแ แแขแแ แแแก แฉแแแแงแแแแแแแแกแ แแ แจแแแแแแแช แแ แแ แแแแแแแแกแฌแแแแแฃแแ
แแแชแฃแแ แขแแ แแขแแ แแแแแก แแแแแแแแ แแ แแ แแแก แขแแ แแขแแ แแแแ แแชแฎแแแ แแแ
แแแกแแฎแแแแแแก แแแแแแแแฃแ แ แแแขแแ แแกแแแ, แแแแแแแแ แแฅ แแ แกแแแฃแแ แซแแแแ
แจแแแฆแฃแแฃแแ แ แแแแแแก แฌแแกแแแแก แกแแแแชแแแแ แแ แแ แฃแแ แฃแแแแแงแแคแแแ
แแแกแแฎแแแแแแก แแแขแแ แแแขแแฃแแ แ แแกแฃแ แกแแแแ แแแแแ แแแแแ แแ แแแกแแฅแแแแ;
โ แซแแแแ แแแแแแ แแแ แแแแกแแแชแแแแ แชแแแแแแ แแแ.
แกแฃแ . 1. Rhododendron ungernii
แแ แแแแแแแแแก แแแแแฌแงแแแขแแก แแแแแ โ แฉแแขแแ แแแก แแฃแกแขแ แแแชแแแแ แฃแแ แแแแแแแแ. แ แแก แจแแแแแแแแช แแแแแแแแแแแแ แแ
แจแแแ แฉแแแ:
o แแฃแแแแแก แแแแชแ แ แแแชแแแก แแแฃ แแแ แแแฃแแ แแแแ
o แแฆแแแแแแก แแแแ
o แแฃแคแแ แฃแแ แแแฃ แแ แแแแแแฎแ แแแ แแแแแงแแแแแแก แขแแ แแขแแ แแ
o แแแแแขแแ แแ แแแแ
โ แแแแแ แแแแแคแแ แแแแแแแก แแแแแขแแ แแแแแก แกแฅแแแแก แจแแแฃแจแแแแแ แแแชแฃแแ
แขแแ แแขแแ แแแแแกแแแแแก;
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โ แแแแแชแแแแ แแแแแก แแ แแ แกแแแแแ, แแจแแแแแ แแ แฅแ แแแแแ แกแแฎแแแแแแแก แแ แแแฃแกแขแ
แแแแแชแแแแแ;
โ แจแแกแแแแแแกแแ แฃแแแ แแแแแแแแแก แแแแแฏแแแแขแแก แแแแแ;
โ แแแคแ แแกแขแ แฃแฅแขแฃแ แแก แฉแแแแงแแแแแแแ แแ แแแแขแฃแ แแแแแก แแแแแแแแ แแแแก
แฎแแแจแแฌแงแแแ.
แแแขแแ แแขแฃแ แ 1. แแแแฎแแแแก แแ แแแแฃแแ แแแ แแแก แแแแแฏแแแแขแแก แแแแแ, 2005 แฌแแแ, แแแชแฃแแ
แขแแ แแขแแ แแแแแก แกแแแแแแขแ.
2. แฅแแแฃแแแแแก แแแแ แซแแแแกแ แแ แฅแแแฃแแแแแก แแฆแแแแแแแแก แแแแแฏแแแแขแแก แแแแแ, 2005
แฌแแแ, แแแชแฃแแ แขแแ แแขแแ แแแแแก แกแแแแแแขแ.
3. แแขแแ แแแแก แแ แแแแฃแแ แแแ แแแก แแแแแฏแแแแขแแก แแแแแ. 2009 แฌแแแ, แแแชแฃแแ
แขแแ แแขแแ แแแแแก แกแแแแแแขแ.
4. http://apa.gov.ge.
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State of the Ecology of Kintrishi River
Khatia Meskhidze ([email protected]), Nino Tsilosani Faculty of Natural Sciences and Health, 3rd Grade Students of Ecology Speciality
Batumi Shota Rustaveli State University, 3 Ninoshvili Street, Batumi, Georgia Scientific Supervisor: Assist. Prof. Guguli Dumbadze
Abstract River Kintrishi takes its source from Khino Mountain and enters the Black Sea near the Kobuleti resort town. Its overall length is 45 kilometres. The river is fed by precipitation, groundwater and snow melt.
This research is concerned with the current hydro-chemical and ecological state of the Black Sea Basin River Kintrishi and establishes the level of the anthropogenic impact. Research performed along the field visit routes established, that the river quality from the sources to almost its mouth is clean, not discoloration is observed and transparency is perfect, only in rare occasions in downstream reaches solid waste debris are becoming noticeable. In these downstream areas, near the villages Khutsubani and Kobuleti signs of eutrophication is apparent.
Hydro-chemical analysis of samples established, that the level of contamination by chemical substances is within norms and do not exceed maximal permissible concentrations.
แแแแแแ แ แแแแขแ แแจแแก แแแแแแแแฃแ แ แแแแแแแ แแแแ
แฎแแขแแ แแแกแฎแแซแ, แแแแ แฌแแแแกแแแ
แกแแแฃแแแแแกแแแขแงแแแแ แแแชแแแแ แแแแแ แแ แฏแแแแแชแแแก แคแแแฃแแขแแขแแก
แแแแแแแแแก แกแแแชแแแแแแแก III แแฃแ แกแแก แกแขแฃแแแแขแแแ
แแแแฃแแแก แจแแแ แ แฃแกแแแแแแแก แกแแฎแแแแฌแแคแ แฃแแแแแ แกแแขแแขแ
แแแแฃแแ, แแแแแจแแแแแก 35. แแแแฅแขแ แแแฃแแ แคแแกแขแ: [email protected]
แกแแแแชแแแแ แ แฎแแแแซแฆแแแแแแ: แแกแแกแข. แแ แแค. แแฃแแฃแแ แแฃแแแแซแ
แ แแแแฃแแ แแแแแแ แ แแแแขแ แแจแ แกแแแแแแก แฎแแแแก แแแแแแ แแฆแแแก แแ แแฃแ แแ แข แฅแแแฃแแแแแก
แกแแแฎแแแแแก, แจแแ แแฆแแแจแ แฉแแแแแแแแ. แแแกแ แกแแแ แแ แกแแแ แซแ 45 แแแแแแแขแ แแ. แแก
แกแแแ แแแแแก แฌแแแแแก, แแแฌแแกแฅแแแจแ แแ แแแแแแก แฌแงแแแ.
แแแแแแ แแแแชแแแก แแแคแแ แแแชแแแก แกแแฅแแ แแแแแแก แจแแแ แแฆแแแก แแฃแแแก แแแแแแ แ แแแแขแ แแจแแก
แแฆแแแกแแแแแก แแ แกแแแฃแแ, แฐแแแ แแฅแแแแฃแ แ แแ แแแแแแแแฃแ แ แแแแแแแ แแแแแก แจแแกแแฎแแ,
แแแแแขแแคแแชแแ แแแฃแแแ แแแแ แแแแแแแแก แแแแแแ แแแ แแแแแฅแแแแแแแก แแแแแแแ แแแแ.
แแแ แจแ แฃแขแฃแแ แแแแแแแแแแแแแ แแแแแแแแ, แ แแ แแแแแแ แแก แฌแงแแแ แกแแแแแแแแ
แจแแกแแ แแแแแแแ, แกแฃแคแแแ, แแ แแฅแแก แคแแ แ แจแแชแแแแแ แแ แแแแญแแแ แแแแแ, แแฎแแแแ แแจแแแแ
แจแแแแฎแแแแแแจแ, แแแแแแ แแก แฅแแแแ แแแแแแแจแ แแแฎแแแแแ แแงแแ แ แแฃแแแชแแแแแฃแ แ
แแแ แฉแแแแแแ แแแแแแซแฃแ แแแแก แจแแแแฎแแแแแแ. แแกแแแ แฅแแแแ แแแแแแแจแ, แกแแค. แฎแฃแชแฃแแแแกแ แแ
แกแแค. แฅแแแฃแแแแแก แแแแแแแแแจแ แจแแแแแจแแแแแแ แแแขแ แแคแแแแชแแแก แแแจแแแแ.
แฌแงแแแก แกแแแฏแแก แฐแแแ แแฅแแแแฃแ แ แแแแแแแแ แแแแแแแแ, แ แแ แแแกแจแ แกแฎแแแแแกแฎแแ
แฅแแแแฃแ แ แแแแแแแ แแแแแแก แ แแแแแแแแ แแแ แแแก แคแแ แแแแแจแแ แแ แแ แแฆแแแแขแแแ
แแฆแแ แฃแแแ แแแจแแแแฃแ แแแแชแแแขแ แแชแแแแก.
156
แจแแกแแแแแ แแแแแแ แ แแแแขแ แแจแ (แซแ. แฎแแแแก แฌแงแแแ) แกแแแแแแก แแฆแแแก แแแกแฎแแแแก แฅแแแแก แฉแ แแแแ-
แแแกแแแแแ แแแแแแแ, แแแ แฎแแแแก แแแฎแแแแแแ, แแฆแแแก แแแแแแแ 2599 แ-แแ. แแก
แแแแขแ แแจแแก แแแชแฃแแ แขแแ แแขแแ แแแแแก แฌแงแแแก แแแแแแ แ แแ แขแแ แแแ. แแแแแแ แ
แฅแแแฃแแแแแแ แแ แแแแก แจแแ แแฆแแแก, แกแแแ แซแ 45 แแ, แแฃแแแก แคแแ แแแแ 291 แแยฒ. แกแแแ แแแแแก
แฌแแแแแก, แแแฌแแกแฅแแแจแ แแ แแแแแแก แแแแแแแ แฌแงแแแ. แฌแงแแแแแแแแ แแชแแก แแแแแคแฎแฃแแแ,
แฌแงแแแฃแฎแแแ แจแแแแแแแแแแแช, แฌแงแแแแชแแ แแแ โ แแแแแแ แกแ แแ แแแคแฎแฃแแจแ.
แแแแขแ แแจแ แแแแแแแแแก แกแแคแแแแก โ แฎแแแแก, แแแกแฎแ แฅแแแก, แแแ แแแแกแแแก, แชแฎแแแแแแก,
แญแแฎแแแก, แแแฎแก, แกแแค. แฅแแแฃแแแแก, แฎแฃแชแฃแแแแก, แแแแแฃแ แก แแ แแแแ แแแแก. แแแแแจแ แแ แแแแก
แกแแแแแแ แแแแ แแแแ แแแแแแ แ แแแแแแจแ, แ แแแแแแแแแช แแแแแฃแแแ โแแแแแแจแแก แฐแแกแโ. แแแ แแ แแแแกแ, แแแก แแแแ แ แแแแ แแแแ แแแแแแ แ แแ แแแแแแฃแแ แฃแแ แแแแแ. แแกแแแแ:
แฎแแแแแ แ, แแแ แแแแ, แแแแแแแฆแ, แแแแฆแแแ, แแแกแแแแแแก แฆแแแ (30-แแแขแ แแแแ
แแแแแฌแแ แแขแแชแ แฉแแแฉแฅแแ แแ), แแแแฅแแแซแแแแแก แฆแแแ แแ แฉแ แแแแ (แแ แกแแคแแฎแฃแ แแแแ 70
แแแขแ แแก แกแแแแฆแแ แฌแงแแแแแ แแแแแแ) แแ แกแฎแแ, แจแแแแแแ, แแแแแแ แแก แแแแแแแขแ
แคแแ แแแแแแแ แแ แจแแ แแฆแแแก แแ แแแแก แฅแแแแฅ แฅแแแฃแแแแจแ. แแ. แแแแขแ แแจแแ
แจแแแแ แฉแแแแแแ แแแแแ แแแคแแก แแ แแแแแแแ แแแแแฃแแ แ แแแแแแแแ แแแฆแแแแแ แฎแแแ.
แแ. แแแแขแ แแจแ แแแแแแ แแ แกแแฃแแแแแกแ แฎแแ แแกแฎแแก แแแแแแแแ. แ แแแแ แแชแแ: แแแแแแฎแ,
แกแแแแแ, แฌแแแ แ แแ แ. แจ. แจแแแแแฆแแแกแแแ แแแจแ แจแแแ แแฆแแแก แแ แแแฃแแแก แแฆแฌแแ แแแแแแกแแแแแก
แแแ แแแกแ แแแแแแ แ แแฎแแแแ แกแแฅแแ แแแแแแจแแ แจแแแแ แฉแแแแแ. แแ แกแฌแแ แแ แแแ แจแแ แแก
แแ แ-แแ แแ แแแแขแ แแจแแ. แแ. แแแแขแ แแจแแ แแแแแแแ แแแแแแแแ แแแแก แแแแฅแขแ แแกแแแแฃแ แแก
แแจแแแแแแแแ.
แแฅแขแฃแแแแแ แแแแ แฌแแแแแก แแแแแแแแแแแจแ แกแฌแ แแคแ แขแแแแแ แแแแแแ แแ แฌแงแแแก แแแฎแแแ แแแ, แ แแแแ แช
แฅแแแแฅแแแจแ, แแกแ แกแแคแแแแจแ. แจแแกแแแแแแกแแ, แแแแแแขแ แแ แคแแ แแ แฎแแกแแแแ แแแแฆแ แฌแงแแแก
แแแญแฃแญแงแแแแแแแแแช, แ แแช แแแแแแ แแแแกแ แแ แฌแงแแแกแแขแแแแแจแ แญแฃแญแงแแแแ แฌแงแแแก แจแแ แแแแแแ
แแแแแแ แแแแแฃแแ.
แแฆแแแก แแแแแแซแฃแ แแแแก แฌแงแแ แแแแแก แแแแแแแก แแ แแก แฃแแแแจแแแแแแแแแแกแ แแแแแแ
แฃแญแแ แแแก แแฆแแแจแ แฉแแแแแแแ แ แแแแแแ แแแแแก แแแแแแแแฃแ แ แแแแแแแ แแแแแก แจแแกแฌแแแแแก.
แแแแแแแก แแแแแแ แกแแฅแแ แแแแแแก แจแแแ แแฆแแแก แแฃแแแก แแแแแแ แ แแแแขแ แแจแแก แฐแแแ แแฅแแแแฃแ แ แแ แแ แกแแแฃแแ
แแแแแแแแฃแ แ แกแแขแฃแแชแแแก แแแแแแแแแแ, แแแแแแซแฃแ แแแแก แแแแแแแ แแแแแกแ แแ
แแแแแแแแซแฃแ แแแแแ แฌแงแแ แแแแแก แแแแแแแ, แแฃแแแแ แแแ แแ แแแแ แแแแแแแฃแแ
แคแแฅแขแแ แแแแก แแแแแขแแคแแชแแ แแแ.
แแแแแแแก แแแกแแแ แแ แแแแแแแแ แแแแแแแก แแแแแฅแขแก แฌแแ แแแแแแแแแ แแ. แแแแขแ แแจแ แแ แแแกแจแ แจแแแแแแแ แกแฎแแ แแแแแแ แแแแ
แแฃ แแแแแแฃแแแแ. แแแแแแ แฉแแขแแ แแ แแแ แจแ แฃแขแฃแแ แแแแแแแ, แแแแแแ แแก แแฆแแแก
แจแแกแแ แแแแแแแ แแแฌแงแแแฃแแ, แฎแแแแก แแแแก แแแกแแฌแงแแกแแแแ, แแกแแแ, แแแแฃแแแฃแ แแ
แแแแแแแแแฃแแ แแฅแแ แแแกแจแ แจแแแแแแแ แแ. แแแแแแจแ แแแก แแแกแแฌแงแแกแแแแ แแ แกแฎแแ แแชแแ แ
แแแแแแ แแแแ แแ แฆแแแแแแ.
157
แแกแแแ แฉแแขแแ แแ แฌแงแแแก แฐแแแ แแฅแแแแฃแ แ แแแแแแแ แกแฎแแแแแกแฎแแ แแแแแ แแแแแแแ แแแแแ
แจแแแชแแแแแแแแ, แ แแแแแกแแแแกแแช แกแแแฏแ แแฆแแแฃแแ แแฅแแ แแ. แแแแขแ แแจแแก แแฆแแแกแแแ
แจแแกแแ แแแแแแแ แแแแฎแแแแแแ 200-300 แแแขแ แแก แแแจแแ แแแแ.
แแแแแแแก แจแแแแแแแ แแแ แจแ แฃแขแฃแแ แแแแแแแแแแแแแ แแแแแแ แฉแแแแ, แ แแ แแแแแแ แแก แฌแงแแแ แกแแแแแแแแ
แจแแกแแ แแแแแแแ, แกแฃแคแแแ, แแ แแฅแแก แคแแ แ แจแแชแแแแแ แแ แแแแญแแแ แแแแแ, แ แแช แแซแแแแ
แแแกแจแ แแแชแฃแ แแแ แกแฎแแแแแกแฎแแ แกแแฎแแแแแก แแแแแแแแก แแแแแกแฃแคแแแ แฎแแแแแก แกแแจแฃแแแแแแก.
แแแฃแฎแแแแแแ แแแแกแ, แ แแ แแแแแแ แแก แแฃแแแก แแ แแแแแ แฎแแแแแแจแ 10 แกแแคแแแแ
แแแจแแแแแฃแแ, แฃแจแฃแแแแ แแแแแแ แแก แกแแแแแแ แแแแแ แแแฎแแแแแ แแฎแแแแ แ แแแแแแแแ
แแฏแแฎแ, แแ แกแแ แแแฎแแแแแ แแแขแแกแแแ แแชแฎแแ. แแแแแแ แแก แกแแแแแแ แแแ แแแแแแ แแแแก
แกแแงแแแแแแ แแแฃแ แแแแแ, แแแแฎแแแแแแ 50 แ-แแ แแแจแแ แแแฃแแแ แคแแแแแแก แแฌแแ แแแแแแแ
แแชแแ แ แกแแฌแแ แแ. แแแคแฎแฃแแแแแ แแแแแแ แแก แแแแแ แแแ แฎแจแแ แแ แแแแแแงแแแแแ แกแแแแแแแแแ,
แแกแแแ แคแฃแแฅแชแแแแแ แแแก แแแคแ แแ แ แแกแขแแ แแแ. แกแแคแแแแจแ แแ แแ แกแแแแแก แกแแแแแแแแแแชแแ
แกแแกแขแแแ, แกแแกแแคแแ-แกแแแแฃแ แแแ แกแแแแ แแฃแแแแ แแแแแแแแฃแแแ แกแแแแกแขแแ แแฃแแขแฃแ แแแแ,
แกแแแแแแแ, แชแแขแ แฃแกแแแแแแ แแฃ แกแฎแแ แฎแแฎแแแแแแ แแชแแแแ แแแ แแแ แแแแแแแแ.
แงแแแแแแแ แแฆแแแจแแฃแแ, แแแแแแ แแก แฅแแแแ แแแแแแแจแ, แแแก แแแแแ แแแแ แฅแแแแก
แแฃแแแชแแแแแฃแ แ แแงแแ แ แแแ แฉแแแแแแ แแชแแ แแ แแแแแแซแฃแ แแแแก แจแแแแฎแแแแแแก. แแกแแแ แแ
แแ แแก แแแแแ แแชแฎแฃแแ แฌแงแแแก แแแแแแซแฃแ แแแ แคแแแแแฃแ แ แแแกแแแแ, แแแแแแแ แฃแแแ
แแฅแขแแฃแ แ แแแแแแแ แแแแแแแ แแ แแแกแขแแชแแแแแแ.
แแแขแ แแคแแแแชแแแก แแแจแแแแ แแแแแแแแแแ แ แแแแแแแแ แแแแแแแก, แแ. แแแแขแ แแจแแก แแฃแแแก
แฅแแแแ แแแแแแแแจแ, แกแแค. แฅแแแฃแแแแแก แแแแแแแแแจแ. แแแ แซแแ, แกแแค. แฅแแแฃแแแแแแ
แแฎแแแก, แแแแแแ แแก แแแแแ แแแแ แแแแแแแกแแแฃแแ แกแแชแฎแแแ แแแแแ แกแแฎแแแแแก แกแแแฎแแแแแก,
แแกแแแ, แกแแค. แฎแฃแชแฃแแแแก แชแแแขแ แแแ แแ แกแแค. แแแแ แแแแก แชแแแขแ แจแ, แกแแแแแแแแแแ แแแ
แแแแจแแงแ แแก แแแแแแแก.
แแแแแแ แแก แแฆแแแกแแแ แจแแกแแ แแแแ แแฆแแแก แแแแแแแ, แ แแก แแแแแช แคแแ แฎแแแแ แฌแงแแแก
แแฆแแแจแ แจแแกแแแ แแ แแแแ แแแ แจแแแแแแแ, แแแแ แแ แแแแแแแแ แ แแแแแแฃแแแแแก
แแแญแแแแแแแก แแฌแแแแก.
แแแแแแแแ แ แฌแแแก 9 แแแแแกแขแแก แฉแแขแแ แแ แแ. แแแแขแ แแจแแก แฌแงแแแก แฐแแแ แแฅแแแแฃแ แ
แแแแแแแ, แ แแแแแแช แแฆแแแฃแแ แแฅแแ แแแแแแ แแก แแฆแแแกแแแ แจแแกแแ แแแแแแแ 200-300
แแแขแ แแ แแฆแแ. แแแแแแกแขแฃแ แแ, แ แแ แแแกแจแ แกแฎแแแแแกแฎแแ แฅแแแแฃแ แ แแแแแแแ แแแแแแก
แ แแแแแแแแ แแแ แแแก แคแแ แแแแแจแแ. แแแ แซแแ, pH โ 7.15; แแแฎแกแแแแ แแแแแแแแ โ 12,7 แแ/แ;
แแแแแแแแแก แแแแ. แแแแฎแแแแแแแแ โ 0.39 แแ/แ (แแแ 5.8), แฎแแแ แแแแแแฃแแแก แแแแขแแก
แแแแชแแแขแ แแชแแ โ 0.003 แแ/แ (แแแ โ 1.4). แแแแแแ แแก แฅแแแแแฌแแแจแ แฌแงแแแก
แแแแแ แแแแแแชแแ แแแแแแแ โ 70.0 แแ/แ, แแแแแแแ แฌแงแแแก แกแแแฆแแ แแแแช. แแแแแแ แแก
แคแกแแแ แ แแแคแแ แฃแแแ แฌแงแแแก แแชแแแแ แแแแแ.
แแแกแแแแ แแแ แแแแ, แแ. แแแแขแ แแจแแก แแแแแแแแฃแ แ แ แแกแแแก แคแแฅแขแแ แแแแ แกแแงแแคแแชแฎแแแ แแแ
แแแแแแซแฃแ แแแ, แแแฃแแแ แแแแ แแ แฎแจแแ แจแแแแฎแแแแแจแ แแ แแ แกแแแฃแแ แกแแแแแแแแแแชแแ
แกแแกแขแแแ แแ แกแแคแแแก แแแฃแ แแแแแ, แ แแแแแแแช แฅแแแแแ แกแแงแแคแแชแฎแแแ แแแ แแงแแ แ
แแแ แฉแแแแแแ, แคแแแแแฃแ แ แแแกแแแแ, แแแแแแแ แฃแแแ แแฅแขแแฃแ แ แแแแแแแ แแแแแแแ แแ
แแแกแขแแชแแแแแแ แแแแแแซแฃแ แแแแก แ แแกแแก.
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แแแฃแฎแแแแแแ แแแแกแ, แแฆแแแกแแแแแก แแแแแแ แแก แฌแงแแแ แกแฃแคแแ แแ แแแแแแแแ แแแกแจแ แแชแแ แแ
แแงแแ แ แแฃแแแชแแแแแฃแ แ แแแ แฉแแแแแ, แแกแแแ แฅแแแแฃแ แ แแ แแฃแแแแ แแแ แแแแแ แแแแแแก
แจแแแชแแแแแแ แแชแแ แแ แแ แแ แแฆแแแแขแแแ แแฆแแ แฃแแแ แแแกแแจแแแ แแแ แแแแก. แแแ แแแ แฉแแแก
2015 แฌแแแก แกแแฅแขแแแแ แแก แฃแแแแฃแ แแกแ แฌแงแแแแชแแ แแแ.
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Anthropogenic Impacts on Habitats of Kolkheti Lowland Shorelines
Natela Tetemadze Faculty of Natural Sciences and Health
Batumi Shota Rustaveli State University Email: [email protected]
Abstract Kolkheti Lowland is the relic refugium of glacial period, hosting relic species of flora and fauna. Most of its habitats and species are under intense anthropogenic pressure. Floral elements present here are living monuments of the ancient nature. For centuries changing environmental conditions lead to loss of many species. Their destiny is not caused only by
natural, climatic and geographic variability; most significant impact is of anthropogenic nature. All those species, which disappeared in the human historic time span, are results of the wrongdoings of the mankind. Anthropogenic impacts drastically modify vast landscape spaces, bringing them to the edge of ecological catastrophe. Removal of some species from biocenosis of interconnected species leads to ultimate degradation of the overall ecosystem.
แแแแ แแแแแแแฃแ แ แแแแแแแแแ แแแแฎแแแแก แแแแแแแแก แกแแแแแแ แ แแแแแก แฐแแแแขแแขแแแแ
แแแแแแ แขแแขแแแแซแ
แแแแฃแแแก แจแแแ แ แฃแกแแแแแแแก แกแแฎแแแแฌแแคแ แฃแแแแแ แกแแขแแขแ
แกแแแฃแแแแแกแแแขแงแแแแ แแแชแแแแ แแแแแ แแ แฏแแแแแชแแแก แคแแแฃแแขแแขแแก แแแฅแขแแ แแแขแ
แแแแฅแขแ แแแฃแแ แคแแกแขแ: [email protected]
แ แแแแฃแแ แแแแฎแแแแก แแแแแแแ แแแแงแแแแแ แแแแก แแ แแแแแแแ แ แแแแฅแขแแ, แ แแแแฅแขแฃแ แแ แแแ แแแแ
แแแกแ แคแแแ แแชแ แแ แคแแฃแแแช. แแแแ แแแแจแแแแแแแแแ แแแฌแแแ แฃแแแแแกแ แแแแ แแแแแแแฃแ แ
แแแแแฅแแแแแแแก แฅแแแจ แแแงแแคแแแ. แแฅ แแ แกแแแฃแแ แคแแแ แแก แแแแแแแขแแแ แฃแซแแแแแกแ
แแฃแแแแแก แชแแชแฎแแแ แซแแแแแแแ. แกแแฃแแฃแแแแแแก แแแแซแแแแ, แแแ แแแ แแแ แแแแแแก
แชแแแแแแแแแแแ แแ แแแแแ แกแแฎแแแแแก แแแฆแฃแแแ แแแแแแฌแแแ. แแแแ แแแแแกแฌแแ แ แแแ แขแ
แแฃแแแแ แแ-แแแแแแขแฃแ แ แแแแแ แแคแแฃแแ แคแแฅแขแแ แแแแก แจแแแแแ แ แแแแ. แแแแแ
แแแแกแแแฃแแ แแแฃแแ แแแแแ แแแขแแแ แแแแ แแแแแแแฃแ แแ แคแแฅแขแแ แแ. แงแแแแ แกแแฎแแแแ,
แ แแแแแแช แแแแแแแแแก แแกแขแแ แแฃแ แแแแฅแแจแ แแแแฆแฃแแ, แแแกแแฎแแแแแแก แฃแกแฃแแแฃแแ
แแแแแแแแแแฃแแแแแก แแกแฎแแแ แแแ แแแฎแแ. แแแแ แแแแแแแฃแ แแ แคแแฅแขแแ แแ แกแแฎแ แฃแชแแแแ
แแกแแคแแแแก แแแแแฃแฌแแแแแแแ แกแแแ แชแแแแแก แแแแแจแแคแขแแแก แแ แแแแแแแแฃแ
แแแขแแกแขแ แแคแแแแ แแแแงแแแแ แแกแแแ. แแแแแแแแแ แฉแแแแงแแแแแแแฃแ แแแแแกแแแแแแแแแแแจแ
แกแแฎแแแแแแ แแกแ แฃแแแแจแแ แแแแแแ แแ แแแแแแแก, แ แแแแ แช แแ แแแแแแแจแ แแ แแแแแแแ แแ
แแแแชแแแแแแแแ แ แแแแแแแ แกแแฎแแแแแก แแแแแแ แแแ แแแแแก แแงแแแแแก แแแก แแ แแฌแแแแก
แแแแแแแแ แแแแก.
160
แจแแกแแแแแ แแแแฎแแแแก แแแแแแแแก แฐแแแแขแแขแแแก แจแแแฅแแแ แแ แแแแแ แแ แแแแแแ, แ แแแแ แแแแชแแ:
แขแแ แคแแก แแแแแแแแแกแ แแ แแแแแแ แแชแแฃแแ แแ แแแฅแขแแแ แฌแแแแแก แแแแแแแแแแแจแ แแแแจแ แแก แแแแฎแแแแก แขแแ แคแแแ แแแแก 140000 แฐแ แขแแ แแขแแ แแ.
แขแแ แคแแก แแแแแแแแแกแแก แแฆแแแแแแ แขแแ แคแแก แแแแ 2 แ แกแแกแฅแแก แคแแแแก, แ แแก แแแแแช
แแ แแแแแแแแ แแ แจแแแฌแงแแแแ แแแแแ แแแก แแแแแกแ แแ แแแแแแแฃแ แ แแแแแจแแคแขแจแแแฅแแแแแ
แคแฃแแฅแชแแ, แแแแแแ แแ แแแฎแจแแ แแแแแก แแแแกแแ แแขแแแกแคแแ แแจแ แแ แแแแ แแฅแชแแแ
แแแญแฃแญแงแแแแแแแก แฌแงแแ แแ. แแแชแฎแ แแแแขแ แกแแฃแแฃแแแก แแชแแแแแแแแ แฌแแแแแแแ แแแแฎแแแจแ
แแแแแแแแ แแแแแ แแแแแแ แแชแแฃแแ แแ แแชแแกแแแ, แ แแก แแแแแช แแแแจแ แแก แแแแฅแแแก 200 000
แฐแ แคแแ แแแแ. แแแแแแงแแแ แแ แแ แแแแแแแแ แแแแแ แแแก แแแแแแแแ แแแ แแแแแแแ แกแแฎแ, แแแ
แแแแแแแก แแแแ แแแ แแแแแแแแ แฉแแแแงแแแแแแ, แแแแแแแฃแ แ แกแแฎแแแแแแแ
แฌแแ แแแแแแแแแ.
แฎแแแงแแคแแแ แแ แแแแ แแแฃแแ แแแแแแแแ แฎแแแกแแงแ แแแ แแแ แแแ แแฆแแแฉแแแ แแแแแแแฃแ แ
แกแแฎแแแแแแแกแแแแแก, แฃแแแขแแกแแแ แแแแแแแแกแ แแแแแแฃแ แแแฃแแแ แแ แกแฎแแ แแแแแแขแฃแ แแแแแ
แแ แแแ แกแแฎแ แฃแชแแแแแก แแแแฎแแแแก แฃแแแแแแฃแ แแแแแจแแคแขแแแก.
แแแแกแแแฃแแ แแแฃแ แแแแแก แแงแแแแแก แขแแ แคแแแ แก แฎแแแซแ แแแ. แฎแแแซแ แแแ แแแแแแ แกแ แแ แแแ แแ
แแแแแคแฎแฃแแแแ. แแแแกแแแฃแแ แแแแ แแแจแแ, แ แแชแ แคแ แแแแแแแแแก แแแแ แแชแแแ. แแแแแแแ แแแแ
แฃแแแแแแแ แชแแชแฎแแก แขแแ แคแแแ แก แ แแแ แแฌแแแแ แแ แแแแแแแ แแแแแแก แแแแแแแ แแแ. แแ แแ แแก
แแแแแแแแ แแฌแแแก แแแแแแแก แขแแ แคแแแ แ. แฎแแแซแ แแแ แแฌแแแแก แจแแแแแ แชแแแแแแแแแก:
โ แกแแฎแแแแ แแแ แจแแแแแแแแแแแแก แชแแแแก แแแฃ Sphagnum imbricatum-แแก แแแฅแ แแแแก;
โ แแฆแแ แฎแแแแ แขแแ แคแแก แแแฃแแฃแแแชแแแก แแ แแชแแกแ;
โ แแ แฆแแแแ แชแแชแฎแแแ แกแคแแแแฃแแแแแ แขแแ แคแแแ แแก แแแแ แแ แแแแแคแ, แฉแแแแแ
แฉแแฆแ แแแแแแแแ แแ แแแ แชแแแแแแ.
แกแแแแแแ แ แแแแแก แแแแแแแ แแแแแกแแแ แแแแฅแแแก แแแแแแแฃแ แแแฃแแแ แกแแแแแแ แ แแแแ, แแแกแ แแชแแแแ แแฃแแ แกแแคแแ แ, แ แแช แแฌแแแแก
แแ แแแแฃแ แแ แแชแแกแแแก.
แกแแฅแแแแแก แซแแแแแ แแ แแแแแแแก แฌแแ แแแแแแแแก แกแแฅแแแแแก แฃแกแแกแขแแแ แฃแแแแแ แแแ แซแแแแแ, แ แแช แฃแแแแแก
แแแแแก แแงแแแแแก แแแแจแแแแแแแแ แชแแแแแแแก, แแ แฎแแแแ แแแแแแแแแฎแแแแแก แแ แแชแแกแ.
แกแแฅแแแแแก แซแแแแแ แแฌแแแแก แฎแแแแแแฃแ แแ แแแแแก, แแแขแฃแแแแก แขแแ แคแแก แฎแ แฌแแแก แแ แแชแแกแ.
แแแชแฃแแ แขแแ แแขแแ แแแแ, แแแแแแ แแแแแ, แ แ แแแกแแฎแแแแฃแ แแฃแแฅแขแแแแแ แแ แกแแกแแคแแ
แกแแแแฃแ แแแ แกแแแแ แแฃแแแแแแ แแฎแแแก, แแแแแชแแแแ แฃแแแแแก แแแแ แแแแแแแฃแ
แแแแแฅแแแแแแแก, แ แแแแ แแแแชแแ:
โ แฃแแแแแ แแแ แฉแแฎแแ แขแงแแแแแกแ, แ แแแแแแแช แจแแแซแแแแ แแแแแแฌแแแแก แฃแแแแแแฃแ แ
แแแแกแแกแขแแแแแแก แแแแแแแฃแ แแแ;
โ แแแแแ แแแ แแจแแแแ แแ แแแแแจแแแแแแก แแแ แแก แแแแแ แกแแฎแแแแแแแ;
โ แแแแแ แแแ แแแแแแคแ แแ แคแ แแแแแแแแแ;
โ แฃแแแแแแ แแแแแญแแ แ;
โ แขแแ แคแแก แแแแแแแแ;
โ แแ แแแแแ. แแแแจแ แแก แญแแแแแแแก แแแแจแแแแแแแแแ แแแฌแแแ, แ แแแแแแช แกแแกแแคแแ-
แกแแแแฃแ แแแ แกแแแแ แแฃแแแแแ แแ แแกแแ แแก แแแแแฃแงแแแแแแแ. แแแแ แแแแแแแฃแแ
161
แแแแแฅแแแแแแแก แจแแฌแงแแแขแแก แจแแแแแ แแแแ แแแแจแแแแแแแแแ แแแฌแแแ แแแแแ
แแแญแแแแแ;
โ แกแแกแแคแแ-แกแแแแฃแ แแแ แกแแแแ แแฃแแแแแ แแ แกแแซแแแ แแแแ แแแแแงแแแแแ, แ แแช แฃแแแแแก
แแแแแก แแงแแแแแก แแแแจแแแแแแแแ แชแแแแแแแก. แแ แฎแแแแ แแแแแแแแแฎแแแแแก แแ แแชแแกแ;
โ แแแแฎแแแแฃแ แแแแแแแแ แแแฃแ แแแแกแแกแขแแแแแแ แฉแแแแงแแแแแแ แแแแ แแแ
แแแแแแแแ;
โ แแแแกแแกแขแแแแแแก แแแแแแซแฃแ แแแแก แแแแจแแแแแแแแ แฌแงแแ แแก แฌแแ แแแแแแแแก
แกแแงแแคแแชแฎแแแ แแแ แแแ แฉแแแแแ;
โ แขแงแแแแแก แแแฉแแฎแแ แแแแจแ, แ แแช แกแแจแแจแ แแแแแก แฅแแแแก แฌแงแแแแแแแ แแแแแแกแ แแ
แฆแแแ แชแแคแแแแกแแแแแก แกแแแแแแ แ แแแแจแ. แแขแแแ แแแ แฅแแแแฅแ แฅแแแฃแแแแ,
แกแแคแแแแ.
โ แแแแแแ แกแแแแแแแแแแ แแแ แชแแแแแแ แแแ.
แแแแแแกแแงแ แแแแแ แแแแกแแกแขแแแแแแก แแแญแฃแญแงแแแแแแแก แแแแจแแแแแแแแ แฌแงแแ แแก แฌแแ แแแแแแแแก แกแแงแแคแแชแฎแแแ แแแ
แแแ แฉแแแแแ.
แแแแแแ แแแ แแแแกแแแชแแแแ แชแแแแแแ แแแ แแแกแแฎแแแแแแก แแแ แแแแกแแแชแแแแ แชแแแแแแ แแแ แฏแแ แแแแแ แแแแแแแ. แแแชแฃแแ แขแแ แแขแแ แแแแ (แแแแฎแแแแก แแ แแแแฃแแ แแแ แแ, แฅแแแฃแแแแแก แกแแฎแแแแฌแแคแ แแแแ แซแแแ)
แแแแแแ แแแแแ แแแกแแฎแแแแฃแ แแฃแแฅแขแแแแแ แแ แกแแกแแคแแ-แกแแแแฃแ แแแ แกแแแแ แแฃแแแแแแ
แแฎแแแก, แ แแช แแ แแแแแแแแก แฃแฅแแแแก แแแชแฃแ แขแแ แแขแแ แแแแก.
แแฃแชแแแแแแแแ แฎแ-แขแงแแก แแแแแแแแแแแ แแแ แแขแแ แแฃแแแก แฃแแ แฃแแแแแงแแคแ
แแแแแแแ แแแฃแแ แขแงแแก แแแ แแแแแแกแ แแ แแแฆแแแ แแแแกแแ แแแชแแฃแแ แฆแแ แแแฃแแแแแก แแฅแแแ
แขแงแแแแจแ แแ แแ แแแ แแแแแแก แแแชแแแก แแ แแแ แแขแแขแฃแแแแแก แแ แแแชแแแแก แแแแแงแแแแแ,
แแแชแแแแ แแแแก, แแ แแกแแแแแแ แแแ แแ แแแแแแแชแแแแ แแ แแแแแแแแ แแแ แแแกแแฎแแแแแแก
แฉแแ แแแ แแ แฆแแแแกแซแแแแแแจแ.
แแแแแแแแฃแ แแ แแแฃแแแ แแแแแแแ แแ แแแแแแแฃแแ แแจแแแแแแแแแแ แงแฃแแแแแก แขแแ แแแแแแแก แแจแแแแแแแแแกแแก แแแแฉแแฎแ 1000 แฐแ แ แแแแฅแขแฃแ แ แแแแฎแฃแ แ แขแงแ,
แกแแคแ แแฎแ แแแฃแฅแ แแแ แแแแฎแแแแก แแ แแแแฃแแ แแแ แแแก แแแแชแ แ แแแชแแแก (แแแ แแแฃแ) แแแแแจแ
แแงแแค แญแฃแ แแแก แขแแ แคแแแ แกแ แแ แแแก แฃแแแแแแฃแ แแแแแ แแแแแคแแ แแแแแแแก; แฐแแแแขแแขแแก
แ แฆแแแแแ แแ แแแแแแแแ แฃแแ แแแแฎแฃแ แ แฎแแฎแแแแก แแแฅแ แแแ แแแแแแฌแแแ.
แแแแกแแกแขแแแแแ แแแแแกแแแแกแแก แแ แแ แกแแแแแก แแฃแกแข แแแชแแแแ แฃแ แแแกแแแแแแแ
แแแคแฃแซแแแแฃแแ แแแ แแแแแ แแแแแฅแแแแแแแก แจแแคแแกแแแ (แแแจ). แแแแก แแแแแแแแแ แงแฃแแแแแก
แฃแแแ แแแแแ แ แขแแ แแแแแแแก แแจแแแแแแแแแช แแแแ แ. แแแแฎแแแแก แชแแชแฎแแแ แขแแ แคแแแ แแแแก
แฉแแแแงแแแแแแแแก แแแแกแแแฃแแ แแแฃแแ แแแแแแแแฃแ แ แแ แแชแแกแแแ, แแฆแแแก แขแ แแแกแแ แแกแแฃแแ
แแ แ แแแ แแกแแฃแแ แแแแแแแแแ แฃแซแฆแแแ แฌแแ. แแ แแฎแ แแ แแแแกแแแฃแแ แแแฃแแ
แแแแจแแแแแแแแกแแ แแฎแแแจแแแแฆแแฃแ แ แขแแ แแกแแก แแแฃ แแแฃแแแก แฉแแแแงแแแแแแแแก แแ แแชแแกแ.
แแแแฎแแแแก แงแแแแ, แแแแกแแแฃแแ แแแแ แแ แญแฃแ แแแกแ แแ แแแแแแแก แชแแชแฎแแแ แขแแ แคแแแ แแแ,
แแฆแแแก แแแแแแ แแแแแ แแแแแแ แแแแแ. แแแแฉแแฎแ แแกแแฃแแ แฐแแฅแขแแ แ แฎแแแฃแฎแแแแแแ
แ แแแแฅแขแฃแ แ แแแแฎแฃแ แ แขแงแ, แกแแแแช แแแ แแแแแแ แแแคแแแ, แแแแฎแฃแ แ แแแ, แแแแ แฃแแ
แแ แฐแแ แแแแกแแก แแฃแฎแแแ. แแก แกแแฎแแแแแแ แแฆแแแกแแแแแก แกแแฅแแ แแแแแแก แแฎแแแ โแฌแแแแแ
แแฃแกแฎแแกโ แกแแฎแแแแแแแ. แแฆแแ แแ แฃแแแแแแแกแ แแฆแแแกแแแ แ แแแฃแแ แแ แแแแฅแ แแแแ แแแกแ
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แแชแแแแ แแฃแแแแ, แ แแแแ แแชแแ แแฆแแแกแแแ แ แแกแแแ แแแฃแกแ, แกแแแ แกแฃแแ แฎแแแ แแฅแแ. แแแฅแ แแแแก
แกแแคแ แแฎแ แแแฃแฅแ แแแ แแแแฎแแแแก แแจแแแแ แแแแแแก โ แแแคแแแก. แฐแแแแขแแขแแแแก แ แฆแแแแแ
แแแแแแแแ แฃแแ แแแแฎแฃแ แ แฎแแฎแแแแก แแแฅแ แแแแช แแ แแแแแแฌแแแ. แกแแแแแแแ
แแแแแแแ แแแแแจแแ แแแแแแแแ แแแ แแแกแแฎแแแแแ. แแแฃแแแแแฃแ แแก แแจแแแแแแ แ
แกแแแแ แแแแแแ. แขแแ แคแแแ แแแแก แแแจแ แแแแก แแแแแแ แแแงแแแแแแแ แกแแแ แแแแแ แแ แฎแแแ, แ แแก
แแแแแช แแ แกแแแแแก แแแแแ แแแก แแแแแแแแแก แกแแจแแจแ แแแแ. แแแแแแแแ แแ แแแกแแฎแแแแแแก แแ แ
แแฅแแก แกแแกแแแแ แฌแงแแแ, แ แแแแแกแแช แแแแฃแแแแแ แแแแแแแขแ แแก แแแแซแแแแแแ แแแแแแแ.
แแแแก แแแแ, แ แแ แแ แแ แกแแแแแก แแฃแกแข แแแชแแแแ แฃแ แแแกแแแแแแแ แแแคแฃแซแแแแฃแแ แแแ แแแแแ
แจแแแแฅแแแแแแแก แจแแคแแกแแแ, แแแแ แแ แแแแแแแฃแแ แงแฃแแแแแก แขแแ แแแแแแแช แแ แกแแคแ แแฎแแก
แฌแแแแจแ แแแแก.
แแแ แฌแแแแแแแแ แแแฉแแฎแแแ แแแแแแแแก แ แแแแฅแขแฃแ แ แแแแฎแฃแ แ แขแงแ. แจแแแแ แฉแแแแแแ
แแฎแแแแ แแฃแ แงแแแ แ แแแแแกแ แแแ แแแแกแแแแ แแแฆแแแ แ แแแฅแชแแแก แแแแ. แแแแกแแแแแแ
แแแกแแฎแแแแแแก แแแแฎแแแแแแแแแ แขแงแแก แแแแ แแแแแ แแแแงแแแ. แแแแก แแ แ-แแ แแ แแแแแแ
แแแขแแ แแแขแแฃแแ แกแแแแแแแก แแ แแ แกแแแแแ แแ แฌแแแแแก แแแแแแแแแแแจแ แแแแแคแแแฃแแ
แแแ แฃแคแชแแแ. แแแแแแแแ แฉแแฎแแแก แขแงแแก แแ แแแก แแแแแแก แแญแแ แก แกแแกแแคแแ-แกแแแแฃแ แแแ
แกแแแแ แแฃแแแแ, แแ แแกแแแ แแ แแแแแแ แแ แแกแแ แแก แแแแแฃแงแแแแแแแ. แแแฉแแฎแแ แแแแ แแ
แแแแแแแแแ แฉแแแแงแแแแแแ แแแแแแ แฎแแ แแกแฎแแก แกแแซแแแ แแแ แแ แแแแ แแแ แชแแแแแแแ.
แแซแแแ แกแแชแแแแฃแ -แแแแแแแแฃแ แ แแแ แแแแแ แแแกแแฎแแแแแแก แแซแแแ แกแแชแแแแฃแ -แแแแแแแแฃแ แแ แแแ แแแแแแ, แแแแแแ แแคแแฃแแแ
แกแแแญแแแ แแแแ, แแแแ แแแแแแแฃแ แแ แคแแฅแขแแ แแแแ (แขแงแแก แญแ แ, แกแแฅแแแแแก แซแแแแแ,
แแแแแ แแแ, แฃแแแแแแ แแแแแญแแ แ, แฎแแแซแ แแแ) แกแแคแ แแฎแ แจแแฃแฅแแแ แแแชแฃแ แขแแ แแขแแ แแแแก
แแ แฃแแแแแแฃแ แฐแแแแขแแขแแแก. แแแชแฃแแ แขแแ แแขแแ แแแแ แแแแแแ แแแแแ แแแกแแฎแแแแฃแ
แแฃแแฅแขแแแแแ แแ แกแแกแแคแแ-แกแแแแฃแ แแแ แกแแแแ แแฃแแแแแแ แแฎแแแก, แ แแช แแ แแแแแแแแก
แฃแฅแแแแแ แแแชแฃแ แขแแ แแขแแ แแแแแก แแแแแ แแแแแคแแ แแแแแแแก แแแฆแแ แแแแแแก.
แแแแฎแแแแกแแแแแก แแกแแแ แขแแแแฃแ แ แฐแแแแขแแขแแแแก, แ แแแแ แแชแแ แฐแแแ แแคแแแฃแ แ
แแฃแ แงแแแ แ แขแงแ, แกแคแแแแฃแแแแแ, แแแแแ แแแฃแ แ แแแฃ แแฆแแแกแแแ แ แฅแแแจแแแแ
แแชแแแแ แแฃแแแแ, แแแชแแแแ แฌแแ แแแแฅแแแ แแฎแแแ แฃแ แแแแแแแ แแแฃแแ แแ แขแแฅแแแแแ
(แแแแแแแแก แชแแกแขแแ แแแแ, แ แแแแแแแ, แแแฆแแแ แซแแแแแก แแแแฅแขแ แแแแแแแชแแแ แฎแแแแแ)
แแแแแขแแแ แแฃแแ แขแแ แแขแแ แแ. 1999 แฌแแแแแ แแแแแแแฃแ แแ แงแฃแแแแแก แขแแ แแขแแ แแ แแ
แจแแกแแแแแแกแแ แแฅ แแ แกแแแฃแแ แขแแแ แแแ แกแ แฃแแแแ, แจแแกแแแแแแกแแ แงแฃแแแแแก แกแแแแแแ แ
แแแกแฌแแ แแแ แแแแ แคแแแแแแ แ แแแแแแแแก แแแแแก แแจแแแแแแแแแกแแแแแก. 2002 แฌแแแแแ แแก
แขแแ แแขแแ แแ แแแแฎแแแแก แแ แแแแฃแแ แแแ แแแก แแแแชแ แ แแแชแแแก แแแแแจแ แแแแแฎแแแแแแแ.
1999 แฌแแแแแ แแฅ แแแแฌแงแ แแแคแ แแกแขแ แฃแฅแขแฃแ แแก แแแแแแแแ แแแ แแกแ, แ แแ แแ แแแแแแแแฃแแ
แแแ แแแแแ แแแแแฅแแแแแแแก แจแแคแแกแแแ, แแฃ แ แ แกแแคแ แแฎแแก แจแแฃแฅแแแแแ แแจแแแแแแแแ แแฅ
แแ แกแแแฃแ แแแแแ แแแแแคแแ แแแแแแแก. แแแแแแแฃแ แแ แแฅ แแแแแแ แ แแขแแแแ แฌแงแแแแแ แขแแแ แแแ
แแ แจแแกแแแแแแกแแ แกแแฎแแแแแแ. แแ แแแ แแก แขแแ แแขแแ แแแแ แ แแแกแแ แแก แแแแแแแชแแแก
แแแแจแแแแแแแแแ แญแแ แแขแแแแแแ แขแแ แแขแแ แแแ แแแแ แแ แแแแแ แแ แแแแแแคแ แแแ
แคแ แแแแแแแแแกแแแแแก.
1999 แฌแแแก แแ. แฎแแแแก แแแ แชแฎแแแ แกแแแแแแ แแแ แแแแฌแงแ แแแแแแแขแแ แแแแแแแก แแจแแแแแแแแ.
แฎแแแ แแแแแแแแ แฌแแแก แแ แแแแแแ แ แฎแแแแกแฌแงแแแแก แแแ แฏแแแแ แกแแแแแแ แแแ แกแแแฆแแแ
แแแ แขแแก แแจแแแแแแแแ แแแแแแแแ. แงแฃแแแแแก แขแแ แแแแแแแก แแจแแแแแแแแแ แกแแแ แซแแแแแแ
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แแแแแแแแ แแ แจแแชแแแแ แแฃแแแแ แแแ แแชแแแแ แแฃแแ แกแแคแแ แ. แแฃแชแแแแแแแ แแแฎแแ แแ
แขแแ แแขแแ แแแก แแแชแแแแ แแฎแแแ (แกแแแแแแแแกแแชแแ) แแแชแฃแแ แแแแแฅแขแแก (แแ แแแแแฅแขแแแแก)
แแแแแงแแคแ.
1989 แฌ.
แงแฃแแแแแก แขแแ แแแแแแแแแ 2012 แฌ.
แแแแแแแฃแ แแแฃแแ แกแแแแแแ แ แแแฃแแ
2015 แฌ.
แงแฃแแแแแก แขแแ แแแแแแ แแฆแแก
แกแฃแ . 1. แกแแคแ แแฎแแแแ, แ แแแแแแแช แแแแแฅแแแแแแแ แกแแแแแแ แ แฐแแแแขแแขแแแแ
แแแขแแ แแขแฃแ แ: แแแแฅแแแซแ แ., แแแญแฃแขแแซแ แ., 2013, โแแแแฎแแแแก แแแแแแแแก แกแแแแแแ แ แแแแแก
แแขแแแแ แฌแงแแแแแ แขแแแ แแแแก แแชแแแแ แแแ แกแแฎแแแแแแแก ex-situ แแแแกแแ แแแชแแแก
แแฃแชแแแแแแแแ แแแแฃแแแก แแแขแแแแแฃแ แแแฆแจแ. แแแขแแแแแฃแ แ แแแฆแแแแก
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แแแแจแแแแแแแ แแชแแแแ แแแ แแ แแแแแคแแ แแแแแแแก แจแแแแ แฉแฃแแแแแจแ, แแแแฃแแแก
แแแขแแแแแฃแ แ แแแฆแ แกแแแฃแแแแแ แแ แแแฃแแ, แแ. 48.
แแแแฅแแแซแ แ., แแแญแฃแขแแซแ แ., 2013, โแแแแฎแแแแก แแแแแแแแก แกแแแแแแ แ แแแแแก
แแขแแแแ แฌแงแแแแแ แขแแแ แแแแก แแชแแแแ แแแ แกแแฎแแแแแแแก ex-situ แแแแกแแ แแแชแแแก
แแฃแชแแแแแแแแ แแแแฃแแแก แแแขแแแแแฃแ แแแฆแจแ. แแแขแแแแแฃแ แ แแแฆแแแแก
แแแแจแแแแแแแ แแชแแแแ แแแ แแ แแแแแคแแ แแแแแแแก แจแแแแ แฉแฃแแแแแจแ, แแแแฃแแแก
แแแขแแแแแฃแ แ แแแฆแ แกแแแฃแแแแแ แแ แแแฃแแ, แแ. 48.
แแแฎแฃแชแ แแจแแแแ แ., แแแญแฃแขแแซแ แ., 2014, โแงแฃแแแแแก แขแแ แแแแแแแก แแแแแแแแ แ
แขแแ แแขแแ แแแแแก แฐแแแแขแแขแแแแกแ แแ แแชแแแแ แแฃแแ แกแแฎแแแแแแแก แจแแคแแกแแแ แแ
แแแแแขแแ แแแแแก แแ แแแ แแแแก แจแแแฃแจแแแแแ.โ แแแแฎแแแแก แแแแแแแแ แแแแก แคแแแแ.
แแ. 54.
Izolda Matchutadze, Tamar Bakuradze, Mamuka Gvilava, Bulbuli Bolkvadze and David Baratashvili, 1013, Coastal Sand Dunes and Freshwater Ponds in Kolkheti โ Threats and Needs for Conservationโ, Lagoons: Habitat and Species, Human Impacts and Ecological
Effects Chapter, pp. 195-21, ISBN: 978-1-62808-092-6.
Matchutadze I., Bolkvadze B., Jakeli J., 2014, Kolkheti refugee-Habitat and species biodiversity
(Georgia), World Biodiversity Congress, SriLanka.
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Relic Kolkhic Forests of Kolkheti Lowland
Merab Tsinaridze PhD Student of Batumi Shota Rustaveli State University
Email: [email protected] Abstract Imeretian and hartwissian oaks, wingnuts, Kolkhic box-tree, Pontic marshmallow are species of red list and red book of Georgia, nominated recently to IUCN for designation as internationally threatened species. Due to the demand for hardwood timber these species were massively harvested on lowlands of Kolkheti and Kobuleti. No measures for ex-situ conservation are in place. Secondary meadows are formed on the harvested areas, with low quality grazing lands. It
should also be stressed, that regeneration of these species in natural ecosystems is proceeding with very slow rate (Matchutadze, 2003; Matchutadze, 2008).
แแแแฎแแแแก แแแแแแแแก แ แแแแฅแขแฃแ แ แแแแฎแฃแ แ แขแงแแแแ
แแแ แแ แชแแแแ แแซแ
แแแแฃแแแก แจแแแ แ แฃแกแแแแแแแก แกแแฎแแแแฌแแคแ แฃแแแแแ แกแแขแแขแแก แแแฅแขแแ แแแขแ
แแแแฅแขแ แแแฃแแ แคแแกแขแ: [email protected]
แ แแแแฃแแ แแแแ แฃแแ แแ แฐแแ แแแแกแแก แแฃแฎแแแ, แแแคแแแ, แแแแฎแฃแ แ แแแ, แแแแขแแก แขแฃแฎแขแ โ โแฌแแแแแ
แแฃแกแฎแแกแโ แแ โแฌแแแแแ แฌแแแแแกโ แกแแฎแแแแแแแ, แ แแแแแแแช แฌแแ แแแแแแแแ แแแแแแแชแแแแ -
แแฃแแแแแก แแแชแแแก แกแแแ แแแจแแ แแกแ แแแแจแแ แจแ, แ แแแแ แช แกแแแ แแแจแแ แแกแ IUCN แกแแคแ แแฎแแก
แฌแแแแจแ แแงแแคแ แกแแฎแแแแแแ. แซแแแ แคแแก แแแ แฅแแแแ แแแแฎแแแแแแแแแก แแแแ แแแแฎแแแแกแ แแ
แฅแแแฃแแแแแก แแแแแแแแ แแก แกแแฎแแแแแแ แแแกแแฃแ แแ แแแแฉแแฎแ. แแ แจแแแฃแจแแแแแฃแแ แแแแแ
ex-situ แแแแกแแ แแแชแแแก แฆแแแแกแซแแแแแแ. แแแฉแแฎแแ แขแแ แแขแแ แแแแแ แฉแแแแงแแแแแแ
แแแแ แแแ แแแแแแแแ, แแแแแแ แฎแแ แแกแฎแแก แกแแซแแแ แแแ. แแแแกแแแแแแ, แฃแแแ แแฆแแแแจแแแก,
แ แแ แแฃแแแแ แแ แแแแกแแกแขแแแแแจแ แแแแ แแแแแฎแแแแ แซแแแแ แกแฃแกแขแแ แแแแแแแแ แแแแก
(แแแญแฃแขแแซแ 2003, แแแญแฃแขแแซแ 2008).
แจแแกแแแแแ แ แแแแฅแขแฃแ แ แแแแฎแฃแ แ แขแงแแก (แฐแแ แแแแกแแก แแฃแฎแแแแกแ แแ แแแคแแแแก แจแแแแ แฉแแแแแ
แฎแแแฃแฎแแแแแแ แแแ แแแแแ) แคแ แแแแแแขแแแ แจแแแแ แฉแแแแแแ แแแแแแแแจแ แแ. แคแแฉแแ แแก
แแแแแ แแ, แแแแแแแก แขแแ แคแแแ แแก แกแแแฎแ แแแแ, แฅแแแฃแแแแจแ แแกแแแแ II แแแแ แซแแแแก
แฃแแแแฃแ แแก แกแแแฎแ แแ แแฆแแแกแแแแแ แแแแแ แแก, แแชแแ แ แ แแแแแแแแแ แแแแแ แจแ.
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แจแแแแแแแ แแแคแแแแก Pterocarya fraxinifolia-แก แแแแฃแแแชแแแแ แแแแฎแแแแก แแ แแแแฃแ แแแ แแจแ
1. แกแแแแแ, แกแแแแแแก แกแแขแงแแ แฃแแแแ, แกแแค. แกแแ แแแฉแแแแแแ, แกแแแแช แแแกแ แงแแแแแแ แแแแ
แแแแฃแแแชแแแ;
2. แแ. แคแแฉแแ แแก แแแแแ แแแ, แแฅ แฌแแแแแก แฌแแ แแฉแแฎแแแแแ แแแแแ แกแแแแ แแ แแแแฃแแ แแแ แแ
แแแแ แกแแแแแแ;
3. แแแแแแแก แขแแ แคแแแ แแก แแแแแแแแ แ แกแแแฎแ แแแ แขแงแแแแ. แแฅ แกแแแ แซแแแแแแ แแแแ
แแแแฃแแแชแแแ แแแแฎแแแแแแ แแแแแแแแแ แแแ. แแแแแกแแแแแแแแแแแจแ แฐแแ แแแแกแแก
แแฃแฎแแกแแแ;
4. แแแแฎแแแแก แแ แแแแฃแแ แแแ แแแก แแแแแแแกแขแ แแชแแแก แจแแแแแแแแ แฉแ แแแแแแแแ.
แแฅแแช แแแแ แแแแ แแแแฃแแแชแแแ, แแแฃแฎแแแแแแ แแแชแฃแแแก แกแขแแขแฃแกแแก แแ แฅแแแแกแ
แแแแแช แจแแแแ แฉแแแแแแ แแแคแแแ;
5. แญแฃแ แแแก แขแงแแแแจแ แแแคแแแก แงแแแแแแ แแชแแ แ แแแแฃแแแชแแแ ;
6. แแ. แคแแฉแแ แแก แฉแ แแแแแแแ แขแงแแแแ แ. แฌ. โแแญแแ แแแแแกโ แแแกแแฎแแแแแกแแแ
แแ แกแแแฃแแ แขแงแแแแ, แกแแแแช แแกแแแ แจแแแแ แฉแแแแแแ แแแคแแแแแก แแแ แแแ แแ แแแกแ
แแแแขแแแแแแแ แแ แแแแแฌแแแแแ แแแแแแแแแ แแแ.
แกแฃแ . 1. Pterocarya fraxinifolia แกแฃแ . 2. Quercus hartwissiana
แฐแแ แแแแกแแก แแฃแฎแแก แแแแฃแแแชแแแแ แแแแฎแแแจแ
1. แแกแแแแ 2 แขแแ แคแแแ แแก แกแแแฎแ แแ-แแฆแแแกแแแแแแ แขแงแ;
2. แแแแแ แแก แขแงแ;
3. แแ. แคแแฉแแ แแก แแแแแ แแแ;
4. แแแแแแแก แขแแ แคแแแ แแก แกแแแฎแ แแแ แขแงแแแแ;
5. แแแแฎแแแแก แฃแแแแ แกแแค. แแแ แกแแกแแแ;
6. แแแแแแแแจแ แแ. แชแแแแก แแแแแ แแแ.
167
แ แแแแแแแแแชแแแแ แแแแกแแ แแแชแแแกแแแแแก แแฃแชแแแแแแแแ:
แแแฌแแก แแแแแงแแแแแแก แแแแแฏแแแแขแ -> แฐแแแแขแแขแแก แแ แแฃแแแแ แแแ แแแ แแแแก แแฆแแแแแ,
แฃแแแแแแก/แแแแแแแแแก แแแชแแ: แกแแฎแแแแแก แแแแแฏแแแแขแ -> แกแแฎแแแแแก แแฆแแแแแ; แกแแฎแแแแแก
แแแแแฏแแแแขแ -> ex-situ แแแแกแแ แแแชแแ -> แกแแฎแแแแแก แแแแแก แแแแแแก แจแแฅแแแ; แแแแแแแแแ แแ
แแแ แแแแกแแแชแแแแ แจแแแแแแแก แฉแแแแงแแแแแแแ -> แแแแแแแแแ & แแแแฃแแแแแชแแ; แคแแ แแแแฃแ แ
แแแแแแแแแ; แแแแแแ แแ แแแแแขแแแ -> แแแแแขแแแ แแ แ แแแฃแแแชแแ.
แแแแแแแแแก แแฃแชแแแแแแแแ:
แแแแแแแแ -> แกแแคแ แแฎแแแแ; แแแแกแแ แแแชแแแก แแฃแชแแแแแแแแ -> แกแแฎแแแแแก
แจแแกแฌแแแแ/แแแแกแแ แแแชแแแก แแแแแ; แแแแกแแ แแแชแแแก แแแแแ -> แแ แแแแแ แแแคแฃแซแแแแฃแแ
แแแแแฏแแแแขแแก แแแแแแก แจแแแฃแจแแแแแ; แแแแแขแแ แแแแ -> แแแแฃแแแชแแฃแ แ แ แแชแฎแแแแแแ;
แแแแแขแแ แแแแ -> แฐแแแแขแแขแแก แกแแ แแแกแ.
แแแขแแ แแขแฃแ แ Matchutadze, I. Goradze, I. Tsinaridze, M. Jakeli, E. โInventory of height conservation value
forest in Adjara, 2010, 1st International Turk-Japan conference in Trabzon, vol. 1, pp. 33-65.
แชแแแแ แแซแ, แ., แแแฆแแแแแแกแแ แแแชแแฃแแ แขแงแแก แแแ แแแแแ แแญแแ แแจแ, แ แฃแกแแแแแแแก
แฃแแแแแ แกแแขแแขแแก แกแขแฃแแแแขแแ แแ แแฎแแแแแแ แแ แแแชแแแแ แแ แแแแคแแ แแแชแแ
แแแ แแแแก แแแชแแแก แแฆแแกแแแแ แแแซแฆแแแแแ, แแญแแ แ, แแแแ แแแ แแแแแแแแ แแแ,
แแแแแแแแ. 2011. Matchutadze I., Kurkhuli T., Tsinaridze. M. โWhy Kolkheti relict forest is so valuable and
significantโ, 1st International Turk-Japan conference in Trabzon, vol. 2010. Matchutadze. I. Tsinaridze. M. Tsiklauri. X. IUCN Globally Critically Endangered Woody Plant
Species of Relict Forest of Kolkheti Lowland 2013. Matchutadze I., Bolkvadze B., Tsinaridze. M. Jakeli J., 2014, โKolkheti refugee-Habitat and
species biodiversity (Georgia), World Biodiversity Congress, SriLanka.
Instruments for Modelling Black Sea River Basins: Research Proceedings for Guria Region of Georgia
Integrated Landโuse Management Modelling of Black Sea Estuaries (ILMMโBSE) is a project supported by the second call of the EU Joint Operational Programme "Black Sea Basin 2007 โ 2013". Partners of the project are: Applicant Bourgas Regional Tourism Association BRTA, Bulgaria ENPI Partners Bourgas Prof. Assen Zlatarov University BTU, Bulgaria Ukrainian Marine Environment Protection Association UkrMEPA, Ukraine International Association CIVITAS GEORGICA, Georgia IPA Beneficiary Hayrabolu Municipality HBM, Turkey IPA Partners Turkish Marine Environment Protection Association TURMEPA, Turkey Namฤฑk Kemal University NKU, Turkey The overall objective of the project is to develop, enhance, and evaluate, impact assessment and other management tools for sustainable land use of the watershed areas of coastal river basins and mouths. The specific areas covered by the ILMMโBSE project include river basins of Ergene in Turkey; Ropotamo and Veleka in Bulgaria; estuaries of Danube, Dniester and Dnieper in Ukraine; and river basins of Guria Region in Georgia. This publication, produced by the Georgian Partner International Association Civitas Georgica, collates the thematic research material, generated through coordinated action together with Black Sea partners and Georgian stakeholders.