mediterranean_basin_en.pdf

8/22/2019 Mediterranean_basin_En.pdf

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Report by Goffredo La LoggiaUniversity of Palermo

Laboratorio diTelerilevamento eSistemi Informativi

Territoriali

Universit di Palermo -Dipartimento di

Ingegneria Idraulica edApplicazioni Ambientali

Mediterranean basin, floods, droughts, coastal

problems

Promozione dellinnovazione nella Regione

di Sviluppo Sud-Est della Romania

Sicilia - Romania

Strategia Regionale per lInnovazionedella Regione di Sviluppo Sud-Est


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MEDILAB Laboratorio diTelerilevamento e Sistemi

Informativi Territoriali

DIIAA Dipartimentodi Ingegneria Idraulica

ed Applicazioni Ambientali

E. Cox

H. Nollet

G. La Loggia

V. Noto

G. Ciraolo

A. Maltese

Manager/director/

supervisor andcoordinator

Modelling andimageprocessing

GIS programmingand geostatistics

Model Makerprogramming and

Image processing

IDL programmingand ImageProcessing

Image Processingand databases

Personnel Equipments

Hardware

Software

GIS Platforms

- ARCINFO

- ARCVIEW GIS

- ARCIMS

- ARCPAD

Image Processing

- IDL

- ENVI 3.x

- ERDAS IMAGINE 8.x:

- ER MAPPER 6.x

- IDRISI 32

- EASY TRACE

- CARTALINX

- MSTAR COMMUNICATOR

Data Processing


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ACTIVITY

Remote SensingGeographical

Information SystemsFieldwork

WEB-GIS in an open-source environment

Integrated models within GIS systems

Environmental applications (Monte Pellegrino, Stagnone di Marsala,etc.)

Hydrological applications (GIS to calculate river levels, precipitationanalysis, etc.)

GeographicalInformation Systems

Fieldwork

Bathymetric measurements with echosounder

LAI (Leaf Area Index) measurements

GPS measurements

Spectroradiometric measurements

m

8m

10m

15m

20m



Numericalmodeling


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ACTIVITY

Remote Sensing 1) Monitoring submerged vegetation in coastal areasusing multi-platform data;

2) Monitoring coastal water quality using remotesensing;

3) Integrating numerical modelling and remote sensingdata to simulate water circulation in coastal lagoons;

4) Applying remote sensing techniques to determinemarine fronts;

5) Analysis of terrestrial vegetation dynamics using timeseries analysis of satellite imagery;

6) Remote sensing and GIS for archaeologicalapplications.




Information SystemsFieldwork Numerical

modeling


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ACTIVITY

Implement, validate and calibrate numericalmodels for the simulation of hydrodynamicconditions and solute transport in coastalareas

Understanding the interaction betweenhydrodynamic local conditions and submergedvegetation (phytobentos) in a coastal lagoon

To set up a field measurements system inorder to understand the dynamical behaviourof the simulated variables (velocities, water

elevations, etc)

To forecast the evolution of the ecosystemwhen the hydrodynamic regime varies

To map flooded areas both in rural and urban

catchments using one and two dimensionalmodels




Information SystemsFieldwork Numerical

modeling

Numerical modeling


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Water resources and hydrology

Keywords:

Flood extent

Flood forecasting

Water resources assessment


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Floods

The importance of a correct modeling of thehydrological processes in flood

Field Data and Remote Sensing

Continuous in-situ measurementsover spatially distributed locations

within nested watersheds.

Repeat-visit, high-resolution, hyper-spectral observations from spaceborneand airborne sensor platforms.

Physically-based DistributedModels

Process-based representations of

basin hydrology, geomorphology andlandatmosphere interactions.

Incorporation of spatial andtemporal distribution of topography,

rainfall, soils, vegetation,meteorology, soil moisture.


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Floods

Field and Remote Sensing Dataover Hydrologic Catchments

Precipitation Hydrologic Statedata estimates

Satellite & AircraftMeteorological Data

EM Data


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Floods

Hydrologic Observations

Measurements ofEarths Topography

Major advances in remote sensing have improved ourcapabilities to simulate and forecast watershed

hydrology. Numerical models capable of utilizing thesedata sources at multiple scales are required.

Measurements ofEarths Precipitation

Measurements of

Earths HydrologicVariables


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Floods

Modeling hydrological processes

Lake

Coastal

Mountain

Riverine

Rainfall-Runoff Transformation

Surface-Groundwaterinteractions in different

scales and lanscapes


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GIS and Floods

SIRI is based on the ArcView GIS environment andallows the determination of maximum probabledischarge all over the Sicilian territory.

The prediction of flood discharge is performed usingtwo different approaches: direct and indirect analysis

DIRECT METHODS

Regional analysis of data

coming from the streamgauge using GEV or

TCEV

INDIRECT METHODS

Hydrologic models

transform the rainfallrecorded on the gaugesover the watershed intodischarge at the outlet.

INDIRECT ANALYSIS

1. RAINFALL MODULE: probabilistic analysis of therainfall using one of the many rainfall pdf implementedin the system (GEV, TCEV; EV1, EV2).

2. LOSS MODULE: transformation of rainfall into runoff

(the runoff coefficient and the SCS-CN method).

3. ROUTING MODULE: the transformation of rainfallexcess to direct runoff (unit hydrograph approach).

SIRI offers the possibility to compare estimation of QT

obtained by different approaches and by different models.


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Flooding delimitation

One image taken by Shuttle astronauts, using SIR-C, appears on the left. On theright is an image of merged JERS-1 radar and a SPOT 3-band composite, whichoffers considerable detail (notice how farmlands show through the water).Mississippi River basin

http://rst.gsfc.nasa.gov/Sect14/Sect14_16.html


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Radar data is used to issue special weather statements

This image shows a map of radar-estimated precipitation totals for a 12 hour period.Since the radar reflectivity is closely related to the precipitation rate, the total amountof precipitation falling on a region over a fixed period of time can be determined byanalyzing reflectivity field over that period.

Flash Floods

http://ww2010.atmos.uiuc.edu/(Gl)/guides/rs/rad/appl/flood.rxml


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Radar data is used to issue special weather statements

This image shows a map of radar-estimated precipitation totals for a 12 hour period.Since the radar reflectivity is closely related to the precipitation rate, the total amountof precipitation falling on a region over a fixed period of time can be determined byanalyzing reflectivity field over that period.

Storm Tracking using raingauges


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Flooding in urban areas


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Urban planning and retrofitting

Planning new urban areasRetrofitting extisting dense

urban contexts

Best Management Practices (BMP)

Road runoff reductionSustainable Urban

Developments (SUDS)


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Infiltration BMPs

Trenches Basins

Pavements Pits and wells


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Storage BMPs

Under parking lots and

streets

Available open spaces


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Vegetated surfaces (diversion BMPs)


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BMP integration example in road engineering

Pervious pavements

Infiltration trenchesVegetated area diversion

Sidewalk storage


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Storage BMPs application

0

0,5

1

1,5

2

2,5

3

3,5

0 40 80 120 160 200 240

tempo [min]

portata

[lps]

T=10 anni

T=5 anni

T=3 anni

T=2 anni

26,1523,1220,618Before [lps]

3,022,852,72,53After [lps]

10532Return period [yrs]

88,5%87,7%86,9%86%Mitigation effect



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Parking area with pervious pavement

26,1523,1220,618Before [lps]

17,6613,6410,248,82After [lps]


32,5%41%50,3%51%Mitigation effect


0

4

8

12

16

20

0 15 30 45 60 75 90

tempo [min]

portata

[lp

s] T=10 anni

T=5 anni

T=3 anni

T=2 anni


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Droughts

Keywords:

Correlation of climatic data vegetation indices;

Correlation analysis;

Water saving measures in rural and urban catchments


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ARIDITY INDEX

Location of areas vulnerable to desertification

and climate change

MSAVI1 VEGETATION INDEX

CLIMATIC DATA REMOTE SENSING DATA

Improvement of the understanding of the relationship betweenclimatic variables and vegetation indices


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LANDSAT 7 ETM+

Satellite: LANDSAT7Sensore: ETM+Data di Acquisizione: 07/07/2002


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MSAVI1


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Aridity Index (Thorntwaite)


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Correlation between climatic parameters and

vegetation indices

Correlazione NDVI-AI

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

1986 1988 1990 1992 1994 1996 1998 2000 2002

Anni

Indice

dicorrelazione

Correlazione NDVI-P

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

1986 1988 1990 1992 1994 1996 1998 2000 2002

anni

Indice

dicorre

lazione

Correlazione MSAVI-P

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

1986 1988 1990 1992 1994 1996 1998 2000 2002

Anni

Indice

dicorrela

zione

Correlazione MSAVI-AI

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

1986 1988 1990 1992 1994 1996 1998 2000 2002

Anni

Indice

dicorrela

zione


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REMOTE SENSING AND AGRO-HYDROLOGICAL MODELLING

Aim of the research:

improving of crop water requirement estimation at regional scale in

semiarid regions (water scarcity)

Arguments studied:

1. Evaluation of different Remote Sensing systems for biophysical

variables estimation.

2. Integration use of Remote Sensing and agro-hydrological models:

2.a Soil Water Balance models applications

2.b Surface Energy Balance models applications


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REMOTE SENSING AND AGRO-HYDROLOGICAL MODELLING:

a combined approach aimed to improve the crop water requirement estimation in semiarid regions

S

W

A

P

Lower boundary

a) Groundwater tableb) No flux

c) Free drainage

d)

Water flow in unsatured soils:

Richards equation

+

Feddes model

Soil-Water and root interactions

Upper Boundary:Crop & Canopy parameters

The Soil-Plant-Atmosphere (SPA) system: Processes and interactions

LAImax = 3.5

LAImin = 0.0+

PotentialEvapotranspiration

Plant - Atmosphere (SPA) interactions


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TEST AREAS

Test area 2002

Menfi

Test area 2005CASTELVETRANO




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Scene 189/34

Center Lat: +37:28:31

Center Long: +013:49:05

Satellite data: LandSat TM7

(3 bands VIS + 1 NIR 30m x 30m)

22/09/2001

13/02/2002

27/05/2002

07/07/2002

Test data

Airborne sensor: MIVIS

(20 bands VIS + 8 NIR 3m x 3m)

Test area

19/06/2002

Acquisition date

REMOTE SENSING DATA


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Applications and results in the MENFI test AreaLeaf Area Index (LAI) estimation

METHODOLOGY

Canopy Radiative Transfer s Models

SAIL+PROSPECT (Verhoef, 1984)

Semi-empiricals Models

CLAIRS (Clevers, 1989)

13/02/2002LAI value

[Minacapilli, DUrso, Qiang, 2004]

Simulation and Management of Irrigation System

Approach:Use of Remote Sensing data

into a Soil Water Balance

model (SWAP code)

[DUrso, Iovino, Minacapilli, 2005]

Etp [mm/d]


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METHODOLOGY

The application of the SEBAL (Surface Energy BAlance for Land) model has been investigated using

hyperspectral (VIS/NIR + TIR) and high resolution airborne data.

HGRET0n

=



Applications and results in the MENFI test Area

ESTIMATING EVAPOTRANSPIRATION BY MEANS OFHYPERSPECTRAL AIR-BORNE REMOTE SENSING DATA

VIS NIR Mivis bands TIR Mivis bands

Net Radiation Soil Heat Flux Sensible Heat Flux

[Ciraolo, Minacapilli, Sciortino, 2006]

SEBAL OUTPUT: Real Evapotranspiration map


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INVESTIGATIONS

We are validating SWAP and SEBAL model has by means of soil water content and scintillometer flux

and/or eddy tower measurements.



Applications and results in the CAstelvetrano test Area


Test area and instruments

location


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Field data acquisitions

- 3D sonic anemometer;

- Fine-wire thermocouple;

- Infrared Gas Analyzer :

CO2. H2O

- Electronic tipping-bucketrain gauge;

- Pyranometer;

- Radiation shield;- Infrared temperature soil

sensor;- Net radiometer;- 107 soil temperature

probes;

-Data logger.

MICRO-METEOROLOGIC MEASUREMENTS: EDDY CORRELATION METHOD

Fastre

sponse

instru

ments


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- 3D sonic anemometer;

- Fine-wire thermocouple;

- Infrared Gas Analyzer :

CO2. H2O

- Electronic tipping-bucketrain gauge;

- Pyranometer;

- Radiation shield;- Infrared temperature soil

sensor;- Net radiometer;- 107 soil temperature

probes;

-Data logger.

MICRO-METEOROLOGIC MEASUREMENTS: EDDY CORRELATION METHOD

Fastre

sponse

instru

ments


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Field truth data acquisition

Capanno contenente la centralina

di rilevamento dati

Pluviografo

0

0

0

0

0 2 4 6 8 1 0 12

Scala portate

LAI (Leaf Area Index) and SPAD: 52 ground stations

Humidity TDR (Time Domain Reflectometry): 15 ground

stations.

Humidity thermo-gravimetric method: 22 soil samples.Soil temperature soil thermometer: 15 ground stations.

Soil and vegetation temperature non contact infrared

thermometer: 15 ground stations.

Spectroradiometric measurements: spectral solar radiance and

irradiance.

Discharge measurements.

Soil use: field prospecting.

Satellite positioning (GPS) WAAS: 3m accuracy.


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The application range is

the visible and the near

infrared (350-2500nm).

Vegetation is one of the

most interesting object to

measure due to its

particular shape in the red-

infrared region.

The spectroradiometer is

an instrument useful to

measure radiance,

irradiance and reflectance

of different material


RADIOMETRIC MEASUREMENTS: SPECTRORADIOMETER

0.00

0.10

0.20

0.30

0.40

0.50

443 523 603 683 763 1175

Wavelength [nm]

Reflectance

2. Vineyard

1. Bare soil

1

2

2

1

3

4

50.00

0.10

0.20

0.30

0.40

0.50

443 523 603 683 763 1175

Wavelength [nm]

Reflectance

4. Meadow

3. Light Asphalt

5. Sand

4

53

REMOTE SENSING AND AGRO HYDROLOGICAL MODELLING:


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FIRST RESULTS:

Scintillometers measurements and SEBAL model validation





0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

17-mag 18-mag 19-mag 20-mag 21-mag

ET[mm/h]

Scintillometro

ETrif

Erba medica

17 - 20 maggio 2005

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

8 9 10 11 12 13 14 15 16 17 18

ore

ET[mm/h]

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Scintillometro

Sebal

15/07/2005

ETr

Scintillometro

SEBAL

b)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

8 9 10 11 12 13 14 15 16 17 18

ore

ET[mm/h]

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

ETref

Scintillometro

SEBAL

EFm

14/07/2005

a)

ETr

Scintillometro

SEBAL

REMOTE SENSING AND AGRO HYDROLOGICAL MODELLING:


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FIRST RESULTS:

Soil Water content measurements and SWAP model validation





0

10

20

30

40

50

60

70

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0

[% vol]

z

[cm

]

22 lug

05-ago

08-ago

12-ago

16-ago

Pozzetto A1 D=0

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

19-giu 05-lug 21-lug 06-ago 22-ago 07-set 23-set

10-20 cm

30-40 cm

40-50 cm

50-60 cm

SWAP

DIVINERMeasure



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NEXT INVESTIGATIONS:

Use of ASTER Satellite and NERC Airborne remote sensing data for precision farming

applications





Aster 16/08/2005 (VIS/NIR 15m) Hyperspectral CASI2 image (16 bande 3m x 3m)


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EMERGENCY

Insufficient water resources:Hydrological drought (act of Nature)

Antropic Drought (human responsibilities)

PLANNING (LONG TERM)

INTERMITTENT DISTRIBUTION

PRIORITY USES PRESERVATION

ALT. RESOURCES HARVESTING

REAL TIME NETWORK ANALYSIS

ADVANCED WATER METERINGWATER DEMAND REDUCTION

RAIN/GRAY WATER REUSE

Impact of hydrological drought in urban areas


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Definition ofdistributiondistricts

Remotelyautomatedcontrol


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I t f h d l i l d ht i b


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M-BUS connectionto the operator

REAL TIME USER MONITORING AND CONTROL

Pulse electronic meters

Local data logger

GPRS/GSM WiFiconnection

Early failure warning

Advanced water metering


I t f h d l i l d ht i b


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Reducing residential water consumption




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Noo!!

Introduction of best water practice




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Raccoglie acqua dal tetto e

rifornisce esterno, toilet elavatrice

Alternative resources for non potable uses


Landslides


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Keywords:

Differential interferometry;

Monitoring.


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The 1983 Thistle landslide at Thistle, Utah

http://landslides.usgs.gov/html_files/landslides/slides/landslideimages.htm

The 4th November 1963 Vajont landslide

www.vajont.net

Comparison of radar scattering mechanisms determined from L-band AIRSAR

polarimetry (Left) and IRS panchromatic data (Right) over the Tsaoling mega


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polarimetry (Left), and IRS panchromatic data (Right) over the Tsaoling mega-

slide south (09/1999).

Jeffrey Weissel and Kristina Rodriguez http://www.slamservice.info/

Coastal


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Keywords:

distribution and dynamic of submerged vegetation;

water column correction; airborne, satellite and field data;

numerical modeling of water circulation and transport

Field Truth Data


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Salt field spectrum

0.0000.1000.2000.3000.4000.5000.600

0.7000.8000.9001.000

350 565 780 995 1210 1425 1640 1855 2070 2285 2500

Wavelength [nm]

Reflectan

ce

Submerged vegetation spectra

0 . 0 0 0 0

0 . 0 10 0

0 . 0 2 0 0

0 . 0 3 0 0

3 5 0 4 0 0 4 5 0 5 0 0 5 5 0 6 0 0 6 5 0 7 0 0 7 5 0 8 0 0

w a v e le n g t h [ n m ]

Posidonia

oceanica

Sand

Deep Water

Deep water

Reflectance measurements on a salt field close the Stagnone, and on submerged vegetation - ASD Field Spec Pro FR

Surveying and precise positioning of field data: Cressi Sub bathyscope and a pair of MagellanProMARK X CPTM GPS

July 2002 and July 2003

Water column correction


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R0 i( ) = R i( )+ Rb i( ) R i( )[ ] e2Kdz( )

LyzengaBen Moussa

Xj =Kd i( )

Kd j( ) Xi + ln

Rb i( ) R i( )[ ]

Rb j( ) R j( )[ ]Kd i( )

Kd j( )

Ben Moussa method require the

knowledge of the opticalproperties of the water

a slight error in the bathymetry causessubstantial over-correction of theinfluence of the water column

Water column correction


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Original image5 m

8 m

10 m

15 m

20 m

Bathymetry Corrected Image

Daedalus AADS 1268 CZCS

Classification of submerged vegetation at the Gulf ofMondello


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Original image

Dipartimento di Ingegneria Idraulica edApplicazioniAmbientaliUniversity of Palermo (Italy) Numerical modelling


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Torna alla primapagina

University of Palermo (Italy)

To implement, validate and calibrate numerical models for the

simulation of hydrodynamic conditions and solute transport incoastal areas

Understanding the interaction between hydrodynamic localconditions and submerged vegetation (phytobentos) in a

coastal lagoon

To set up a field measurements system in order to understandthe dynamical behaviour of the simulated variables (velocities,

water elevations, etc)

To forecast the evolution of the ecosystem when thehydrodynamic regime varies.

Numerical modelling

of water circulationand transport

Dipartimento di Ingegneria Idraulica edApplicazioniAmbientaliUniversity of Palermo (Italy)

Th t d


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University of Palermo (Italy)The study area

shallow water (mean depth 0.95 cm) two openings connecting the lagoon with the open sea

northern mouth characterised by low depths (20 cm;

dry during low tide)

two main sub-basins (northern and southern) water exchange given by wind and tidal effects

presence of islands within the lagoon

presence of a submerged road connecting Mothia with

the coast in the north-south direction presence of seagrasses: Posidonia oceanica

(sometimes emerging during low tide) and Cymodocea

nodosa

Stagnone is a coastal lagoon (2200 ha - naturalreserve) characterised by:

Mothia


The study area:


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University of Palermo (Italy) y

batimetry

channel

Dredged3

1


Hydrodynamic numerical


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y ( y)

models (SWE, in-house)

2D model (finite difference) Quasi-3D model (finite elements)


Transport numerical


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y ( y)

model (in-house)

Passive solute transport Residence time (tide only)

Tide only

Dipartimento di Ingegneria Idraulica edApplicazioniAmbientaliUniversity of Palermo (Italy) Field measurements


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Field measurements

In order to acquire information about the

forcing factors, a tidal gauge (1) and a

meteo station (2) have been installed in2002

The first measurement campaign of

velocities and water levels inside the lagoon

was carried out in July 2003

Two other measurement campaigns were

performed in July and December 2004Mediterra

neanSea

0

South MouthValeport < 0.5 Hz

7, 8 Vector 1-16 Hz

9 ADV 25 Hz

3, 4, 5, 6

2 Meteo gauge

1 Tidal gauge

S.Maria

Northern

8

5

9

4

1

3

2 km

Grande

Isola

6

72

Mothia

East

North

x

y

channelDredged3

1

Mouth

Dipartimento di Ingegneria Idraulica edApplicazioniAmbientaliUniversity of Palermo (Italy) The equipments


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The equipments

Special platforms rising above the sea

surface were designed and built using

aluminium metal tubes (3 m times 3 m) The platforms enable us to shift the

instruments upwards and downwards

MediterraneanSea

0South Mouth Valeport < 0.5 Hz

7, 8 Vector 1-16 Hz

9 ADV 25 Hz

3, 4, 5, 6

2 Meteo gauge

1 Tidal gauge

S.Maria

Northern

8

5

9

4

1

3

2 km

Grande

Isola

6

72

Mothia

East

North

xy

channel

Dredged3

1

Mouth



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Offshore


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Keywords:

Water quality

Primary production Phytoplancton

Sea Surface Temperature

Suspended solids concentration map - Landasat TM(no atmospheric correction applied)


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South-westSicily

Suspended solids concentration map - Landasat TM(atmospheric correction applied histogram method)

WATER

QUALITY



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Sicily- Palermoand Carini area


WATER

QUALITY



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Suspended solids concentration map. Landasat TM(atmospheric correction applied histogram method)

Sicily- North-east (Milazzo)

WATER

QUALITY



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Sicily- North-west (Trapani)

WATER

QUALITY

Chlorophyll distribution


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Chlorophyll distributionby Seawifs

20 june 98 12 june 9825 june 98

WATERQUALITY


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Marine thermal fronts usingSST by NOAA


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Please visit our website:

www.idra.unipa.it

And then click on MEDILAB

Contact:

Prof.Goffredo La Loggia

[email protected]


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Please visit our website:

www.idra.unipa.it

And then click on MEDILAB

Contact:

Prof.Goffredo La Loggia

[email protected]

Thanks!

mediterranean_basin_en.pdf

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