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Conceptual models and first simulations Deliverable D5.2
Partner: Joanneum Research Forschungsgesellschaft mbH
H. Kupfersberger, with contributions from M. Pulido-Velazquez and P. Wachniew
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Deliverable summary
Project title
Groundwater and Dependent Ecosystems: New Scientific and Technological Basis for Assessing Climate Change and Land-use Impacts on Groundwater
Acronym GENESIS Contract number 226536
Date due Month 28 in GENESIS
Final version submitted to EC Month xx in GENESIS
Complete references
Contact person Hans Kupfersberger
Contact information Joanneum Research, Institute for Water, Energy and Sustainability, Elisabethstr. 18, A-8010 Graz, Austria, [email protected]
Authors and their affiliation Hans Kupfersberger (JR)
Project homepage www.thegenesisproject.eu
Confidentiality The deliverable has been submitted for publication to xxxxx. The publication is confidential until it has been published. The deliverable can be used in WG C and other EC working groups.
Key words Conceptual hydrogeoloic model, uncertainty, iterative development, numerical groundwater flow model
Summary (publishable) for policy uptake
The WFD, the GWD and several CIS Guidance Documents recognize conceptual models as an essential tool in groundwater management. However, there is no single definition, but only a common understanding that a conceptual hydrogeologic model represents a perception of how the real aquifer systems works based on the available information. In general, conceptual models address both the quantitative and qualitative (chemical) status of groundwater. Complexity of conceptual models, their spatial coverage and temporal resolution may vary in wide ranges depending on the context of their use. Refinement of conceptual models is an iterative process in which observations and numerical modelling performed to test the model provide new knowledge on the system that in turn is incorporated in the improved version of the conceptual model. Typically, development of conceptual models of groundwater bodies includes spatial delineation of recharge and discharge areas, identification of pathways from the unsaturated zone through the saturated zone to groundwater receptors, analysis of processes and their interactions and quantification of fluxes and time scales of flow and transport to design measures for use and protection of groundwater against pollution and deterioration.
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List of GENESIS partners
Norwegian Institute for Agricultural and Environmental Research (CO) Bioforsk Norway
University of Oulu UOULU Finland
Joanneum Research Forschungsgesellschaft mbH JR Austria
Swiss Federal Institute of Technology Zurich ETH Switzerland
Luleå University of Technology LUT Sweden
University of Bucharest UB Romania
GIS-Geoindustry, s.r.o. GIS Chezk Repulic
French National institute for Agricultural research INRA France
Alterra - Wageningen University and Research Centre Alterra The Netherlands
Helmholtz München Gesundheit Umwelt HMGU Germany
Swiss Federal Institute of Aquatic Science and Technology EAWAG Switzerland
University of Science and Technology AGH Poland
Università Cattolica del Sacro Cuore UCSC Italy
Integrated Global Ecosystem Management Research and Consulting Co. IGEM Turkey
Technical University of Valencia UPVLC Spain
Democritus University of Thrace DUTh Greece
Cracow University of Technology CUT Poland
University of Neuchâtel UNINE Switzerland
University of Ferrara UNIFE Italy
Athens University of Economics and Business- Research Centre AUEB-RC Greece
University of Dundee UNIVDUN United Kingdom
University of Zagreb - Faculty of Mining, Geology and Petroleum Engineering
UNIZG-RGNF Croatia
Helmholtz Centre for Environmental Research UFZ Germany
Swedish Meteorological and Hydrological Institute SMHI Sweden
University of Manchester UNIMAN United Kingdom
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Table of content
1 Introduction ............................................................................................ 6
2 Definition of conceptual model ..................................................................... 6
3 Purpose of conceptual models ...................................................................... 9
4 How to set up a conceptual model ................................................................ 11
4.1. main characteristics ............................................................................ 12
4.2. Groundwater ecosystems and conceptual model development .......................... 12
4.2.1 What GDEs can tell us about characteristics of groundwater flow ................. 13
4.2.2 Do we sufficiently understand the functioning of GDEs ............................. 14
4.3. parameterization/quantification ............................................................. 15
4.4. application of tracer methods within the conceptual model development ............ 16
4.5. qualitative/quantitative description of impacts ........................................... 18
4.6. description of effects of measures ........................................................... 19
5 Management and socioeconomic issues in conceptual models ................................ 19
6 Uncertainty of conceptual models ................................................................. 22
6.1 Approaches to account for conceptual model uncertainty ............................... 24
6.2 Sources of conceptual model uncertainty ................................................... 25
7 Scopes of first simulations on climate and land use change ................................... 26
8 references ............................................................................................. 29
9 Appendix - conceptual models and first simulations regarding climate and land use
change of example GENESIS test sites .................................................................. 32
9.1 Mancha Oriental System ....................................................................... 32
9.2 Lulea .............................................................................................. 33
9.3 Grue .............................................................................................. 34
9.4 Vomvoris ......................................................................................... 35
9.5 Caretti site ...................................................................................... 35
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9.6 Murtal aquifer ................................................................................... 36
9.7 Rokua esker ...................................................................................... 37
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1 Introduction Compared to surface hydrology hydrogeologists face a fundamental problem, i. e. they have
only very limited access to the subsurface domain where groundwater flow and transport
processes take place. Thus, they have to come up with a consistent interpretation of point
data and/or data that integrate information over a certain area. Hydrogeologists need to
build the spatial structure of the subsurface (e.g. extent of geological units) and to find a
distribution of hydraulic parameters that can be applied to reproduce the observed system
state (e.g. groundwater levels).
To accomplish this task hydrogeologits make use of various methods from neighbouring
disciplines like geophysics, geochemistry, geography, soil physics, (groundwater) hydrology or
river hydraulics in addition to their own field of expertise. Corresponding data (newly
acquired or already published) that are being evaluated may consist of e.g. well cores,
seismic or georadar images, time series of several groundwater components including
temperature as a natural tracer, remote sensing series that provide land and in particular
water use information, soil lab analyses revealing grain size distributions, calculations of
evapotranspiration to infer groundwater recharge, pump test data and surface water level,
among many others. Some of the mentioned methods yield only indirect or soft data that first
need to be translated into hydrogeological characteristics (typically true for geophysical or
remote sensing data by using separate models for interpretation).
From this example list it becomes clear that there is no single or unique procedure to explore
and characterize a subsurface system. Groundwater environments are open and complex,
rendering them prone to multiple interpretations and conceptualizations. This is particularly
true for regional-scale modeling, in which parameter measurements and field observations
are sparse relative to large modeling domains (Ye et al, 2010). Investigating an aquifer
depends on a lot of different conditions like extent of the area, problem to be solved, already
existing information and last but not least time and financial resources which all individually
influence the level of detail of the associated investigations.
2 Definition of conceptual model All the knowledge about the subsurface system gained from data interpretation is being
coherently synthesized into a perception about how the real system works that is generally
being referred to as a conceptual model (Spijker et al, 2010). It is the primary goal of building
a conceptual model to gain an adequate system understanding (predominant static
characteristic) and to identify the relevant physical processes within and pressures onto the
system (dynamic component). Due to the in every case limited access to information (we
won’t have an observation per square meter) about the subsurface this perception inherently
includes simplifications and assumptions, which have always to be kept in mind when the
conceptual model is being used.
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Given the site and problem specific background of a conceptual model there is no single or
simple definition of a conceptual model. Nevertheless, we can formulate a basic
understanding of common features that are part of the conceptual model building process.
Reiterating the above, a conceptual model constitutes a simplified representation, working
description or schematization of how hydrogeologists believe the real subsurface system
behaves (CIS guidance document no 7 and 15). It might include a description of reality in
terms of verbal descriptions, equations, governing relationships or ‘natural laws’ that imply to
describe reality (Refsgaard, 2002).
Several justifiable simplifying assumptions might have been used to illustrate the principal
characteristics of the real system. Expressed in hydrogeological terms, a conceptual model
should describe the relevant geological characteristics, groundwater and surface water flow
conditions, hydrogeochemical and hydrobiological (including terrestrial and aquatic
ecosystems) processes, anthropogenic activities (relevant land uses) and their interactions
(CIS guidance no 17).
From this definition some implications can be drawn. First, building a conceptual model is a
continuous task that evolves during the process; i.e. the hydrogeologist starts with the
interpretation of very significant data (e.g. groundwater level readings) and refines his
system understanding by adding the interpretation of further hydrogeologic data (e.g.
localisation of geological units, delineation of hydraulic conductivities). There are approaches
reported in the literature on when the effort of additional data collection exceeds the
knowledge gain so that we have some indication when the development of the conceptual
model can be stopped.
However, the development also includes the possibility that the incorporation of new data
does not correspond with the current status of the conceptual model, i.e. it is being falsified
by the new data. In such a case, the perception of key hydrological processes, associated
assumptions or spatial features might have to be adapted so that all available information
consistently fits together. Thus, a number of iterations may be needed until the improved
conceptual model can describe all the measured data in a consistent way and with sufficient
accuracy (see Figure 1). Yet, this process cannot be taken for granted since sometimes new
data increases uncertainty in the short term. It will not be reduced until it is discovered what
was wrong about the earlier understanding of the system. Sometimes it is even difficult to
build a single appropriate conceptual model.
In this respect, it has to be mentioned that a conceptual model cannot be confirmed or
validated with a (predefined) suitable level of accuracy which is also true for a numerical
groundwater model, where a lot of corresponding literature exists (e.g. Konikow and
Bredehoft, 1992), or any scientific hypothesis. Yet, as discussed above, a conceptual model is
always related to a certain level of detail of investigation and directed to deal with a specific
problem that in turn allows for suitable simplifications (or just doesn’t). Hence, any
validation of a conceptual model is always limited to the given circumstances (i.e. intended
application with accepted level of detail agreed) but not of general applicability. Moreover,
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due to the imperfect data and related knowledge conditions a conceptual model is also
always uncertain, i.e. different interpretation of the same set of data or different integration
of data from several disciplines might lead to equiprobable but different conceptualizations
of subsurface system characteristics. This uncertainty has to be considered if conclusions
from the conceptual models are drawn or if findings are being introduced to further
associated models. Ignoring conceptual model uncertainty may result in biased predictions
and/or underestimation of predictive uncertainty. The topics of uncertainty and resolving of
contradictions by adding new data will also be discussed in successive chapters.
Figure 1: Iterative development of a conceptual model (CIS guidance document no 26).
In general, the development of a conceptual model starts with a focus on qualitative
components and gradually turns to quantitative descriptions. To solve advanced groundwater
management tasks the conceptual model is often being transferred into and serves as a basis
for a numerical groundwater flow (and transport) model. In that case, the choice of selecting
the right equations to describe the most important physical (chemical, biological) processes,
their associated parameterization and appropriate numerical solving routines have to be
made by the hydrogeologist (modeller). These tasks as well as the more general relation
between conceptual and numerical model and model validation, respectively, are shown in
Figure 2.
Conceptual models are more difficult in some domains than in others (Refsgaard, 2002). For
example, the process descriptions in a hydrodynamic river flow system are relatively easy to
identify as compared to a groundwater or an eco-system, because the geology will never be
completely known and the biological processes may not be well understood in an ecosystem.
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The guidance document no. 6 (about groundwater monitoring) distinguishes between two
types of conceptual models: a) The regional conceptual model which provides insight into the
factors that play a role at the level of a groundwater body (e.g. representativeness of the
monitoring network and the interpretation of monitoring data). b) The local conceptual
model, which provides insight into the factors that affect the behaviour of individual
monitoring points.
Figure 2: Model simulation environment (Refsgaard, 2002). The inner arrows describe the
processes that relate the elements to each other, and the outer circle refers to the procedures
that evaluate the credibility of these processes.
3 Purpose of conceptual models The intrinsic goals of developing a conceptual model can be divided into four main categories.
The first purpose has been introduced already in the previous chapter and is related to the
process of building the conceptual model. It consists of being a tool for consistent data
integration. Data are being interpreted as they are collected and may identify knowledge
gaps where additional information is needed to complete the conceptual understanding. In
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some cases, the development of a conceptual model of a groundwater system can be an end
in itself, as it forms the basis for the majority of hydrogeological projects where the
understanding of the system provided by the conceptual model allows for decisions to be
made and the risks associated with new developments to be evaluated to a satisfactory level
of accuracy (Brassington & Younger, 2010).
When new data become available in this procedure the assumptions made setting up the
current conceptual model have to be tested. When there is significant divergence, this has to
be explained. This could require the collection of more data (e.g. extension of monitoring
network, increased monitoring frequency) or additional data (e.g. conditions of input of
substances, degradation/retention capacities, flow/spreading velocities in
groundwater/leachate) that explore previously unobserved processes. This process may need
to be continued until the improved conceptual model can describe the measured data in a
consistent way, with sufficient certainty and appropriate scales and complexity (see Figure
1). This improvement of the conceptual model is an important element in the groundwater
management process in order to increase system understanding and to develop effective
planning and control measures.
Second, a conceptual model can be regarded as an instrument for communication with other
hydrogeologists as well as with regulators, politicians or the general public. Through
discussions the experts can complement their views and reach a common understanding of
the groundwater system and will in particular be able to differentiate between geogenic and
anthropogenic impacts. The drafting of the models not only leads to the formulation of
knowledge questions but it also reveals where gaps in knowledge are still present. In this
respect, visualization of the most significant relationships and processes is an important way
to communicate conditions in even complex groundwater bodies in an understandable way.
Thus, also non-experts will be able to comprehend how an aquifer system is working and how,
where and when risks may impact groundwater.
Third, a conceptual model represents a (quantitative) basis where the understanding of the
system allows to delineate first measures for groundwater protection without the
application of further (i.e. numerical) models to a satisfactory level of accuracy. This also
includes predictions of the effects of any measures, assessing risks related to groundwater
and planning of monitoring systems. With relation to the WFD a conceptual model can be used
to identify the reasons why a groundwater body fails to achieve any status objectives.
Moreover, it allows for evaluation of potential measures that are most likely to remedy the
situation in an effective and sustainable manner. When there is a risk of failing to achieve
good groundwater status the conceptual can justify exemptions or provide alternative
objectives.
Finally, a conceptual model may serve as a preparatory work for setting up a numerical
model. That is, it offers a sufficient understanding of the relationships between the principal
characteristics of a system so that mathematical methods can be used to predict processes in
groundwater and to evaluate possible outcomes of changes within the system for a range of
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feasible situations (Brassington & Younger, 2010). If the conceptual model is represented
mathematically to render quantitative predictions, the model parameters are the quantities
required to obtain a solution from the model and thus are model-specific (Meyer et al, 2004).
The mathematical model can be thought of as a process to test the conceptual model
hypothesis, possibly simplifying the conceptual model. Moreover, Meyer et al (2004)
emphasize that a single conceptual model may be implemented in more than one way: for
example, a fractured rock may be represented as an equivalent porous medium or as a
discrete network of fractures.
In essence, what is not properly considered in the conceptual model (e.g. processes,
structures, pressures) will most likely also not be revealed in the numerical model. This will
rather lead to misguiding distribution of (fitted) parameters which are of particular harm in
computing predictions (i.e. scenarios applying different boundary conditions).
4 How to set up a conceptual model In this chapter practical aspects of how to set up a conceptual model shall be discussed with
an emphasis on hydrogeological issues and the subsequent application of a numerical model.
This means, that at that point potential discrepancies between data and their interpretation
have been resolved to an acceptable level and that the conceptual model can be put together
for further use. Data acquisition and validity check is not at the heart at this stage anymore.
However, it might be the case that the system behaviour calculated by the numerical model,
which in turn is inferred from the conceptual model, cannot be matched by observations. In
that case, the conceptual model needs to be re-evaluated to further develop the
understanding of the groundwater system. If complex interacting processes are present, a
numerical model may be needed to verify whether hydrogeological parameters and processes
are suitably described within the conceptual model which sometimes takes a number of
iterations to accomplish.
The process of setting up a conceptual model can be divided into four separate components
as follows: main characteristics, parameterization/quantification, qualitative/quantitative
description of impacts, quantitative description of current and future effects of measures
together with socioeconomic and legal issues. Within the first step the geometry of the
system is defined and major land and groundwater uses as well as relevant (physical,
chemical, biological) processes identified. Appropriate consideration of groundwater
dependent ecosystems represents an important part within this step (this issue also inspired
our project); thus, a separate subchapter will be devoted. Then, hydrogeological parameters
are being assigned to the individual system components and boundary conditions are being
quantified. At that point a special subchapter on the use of tracer methods within the
development of a conceptual model is being introduced. After that, which in the most cases
involves the application of a numerical model, a quantitative cause and effect relationship
between pressures and impacts on groundwater quantity and quality is established. In the
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fourth step the potential effects of future measures are predicted, assessed and optimized
within a scenario framework. In a separate chapter following socioeconomic as well as legal
considerations are being included.
4.1. main characteristics
The component “main characteristics” summarizes static (imaging the subsurface structure;
identification of land uses) as well as more dynamic (identification of processes) elements
and comprises the following hydrogeological issues (non-exclusive list):
• determination of the relevant area including hydrogeological boundaries
• the relevant geological characteristics (i.e. facies distribution)
• hydrogeological characteristics of the groundwater body (e.g. conductivity
distribution)
• characteristics of the superficial deposits and soils in the catchment from which the
groundwater body receives its recharge
• position of important terrestrial and aquatic ecosystems within the groundwater body
• the distribution of relevant land uses
• groundwater flow directions in relation to the main watercourses
• hydrogeochemical and hydrobiological processes
• consideration of processes with slow kinetics (e.g. solution processes, climate
variations)
It is important to determine the areal extent and the boundaries of a conceptual model. In
case of doubt, it is better to choose the hydrogeological boundaries well beyond the area of
interest (independent from the scale of interest) albeit they may subsequently be reduced as
hydrogeological/physical information allows the zone of potential influence to be delineated.
The smallest scale of a conceptual model is the catchment area of a sampling point.
4.2. Groundwater ecosystems and conceptual model development
Most of the topics in this subchapter are taken from Bertrand et al. (2011) which was
published within GENESIS. Ecosystem goods (e.g. production of fishes) and services (e.g. flood
controls) are the conditions and processes through which biotopes and biocenoses help sustain
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and fulfill human life. Groundwater has ecological roles within aquifers and in ecosystems
located close to the discharge zone or water table, referred as groundwater-dependent
ecosystems (GDEs).
From a hydrogeological point of view, groundwater systems are mainly viewed as fluxes of
water, heat and chemical compounds. From an ecological point of view, groundwater is a
milieu (biotope) featured by environmental conditions (e.g. variability of water temperature
and nutrients) in which fauna and flora (biocenoses) adapt and interact.
GDEs may belong to wetlands, which are defined as areas of land saturated with water long
enough to promote wetland or aquatic processes. They are featured by poorly drained soils,
hydrophytic vegetation or various kinds of biological activity which are adapted to a wet
environment. GDE biocenoses not only depend on the mere emergence of water at a location
but also on the temporal variability of the water supply, the quality of discharging
groundwater and the morphology of outlets, which are all related to aquifer/landscape
processes. Water regimes constrain abundance and diversity of biocenoses. Dissolved or
suspended elements in groundwater have to be viewed as potential nutrients which impact
the productivity in springs, rivers, lentic systems or terrestrial GDEs.
4.2.1 What GDEs can tell us about characteristics of groundwater flow
Groundwater arrivals are favored by specific reach morphologies. Upwelling GDEs are located
where high bed permeability (e.g. paleochannel in lattice-like alluvium) allows a great
discharge of groundwater (like a spring). Dam GDEs are located where groundwater is flushed
due to a difference in pressure between the upstream and downstream of a dam. Meander
GDEs are situated at the end of river elbows, where groundwater seeps preferentially and
follows the general hydraulic gradient.
At the emergence scale, morphology and/or pedology may give information about the
physical environments of GDEs, both on short and longterm scales and may also constrain
nutrients availabilities. The identification of taxa preferring ephemeral or permanent flow
sites can potentially indicate flow permanence. Surface water reaches with a strong
groundwater discharge favor an enhanced plant biodiversity. These trends could be explained
by (1) a lower drought stress along the hydrological year; (2) higher nitrate concentration in
groundwater due to anthropogenic impacts and aerobic microbiological degradation of organic
matter in soils or aquifers.
Considering that the hyporheic zone can be viewed as a chemical reactor promoting
transformations, rich vegetation (e.g. bryophytes and macrophytes) can be used as an
indicator of upwelling hyporheic water rather than pure groundwater, i.e. water that has
been enriched by nutrients coming from biotransformation of organic material and promoting
plant fertilization. It seems that equilibrium between surface water (providing oxygen) and
groundwater contributions (providing thermal stability) needs to be reached. Groundwater
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discharge zones support a wide biodiversity and long-term groundwater conditions may be
evaluated by knowing representative ecosociology. For management purposes, this
inexpensive approach may complement classical hydrological measurements and complete
surveys
Groundwater use by terrestrial ecosystems is constrained by (1) dynamics of the adjacent
river compartment, (2) stream order, in particular in mountains because riparian ecosystems
are not inevitably on alluvium but may be on rocky edges of the river, (3) river style (e.g.
braided, anastomosed, meandering) and (4) the plant water-use spatio-temporal variability,
depending on the forest stage (pioneer, mature) and type (mesoriparian, xeroriparian). The
soil structure may favor capillary rise of groundwater and could be a key driver for water
usage by plants.
4.2.1.1 Special case peatlands
Peatlands form where soil-water saturation retards the decomposition of organic matter,
allowing it to accumulate. The production peat depends on hydrology which in turn is
modified by the ecological conditions at the emergence scale. Peatlands can be described as
a coupling of redox reactors. An oxidized (aerobic) layer is present near the surface and there
is a deeper anaerobic layer. The water level and fluctuation are key factors because they
directly influence the existence and spatio-temporal extent of anaerobic and aerobic layers.
Peatlands may be supplied by rainwater, surface water and groundwater whose proportions
depend on their position in the landscape, surrounding geology (terrains permeability) and
maturity of the ecosystems. When low flow occurs, water is retained longer in peatlands and
remains accessible for plants. This inertia is a key factor for maintaining wet conditions close
to surface in case of temporal water level decrease. Therefore, in peatland GDEs, the water
availability period may diverge from the supplying groundwater system hydroperiod.
Focusing on groundwater dependent peatlands, poor to moderately rich systems should be
significantly fed by crystalline (igneous, metamorphic) aquifers or may use rainwater. In
contrast, extremely rich and calcareous fens are usually fed by calcareous aquifers. These
latter are delineated by the fact that Sphagnum mosses cannot grow and that carbonate
saturation is reached.
4.2.2 Do we sufficiently understand the functioning of GDEs
While large scale groundwater flow patterns and aquifer characteristics control the location
of GDEs and some of their features, their characteristics and functioning also depend on the
detailed morphology of the aquifer-GDE interface. In hyporheic zones, groundwater mixes
with surface water in various proportions depending on the hydraulic conditions of the bed
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material and on the hydrologic situation (loosing, gaining or flow-through water body). This
results in mosaics of hydrological and ecological patches, each having a particular faunal
composition.
Groundwater and surface-water mixes provoke sharp changes of chemical concentrations in
hyporheic zones. Surface water is often rich in oxygen and organic matter but contains lower
concentrations of inorganic compounds than groundwater. Consequently, the hyporheic zone
can be considered as a sink for organic nutrients derived from the catchment and the
floodplain, as well as a source of nutrients (organic and inorganic) for the river. Reactions are
facilitated by bacteria and geochemically active sediment coatings. Aerobic species may
completely use up oxygen at some distance into the streambed, and then may be replaced by
organisms adapted to or specialized for hypoxic conditions. These processes affect the
movement of nutrients and contaminants between groundwater and surface water
Organic soils in peatland ecosystems have lower bulk densities and higher water-holding
capacities than mineral soils. Moreover, hydraulic conductivities typically decrease with
depth from the least decomposed upper layer (acrotelm) to the more decomposed lower
zone. As for groundwater fed peatland, groundwater abstraction may have nutritive impact
altering spatio-temporal patterns of reduction and oxidation processes. At the same time,
ammonification provokes an increase of NH4+ which can reach the surface by upward
diffusion, due to concentration gradients between reduced and oxidized layers.
The catchment surface use and the buffering capacity of bedrock and surrounding soils may
be considered as drivers controlling the repartition and diversity of plant species in terrestrial
GDEs. There are many implications of groundwater physical and biotic uplifts, including
chemical modification (mixing, change of redox conditions) of the surface water input,
nutrient acquisition, facilitation of neighboring plants with shallow roots systems or
prolongation of activity (growth and solute uptake) during drought conditions.
4.3. parameterization/quantification
In the previous step a sort of the inner and outer frame of the subsurface structure is build
and the major processes for the task to be addressed are incorporated. In the subsequent
phase, parameters are assigned to all the substructures, that describe their hydrogeological
characteristics. Furthermore, variables that represent (quantify) the exchange of fluxes or
mass between different compartments are introduced. The hydrogeological parameters and
variables include the following (non-exclusive list):
• thickness, porosity, hydraulic conductivity, confinement and absorptive properties of
aquifer deposits and soils
• exchange of water between the groundwater body and associated surface systems
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• calculation of distributed recharge
• description of the most important meteorological parameters
• spatial delineation of concentrations and fluxes
4.4. application of tracer methods within the conceptual model
development
Principles of environmental tracers application
Environmental tracers have proven an effective tool in conceptualization of groundwater
systems, yet their application in hydrogeology and in groundwater resources management is
still limited and insufficient. Environmental tracers are understood here as naturally occurring
and man-made substances that pervade environment and can be traced throughout different
environmental compartments and processes.
Application of tracers in the field of groundwater resources is not restricted to tracing of
water flow and of solute transport through geological media but can be extended to cover
hydrological and biogeochemical interactions between groundwater bodies and related
environmental compartments like soil, surface water bodies and ecosystems. Information
provided by tracers contributes to both qualitative and quantitative characterization of
groundwater systems and can be used at all stages of conceptual models development, from
delineation of system boundaries, to calibration and validation of numerical models, to
monitoring effects of impacts and measures on the system.
A basic division can be made between tracers used to infer properties of groundwater systems
that are related to:
origin, movement and mixing of water (including determination of groundwater flow
pathways and mixing ratios as well as quantification of groundwater flow timescales
and of matrix properties);
origin, transport and transformations of reactive solutes.
Stable isotope systematics of carbon, nitrogen and sulphur provide information on sources and
transformations of these biogenic elements in the subsurface and in the terrestrial
environments. Such information may be of value for understanding of behaviour and transfers
of nutrients and contaminants in and between groundwater and related ecosystems. For
example, stable isotopes are commonly used to identify natural and anthropogenic sources of
nitrates or sulphates, to provide evidence for denitrification and in studies on biodegradation
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of organic pollutants. Use of stable isotopes in ecology in relation to hydrological and
biogeochemical cycles is presented by Fry (2006) and Michener and Lajtha (2007) while
application of compound specific stable isotopic analysis in studies of pollutant degradation is
described by Hunkeler et al. (2008) and Aelion et al. (2009). Hydrochemical patterns in wells
and springs reflect origin of dissolved substances and thus indirectly provide information on
subsurface lithology, geochemical conditions, weathering processes, sources of contamination
and even on groundwater flow structure (Herczeg and Edmunds, 2000).
Tracers most appropriate for tracing water movement are those which possess conservative
properties, i. e. their concentrations are not altered by any physicochemical or biological
processes that operate along groundwater pathways. All tracers can be, in specific conditions,
subject to such alterations but abundances of the isotopic species of hydrogen (1H, 2H, 3H)
and oxygen (16O, 18O) contained in water molecules are closest to being the ideally
conservative tracers unless groundwater comes into contact with atmospheric air when
isotopic modification of water becomes possible. Atoms forming water molecules can also
undergo isotopic exchange with minerals of rock matrix but this process is usually extremely
slow and negligible except exchange of oxygen between water and carbonate minerals in
hydrothermal conditions. The isotopic species of water and the non-reactive dissolved
substances are good tracers of the advective water flow but molecular diffusion influences
their behaviour in systems with significant matrix porosity, which is of great significance in
groundwater dating applications (Zuber et al., 2011).
Some tracer applications can be, in principle, based on a single measurement of tracer
content (e. g. radiocarbon dating or lack of tritium as indicator of “old” groundwater) but
information provided by tracers is usually contextual and case-specific. Understanding of the
extent and internal structure of groundwater systems, of pathways, directions and timescales
of groundwater flow is inferred from observed patterns of tracer concentrations in
precipitation, infiltration and groundwater.
Role of environmental tracers in conceptual models development
Applicability of particular environmental tracers depends on the nature of problems to be
solved and on peculiarities of studied systems. Generic rules for tracer application are
therefore difficult to formulate and tracers are not a ready-to-use tool. Proper use of tracers
relies on thorough understanding of their sources, pathways and behavior in groundwater
systems and, primarily, on understanding of principles of these techniques. Environmental
tracers can provide critical improvements in conceptual models but on the other hand
selection of the appropriate tracers and their correct application require some degree of
knowledge of the inquired system. Tracer tools are from this perspective an inherent part of
building and testing of conceptual models, which is an iterative process relying on qualitative
and quantitative information provided by various methods, including tracers. The advantage
of tracer techniques lies in their ability to provide information integrated over different
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scales from the mesocosm to regional flow system scale and over the corresponding temporal
scales (e.g. recharge). Even limited number of tracer analyses can provide crucial information
on groundwater flow rates equivalent to results of long-term hydraulic observations.
Environmental tracers are particularly important in highly heterogeneous systems where
hydraulic approaches give very uncertain results due to large spatial variability of hydraulic
conductivity and porosity. It must be however underlined that tracer techniques have their
limitations and sources of uncertainties which are specific for each category of tracers.
Simultaneous application of several tracers supplemented by the conventional methods, like
hydraulic head and chemical observations, is commonly advised as a way to overcome those
difficulties.
Estimation of the temporal characteristics of solute transport is a particular contribution of
environmental tracer techniques to the development of conceptual models. Knowledge of
contaminant travel times is an indispensable element of risk assessment schemes which
depend on determination of trends and prediction of future pollutant behaviour. Detection of
trends is aided by dating of the contaminant-bearing samples of groundwater what allows to
relate pollution levels with times of recharge and thus to reconstruct time-series of
contamination and assess extent of non-conservative pollutant removal. Prediction of future
trends in pollution also depends on groundwater dating because knowledge of time lags
associated with response of groundwater systems to commencement or cessation of pollution
is essential for such projections.
Finally, environmental tracers can be followed through groundwater dependent ecosystems
and used to evaluate degree of their dependency and vulnerability to deterioration of
groundwater quantity and quality. Application of tracers assists integration of groundwater
dependent ecosystems into conceptual models of groundwater systems.
4.5. qualitative/quantitative description of impacts
In the next stage of constructing the conceptual model the focus is on a qualitative and
quantitative processing of impacts, i.e. understanding the significance of external pressures
on the groundwater system. One of the most significant topics is to distinguish between
anthropogenic and non-anthropogenic effects. Moreover, the kind of inputs into the
groundwater systems can be classified with respect to their temporal and spatial evolution
since this feature might influence the further modelling procedure. The specific issues are:
• evaluating anthropogenic activities and their interactions
characterization of the chemical composition of the groundwater, including
specification of the contributions from human activity
• description of actual or potential inputs (distinction between direct/indirect,
point/diffuse, actual/historical, permanent/periodic features)
19
• establishing plausible pathways between hazards and receptors (including analysis of
magnitude and probability of unacceptable impacts at receptors)
• identification of emerging issues that could pose a potential risk
4.6. description of effects of measures
The last phase is already oriented to applying the conceptual model rather than the
continuation of it’s development. The conceptual model, which is most likely by now turned
into a numerical model for this purpose, is used to predict and assess the effects of current
and future measures on groundwater quantity and quality. At this point WFD and GWD
relevant issues can be covered which include the following:
• the time for reaching a trend reversal
• the time for reaching a good status / natural background level
• identification of the reasons why a groundwater body fails any status objectives
• ranking of potential measures that are most likely to resolve the situation in an
effective and sustainable manner
• justification of exemptions and/or elaboration of alternative objectives for aquifers
where there is a risk of failing to achieve good groundwater status
In addition, Brassington & Younger (2010) state that it is an essential feature of the
conceptual modelling process that it should be auditable. The purpose of the audit trail is to
record the sources of data that have been used and the reasons for the way that the data
have been interpreted.
5 Management and socioeconomic issues in conceptual models The complexity of groundwater management requires methods for the integration of
technical, economic, environmental, legal, and social issues within a framework that allows
for the development of efficient and sustainable water use strategies. The operation of
groundwater systems is mostly a multiobjective problem with some economic, hydraulic,
water quality, and environmental objectives in conflict. System analysis techniques helps
define and evaluate numerous alternatives that represent various possible compromises
among conflicting groups, values, and management objectives, helping to identify the
possible trade-offs between quantifiable objectives so that further debate and analysis can be
more informed (Loucks et al., 1981).
20
If we define a conceptual model as a representation of how a system works aiming to deal
with certain relevant policy questions, a first step in the definition of the model will be to
define the scope and the relevant issues to be addressed by the model. The degree of detail
and complexity of the conceptual model will depend on the final objective in the
development of this model. The model should represent appropriate and necessary sensitivity
while remaining as simple as possible (Letcher et al., 2007).
In these sense, the socioeconomic data is not just one more input for the model, but essential
information for the definition of the conceptual model. One aim of conceptual models is to
describe the relation between groundwater resources/quality, local conditions and
anthropogenic inputs/impacts in understandable way (CIS Guidance Document n. 26, 2010).
Several attempts of conceptual model fail when dealing with the many interconnection and
complexities within and between the physical and the human environment (Jakeman and
Letcher, 2003). Some aspects in which the socioeconomic information is essential in the
definition of conceptual models are:
Spatial and temporal scale. The definition of the spatial and temporal scale of the
model should take into account not only the physical variability/heterogeneity of the
system, but also the variability of the water and land uses and the different pressures
on the system, not only in the present but also in the future, at the right scale.
Definition of scenarios. For using a conceptual model for planning and management of
groundwater development and operation, we need to create plausible scenarios for
the future, involving issues as climate and land use changes (global change). And any
realistic projection of scenarios should be based on the current socioeconomic
conditions and expected trends. So, an economic analysis of water uses and the study
of the main driving forces of the change are essential for the definition of the
scenarios. The socioeconomic driving forces are used to predict potential changes in
the pressures on the system, which will affect groundwater quantity and quality. The
conceptual model has to be defined with the right detail and complexity to give useful
insight on the impact of the expected scenarios. Some of these scenarios might imply
a very different operation of the system with important environmental and economic
consequences and the model should be useful for the identification and analysis of
those impacts.
Definition of objectives. Water management must take into account multiple users,
multiple purposes, and multiple objectives. Identification of the relevant objectives
and their relative importance is one of the most difficult tasks in water resources
management in general. The objectives should be described by a reasonable tractable
mathematical representation (Loucks et al., 1981).
Identification and assessment of measures and policies. The model should serve to
predict the environmental, social and economic consequences (impacts) of different
measures/policies. The complexity of the model should be enough to simulate how
21
efficient the measures are in reaching the objectives and for predicting the time and
level of recovery from the implemented policies. The identification and preliminary
selection of potential measures will be mainly dependent on their cost and
effectiveness. The measures can be technical, affecting directly the pressures or
groundwater status (e.g. remediation actions in a groundwater body) or policy
instruments directed to the driving forces (e.g. monetary incentives, pricing policies
to control water demands, regulations, etc.). Economic tools are needed to simulate
the impact of certain instruments on groundwater users.
Integration of groundwater conceptual model in a decision-making framework. For
groundwater management and planning, the conceptual simulation model of the
groundwater system is usually embedded within a decision-making framework. The
main techniques are simulation and optimization. Simulation addresses “what if”
questions, i.e., it is a descriptive technique to evaluate the system performance under
a set of inputs and operating policies. Simulation represents a trial-and-error process.
In optimization, simulation models of groundwater flow are combined with methods of
mathematical optimization (system analysis) in order to automatically search for
efficient solutions to a particular objective. The optimization approach requires
definition of the decision variables, the constraints to be imposed on the management
model and the objective to be optimized. Optimization techniques have been used in a
broad range of groundwater management problems (groundwater operation, capacity
expansion, water allocation, conjunctive use, groundwater quality, parameter
identification, etc.) (see Ahfeld and Mulligan, 2000). In this case, the formulation of
the management model plays the role of the conceptual model.
The definition of the conceptual model of the groundwater system to be included in a
decision-making framework will depend on the mathematical structure of the optimization
problem and the limitations of the optimization techniques (for example, in a linear
optimization model the conceptual groundwater model has to be linear; if we are going to use
Dynamic Programming, the number of decision variables will be limited by the “curse of
dimensionality”, etc.), but also on the scope of the study and the available information. Not
only numerical models are used, but also analytical or semi-analytical solutions (e.g. Pulido-
Velazquez et al.) or heuristic simulations (for example, using Artificial Neural Networks
techniques). Two major techniques have been reported for incorporating distributed
groundwater flow simulation within a management optimization model: the embedding and
the response matrix methods (Gorelick, 1983). In the “embedding method”, the system of
equations obtained by numerical approximation of the governing groundwater flow equation
is embedded within the optimization model constraint set. When linearity of a system is
acceptable (time-invariant hydraulic parameters and boundary conditions), the principles of
superposition and translation in time are applicable. The main advantage of the response
matrices is their condensed representation of external detailed groundwater simulation
models. The response matrix is included as a substitute of the aquifer model making it
unnecessary to solve the full groundwater flow models within the management model.
22
Economics has always had a very important role in the development and operation of
groundwater systems. The WFD explicitly recognizes the role of economics in groundwater
management, and calls for the application of economic principles (e.g. polluter pays
principle), approaches (e.g. cost-effectiveness analysis) and instruments (e.g. water pricing).
The most cost-effective program of measures should be selected in order to meet the WFD
environmental objectives. In this context, hydro-economic models have a decisive role (e.g.
Harou et al., 2009). Groundwater hydro-economic models explicitly integrate groundwater
flow (and transport) and economics within a decision-making framework.
One example is the framework developed by Pena-Haro et al. (2009) for determining optimal
management of groundwater nitrate pollution from agriculture. A holistic optimization model
determines the spatial and temporal fertilizer application rate that maximizes the net
benefits in agriculture constrained by the quality requirements in groundwater at various
control sites. Since emissions (nitrogen loading rates) are what can be controlled, but the
concentrations are the policy targets, we need to relate both. Agronomic simulations are used
to obtain the nitrate leached, while numerical groundwater flow and solute transport
simulation models are used to develop unit source solutions that will be assembled into a
pollutant concentration response matrix. The integration of the response matrix in the
constraints of the management model allows simulating by superposition the evolution of
groundwater nitrate concentration over time at different points of interest throughout the
aquifer resulting from multiple pollutant sources distributed over time and space. In this way,
the modelling framework relates the fertilizer loads with the nitrate concentration at the
control sites. It has been applied to the GENESIS’s Mancha Oriental case study (see Appendix).
Deliverable 6.1 of the GENESIS project describes the role of groundwater hydro-economic
models in the application of the WFD/GWD, and discusses principles and tools of economics
that are relevant for groundwater system management.
6 Uncertainty of conceptual models As described already above, developing a conceptual model is an iterative process where the
system understanding is compared several times against existing and newly measured data
and if necessary adapted. This also involves the use of assumptions and simplifications
depending on the purpose and the conditions during the development of the conceptual
model. Yet, different hydrogeologits will most likely adapt their system understanding (view
of reality) in slightly different ways given the same newly available data which will result in
equiprobable but non-unique conceptual models. Meyer at al (2004) argue that it is not
generally possible to specify the complete set of possible conceptual model alternatives. As a
result, conceptual model uncertainty has generally been represented as a discrete
distribution, with a small number of model alternatives taken as the complete set of
possibilities.
23
Compared to the calibration or validation of a numerical model developing a conceptual
model is much more linked to interpretative conclusions. This is also due to the fact that the
process of building a conceptual model is less related to quantitative results (at least before
the point where the conceptual model is turned into a numerical model). Running the
numerical model is the first time where the quantification of boundary conditions and systems
parameters as a result of the conceptual model development are subject to the spatial and
temporal distributed solution of basic groundwater flow principles like the Darcy flow
equation and the law of mass conservation. A single conceptualization may be adequate in
characterizing the natural behaviour of a groundwater system after calibration, because the
calibration procedure is able to compensate for errors in the data or in the conceptual model
through biased parameter values (Refsgaard, 2002). However, in most situations there will
not be only one possible conceptualization.
And even if a site-specific model is eventually accepted as valid for specific conditions, this is
not a proof that the conceptual model is true, because, due to non-uniqueness, the site-
specific model may turn out to perform right for the wrong reasons. This implies that it is
never possible to prove the truth of the applied hypotheses and as such of a conceptual
model. Still, it is important to clearly identify theories and assumptions underlying the
conceptual model to ensure adequate transparency. Furthermore, a model’s validity will
always be confined in terms of space, time, boundary conditions, types of application, etc.
Instead of working with implausible parameter distribution resulting from a (automated)
calibration procedure additional components of the conceptual model, which are typically not
considered within a routine model calibration but are relevant to the specific aquifer, could
be modified (i.e. feedback from numerical to conceptual model) like interpolation of the
aquitard or estimation of losses from sewage pipes or inflow into drainage ditches where just
no measurements are available. Once extrapolation beyond the calibration base is attempted,
different conceptual model formulations may result in significantly different results.
Due to parameter variability, for a given model structure, there will be multiple sets of
parameter values that provide valid representations of observed system behavior (Meyer at
al, 2004). When multiple model conceptualizations are consistent with the available data, it
may not be justifiable to rely on a single model structure. Relying on a single conceptual
representation of a system might lead to underestimation of uncertainty by under-sampling
model space and biased results by relying on an invalid model.
Methods for conceptual model confirmation should follow the standard procedures for
confirmation of scientific theories. In general, a model should be tested to show how well it
can perform the kind of task for which it is specifically intended (Klemes, 1986). However,
models are often intended to be used as management tools to help answer “what if”
questions. In such a case no site-specific test data exist and the question of defining a
validation test scheme becomes non-trivial. De Marsily et al. (1992) argue that using the
model in a predictive mode and comparing it with new data is not a futile exercise. It cannot
be expected that the model will be correct for all circumstances.
24
6.1 Approaches to account for conceptual model uncertainty
It is crucial that uncertainty is considered explicitly in the definition phase of the model
study. A very important task is to analyse and determine what are the various requirements of
the modelling study in terms of the expected accuracy of modelling results. The need to
model certain processes in alternative ways or to differing levels of detail in order to enable
assessments of model structure uncertainty should be evaluated. Following Rojas et al (2010)
it has recently been suggested that predictive uncertainty in groundwater modeling is largely
dominated by uncertainties arising from the definition of alternative conceptual models and
that variation in parameter distribution solely cannot compensate for conceptual model
uncertainty. Thus, rather than relying on a single conceptual model, it seems more
appropriate to consider a range of plausible system representations and analyze the combined
multimodel output to assess the predictive modeling uncertainty.
In that respect Meyer at el (2004) caution that any approach based on evaluation of a discrete
set of alternative models will only be as good as the set of alternatives. That is, if the set of
alternatives does not represent the full range of possibilities, conceptual model uncertainty
will be underestimated. Because the set of alternative conceptual models is unlikely to
represent the full range of possibilities, evaluations of model uncertainty should only be
viewed as relative comparisons (one model better than another for the intended purpose),
i.e. they cannot necessarily be used to conclude that any model is a good model.
Considering an ensemble of conceptual models avoids problems with overfitted individual
models, under‐dispersive uncertainty estimations, and (potentially) biased parameter
estimates obtained to compensate for the unknown errors in the conceptualization of the
system. Multimodel methods, like Bayesian Model Averaging or the Generalized Likelihood
Uncertainty Estimation (GLUE), seek to obtain an average prediction from a set of plausible
conceptual models by linearly combining individual model predictions. In principle, the
methods weigh the predictions of competing models by their corresponding posterior model
probability, representing each (conceptual) model’s relative skill to reproduce system
behavior in the observation period.
Rojas et al (2010) advise to complement the information content of heads with measurements
of key parameters and observations of other system‐state variables (e.g. flow‐related
observations with a global character) to further discriminate among alternative conceptual
models. To decide on the validity of individual model predictions, or to identify
conceptualizations that may be too simplistic or erroneous representations of the true flow
system, their relative contribution to the conceptual model uncertainty and to the predictive
uncertainty must be established. This knowledge may be useful to guide, for example, data
collection campaigns or to decide on conceptualizations worth to be explored in more detail.
25
Ye et al (2010) incorporate real world conditions to enhance the understanding of model
uncertainty and the model averaging methods currently in use for assessing model
uncertainty. As an example, recharge estimated using different methods and different
geological interpretations may be incorporated in a groundwater modeling framework.
Propagation of model uncertainty through groundwater modeling gives rise to predictive
uncertainty, as different models lead to different model predictions. The predictive
uncertainty is also attributed to propagation of parametric uncertainty due to spatial
variability and paucity of field measurements. As a result, for each model, predictive
uncertainty is reflected by multiple realizations of model predictions. When alternative
models are considered, predictive uncertainty is quantified by aggregating predictive
uncertainty of each model using the model averaging method.
For estimating the model likelihood function Ye et al (2010) apply different model
information criteria as well as the generalized likelihood uncertainty estimation (GLUE).
Because model probabilities based on model residuals are similar, calibration against
observation data is not critical to discriminate between the alternative models. This finding is
important for guiding data collection for further evaluation and reduction of the model
uncertainty because it is more efficient to target areas where data collection will most
effectively discriminate between alternative models. Furthermore, the standard deviation of
posterior head variance are dominated by those of the between model variance, as compared
with the within-model variance. This demonstrates that model averaging avoided
underestimation of the magnitude and spatial distribution of the predictive uncertainty.
To evaluate conceptual model uncertainty Meyer et al (2004) favor the Maximum Likelihood
Bayesian Averaging Method since it enables to put more weight on simpler models with fewer
parameters that nevertheless reflect adequately the underlying hydrologic structure (i.e.
supported by key data) and phenomena (principle of parsimony). Model-averaged predictions
are weighted by posterior model probabilities which are modifications of subjective prior
model probabilities based on an objective evaluation of each model's consistency with
available data. In general, bias and uncertainty resulting from an inadequate model structure
(conceptualization) are often more detrimental to a model's predictive reliability than are
suboptimal model parameters.
6.2 Sources of conceptual model uncertainty
Errors in the development of a conceptual model will be perpetuated throughout the further
use of the conceptual model and, for example, are likely to result in developing a sampling
and analysis plan that may not achieve the data required to address the relevant issues.
Consequently, it is crucial to take model conceptual uncertainty into account when making
predictions beyond the calibration phase. Processing ensembles of equiprobable conceptual
models through a groundwater flow model and statistically evaluation of the respective
results is one option to quantify the uncertainty associated with a conceptual model.
26
Potential sources of uncertainty in developing a conceptual model include, among other:
• Data availability: In academic environments spatial and temporal coverage of
collected data might be sufficient to falsify a working hypothesis (aka conceptual
model); however, in the engineering practice too few data might be available to
prove an assumption wrong. This relates to the geometry/structure of an aquifer,
its hydraulic parameterization (i.e. variability in the system's properties) as well as
the delineation of relevant processes. Meyer et al (2004) further add measurement
or sampling error in characterizing the system's features, events and processes as
well as disparity among the sampling, simulation and actual scales.
• Human impact: Another, sometimes less considered, source of uncertainty is the
anthropogenic land use and its change with time. This particularly addresses the
impact on spatial and temporal distribution of groundwater recharge and input of
substances. An example, which has often been studied in the past, is given by a
single point source like a seeping landfill or chemical plant. However, delineating
the input function in the case of non-point source pollution like nitrate leaching
from agricultural practices is much more challenging since it often changes in a
short time and small scale context.
Refsgaard et al (2005) distinguish between the following sources of uncertainties within the
model based water management process: context, (e.g. external economic, environmental,
political, social and technological circumstances), input uncertainty (external driving forces),
model structure uncertainty (conceptual uncertainty due to incomplete understanding and
simplified descriptions of processes as compared to nature), parameter uncertainty (i.e. the
uncertainties related to parameter values), model technical uncertainty (arising from
computer implementation of the model) and model output uncertainty (i.e. the total
uncertainty on the model simulations taken all the above sources into account). Depending on
the framing of the model study some of these uncertainties may be located as external non-
controllable sources. Meyer at al (2004) adopt the view that distinctions between types of
uncertainty are largely related to sources of information and that it is more useful to think in
terms of what is needed to accomplish the modeling task: adequate decomposition of the
problem, combining various sources of information, assessing the value of additional data,
and effectively utilizing sensitivity analysis.
7 Scopes of first simulations on climate and land use change In addition to the diverse geologic and climatic characteristics within the GENESIS test sites
very different development stages and modeling goals with respect to scenarios of climate
and land use change (e.g. scale, nature of impact) of the individual sites exist. The results of
the Special Report Emission Scenarios (i.e. temperature and precipitation time series) that
27
are being used in this context are inherently based on (steady state) assumptions about land
use, agricultural production, irrigation needs, city populations, groundwater abstraction on a
coarse scale. The adequacy of this procedure has to be evaluated for each test sites if the
resulting data sets are being implemented as input for further hydrologic modeling.
The test sites that deal with these impacts on quantity and/or quality of groundwater will
investigate the following problems, which are divided into sole effects of climate change,
feedback effects between climate, vegetation and land use and sole effects of land use
change, in further detail:
sole effects of climate change:
• evaluating the effects of climate change on recharge rates, groundwater flow,
groundwater levels and migration of polluted groundwater
• creating the ability to analyze the effects of climate adaptation strategies on
groundwater levels, groundwater quality and inputs to terrestrial ecosystems
• forecasting the energy production under new conditions of precipitation, river
discharge and snowmelt regime
modified geochemical processes in the hyporheic zone are expected
• assessing the impacts on groundwater, groundwater surface water interaction,
ecosystems and yield efficiency
• simulation of optimal groundwater exploitation for GDE protection due to changes in
recharge
• designing countermeasures to increasing soil vapour emissions due to decreasing soil
moisture
feedback effects between climate, vegetation and land use:
• simulating the evolution of nitrate concentration in groundwater (as crossover from
recharge and agricultural practice changes)
• analysis of agricultural products, their water demand and groundwater extraction
changes for mitigation of impacts of climate change with respect to impacts on
groundwater, groundwater surface water interaction, ecosystems and yield
efficiency
28
effects of land use changes:
• simulating different scenarios (compared to business as usual)
Fertilizer prizes vs. nitrate concentration standards vs. max benefits
• finding combinations of crop rotation and fertilizer use that guarantees (not
maximizes) sustainable farmer income under the constraint of meeting
groundwater nitrate concentration thresholds
• considering future residential land use
• studying different forestry scenarios (e.g. more logging, more protection, continuation
as now) in combination with peatland use (e.g. prolongation of status quo,
expansion of protected areas, restoration policy)
Furthermore, subsequent implementation issues might be of relevance to one or the other
test site:
• Climate change scenarios can also be taken from PRUDENCE or ENSEMBLE projections
or compared to this results
• If land use projections are unavailable they can be approximated from EU projects
EuRURALIS or PRELUDE.
• Precipitation and temperature as well as resulting actual evapotranspiration (e.g.
applying Penman Monteith) from climate projections are obtained without
consideration of further feedbacks (e.g. crop selection, irrigation needs)
• The near term (i.e. 2020/2030) will be dominated by climate variability instead of
climatic aspects. Only in the intermediate term (i.e. 2050) climate trends
(irrespective of SRES) will start to have an effect.
• Not only for conceptual hydrogeologic models but also for regional climate scenarios
(e.g. SRES A21B and 50 km resolution) ensemble should be used to sample relevant
uncertainties.
• Consequently, hydrological models (with temperature, precipitation, and potential
evapotranspiration as input) should be run for each ensemble member (not on
ensemble average because of nonlinearities). Averaging will be done on hydrologic
results (compare chapter 6.1).
29
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and transport models, Hydrogeology Journal, 19, 53-60, DOI 10.1007/s10040-010-0655-4.
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9 Appendix - conceptual models and first simulations
regarding climate and land use change of example GENESIS
test sites
9.1 Mancha Oriental System
A stochastic hydro-economic modeling for optimal management of groundwater nitrate
pollution under parameter uncertainty has been developed based on the deterministic hydro-
economic model. The objective of the management model is to determine optimal measures
for nitrate pollution control in the aquifer and to evaluate the influence of climate and land
use changes. In our methodology we are considering 6 different models (see figure below).
Climate and land use scenarios are generated and the impacts on groundwater recharge are
assessed through a calibrated rainfall-runoff (SWAT) model. Later an agronomic model
(GEPIC) has been calibrated for Mancha Oriental in order to provide production and nitrate
leaching functions for different alternatives of water and nitrogen fertilizer uses. A simple
approach based on the kinematic wave has been used to deal with the time delay that
nitrates undergo trough the unsaturated zone. The simulation of groundwater nitrate
pollution is obtained through pollutant concentration response matrices, obtaining unit
pollutant concentration curves by simulation with Modflow-MT3D.
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Each of the models that are being coupled in the general hydro-economic framework requires
a conceptual model by themselves. The stochastic framework has also been extended to deal
with other sources of uncertainty such as crop yield, farmer’s decisions (fertilizer applications
rates, land use / crop pattern scenarios) or unknown future economic scenarios affecting crop
and fertilizer prices and subsidies.
9.2 Lulea
The purpose of the conceptual model and implementation plan is to create a transient one
year flow, heat and mass transport groundwater surface water exchange model that will
include saturated and unsaturated flow with incorporated surface processes (see figure
below).
The following steps in model development will be considered:
• Pure water flux model development
• Inclusion of mass transport
• Inclusion of surface and unsaturated zone processes - coupling
o Unsaturated and surface processes flow model.
o Coupling of the flow models.
o Unsaturated heat and mass transport model.
o Coupling the heat and mass transport models.
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A generalized sensitivity analysis (GSA) is planned to be applied to the existing model in order
to identify the major controlling parameters of model behaviour. The GSA has to be based on
utilization of the model together with a classification algorithm or criterion of acceptability
and distribute the modelled results between behavioural or nonbehavioural. Not an optimum
value but rather a set of parameter values within a model structure or even within different
model structures attuned with data available for calibration has to be found.
Climate change scenarios are available for the Lule River. Modified precipitation/evaporation,
its distribution throughout the year, energy demand, and hydropower production will result in
altered discharge curve of the Lule River. It implies different river water level variations,
possibly shift of seasonality. Modified geochemical processes in the hyporheic zone are
expected.
9.3 Grue
Based on the sedimentological (geological) model and the collected information from the area
a first conceptual hydrogeological model has been made. The conceptual model, results from
numerical modelling based on the first conceptual model, the collected information and the
main issues to be illuminated in the project have been the basis for planning of further
measurements and experiments. Additional information is provided from ongoing
measurements and experiments. This additional information will be used to improve the
conceptual hydrogeological model.
In the figure below a cross-section of a conceptual model at Grue is shown. The soils freeze in
winter, and melt water are gathered and temporary stored in local depressions in the terrain
early in spring. When the frozen soil subsequently thaws, the temporary water storages
rapidly infiltrate the subsurface. It is hypothesized that use of mobile pesticides on such areas
might represents a particular important threat to deterioration of groundwater in cultivated
areas.
DEPRESSIONS WITH FOCUSED INFILTRATION IN SPRING
Hydro-
geological
module
Soil leaching
module
UNSATURATED ZONEGDE
SATURATED FLOW
Winter
process
module
-.
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9.4 Vomvoris
Uncertainty in the conceptual hydrogeological model could arise from two areas:
• Model uncertainty or the possibility that the conceptual model is not appropriate (e.g.
it has assumed inappropriate processes such as river-aquifer or lake-aquifer
interaction).
• Parameter uncertainty related to hydraulic parameters, boundary conditions,
hydrogeologic features which are not well determined in all locations covered by
the conceptual model.
A significant amount of data has been collected, assessed and analyzed and more data is
expected to be available in order to decrease the conceptual model and parameter
uncertainty. This data includes lysimeter monitoring data, which is expected to provide
significant information in order to clarify lake-aquifer hydraulic interaction.
Conceptual model and parameter uncertainty will be assessed in a next step, in groundwater
model flow development. After groundwater flow model calibration, a sensitivity analysis and
a model validation will be conducted. This process includes the model execution with a
different set of groundwater levels, precipitation and groundwater abstractions.
9.5 Caretti site
The conceptual model of Caretti site is to be considered still a work in progress, particularly
in relation to the risk assessment and evaluation of natural attenuation potential of the
contamination. Basically the logical procedure of the conceptual model build-up is a step-
wise approach, started by a classical geological and hydrochemical characterization and
followed up by more specific surveys related to hydrogeological monitoring, soil vapour
intrusion testing and isotopic fingerprinting of the contamination.
The main scope of the conceptual model is to define the exact location of primary sources of
contamination and the pattern of the contaminant occurrence in the local aquifers to put in
evidence conflicts between soil use and hazards (plumes below houses) and the potential of
contamination spreading toward likely off-site receptors.
Main questions are:
• How far are the plumes migrated away from the source and how long could they
potentially migrate further?
• What is the average groundwater velocity and by what boundary conditions is
groundwater flow affected?
• What is the actual risk of vapour intrusion of contaminants at soil surface?
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• What is the actual origin of the contamination? Why are concentrations typical of a
petrochemical plant detected in a fully urbanized area?
• Is there any potential of biodegradability of the solutes inside the plumes?
One of the most interesting, and apparently surprising result, derives from the application of
the flux-chamber approach for soil vapour intrusion monitoring. First results show that,
differently from standard risk assessment evaluation output based on conservative analytical
fate & transport tools, vapors mass flux rate at the surface is very poor down to completely
lacking. This evidence completely disrupts the preceding conceptual model, putting emphasis
on the key role of unsaturated zone water content and dominance of silty-clayey layers above
the shallow aquifer in preventing vapours escaping up to the surface. One of main
consequence of this, valid at the European scale and not only related to the Caretti site, is
that 3rd tier risk assessment, based on site specific monitoring of vapour migration processes,
is crucial in defining risk issues at the site, also before any modeling of the system.
It’s noteworthy that regional geological studies and reports indicated a very simple geological
framework and the occurrence of 2 (and not 3) aquifers. This issue puts in evidence the
importance to construct the conceptual model build up on a site specific investigation,
performed step by step, without assigning an a priori faith on existing studies. Moreover, in
relationship to the origin of the contamination, a very important remark is to consider also a
hydrochemical and isotopic comparison with the broader area where the site is located. Only
through this comparison it was possible to improve the groundwater flow system conceptual
model (environmental isotopes) and to define the origin of the contamination at the Caretti
site (isotopic fingerprinting).
9.6 Murtal aquifer
It is the purpose of the conceptual and numerical model to create a tool capable of
quantitatively reproducing observed groundwater nitrate concentrations and of assessing the
impact of measures to reduce nitrate leachate from the unsaturated zone. Understanding the
processes in the unsaturated zone represents an important aspect of the conceptual model
given only available measurements from the saturated area. It is planned to answer the
complex questions and associated scenarios with the help of numerical models.
A 1d channel flow algorithm was applied to reconstruct river water level time series from
observed water level fluctuations in gauges which are used as Cauchy-type boundary
conditions. Recharge was calculated with the external model STOTRASIM which was calibrated
with lysimeter data. In general, important knowledge (in terms of observations and modeling)
regarding the impacts of different kinds of agricultural land use on recharge and fertilizer
input into the aquifer was gained from operating the research lysimeter station at Wagna. To
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transfer non-point source nitrate input from the field to the aquifer scale land use
information taken from statistics on the cadastral municipality level will be used in a
stochastic manner. Moreover, sequential iterative coupling between unsaturated and
saturated groundwater modeling has been implemented.
9.7 Rokua esker
Initial concerns and need for research in the area was related to changes in closed basin
kettle lake levels on top of the Rokua esker formation. Lake level variations were in previous
studies contributed either to natural climatic variability, land use change surrounding the
esker or effects from both coexisting.
Performed geophysical analysis did not show extensive geological layers referring to perched
lakes. This led to conclusion that lakes are connected to the aquifer. Thus land use changes
leading to changes in aquifer dynamics had to be included in the conceptual model. Climatic
data analysis resulted in the assumption, that also other factors contribute to lake and
aquifer level changes. A pilot restoration project resulted in conclusions, that restoring water
level in the surrounding peat land ditches had an effect in the aquifer hydraulics and amount
of groundwater discharge. Thus, the initial conceptual model was accepted as valid for
further development. Different conceptualization has to made for different scales of the
research area.
Conceptual model can be subdivided into two sections:
1. Plant-soil –system (unsaturated zone, recharge): Need for flow component from the
unsaturated zone to lake systems. Component is possible to exist mainly during spring
melting period, when soil is partly frozen.
Correct modeling of evapotranspiration is a key component in reaching valid estimates
about recharge. Vegetation is assumed as uniform for the whole research area.
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Vegetation includes two plants: pine tree and leachen. Pine tree is responsible for
transpiration from the area. Leachen is considered to influence water retention before
water enters the soil, and producing only interception evaporation, no transpiration.
Soil evaporation is neglected, because lichen is considered to cover the soil blocking
soil evaporation.
Conceptual model of evapotransoiration in Rokua esker
2. lake systems in aquifer, groundwater system (saturated zone) and discharge zone
Lakes in the aquifer can roughly be divided to two categories following the attributes
of lake level variability, trophic status, nutrient/ion contribution, connection to
streamflow and elevation on the esker. Many of the differences between two lake
types can be explained by the conceptual idea of local and regional groundwater flow
systems. Water levels in type 1 lakes depend on groundwater inflow from local flow
system. The GW inflow and outflow are mainly determined by GW-levels adjacent to
the lake. Therefore changes in GW-table (resulting from changes in recharge) can have
a significant impact in GW-SW interaction. Residence time of GW discharging to lake is
relatively short, possibly leading to less leaching and chemically poor water.
In type 2 lakes water level is also affected by regional GW flow system, in addition of
local flow systems. Inflow is less sensitive for GW-table fluctuation/decline, as
regional flow system sustains steady inflow and local inflow can be replaced by
regional inflow during season of low recharge. Also inlets and outlets connecting the
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lakes favor steady water table. Residence time of regional GW-flow is longer, leading
possibly to more leaching from soil to groundwater.
Climate change is thought to influence water flows in many ways because of rising average
temperature and annual precipitation. In principle the effects are seen in all water flow
components. First important effect in the flowchart is seen in changes in snow covered
period. Temperature and changes in water available for transpiration change both lake
evaporation and evapotranspiration, leading to changes in recharge and GW-lake interaction.
With regards to areas ecosystems, on top of the esker can be found rare lake types, old
forests in natural state and lichen coverings supporting endangered vegetation and insect
species. Spring ecosystems in GW discharge area are mostly not included in natural
conservation programs, but they are heavily altered.
Land use scenarios formulated are divided into two categories: land use on top of the esker
and land use in peatlands surrounding the esker. Land use scenarios on top of the esker are
related to management of forestry/loggings, thus changing areas vegetation resulting in
changes in evapotranspiration. Vegetation is also not affected by climate change. Land use
scenarios in peatlands affect mainly GW discharge to peatlands. Changes in peatlands are
related to changes in the resistance to GW discharge of the confining peat layer. Changes in
vegetation are more related to restoration of spring ecosystems. Land use scenarios in esker
area and in peatlands are combined to follow a certain policy lines which can be summarized
as steady state, modest protection, extensive protection and increased logging.
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Conceptual model Rokua esker including relationships to important ecosystems and sceanrios
of climate and land use change