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, hans.kupfersberger@joanneum.at

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

8 references Aelion C. M., Höhener P., Hunkeler D. and Aravena R. (2009): Environmental Isotopes in

Biodegradation and Bioremediation. CRC Press.

Ahlfeld, D.P. y A. Mulligan, 2000. Optimal Management of Flow in Groundwater Systems.

Academic Press, New York, NY, USA.

Bertrand, G., Goldscheider N., Gobat, JM. And D. Hunkeler (2011) Review: From multi-scale

conceptualization to a classification system for inland groundwater-dependent ecosystems.

Hydrogoelogy Journal, DOI 10.1007/s10040-011-0791-5.

Brassington. F.C. and P.L. Younger (2010): A proposed framework for hydrogeological

conceptual modelling, Water and Environment Journal 24(2010) 261-273.

Common Implementation Strategy for the Water Framework Directive (2000/60/EC) Guidance

documents

No. 7: Monitoring

No. 15: Groundwater Monitoring

No. 17: Preventing or limiting direct and indirect input

No. 18: Groundwater status and trend assessment

No. 26: Guidance on risk assessment and the use of conceptual models for

groundwater

chapter 3: Conceptual model overview

Annex II: Setting up conceptual models for groundwater systems

De Marsily, G., Combes, P. and P. Goblet (1992): Comments on 'Ground-water models cannot

be validated', by L.F. Konikow and J.D. Bredehoeft, Advances in Water Resources, 15, 367-

369.

Fry B. (2006): Stable Isotope Ecology. Springer.

Gorelick, S. M., 1983. A review of distributed parameter groundwater management modeling

methods. Water Resour. Res., 19(2), 305–319.

Harou, J.J., Pulido-Velazquez, M., Rosenberg, D.E., Medellin-Azuara, J., Lund, J.R., Howitt,

R.E., 2009. Hydro-economic Models: Concepts, Design, Applications, and Future Prospects. J.

of Hydrology, 375 (3-4), 627–643, doi:10.1016/j.jhydrol.2009.06.037

Herczeg A. L. and Edmunds W. M. (2000): Inorganic ions as tracers. [In:] Cook P. and Herczeg

A. L. (eds.) (2000): Environmental Tracers in Subsurface Hydrology. Kluwer Academic

Publishers.

30

Hunkeler D., Meckenstock R. U., Sherwood Lollar B., Schmidt T. C. and Wilson J. T. (2008): A

Guide for Assessing Biodegradation and Source Identification of Organic Ground Water

Contaminants using Compound Specific Isotope Analysis (CSIA). United States Environmental

Protection Agency. http://www.epa.gov/nrmrl/pubs/600r08148/600r08148.pdf

Jakeman, A. and Letcher, R., 2003. Integrated Assessment and Modelling: Features, Principles

and Examples for Catchment Management. Environmental Modelling and Software, 18m, 491-

501.

Klemes, V. (1986): Operational testing of hydrological simulation models. Hydrological

Sciences Journal, 31, 13-24.

Konikow, L.F. and J.D. Bredehoeft (1992): Ground-water models cannot be validated.

Advances in Water Resources, 15, 75-83.

Letcher, R., Croke, B., and Jakeman, A., 2007. Integrated assessment modelling for water

resource allocation and management: A generalised conceptual framework. Environmental

Modelling and Software, 22, 733-742.

Loucks, D. P., J.R. Stedinger y D.A. Haith, 1981. Water resource systems planning and

management. Prentice-Hall Inc., N.J., USA. 558 pp.

Meyer, P.D., Ye, M., Neumann. S.P. and K.J. Cantrell (2004): Combined estimation of

hydrogeological conceptual model and parameter uncertainty. NUREG/CR-6843. U.S. Nuclear

Regulatory Commission, Office of Nuclear Regulatory Research, Wasington, DC.

Michener R. and Lajtha K. (eds.) (2007): Stable Isotopes in Ecology and Environmental

Sciences. Blackwell Publishing.

Peña-Haro, S., Pulido-Velazquez, M., Sahuquillo, A., 2009. A hydro-economic modelling

framework for optimal management of groundwater nitrate pollution from agriculture. J. of

Hydrology, 373(1-2), 193-203, doi:10.1016/j.jhydrol.2009.04.024.

Pulido-Velázquez, M., Sahuquillo, A., Ochoa, JC., and Pulido-Velázquez, D., 2005. Modeling

of stream-aquifer interaction: the embedded multireservoir model. J. of Hydrology, 313(3-4),

166-181.

Refsgaard, C., ed. (2002): State-of-the-Art Report on Quality Assurance in modelling related

to river basin management; HarmoniQuA: Harmonising Quality Assurance in model based

catchment and river basin management, Contract EVK2-CT2001-00097; chapter 3: Modelling

Guidelines – A Theoretical Framework.

Refsgaard, J.C., van der Sluijs, J.P., Hojberg, A.L. and P. Vanrolleghem (2005): Harmoni-CA

Guidance Uncertainty Analysis Guidance 1, Contract no: EVK1-2001-00192, Fifth Framework

Programme.

31

Rojas, R., Feyen, L., Batelaan, O. and A. Dassargues (2010): On the value of conditioning data

to reduce conceptual model uncertainty in groundwater modelling, Water Ressources

Research, VOL .46, doi:10.129/2009WR008822.

Spijker, J., Liesste R., Zijip, M. and T. de Nijs (2010) Conceptual models for the Water

Framework Directive and the Groundwater Directive, RIVM report 607300015, Bilthoven, NL.

Ye, M., Pohlmann, K.F., Chapman J.B., Pohl, G.M. and D.M. Reeves (2010): A model-averaging

method for assessing groundwater conceptual model uncertainty, Ground Water 48(5), 716-

728, doi:10.1111/j.1745-6584.2009.00633.x

Zuber A., Rozanski K., Kania J. and Purtschert R. (2011): On some methodological problems in

the use of environmental tracers to estimate hydrogeologic parameters and to calibrate flow

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