The impact of groundwater–surface water interactions on the water balance of a mesoscale lowland river catchment in northeastern Germany

HYDROLOGICAL PROCESSESHydrol. Process. 21, 169–184 (2007)Published online 24 May 2006 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/hyp.6182

The impact of groundwater–surface water interactions on thewater balance of a mesoscale lowland river catchment in

northeastern Germany

Stefan Krause* and Axel BronstertCentre for Sustainable Water Management, Lancaster Environment Centre, Lancaster LA1 4YQ, UK

Abstract:The glacially formed northeastern German lowlands are characterized by extensive floodplains, often interrupted by relativelysteep moraine hills. The hydrological cycle of this area is governed by the tight interaction of surface water dynamics and thecorresponding directly connected shallow groundwater aquifer. Runoff generation processes, as well as the extent and spatialdistribution of the interaction between surface water and groundwater, are controlled by floodplain topography and by surfacewater dynamics.

A modelling approach based on extensive experimental analyses is presented that describes the specific water balanceof lowland areas, including the interactions of groundwater and surface water, as well as reflecting the important role oftime-variable shallow groundwater stages for runoff generation in floodplains. In the first part, experimental investigations offloodplain hydrological characteristics lead to a qualitative understanding of the water balance processes and to the developmentof a conceptual model of the water balance and groundwater dynamics of the study area. Thereby model requirements whichallow for an adequate simulation of the floodplain hydrology, considering also interactions between groundwater and surfacewater have been characterized. Based on these analyses, the Integrated Modelling of Water Balance and Nutrient Dynamics(IWAN) approach has been developed. This consists of coupling the surface runoff generation and soil water routines ofthe deterministic, spatially distributed hydrological model WASIM-ETH-I with the three-dimensional finite-difference-basednumerical groundwater model MODFLOW and Processing MODFLOW. The model was applied successfully to a mesoscalesubcatchment of the Havel River in northeast Germany. It was calibrated for two small catchments (1Ð4 and 25 km2), wherethe importance of the interaction processes between groundwater and surface waters and the sensitivity of several controllingparameters could be quantified. Validation results are satisfying for different years for the entire 198 km2 catchment. Themodel approach was further successfully tested for specific events. The experimental area is a typical example of a floodplain-dominated landscape. It was demonstrated that the lateral flow processes and the interactions between groundwater and surfacewater have a major importance for the water balance and periodically superimposed on the vertical runoff generation. Copyright 2006 John Wiley & Sons, Ltd.

KEY WORDS groundwater; surface water; model coupling; wetlands; lateral flow

Received 16 September 2004; Accepted 13 September 2005

INTRODUCTION

In the last century, wetlands and floodplain areas inEurope have been subject to fast-changing conditions,altering between agricultural land use (including theinstallation of widespread drainage systems and inten-sive fertilization) on the one hand and nature conser-vation areas on the other hand (Brunke and Gonser,1997; Prescott and Tsanis, 1997; Sophocleous, 2002;Mohrlock, 2003). In order to achieve the goals of theEuropean Water Framework Directive, and partly causedby changes in European agricultural policy, it has becomemore important to focus on the sustainable use of wet-lands and to promote the natural regulation functions forthe water balance of lowland catchments (Krause andBronstert, 2004; Krause et al., 2005). To improve thewater quality of a lowland river system and to promote

* Correspondence to: Stefan Krause, Centre for Sustainable Water Man-agement, Lancaster Environment Centre, Lancaster LA1 4YQ, UK. E-mail: [email protected]

its natural regulation functions it is necessary to inves-tigate the eco-hydrological processes and water balancecontrolling functions of the strongly connected floodplainand its changes as a result of management practices (Win-ter et al., 1998; Hayashi and Rosenberry, 2002; Sopho-cleous, 2002; Acreman et al., 2003).

In several previous studies, the tight interactionsbetween surface waters and the groundwater of the adja-cent floodplains have been mentioned (Langhoff Heide-mann et al. 2001; Hayashi and Rosenberry, 2002; Sopho-cleous, 2002). The importance of the interactions betweenthe shallow groundwater and surface waters for waterbalance processes of floodplains and wetlands in low-land areas (Waddington et al., 1993; Devito et al., 1996;Andersen, 2004), and subsequently for floodplain ecology(Brunke and Gonser, 1997; Gilvear et al., 1997; Gasca-Tucker and Acreman, 2000; Hayashi and Rosenberry,2002), have been investigated for numerous differentlyscaled streams and catchments.

The characteristics, intensity and direction of ground-water–surface water interactions are controlled bypressure head gradients, hydraulic permeability of the

Copyright 2006 John Wiley & Sons, Ltd.

170 S. KRAUSE AND A. BRONSTERT

hyporheic zone and by the riverbed geometry (Winteret al., 1998; Winter 1999; Woessner, 2000; Sophocleous,2002). As a result of the spatial heterogeneity ofthe controlling factors of groundwater surface waterinteractions and the subsequent variability of the impactof these interaction processes, the floodplain waterbalance is also characterized by highly variable spatialpatterns and temporal dynamics (Cey et al., 1998;Langhoff Heidemann et al., 2001; Sophocleous, 2002).

However, spatially detailed studies concerning thetemporally and spatially variable effects of control-ling functions on the characteristics and intensity ofgroundwater–surface water interactions have been lim-ited to the investigation of cross-sections or small streamreaches (Langhoff Heidemann et al., 2001; Krause andBronstert, 2005; Krause et al., 2005).

Although groundwater–surface water interactions havebeen qualitatively described for different scales, analysesof their temporally and spatially variable impact on thefloodplain water balance are rather rare.

Research objectives and strategy

This study aims to improve the understanding offloodplain water balance processes and their tempo-ral and spatial dynamics within an exemplary low-land river catchment of the glacially formed northeast-ern German lowlands. It is a further objective of thisstudy to investigate the temporally and spatially variablegroundwater–surface water interactions and to analysetheir variable impacts on the floodplain water balance.In the first part of this study, the results of an exten-sive experimental investigation program will be analysed.These analyses focus on soil moisture and groundwaterdynamics and their dependency on surface water stagedynamics in order to gain a qualitative understandingof the complex process structures and of their temporaldynamics and spatial patterns. Subsequently, the mainlyqualitative results of the experimental investigations willbe used for the development of a conceptual model of thewater balance processes of the floodplain which consid-ers the importance of the floodplain water balance controlby groundwater–surface water interactions.

Based on the results of the experimental investiga-tions, and in consideration of the conceptual require-ments analysed for an adequate reflection of the specifichydrological conditions of floodplains, an innovative cou-pled modelling approach will be presented. This modelapproach links the simulation of the water balance andgroundwater dynamics and considers the variable inter-actions between groundwater and surface waters. It willbe tested for several different-sized subcatchments of thelower Havel River and subsequently used for quantitativeanalysis and characterization of the floodplain hydrology.In applying this model, not only will the heterogeneityof the interaction processes between groundwater andsurface water be analysed, but also the temporally andspatially variable impact on the floodplain water balancewill be quantified in particular.

Figure 1. Location of the lower Havel catchment within Germany andinstrumentation of the study area for hydrological and meteorological

measurements

Study area: experimental set-up

The experimental investigations of this study tookplace in the lower Havel River basin (Figure 1). Thestudy area is located in the glacially formed northeasternlowlands of Germany and is part of the drainage basin ofthe Havel, a river with a length of 325 km before it runsinto the Elbe River. The study area covers 198 km2 andis located about 20 km upstream of the Havel confluencewith the Elbe.

The area is characterized by wide lowlands with amean elevation of 27 m a.s.l. in the central part andsurrounding relatively small-scaled plates that consistof Pleistocene moraines and reach heights of up to120 m a.s.l. Mean precipitation is 540 mm year�1 withan enhancement in autumn and spring and higher precip-itation intensities in the eastern hillslope areas caused bylocal thunderstorms in summer and a mean wind direc-tion from the west. For centuries, the catchment hasbeen characterized by the inundation of large parts ofthe floodplain, which led to the development of a densecross-linked drainage network.

High water levels in the Havel River and inundationof the floodplain are, in general, not caused by highdischarge rates in the Havel River itself, but rather areinduced by high water levels in the Elbe River anda successive backwater impact into the Havel (Krauseand Bronstert, 2004). In the area, the meteorologicalparameters, the groundwater and surface water conditionsand the soil moisture values are measured at severalstations (see Figure 1). An overview of the observedparameters and their temporal and spatial resolutions isgiven in Table I.

The meteorological conditions are observed by fourclimate stations located within the floodplain part ofthe catchment and in the surrounding hillslopes. Surfacewater stages and groundwater stages at seven observationboreholes are observed within 1 h time intervals. Addi-tionally, groundwater stages are monitored in weeklytime steps at 14 boreholes. Soil moisture is measurednear to the four meteorological observation stations atfour depths down to 1 m below the surface with hourlytime steps using time/frequency domain reflectometry

Copyright 2006 John Wiley & Sons, Ltd. Hydrol. Process. 21, 169–184 (2007)DOI: 10.1002/hyp

GROUNDWATER–SURFACE WATER INTERACTIONS 171

Table I. Experimental parameter, spatial and temporal resolution of observation parameters with: precipitation (precip), air moisture(am), air temperature (at), solar radiation (sr), wind direction (wd), wind speed (ws), albedo, soil temperature (st), soil moisture (sm)

Station Parameter Observation interval

Guelper Insel precip (1 m), am (2 m), am (0Ð1 m), at (2 m), at (0Ð1 m), sr(2 m), wd (2 m), ws (2 m), albedo (2 m), st (0Ð05 m), st(0Ð2 m), sm (10 cm), sm (20 cm), sm (40 cm), sm (70 cm)

60 min, precip 15 min

Kossaetenberg precip (1 m), am (2 m), am (0Ð1 m), at (2 m), at (0Ð1 m), sr(2 m), wd (2 m), ws (2 m), albedo (2 m), st (0Ð05 m), st(0Ð2 m), sm (10 cm), sm (20 cm), sm (40 cm), sm (70 cm)

60 min, precip 15 min

Kienberg precip, sm (10 cm), sm (20 cm), sm (40 cm), sm (70 cm) 60 min, precip 15 min

Parey precip, sm (10 cm), sm (20 cm), sm (40 cm), sm (70 cm) 60 min, precip 15 min

Groundwater observationboreholes (7 C 14)

Groundwater depths 1 h (7 boreholes), 10 days(14 additional boreholes)

Surface water gauges (7) Surface water levels 1 h

(TDR/FDR) techniques (Gardner et al., 1991; Roth et al.,1992).

EXPERIMENTAL ANALYSES

Groundwater dynamics and soil moisture conditions

Spatially detailed investigations of the hydraulic con-ductivities of the floodplain soils show complex spatialpatterns with moderate hydraulic conductivities at thesmall-scaled lentoid structures of organic soils and peatclays (2Ð1 ð 10�7 m s�1 to 7Ð3 ð 10�5 m s�1) and highlypermeable sandy soils in wide parts of the floodplainand of the hillslopes ��1Ð2–4Ð2� ð 10�4 m s�1� (Krauseand Bronstert, 2004). The high conductivity of most ofthe soils and the spatial limitation of the soils character-ized by moderate conductivities means that the infiltra-tion capacity is always high enough to inhibit infiltrationexcess, including during summer thunderstorms (Krauseand Bronstert, 2004).

The spatially detailed investigations of soil moisturedynamics at different depths (Figure 2) highlight thespatially very heterogeneous distribution and temporally

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Figure 2. Soil moisture dynamics at the Havel River floodplain: stepwisedecrease of soil moisture content at the upper horizons due to ETR lossesduring a drying period (15 May–25 June 2001), test site ‘Guelpe’, river

distance ¾500 m

highly variable dynamics of soil moisture contents in thefloodplain.

Especially notable are the observed daily fluctuationsof soil moisture during dry summer months that can bedetected at test sites characterized by sandy soils andgroundwater depths of less than 3 m. The typical soilmoisture dynamics during dry periods are characterizedby a stepwise daily decrease of the soil moisture asa result of daily transpiration losses. These effects arelimited to the upper soil horizons (10 and 20 cm), whichare affected by plant root uptake. Typically, the soilmoisture within the upper soil horizons decreases inthe morning due to increasing evapotranspiration (ETR).This effect usually holds on until the evening. As aresult of the stagnation of ETR during night-time, notranspiration losses occur and soil moisture is stable.Similarly characterized dynamics have been reportedearlier for other test sites (Feddes et al., 1976; Portge,1996). The appearance of these effects is limited to theinitial states of dry periods only (10–22 May 2001 inthe example presented), because a distinctive minimalamount of soil water content is required.

This specific example highlights once more the impor-tance of the impact and control of meteorological condi-tions on the variable soil moisture conditions, especiallyfor the highly permeable sandy soils, not only for precip-itation events, but also during dry seasons.

The analysis of the results of groundwater stageobservation at boreholes since 1990 shows that theannual groundwater stage dynamics are characterized bya maximum from autumn to spring and a period of lowergroundwater stages during summer. Figure 3 shows theprecipitation and groundwater and river water levels forthe period from October 2001 to October 2002 at twolocations at different distances from the Havel River.Annual groundwater stage fluctuations in the lower Havelcatchment vary between more than 100 cm for areas thatare close to surface waters and ¾40 cm within areas at agreater distance from the river. In Figure 3, it can easilybe seen that whereas the groundwater stages at the closerlocation (left) seemed to be highly influenced by surfacewater dynamics, the groundwater dynamics at a greater



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distance from the surface water seem not to be influencedby river stage dynamics.

To investigate the dependence of the annual ground-water stage dynamics on distance to the river, the meanannual groundwater stage fluctuation was analysed for21 observation boreholes for a 10-year monitoring period(1991–2000). As Figure 4 proves, the annual groundwa-ter stage dynamics decrease significantly with increas-ing distance from the surface water. Whereas the meanannual amplitude of groundwater fluctuation within a1Ð5 km zone surrounding the surface water is, at ¾1Ð2 m,nearly as high as the amplitude of the mean annual sur-face water fluctuation (¾1Ð5 m), the mean annual ground-water stage fluctuation decreases asymptotically to 0Ð4 mwith increasing distance from the river. The thresholdof a 0Ð4 m mean annual groundwater stage fluctuationcan be roughly interpreted as the meteorologically drivenand controlled part of the groundwater stage fluctuation,whereas higher values of fluctuation within the study aremainly controlled by surface water–groundwater inter-actions. For sure, this can only be an approximationbecause, as shown before, the soil physical propertiesare spatially highly heterogeneous.

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Figure 4. Spatial dependence of annual groundwater level fluctuationsfrom surface water distance mean annual fluctuations of a 10 year

observation period (1991–2000) for 24 observation boreholes

Groundwater–surface water interactions

Further evidence for an intensive impact of ground-water–surface water interactions on the floodplain soilwater balance could also be observed in the analysisof the temporally highly resolved soil moisture data.By analysing the temporal dynamics of the observedsoil moisture dependence on surface water stages andprecipitation for the period of October 2001–October2002 in Figure 5, it becomes obvious that, with hydro-meteorological processes alone (e.g. infiltration, rootwater uptake and ETR), the complex soil moisturedynamics of the floodplain cannot be described in asufficiently adequate way. However, it can be seen inFigure 5a that the soil moisture dynamics (measured byFDR probes at depths of 10 and 70 cm) are stronglylinked to precipitation for most of the time. However,for certain events, e.g. August 2002, the soil moisturedynamics cannot be determined by precipitation dynam-ics alone. By looking at the soil water dynamics at the70 cm probe, one can see a soil water increase withoutthe occurrence of any rainfall. In this case, an increasein the soil moisture at the lower probe is not explicableby any vertical infiltration of precipitation; rather, thisincrease in the soil moisture is due to an increase of theriver water level (August 2002 flood event). Thus, it canbe proved that the river water dynamics also influencethe unsaturated zone of the catchment, in particular inregard to the subsoil. Because there is no reaction at theupper FDR probe (the top soil water content measured atthe 10 cm probe is still decreasing), one can neglect anyimpact of vertically infiltrating precipitation water.

Figure 5b and c focuses on this period (August 2002):Figure 5b shows a location close to the surface water andFigure 5c shows an additional soil moisture station at agreater distance from the river (5Ð6 km). Whereas for thecloser site (Figure 5b) there is a detectable reaction of thelower soil horizon to the groundwater rise, no reactioncan be observed for the upper soil horizon and for bothhorizons of the second site (Figure 5c).

Thus, it can be seen how the (unsaturated) soil moisturecontent on a test site within 0Ð7 km distance to the surfacewater is strongly influenced by river water dynamics,



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Figure 6. Surface water–groundwater interaction during dry summer months; a faster decrease of surface water level (compared with groundwaterlevel) causes groundwater exfiltration into the river

whereas vertical processes alone are controlling thedynamics at greater distances.

Groundwater recharge in this typical lowland catch-ment is not only a function of vertical processes e.g.infiltration and percolation, but also of lateral groundwa-ter flow and interactions with surface water. Furthermore,the spatial limitation of the impact of these lateral pro-cesses becomes obvious.

Water balance retention

An additional important effect of the tight surfacewater–groundwater interactions on the floodplain waterbalance become visible when analysing the groundwaterstage and surface water level dynamics at the begin of

drying seasons. Figure 6 shows the retention capacityof the floodplain soils for the catchment water balanceduring an exemplary drying season from April 2003–July2003. The groundwater levels do not decrease as fastas the river water stages. This is caused by a slowergroundwater reaction on the decrease of surface waterstages, which is caused and controlled by maximal filtervelocities of 10�4 m s�1. It can be seen in Figure 6(left)that, owing to this retardation, the surface water levelsoccasionally decrease below the groundwater stages fromJune 2003.

Although the infiltration into the groundwater dom-inates the interactions between both waterbodies, thismore rapid decrease of surface water stages than the



groundwater induces an exfiltration from the aquiferinto the river. These periods of groundwater exfiltra-tion, which occur regularly during dry summer sea-sons and show the sensitivity of these wetlands withrespect to river management, are marked by hatchingin Figure 6(right). Considering that these characteris-tic dynamics affect not only the water balance of thefloodplain, but also influence the water chemistry dueto the change of flow directions and varying infiltra-tion/exfiltration conditions, an important impact on thefloodplain ecology can also be assumed.

CONCEPTUAL MODEL OF FLOODPLAIN WATERBALANCE

The experimental results lead to a qualitative understand-ing of the complex floodplain water balance processesin the lower Havel River basin. It becomes clear thatthe floodplain water balance and soil water dynamicsare controlled by vertical meteorologically driven pro-cesses at the soil–vegetation–atmosphere interface, aswell as by lateral processes at the groundwater–surfacewater interface. However, quantitative analyses of fluxesand a weighting of the importance and impact of certainprocesses are not possible based solely on the experi-mental results. This knowledge can only be obtained bysimulation of the floodplain hydrological processes usingan adequate model. In the following, the experimentallygained process knowledge will be used to build up aconceptual model of the floodplain water balance, whichconsiders the process dynamics analysed before in itsarchitecture.

As the experimental results show, for a sufficientunderstanding and successful modelling of the water bal-ance of lowland catchments it is necessary to focus onthe specific hydrological conditions and runoff genera-tion processes of wetlands, which means considering thesoil water and groundwater processes in an integrativeway (Sophocleous and Perkins, 2000; Sophocleous, 2002,Hughes, 2004; Krause and Bronstert, 2004). One of themost important experimental results is the understand-ing of the tight interactions at the groundwater–surfacewaters interface, which affect the floodplain water bal-ance significantly (Figure 7). Furthermore, it was possibleto show that meteorologically driven influences on thewater balance occur not only by infiltration and per-colation, but also especially by groundwater and soilwater losses due to root water uptake and ETR and arevery important processes for the floodplain water bal-ance.

As one can see in Figure 7, the resulting concep-tual model covers the processes of the soil waterbalance and groundwater dynamics, as well as thevariable interactions between groundwater and sur-face waters. Summarizing the requirements to simulatethe water balance in such an interconnected surfacewater–floodplain–lowland landscape, one can say that anappropriate model has to be able to cope with (Figure 7):

lateral processes:groundwater modelling + groundwater ––surface water interactions –– MODFLOW

vertical processes:runoff generation –– WASIM-ETH-I

unsaturated conditions

saturated conditions - groundwater

Figure 7. Conceptual model of water balance and groundwater dynamicswithin floodplains considering the coupling of vertical soil water dynam-ics of the unsaturated soil zone, lateral groundwater flow processes of thesaturated zone and interactions between groundwater and surface water

ž temporal and spatial dynamics of runoff generationprocesses, including the surface runoff from saturatedareas of a temporally variable spatial extent;

ž soil water dynamics within horizontally and verticallyvariable soil properties and extension of the unsaturatedsoil zone;

ž groundwater flow in the saturated soil zone of variablespatial extent;

ž two-way coupling of the water balance and ground-water processes and dynamics, including the effectivegroundwater uptake in zones of shallow groundwater;

ž direct interaction between groundwater and surfacewater, including the resulting processes of surfacewater infiltration and groundwater exfiltration, andthe representation of their variations in time andspace.

COUPLED SIMULATION OF WATER BALANCEAND GROUNDWATER DYNAMICS

General model concept

Based on the results of the experimental investiga-tions, one can state that the spatial and temporal variabil-ity of the soil water balance and groundwater dynamicsprocesses calling for a non-stationary and spatially dis-tributed model set-up.

Although there have actually been a number of suc-cessful attempts dealing with incorporating some of thespecific model requirements named above into modelsof different types and for different scales none of theseapproaches seemed to be sufficient for an adequate sim-ulation of the coupled water balance and groundwaterdynamics of the study area.

Some exemplary numerical experiments about the spa-tial variability and varying extent of groundwater–sur-face water interaction have been accomplished bydetailed two-dimensional, vertical-plane simulations ofexemplary transects of rivers beds connected with theadjacent floodplains and lowlands by using numericalunsaturated–saturated soil water models, e.g. HYDRUS-2D (Simunek et al., 1994; Joris and Feyen, 2003). Thosesimulations refer to the field scale only.



At the mesoscale, several models have been devel-oped and applied for water balance simulations oflowland–floodplain landscapes that account for ground-water–surface water interactions in a different manner.Some of these models are based on more conceptualapproaches for soil and groundwater (e.g. by consideringdata of groundwater gradient or surface water distances(HECNAR; Jayatilaka and Gillham, 1996; Jayatilakaet al., 1996)), or on a combination of a vertical soil-watersimulation with a simplified representation of groundwa-ter flows towards the main drainage direction (SWATRE;Spieksma and Schouwenaars, 1997), but also on fullythree-dimensional physically based numerical approachesfor both soil moisture and groundwater (VanderKwaak,1999; VanderKwaak and Sudicky, 1999; Sudicky et al.,2000; Weng et al., 2003). Other approaches consist of acoupling of physically based groundwater models (two-dimensional horizontal plane, or fully three-dimensional)with a more conceptual model for the hydrological pro-cesses in the unsaturated zone and at the soil surface(SWAT–MODFLOW, Sophocleous and Perkins, 2000;AGRIFLUX–MODFLOW, Lasserre et al., 1999).

Most of the models mentioned here perform importantsimplifications: e.g. the spatial extent of the interactionsbetween groundwater and surface water is assumed to beconstant, or spatially and temporally transient changes ofprocesses are considered in a rudimentary manner only.The ‘most physical’ approach, a full three-dimensionalnumerical solution of saturated–unsaturated zone pro-cesses (in both the soil and groundwater domain), how-ever, encounters limitations for mesoscale applications,because of the high computation time required, problemswith changing the types of boundary condition (e.g. vary-ing saturation areas in space and time), and insufficientknowledge concerning really representative parameters ofthe soil hydraulic properties for such models.

The groundwater–surface water interactions betweenfloodplains and surface waters are represented in mostof these models. However, the influence of the areasthat are adjacent to the lowland–floodplain landscapes(i.e. their hydrologic conditions at the non-surface-waterboundary) are not accounted for in most models (Hayashiet al., 1998; Krause and Bronstert, 2004, 2005). Addi-tionally, most of these approaches ignore areal soil-water–vegetation interactions, e.g. changes in ETR androot water uptake, caused by higher groundwater levels.

Thus, we decided to create a new model approachbased on the coupling of already existing and approved

model routines that represent the dynamical processes ofcertain compartments of the floodplain successfully.

For simulation of the water balance in the study areawe used the Integrated Modelling of Water Balance andNutrient Dynamics (IWAN) model system (Krause andBronstert, 2004, 2005). With regard to the conceptualmodel developed (Figure 7), it has two main compo-nents that have been coupled in a two-way mode, i.e.feedback effects are taken into account in both directions(Figure 8):

1. runoff generation and vertical soil water dynamicsare simulated by using the relevant routines of thedeterministic, spatially distributed hydrological modelWASIM-ETH-I (Schulla, 1997; Schulla and Jasper,1999, Niehoff and Bronstert, 2001; Niehoff et al.,2002);

2. the flow in the saturated zone and its interactions withthe channel systems are modelled using the three-dimensional finite-element-based numerical groundwa-ter model MODLFOW (Harbaugh and McDonald,1996a,b) and Processing MODFLOW (Chiang andKinzelbach, 1993, 2001).

Realization of the coupled model approach

Both models are based on a raster grid discretizationand have, for reasons of model consistency, been usedwith equivalent cell sizes. Also, the simulation time-steps

Infiltration

Percolation

Surface Runoff

Evapotranspiration

Groundwater Uptake

Interception

Groundwater Exfiltration

Surface Water Infiltration

WASIM-ETH-ISimulation of:ETR, Interception,Infiltration, Soil waterdynamics, Percolation

MODFLOWSimulation of:Groundwater dynamics,lateral groundwater flow,exchange with processeswith surface waterG

rou

nd

wat

er Z

on

e

Surface Water

Un

satu

rate

dS

oil

Zo

ne

IWAN - Water Balance Model

Model CouplingDue to:Exchange of simulatedvertical groundwaterrecharge & uptake

Groundwater Dynamics

Groundwater Recharge(vertical)

Interface - Model Coupling

Figure 8. Concept of coupling the water balance and groundwater dynam-ics routines within the IWAN model: WASIM-ETH for calculation of thevertical soil water dynamics in the unsaturated zone and MODFLOW forapproximation of the lateral groundwater flow for saturated conditions

and for the interaction between groundwater and surface water

Table II. Model set-up for several simulations

Subcatchment Spatial Simulation Time step Areadiscretization (m) time �km2�

WASIM MODFLOW For model coupling

Guelper Insel 25 2 weeks 1 h 1 h 1 day 1Ð43Guelper Insel 25 4 months 1 h 1 h 1 week 1Ð43Muehlengraben 25 4 months 1 h 1 h 1 week 25Ð38Lower Havel 50 1 year (2001–02) 1 h 1 day ¾10 days 189Ð1Lower Havel 50 13 years (1988–2000) 1 h 1 day 1 month 189Ð1



used for both models are the same and vary for eachmodel application depending on the total simulation times(Table II).

Water balance: WASIM-ETH-I. The simulation of thesoil water balance in the WASIM-ETH-I model is basedon an advanced TOPMODEL approach (Beven andKirkby, 1979; Schulla, 1997; Beven et al., 1995). Thetemporal and spatial dynamics of the main storagesof the model (Schulla, 1997) are controlled by thecharacteristics of the saturation deficit SD, which iscalculated for each time-step.

The dynamics of vertical groundwater recharge ordischarge in WASIM-ETH-I are conceptualized as thechange of a subsoil storage SG and are parameter-ized by the time-variable changes of the saturationdeficit SD (Equations (1) and (2)) and the baseflow QBAS

(Equation (3)) (Schulla, 1997).Therefore, the local saturation deficit SDi is calculated

by distributing the spatial mean saturation deficit of asubcatchment SDm by using the spatial distribution ofthe topographic index from the mean topographic index:

SDi D SDm � m

(ln

˛

T0 tan ˇ� y

)�1�

with

y D 1

iA

iA∑iD1

ln˛

T0 tan ˇ

where T0 is the saturated hydraulic transmissivity, ˛ is thespecific catchment size, ˇ is the mean slope of a raster,y is the mean topographic index of the entire subbasinand m works as a scaling factor.

Actualization of the spatial mean saturation deficitSDm,i occurs for each time-step by the spatial meansaturation deficit SDm,i�1 of the antecedent time-steptaking into account the baseflow simulated in WASIM-ETH-I QBAS and groundwater recharge QSUZ from thespecific part of the unsaturated zone that is not affectedby plant roots as well as the groundwater uptake QUP:

SDm,i D SDm,i�1 C QBAS C QUP � QSUZ �2�

Calculation of the baseflow QBAS depends on the meansaturation deficit SDj of the subcatchment of a streamreach j and is based on the assumption of lateralgroundwater fluxes to the river network (Beven andKirkby, 1979). Therefore, the lengths of the particularstream reaches lj, the hydraulic transmissivity T0, theslope ˇ and the scaling parameter m are also taken intoaccount:

QBAS Dn∑

jD1

lj�T0 tan ˇt�e�SDj/m �3�

Calculation of the ETR depends on the actual saturationdeficit using the Penman–Monteith approach (Monteith,1975). Further detailed information about the calculationof infiltration, ETR and fluxes as QUP and QSUZ can be

found in Schulla (1997) or Krause and Bronstert (2004,2005).

Groundwater dynamics: MODFLOW. For the simu-lation of the groundwater flows and the interactionswith the surface water, the modular designed pre- andpost-processing unit Processing MODFLOW (Chiang andKinzelbach, 1993, 2001) has been used.

MODFLOW (McDonald and Harbaugh, 1988; Har-baugh and McDonald, 1996a,b), the groundwater modelimplemented, is one of the most often used numericalgroundwater models. It is based on the approximationof the groundwater flow equation using a finite differ-ence approach (McDonald and Harbaugh, 1988) includ-ing the preconditioned conjugate gradient (PCG2) solvingalgorithm (Hill, 1990). The upper boundary condition isdefined by the vertical groundwater recharge or dischargecalculated in WASIM-ETH-I (Figure 8).

The interaction between surface water and groundwateris calculated in Processing MODLFOW by using a leak-age approach. Therefore, the ‘River Package’ (Prudic,1988; Rembe and Wenske, 1998) is used, which controlsthe fluxes above the river boundary condition.

For calculation of the in- and out-flows at the riverboundary condition QRIV, two cases can be distinguished:the case that the groundwater level h is higher than theheight of the river bottom RBOT (Equation (4)) or where itis below the riverbed bottom (Equation (5)). Furthermore,the flux quantities at the river boundary depend on thehydraulic conductivity of the hyporheic zone CRIV andon the time-variable surface water level hRIV.

QRIV D CRIV�hRIV � h� for h > RBOT �4�

and

QRIV D CRIV�hRIV � RBOT� for h < RBOT �5�

The intensity of the interaction is controlled by theleakage factor CRIV, which is defined as the hydraulicconductivity of the hyporheic zone. This depends ongeometrical riverbed properties, like the river lengthwithin a cell L, the river width WRIV and the thicknessof the hyporheic zone MRIV, but also on the hydraulicconductivity of the riverbed KRIV:

CRIV D KRIVLWRIV

MRIV�6�

Spatial mean values (averaged for similar stream reaches)have been assumed for the riverbed conductivity KRIV.

Coupling procedure. The overall model coupling strat-egy is based on the assumption that the vertical ground-water recharge can be derived by the simulation results ofthe WASIM-ETH-I model being transferred via a transferfunction as vertical supply (percolation in the case of pos-itive values) or losses (uptake in the case of negative val-ues) to the groundwater model MODFLOW (Figure 8).

The coupling of vertical fluxes in and out of the unsat-urated zone with the groundwater module is performed



by transmitting the fluxes into/from the WASIM-ETH-Isoil storage as groundwater recharge or uptake to MOD-FLOW, and vice versa (Figure 8).

The amount of vertical groundwater recharge is cal-culated as the sum of the time-variable alteration ofthe saturation deficit (Equation (3)) (in consideration ofthe effective soil porosity �) and of the baseflow QBAS

(also simulated in WASIM-ETH-I). Thus, the groundwa-ter recharge QReach of a defined transfer period t toMODLFOW is given by

QReach D SD� C QBAS

t�7�

andSD D SDi � SDi�1 �8�

The calculated volumes of QReach will be implementedas inflow and outflow to the upper boundary conditionof MODFLOW (Equation (7)). Because this algorithmaccounts for defined transfer periods, the transmittedvalues are averaged in time. Transfer periods varydepending on the simulation time and simulation time-steps (Table II). At the actual stage, the length ofthe transfer time periods has to be predefined; thedevelopment of an algorithm to realize automatic controlof adaptive transfer time-steps is in progress. Takinginto account that the resulting fluxes above the upperboundary condition in the groundwater model alwaysresult from the difference of the saturation deficit SDbetween two successive time-steps in WASIM-ETH-I, itcan be seen that their values can be positive (groundwateruptake) or negative (groundwater recharge).

The unsaturated soil zone interacts directly with thegroundwater in the floodplain parts of the lower Havelcatchment. In the peripheral moraine areas at the catch-ment boundaries, the groundwater processes and theprocesses in the active upper parts of the unsaturatedzone, which are affected by plant roots and atmosphericprocesses, are decoupled. The different duration timesof water in the unsaturated inactive part of the soilmodel, the transfer zone, are controlled by the predefinedhydraulic conductivity of the hillslopes’ deep soils. Anexplicit calculation of the processes within the transferzone is not implemented in the model.

Limitations of model application

The fundamental conceptual parts of the approachpresented here are based on the TOPMODEL approach(Beven and Kirkby, 1979). The saturation deficit SD,which is a function of the topographic index, is used tocalculate the spatial distribution of the soil moisture in theWASIM-ETH-I soil model. This approach is only validif at least a minimum of the topographical characteristicsis given, in order to distribute the global soil moisture.The difference in actual saturation deficit SD of twoneighbouring cells, which is responsible for soil moisturedistribution, is related to the logarithm of the ratios of

both cells concerning slope ˇ, catchment size ˛ andtransmissivity T:

SD D SDiA � SDiB

D[

SDm � m

(ln

˛A

tan ˇATA� y

)]

�[

SDm � m

(ln

˛B

tan ˇBTB� y

)]

D m ln(

˛B

˛A

tan ˇA

tan ˇB

TA

TB

)�9�

Although the floodplain topography of the study areais rather homogeneous, a very heterogeneous spatialpattern of soil moisture could be observed (Krause andBronstert, 2004). Thus, whether the model is able torepresent this heterogeneity of soil moisture due to thespatial variability of simulated saturation deficits had tobe tested.

It is important to emphasize that the delineation ofsubwatershed surfaces in lowland catchments is not justrelated to the surface topography; rather, it is predeter-mined by the geometry of the network of drains andditches (Krause and Bronstert, 2004, 2005), which causesa high number of heterogeneously sized subcatchments inthe lower Havel catchment. As a result of the glacial gen-esis of the catchment morphology, the soils, soil depthsand subsequently the transmissivities are relatively het-erogeneous. Consequently, in the lower Havel catchment,the deviation of slope, the catchment size and the trans-missivity between two neighbouring cells, which addi-tionally can be scaled by the parameter m, are largeenough, even in the floodplain part of the catchment,to distribute the mean soil moisture. The topographicindex varies between 16 and 40 for the entire catchment;and in the topographically most homogeneous floodplainregions its variation expands from 25 to 37. The pre-requisite of sufficient topographic heterogeneity to usethe TOPMODEL-approach-based soil water algorithm isgiven for the lower Havel River basin.

Generally, before applying this model approach tofurther lowland catchments, it will always be necessaryto check the heterogeneity of the spatial distribution ofthe topographic index and saturation deficits to ensure anadequate reflection of the processes in these catchments.

MODEL APPLICATION

Model parameterization and set-up

A 25 m raster was generated for the simulations of theGuelper Insel and ‘Muehlengraben’ subcatchments, andfor the whole ‘lower Havel’ catchment a 50 m raster wasgenerated. The boundary conditions in the groundwatermodel, given by the pressure heads in the surface waters,are calculated by interpolation of locally measured datafrom gauges and weirs on the Havel River.

Both models run with a 1 h time-step for the calibra-tion simulations. For simulations of 1 year and longer the



MODLFOW time-step is enlarged to 1 day. The trans-fer time-steps for model coupling differ between 12 hand 1 month, depending on the total simulation time(Table II). As a consequence of the enlargement of thesimulation time-steps and of the extension of the transfertime-steps, the simulation results will be less accurate.Uncertainties in the boundary conditions become signif-icantly higher. Effects that are temporally and spatiallylimited, as can be seen during the calibration period usinga transfer time-step of only a few days, probably get lostwhen interpolation takes place by generating the bound-ary conditions for monthly transfer time-steps. Thus, forevent-focused simulations, the transfer time-steps shouldbe as small as possible, but for the analysis of periodicprocesses the transfer time-steps can be longer.

Spatial mean values are used for riverbed hydraulicconductivity, which had to be specified during calibra-tion when there were insufficient spatial data. The soilphysical parameters used within the WASIM-ETH-I soilmodel routines are taken from plot-based experimentalinvestigations at more than 40 test sites within the studyarea. The specific vegetation parameters of root length,leaf area index, etc., are based on the WASIM interndatabases and on the plant parameter database ‘PlaPaDa’(Breuer and Frede, 2003; Breuer et al., 2003). The initialgroundwater stage conditions of MODFLOW are basedon the spatial interpolation of measured data at all 21observation boreholes. Measured soil moisture conditionshave been used for the initial characterization of the soilwater storages of the WASIM-ETH-I model. Both modelsuse a preliminary simulation period to ensure the integrityof the initial conditions. These periods vary depending onthe simulation time-steps between 3 months and 1 year.

Model calibration

Calibration of the model was carried out by comparingmeasured and calculated groundwater stages for differentobservation points in the floodplain and in the hillslopeparts of the catchment. As mentioned before, the surfacewater stages and discharges of the Havel River are com-pletely controlled by a complex management system ofweirs. Thus, an evaluation of the goodness of fit basedon simulated and measured river discharges would not bepossible for this catchment without using temporally andspatially very detailed management information that isnot available and, consequently, not implemented in themodel concept. Owing to the use of measured groundwa-ter stage dynamics at more than 20 observation boreholesfor the calibration process, a specially detailed evalua-tion of the goodness of fit can be ensured. Additionally,the simulation results have been evaluated in a previousstudy by the comparison of simulated saturation areasand remote-sensing data for specific events (Krause andBronstert, 2004).

Because of the availability of clearly defined boundaryconditions and in order to investigate the relevance ofthe interactions between surface water and groundwater,the model was first tested for the small area Guelper

Insel, which is an island surrounded by the Havel mainchannel and a side channel (Figure 9a). A second testwas performed in the more heterogeneous hilly area ofthe Muehlenfliess catchment, with a focus on the spatialpattern of the interaction processes between groundwaterand surface water (Figure 9a).

As mentioned earlier, the parameter that has beenused for calibration was the leakage factor (Equation (6)).Although the values of the river length L and river widthWRIV in a flow cell in Equation (6) are well known,knowledge about the spatial distribution of the hydraulicconductivity of the riverbed KRIV and about the thicknessof the hyporheic zone MRIV is poor. Thus, the leakagefactor has been calibrated by varying the ratio betweenriverbed hydraulic conductivity and riverbed thickness.

The ratio between the two parameters was varied from10 : 1 [T�1] up to 1 : 100 [T�1]. The goodness of fitwas evaluated by using the Nash and Sutcliffe efficiency(NSE). For calibration, the IWAN model was tested tosimulate the specific floodplain hydrological conditionsand characteristics of several time periods (6 weeks to4 months) in an adequate way. The time periods, charac-terized by temporally and spatially very detailed observa-tion data, were chosen with the intention to test whetherthe model was able to reflect the specific water balancedynamics of the calibration periods. Additionally, calibra-tion of the model for the entire lower Havel River basinwas carried out for a perennial time period using spatiallyand temporally less detailed observation data. The resultsof a 4 month calibration period (1 September–31 Decem-ber 2001) are presented in Figure 9b as an example.

Figure 9b shows the sensitivity of the model resultsto the leakage factor. The model produces the best fitof simulated and measured data for ratios from 1 : 1[T�1] to 10 : 1 [T�1] (Table III). The simulated andmeasured groundwater stages are in good agreement forboth catchments for this ratio (Table III).

Comparing the range of variability of both catchmentsin the particular calibration period, an increasing sensitiv-ity to the variation of the KRIV/MRIV ratio with greaterdistances to the next river cell could also be observed(observation points in the Guelper Insel catchment areat a distance of less than 100 m; observation points in‘Muehlengraben’ catchment are at a distance of more than400 m to the next river cell).

Model validation

To validate the model, the coupled approach wasapplied to the entire lower Havel catchment for two yearswith very different climatological boundary conditions.The first year, October 2001–October 2002 was a rela-tively wet year, including an intensive summer flood inAugust 2002, when large vast areas of the floodplain wereinundated for several weeks. The second year, 2002–03was very dry, with less than 80% of the mean annual pre-cipitation. The simulation results of these two years areshown in Figure 10. Simulated groundwater stages areplotted against measured values for different observationpoints, located in the floodplain and in the hilly areas.



Guelper Insel Muehlenfliess

elevation (m) elevation (m)22 - 2323 - 2424 - 2525 - 26

20 - 3030 - 4040 - 50> 50

N

N

0.3 0 0.3 0.6 km

Km2 0 2 4 6 8 10

Observation point 1 (OP1)




25.0

25.0

24.8

24.8

24.6

24.6

24.4

24.4

24.2

24.2

24.0

24.0

23.8

23.8

23.6

grou

ndw

ater

sta

ges

(sim

ulat

ed)

(m a

.s.l.

)

grou

ndw

ater

sta

ges

(sim

ulat

ed)

(m a

.s.l.

)

groundwater stages (measured) (m a.s.l.) groundwater stages (measured) (m a.s.l.)

23.6

KRIV : MRIV - Ratio

1 : 1001 : 101 : 110 : 1

25.80

25.80

25.75

25.75

25.70

25.70

25.65

25.65

25.60

25.60

25.55

25.55

25.50

25.50

25.45

25.4525.40

25.40

KRIV : MRIV - Ratio

1 : 1001 : 101 : 11 : 10

(b)

(a)

Figure 9. Calibration results, comparison of simulated and measured groundwater stages at observation points shown in (a) for subcatchments GuelperInsel and Muehlenfliess. (b) Sensitivity of the model by calibrating the KRIV/MRIV ratio [T�1] for the observation points in the Guelper Insel (left)

Muehlenfliess (right) catchments

Table III. Results of sensitivity analysis for the KRIV/MRIV ratio [T�1]: Nash and Sutcliffe index for calibration periods inboth catchments for different parameter ratios

NSE

KRIV/MRIV D 1 : 100 KRIV/MRIV D 1 : 10 KRIV/MRIV D 1 : 1 KRIV/MRIV D 10 : 1

Muehlenfliess catchmentOP1 0Ð0923 0Ð1004 0Ð2725 0Ð4008OP2 0Ð2741 0Ð1169 0Ð7327 0Ð9624Guelper InselOP1 0Ð9289 0Ð9414 0Ð9475 0Ð9480OP2 0Ð5744 0Ð9802 0Ð9844 0Ð9849

The agreement of the dynamics of the simulated andthe measured values is generally satisfying, with a meanNSE of 0Ð82. The model was able to reflect the seasonaldynamics and spatial variability of groundwater stages,

as well as the different reactions to the summer flooddepending on the distance to the river. The discordancemeasure of simulated and observed groundwater stagesin August 2002 is caused by inundations that are induced



20

B

A

16A18

27.0

26.5

26.0

25.5

25.0

24.5

24.0

23.5

23.0

GW

(m

NN

)

GW

(m

NN

)

OctNov Dec Ja

nFebM

ar AprM

ay Jun Ju

lAug Sep

gauge 16A (simulated)gauge 16A (observed)

NSE = 0.812BIAS = 0.205

01/10/2001 - 30/09/2002 date

OctNov Dec Ja

nFebM

ar AprM

ay Jun Ju

lAug Sep

01/10/2001 - 30/09/2002 date

OctNov Dec Ja

nFebM

ar AprM

ay Jun Ju

lAug Sep

01/10/2001 - 30/09/2002 date

OctNov Dec Ja

nFeb M

ar AprM

ay Jun Ju

lAug Sep

01/10/2001 - 30/09/2002 date

OctNov Dec Ja

nFebM

ar AprM

ay Jun Ju

lAug Sep

01/10/2001 - 30/09/2002 date

27

26

25

24

23

GW

(m

NN

)

27

26

25

24

23

GW

(m

NN

)

27

26

25

24

23

GW

(m

NN

)

27

26

25

24

23

gauge A (simulated)gauge A (observed)

NSE = 0.817BIAS = 0.239

gauge B (simulated)gauge B (observed)

NSE = 0.852BIAS = 0.017

NSE = 0.632BIAS = 0.010

gauge 18 (simulated)gauge 18 (observed)

gauge 20 (simulated)gauge 20 (observed)

NSE = 0.795BIAS = 0.736

Figure 10. Validation results for ‘lower Havel’ catchment: comparison of measured and simulated groundwater stages for several locations at differentdistances from the surface water (October 2001–October 2002)

by dam breaks in the area north of the catchment, whichwere not considered in the model concept.

To evaluate whether there was any systematic over- orunder-estimation, the BIAS was also analysed (Krauseand Bronstert, 2005). For a mean square error MSE 6D 0the BIAS fraction is calculated by

BIAS D �Oi � Pi�2

MSE�10�

with O the observed value and P the simulated value.It was possible to prove that a systematic failure was

limited to minor underestimation mainly in the peripheralregions at the catchment boundary, although it could alsobe detected that a systematic offset occasionally occurseven at observation points with a good fit of the simulated

and observed dynamics proved by a high NSE (Krauseand Bronstert, 2005).

Generally, it can be concluded that the overall simu-lation goodness characterized by the dynamical and sys-tematic model errors is satisfactory and enables use ofthe IWAN model for further water balance analyses.

Analysis of the annual water balance

For the analysis of the annual floodplain water balance,the time period from October 2001 to October 2002 wassimulated with the IWAN model. It was possible to provethat the amount and the direction of flow through theriver boundary varies with time. Figure 11a shows thenet groundwater flow into/out of the aquifer (i.e. thesum of both lateral flow to/from the river and verticalflow to/from the unsaturated zone) and its relation to



S O N D J F M A M J J A S

time (01/10/01 - 30/09/02)

groundwater recharge

groundwater losses

15

10

5

0

-5

10

-15

chan

ge o

f gro

undw

ater

sto

rage

(m

3 s-1

)

gw-discharge (into the river)

gw-recharge (from the river)

1401301201101009080706050403020100

prec

ipita

tion

(mm

/d)

wat

er le

vel H

avel

Riv

er (

m N

N)

20222426

181614121086420

(a) (b)

Figure 11. (a) Dynamics of groundwater recharge in the lower Havel basin (October 2001–October 2002) with the modelled inflow and outflowto/from groundwater, precipitation and surface water stages (given for four gauges at different locations in the river network). (b) Spatial variability

of groundwater recharge and groundwater discharge zones at the river boundary within a single time step

rainfall and to the corresponding surface water stages.Apparently, the net flow direction changes in time,occasionally very rapidly, caused by quick changes insurface water stages or intensive precipitation events.

Although the interaction of the waterbodies is impor-tant for the water balance of the floodplain areas, thedischarge of groundwater into the surface water is nearlynegligible compared with the discharge in the river, evenfor events with rapid surface water decreases like at theend of August 2002. The maximum groundwater dis-charge of 7Ð8 m3 s�1 in the entire catchment of the lowerHavel River corresponds to 3Ð48% of the total runoff atthis time �224 m3 s�1�.

Although Figure 11a shows the time dependency ofexchange flow direction and intensity, it does not describethe spatial variability of the boundary flows. This isbecause only the effective flow as the sum of inflowsand outflows was considered. As a result of the intenseregulation of the river water levels, the absolute flowon several different river cells can vary considerably inspace. Figure 11b shows the spatial variability of theboundary fluxes in different areas of the river network,where it can be seen that infiltration and exfiltrationoccur at the same time in the catchment; this is causedby the spatial variability of the regulated surface waterstages. It can be assumed that, for more than 50% of thetime, fluxes occur in both directions, into and out of thegroundwater (Krause and Bronstert, 2004). This effect iscaused mainly by water level regulations by weirs, etc.

In Figure 12, the simulated total inflow into the aquiferis compared with the vertical groundwater recharge(through the unsaturated zone). It can be seen thatdifferences are apparent. There is no coherence betweenthe temporal dynamics of vertical groundwater rechargeand the total groundwater recharge. The characteristicsof the vertical dynamics are rather superimposed bythe dynamical lateral interactions. This fact demonstratesthat the total groundwater balance must be stronglyinfluenced by the lateral flows. The influences of lateral

S O N D J F M A M J J A Stime (01/10/01 - 30/09/02)

25

20

15

10

5

0

-25

-20

-15

-10

-5

grou

ndw

ater

rec

harg

e (m

3 s-1

)26

24

22

20

18

16

14

12

10

8

6

4

2

0

1401301201101009080706050403020100

prec

ipita

tion

(mm

/d)

surf

ace

wat

er s

tage

(m

.a.s

.l.)surface water stages

precipitationtotal groundwater rechargevertical groundwater recharge

Figure 12. Temporal distribution of vertical groundwater recharge andnet groundwater inflow (including lateral flows and groundwater surface

water interactions) for the year 2001–02

processes are often much stronger than of the verticalgroundwater recharge. Hence, lateral groundwater flowand groundwater–surface water interactions have a majorimpact on the water balance of this catchment.

Another important objective of this study was toanalyse the spatial distribution of the influence of surfacewater conditions on the groundwater dynamics and tounderstand the varying importance of these interactionswith distance to the channel adequately.

Figure 13a shows the spatial distribution of the meanannual groundwater changes for the simulation period1988–2000, which are related to the distance to theriver, the mean river stage fluctuation and soil proper-ties. Figure 13b shows the simulated annual groundwateramplitude for a selected cross-section. The figure illus-trate that, although the minimum and maximum valuesof groundwater stages are rising with increasing dis-tance to the river, the amplitude of groundwater dynamicsdecreases with increasing distance to the channel. Thus,it can be seen again that the model is able to reflect theobserved conditions adequately (Figure 4).

A 13-year period was simulated for analysing thelong-term annual characteristics of the water balance andgroundwater storage dynamics in the lower Havel catch-ment. Thus, meteorological data and surface water stages



2.0

-1.0

1.5

1.0

0.5

0.0

-0.5

1.4

0.2

0.0

1.2

1.0

0.8

0.6

0.4

grou

ndw

ater

bel

ow s

urfa

ce (

m)

grou

ndw

ater

sta

ge v

aria

bilit

y (m

)

0 200 400 600 800 10001200 16001400

distance from river (m)

gwdepth_maxgwdepth_mingw_diff

0 - 0.150.15 - 0.30.3 - 0.450.45 - 0.60.6 - 0.750.75 - 0.90.9 - 1.051.05 - 1.21.2 - 1.351.35 - 1.5

cross section

(a) (b)

gw difference (m)

Figure 13. (a) Spatial distribution of simulated annual groundwater stage variability and (b) maximal (gwdepth max)/minimal groundwater stages(gwdepth min) and variability (gw diff) for a cross-section

2.0

-2.0

0.4

-0.4

-0.1-0.2-0.3

0.30.20.10.0

mea

n gr

ound

wat

erre

char

ge (

m3

s-1 )

grou

ndw

ater

rec

harg

e (m

3 s-1

)

J DF M A M J J A S O Ntime (month)

time (year)

mean groundwater recharge 1988 - 2000

groundwater recharge (1988 - 2000)1.5

1.0

0.5

0.0

-0.5

-1.0

-1.5

88 0089 90 91 92 93 94 95 96 97 98 99

Figure 14. Simulated groundwater recharge for the lower Havel catch-ment for 1988–2000 (top) and mean annual groundwater recharge aver-

age (bottom) over the 13 year period 1988–2000

for interpolation of the river boundary condition for theperiod 1988–2000 were used. Surface water stages at theboundary were interpolated for each month because thetransfer time-step was set to monthly periods. Figure 14shows the dynamics of the groundwater storage by theway of inflows (recharge) and outflows (discharge), aswell as by the mean annual dynamics of the water bal-ance, averaged over the 13-year time period.

As Figure 14 illustrates, groundwater recharge gen-erally occurs from winter to spring. During this timethe surface water stages are normally higher than thegroundwater table and the drains and ditches are wellfilled. Concerning the relation between vertical ground-water recharge and lateral fluxes as shown in Figure 12,it can be assumed that the groundwater recharge dur-ing this period is mainly caused by effective infiltrationof surface water into the groundwater. This tendency isdecreasing until the early summer, when the conditions

begin to reverse and a period characterized by ground-water discharge starts. One explanation for this effect ishigher transpiration losses at this time; but, again takinginto account the results illustrated in Figure 12, it can beconcluded that the most important impact is caused bythe decrease of surface water levels at this time. Drainsbecome empty and are affected by the higher retention ofthe groundwater (Figure 6); larger amounts of exfiltrationfluxes into the surface water occur, which subsequentlycauses a negative water balance during this time. Thisperiod continues until autumn, when the water balancebecomes positive again because of increasing surfacewater levels and subsequently more infiltration out of theriver into the groundwater.

CONCLUSIONS

From the analyses of the results of the experimentalinvestigations of water balance processes, a mainlyqualitative understanding of the specific hydrologicalconditions of the study area was established. It waspossible to demonstrate that even the topographicallyrelatively homogeneous floodplain of the lower HavelRiver is characterized by a complex spatial pattern ofhydrologically relevant parameters, such as hydraulicconductivities or groundwater depths and their annualdynamics. A direct coherence between the decrease ofgroundwater stage fluctuations for increasing surfacewater distances could be shown. It was possible to provethe importance of lateral exchange processes betweengroundwater and surface water for the groundwaterdynamics and for the soil water balance. Furthermore,the floodplain retention capacity for the water balanceof the study area during dry periods could be detected.However, quantitative assumptions based on the analysesof the experimental results are limited.



Based on the experimentally gained knowledge, a con-ceptual model of the hydrological conditions of the studyarea was developed to represent the complex processstructure of the floodplain. For a successful simulation ofthe water balance and groundwater dynamics of the studyarea, it was possible to show that an adequate model hasto deal with the temporal and spatial dynamics of runoffgeneration processes, the interactions between groundwa-ter and surface waters and their variable impacts on thefloodplain water balance, as well as with the effect ofsmall groundwater depths and an effective transpirationcontrol on the soil water balance.

Based on the conceptual perceptions, a coupledmodel system has been proposed that simulates verti-cal groundwater recharge, lateral groundwater transportand exchange fluxes to river networks. The realizationhas been achieved by coupling a finite-difference ground-water model with a deterministic, distributed hydrolog-ical model. It was demonstrated that the coupling ofthese two models is a successful approach to charac-terizing the water balance of a typical lowland catch-ment and to analysing surface water–groundwater inter-actions, including the quantification of transfer fluxes.The model was calibrated successfully using variableratios of riverbed parameters.

It was possible to prove that, although the applica-bility of the TOPMODEL approach as a fundamentalassumption of the model has been limited for lowlandareas, the use of an advanced version of this conceptualapproach is adequate for the simulation of the floodplainwater balance of the study area. Although at least a mini-mum of topographical heterogeneity is required to use theTOPMODEL approach, it could be shown that what isonly given for the moraine areas at the catchment bound-aries and the variation in transmissivity and catchmentsizes, together with the marginal topography of the cen-tral regions, is sufficient to generate heterogeneity of boththe topographic index and the saturation deficit, whichmakes it possible to distribute the available soil mois-ture in the soil water balance part (WASIM-ETH-I) ofthe model.

After successful validation of the model for the lowerHavel River basin (mean NSE D 0Ð82) the model wasapplied for quantitative water balance analyses within thelower Havel River basin.

The importance of lateral exchange processes forthe floodplain groundwater dynamics and water balancecould be quantified. The results of annual water bal-ance simulations prove the impact of variable ground-water–surface water interactions on the change ofgroundwater storage. It was possible to detect thatgroundwater exfiltration and surface water infiltration dooften affect the floodplain water balance simultaneously,whereupon the spatial pattern of exfiltration and infil-tration is mainly determined by the weir management ofriver water stages. An analysis of the annual total ground-water recharges and vertical contributions to the ground-water prove that the overall groundwater dynamics are

mainly controlled by lateral interactions between ground-water and surface waters. The decrease of groundwaterstage fluctuations with increasing surface water distancescould be quantified using perennial water balance simu-lations. These results were additionally evaluated usingthe experimental results obtained.

Considering the promising results of the model val-idation, simulation of the effects of possible land-usechanges and/or river management measures can be per-formed with this model system. Thus, quantitative predic-tions for changes in water balance and boundary fluxesdue to altered environmental conditions can be obtained(Krause et al., 2005).

Future work has to address several problems concern-ing the advancement of the transfer algorithm, includingthe implementation of techniques for adaptive transfertime-step control. Furthermore, additional research needsto be undertaken to address the improvement of water-shed delineation in lowlands and the definition of interac-tion zones between surface water and groundwater in thefloodplain (Krause and Bronstert, 2005). In order to gainan appropriate understanding of the leakage processesbetween surface water and groundwater in distributedwatershed models, it will be necessary to perform andimprove upscaling techniques for the spatially limitedwell-known characteristics of the hyporheic zone (gained,for instance, from tracer experiments from cross-sectionsto stream reaches) to acquire usable information at thecatchment scale.

ACKNOWLEDGEMENTS

This work has been supported by the German FederalMinistry of Education and Research (BMBF). We thankAndreas Bauer and Markus Morgner from Potsdam Uni-versity for extensive fieldwork, data processing and tech-nical assistance. We are very grateful to the reviewers’comments, which greatly improved the manuscript.

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Documents

The impact of groundwater–surface water interactions on the water balance of a mesoscale lowland river catchment in northeastern Germany