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Ecological Modelling 220 (2009) 2543–2558

Contents lists available at ScienceDirect

Ecological Modelling

journa l homepage: www.e lsev ier .com/ locate /eco lmodel

rom meso- to macro-scale dynamic water quality modelling forhe assessment of land use change scenarios

haochun Huang ∗, Cornelia Hesse, Valentina Krysanova, Fred Hattermannotsdam Institute for Climate Impact Research, P.O. Box 601203, Telegrafenberg, 14412 Potsdam, Germany

r t i c l e i n f o

rticle history:eceived 23 January 2009eceived in revised form 5 June 2009ccepted 19 June 2009vailable online 21 July 2009

eywords:ater quality modelling

WIMeso-scale river basinacro-scale river basinncertainty analysisetention parametersand use change scenarios

a b s t r a c t

The implementation of the European Water Framework Directive requires reliable tools to predict thewater quality situations in streams caused by planned land use changes at the scale of large regionalriver basins. This paper presents the results of modelling the in-stream nitrogen load and concentra-tion within the macro-scale basin of the Saale river (24,167 km2) using a semi-distributed process-basedecohydrological dynamic model SWIM (Soil and Water Integrated Model). The simulated load and con-centration at the last gauge of the basin show that SWIM is capable to provide a satisfactory result fora large basin. The uncertainty analysis indicates the importance of realistic input data for agriculturalmanagement, and that the calibration of parameters can compensate the uncertainty in the input data toa certain extent. A hypothesis about the distributed nutrient retention parameters for macro-scale basinswas tested aimed in improvement of the simulation results at the intermediate gauges and the outlet. Toverify the hypothesis, the retention parameters were firstly proved to have a reasonable representationof the denitrification conditions in six meso-scale catchments. The area of the Saale region was classifieddepending on denitrification conditions in soil and groundwater into three classes (poor, neutral and

good), and the distributed parameters were applied. However, the hypothesis about the usefulness ofdistributed retention parameters for macro-scale basins was not confirmed. Since the agricultural man-agement is different in the sub-regions of the Saale basin, land use change scenarios were evaluated fortwo meso-scale subbasins of the Saale. The scenario results show that the optimal agricultural land useand management are essential for the reduction in nutrient load and improvement of water quality tomeet the objectives of the European Water Framework Directive and in view of the regional development plans for future.

. Introduction

The increasing input of nutrients in rivers induced or causedy point sources (sewage treatment plants, industrial enterprisesnd municipal wastewater) and diffuse sources (agricultural land,tmosphere) often leads to pollution and degradation of water qual-ty and can cause ecological changes in freshwater. As the dischargef polluted wastewater has been notably reduced by wastewaterreatment in the recent decades, agriculture is generally perceiveds the main source of nutrient input to rivers in Europe (De Wit etl., 2002). In recognition of the importance of water quality prob-em and the need for integrated management in river basins, the

uropean Water Framework Directive (WFD) was adopted in 2000,iming to restore the “good ecological status” at the scale of largeiver systems until 2015 (EC, 2000).

∗ Corresponding author. Tel.: +49 331 288 2406.E-mail address: huang@pik-potsdam.de (S. Huang).

304-3800/$ – see front matter © 2009 Elsevier B.V. All rights reserved.oi:10.1016/j.ecolmodel.2009.06.043

© 2009 Elsevier B.V. All rights reserved.

Water quality models could provide an important and valu-able support for the assessment and analysis of pollution loadsin river basins and possible measures to improve water quality,and therefore they could be a useful instrument for fulfilling therequirements of the Water Framework Directive. Hence, variouswater quality models for the basin scale were developed and testedin the last decades (Arheimer and Brandt, 1998; Whitehead et al.,1998; Bicknell et al., 1997; Arnold et al., 1998; Krysanova et al.,1998). These models vary in the level of complexity, from statisticaland conceptual models based on statistical and empirical relation-ships to process-based and physically based models derived fromphysical and physicochemical laws and including also some equa-tions based on empirical knowledge. Due to the lack of descriptionof important physical processes, the simplified conceptual modelshave limited applicability, and are not appropriate for simulation of

some essential spatially distributed processes. The physically basedmodels are by definition fully distributed accounting for spatialvariations in all variables. However, these models are often con-sidered too complex without a guarantee of better performance(Beven, 1989; Beven, 1996). The requirement of large amount of

2544 S. Huang et al. / Ecological Modelling 220 (2009) 2543–2558

proces

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Fig. 1. An overview of

nput data and computation resources for such models is also aarticulate concern, especially for large basin simulations (Lunnt al., 1996). This indicates that using the process-based modelsKrysanova et al., 2005a) of intermediate complexity for the basin-cale water quality assessment may be sufficient and even moreromising. Numerous studies have proven that the process-basedodels are spatially explicit and sufficiently adequate to representajor hydrological, biogeochemical and vegetation growth pro-

esses at the catchment scale (Arnold and Allen, 1996; Chaplot etl., 2004; Krysanova et al., 2005b; Stewart et al., 2006; Hattermannt al., 2006). The models SWAT (Arnold et al., 1998), HSPF (Bicknellt al., 1997), SWIM (Krysanova et al., 1998) and DWSM (Borah et al.,004) belong to the process-based modelling tools for river basins.owever, in the most of the published studies the validation is per-

ormed using the catchment outlet data only, which provides nonformation on how they perform spatially (Cherry et al., 2008).

Besides, most of the current catchment scale studies withynamic process-based models focus on micro-scale and smallatchments ranging from 1 to 100 km2 (Du et al., 2006; Chaplot etl., 2004; Eisele et al., 2001 and Bogena et al., 2003) and meso-scaleasins with the drainage area from 100 to 5000 km2 (Abbaspour etl., 2007; Saleh et al., 2000 and Volk et al., 2008). The process-basedodelling at the macro-scale is very rare in literature, where the

tatistical and conceptual riverine load models are usually applied.ne example is by Even et al. (2007), who simulated water qualityonditions in the Seine River basin (78650 km2) using four coupledeterministic models with satisfactory seasonal results at differ-nt stations. Since the WFD requires the assessment on large riverasin scale, more efforts are needed on improving the capabilitiesf describing nutrient fluxes in large basins.

As all necessary input data, such as crop rotations and fertiliza-

ion schedule, and parameters of the current process-based modelssually cannot be precisely defined at the basin scale, there areonsiderable uncertainties in the water quality modelling. More-ver, the process-based water quality models often confront theroblem of over-parameterization, which results in the equal or

ses included in SWIM.

very similar model result using various parameter combinations(problem of equifinality) (Oreskes et al., 1994). There are alreadymany published studies on uncertainty in water quality modellingrelated to physical data such as rainfall (Bertoni, 2001), spatial datasuch as land use maps (Payraudeau et al., 2004), and parameters(Abbaspour et al., 2007; Hattermann et al., 2006). The uncertaintyanalysis allows to find the most important factors controlling themodel behaviour, and to represent the modelling results in a morereliable way.

Recent studies on land use change impacts have demonstratedthat the process-based water quality models are effective tools tosimulate the hypothetic land use scenarios. They are helpful toeither evaluate the long-term impacts of implementation of thecurrent management practices (Santhi et al., 2005), or to investi-gate the optimal solutions in order to reduce the nutrients loads inrivers (Zammit et al., 2005; Hesse et al., 2008 and Volk et al., 2008).However, these studies did not account for some new tendenciesin land use, like the influence of introducing energy plants on thewater quality aspect in the future. In this study the land use scenar-ios are based on various measures of controlling nutrient emissions,and include the potential agricultural practice changes due to thedevelopment of the energy market.

Hence, the objectives of the study were:

- to test the applicability of the process-based ecohydrologicalmodel SWIM for simulating nitrogen dynamics in a macro-scalebasin considering the outlet and intermediate gauges as valida-tion points,

- to investigate the uncertainties related to input data and

parametrization,

- to test the hypothesis about the usefulness of distributed reten-tion parameters for nitrogen simulation in a macro-scale basin,and to establish ranges of retention parameters depending on thecatchment characteristics, and

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to evaluate impacts on nitrate nitrogen load under the potentialland use changes.

In this study, both input data and a set of most sensitive param-ters for nitrogen dynamics were included in the uncertaintynalysis. The most effective factors revealed in the analysis couldxplain the difficulties in simulating nutrient loads simultaneouslyn a large basin and its subbasins, and to suggest the ways formprovement.

. Method, study area and data

.1. Model SWIM

The dynamic process-based ecohydrological model SWIM (Soilnd Water Integrated Model) was developed on the basis of theodels SWAT (Arnold et al., 1993) and MATSALU (Krysanova et

l., 1989). Until now, SWIM was intensively tested, validated andpplied for simulating water discharge in meso- to macro-scaleasins, and water quality in meso-scale basins (Krysanova et al.,998; Hattermann et al., 2006 and Hesse et al., 2008). An overviewf processes included in SWIM is shown in Fig. 1.

SWIM simulates all processes by disaggregating the basins toubbasins and hydrotopes, where the hydrotopes are sets of ele-entary units in a subbasin with homogeneous soil and land use

ypes. Up to 10 vertical soil layers can be considered for hydrotopes.t is assumed that a hydrotope behaves uniformly regarding hydro-ogical processes and nutrient cycles. Spatial disaggregation schemen the model is flexible, e.g. grid cells can be considered instead ofubbasins, and hydrotope units instead of sets of units.

Water flows, nutrient cycling and plant growth are calculated forvery hydrotope and then lateral fluxes of water and nutrients tohe river network are simulated taking into account retention. Aftereaching the river system, water and nutrients are routed along theiver network to the outlet of the simulated basin.

The simulated hydrological system consists of three control vol-mes: the soil surface, the root zone of soil and the shallow aquifer.he water balance for the soil surface and soil column includesrecipitation, surface runoff, evapotranspiration, subsurface runoffnd percolation. The water balance for the shallow aquifer includesroundwater recharge, capillary rise to the soil profile, lateral flow,nd percolation to the deep aquifer.

Surface runoff is estimated as a non-linear function of precip-tation and a retention coefficient, which depends on soil waterontent, land use and soil type (modification of the Soil Conserva-ion Service (SCS) curve number method, Arnold et al., 1990). Lateralubsurface flow (or interflow) is calculated simultaneously withercolation. It appears when the storage in any soil layer exceedseld capacity after percolation and is especially important for soilsaving impermeable or less permeable layer below several per-eable ones. Potential evapotranspiration is estimated using theethod of Priestley–Taylor (Priestley and Taylor, 1972), though theethod of Penman–Monteith (Monteith, 1965) can also be used.

ctual evaporation from soil and actual transpiration by plants arealculated separately.

The module representing crops and natural vegetation is anmportant interface between hydrology and nutrients. A simpli-ed EPIC approach (Williams et al., 1984) is included in SWIM

or simulating arable crops (like wheat, barley, rye, maize, pota-oes) and aggregated vegetation types (like ‘pasture’, ‘evergreen

orest’, ‘mixed forest’), using specific parameter values for eachrop/vegetation type. A number of plant-related parameters arepecified for 74 crop/vegetation types in the database attached tohe model. Vegetation in the model affects the hydrological cycle byhe cover-specific retention coefficient, influencing surface runoff,

ing 220 (2009) 2543–2558 2545

and indirectly controlling transpiration, which is simulated as afunction of potential evapotranspiration and leaf area index (LAI).

The nitrogen and phosphorus modules include the followingpools in the soil layers: nitrate nitrogen, active and stable organicnitrogen, organic nitrogen in the plant residue, labile phosphorus,active and stable mineral phosphorus, organic phosphorus, andphosphorus in the plant residue, and the fluxes (inflows to thesoil, exchanges between the pools, and outflows from the soil): fer-tilization, input with precipitation, mineralization, denitrification,plant uptake, leaching to groundwater, losses with surface runoff,interflow and erosion.

Amounts of soluble nutrients (N and P) in surface runoff, lat-eral subsurface flow and percolation are estimated as the productsof the volume of water and the average concentration. Nitrogenretention in subsurface and groundwater occurs during the sub-surface transport of nitrogen from soil column to rivers and lakes,and is represented mainly by denitrification. Nitrogen retention isdescribed in SWIM by four parameters: residence times (the timeperiod subject to denitrification) in subsurface and groundwater(Ksub and Kgrw) and the decomposition rates in subsurface andgroundwater (�sub and �grw) using the equation (Hattermann etal., 2006):

Nt,out = Nt,in1

1 + K�(1 − e−((1/K)+�)�t) + Nt−1,out · e−((1/K)+�)�t, (1)

where Nt,out is nitrogen output and Nt,in is nitrogen input at timet, the parameter K represents the residence time in days (d), and �(d−1) the decomposition rate. The term �t is the time step. The fourretention parameters are identified within ranges specified fromliterature, and are subject to calibration. This approach was devel-oped for the SWIM model and tested for modelling water quality(Hattermann et al., 2006).

Four maps (Digital Elevation Model, land use map, soil map andsubbasin map) are essential spatial input data for SWIM. They areused to generate the hydrotope classes, basin structure and rout-ing structure, as well as the attributes of subbasins and rivers. Thedaily climate data (minimum, maximum and mean temperature,precipitation, solar radiation and air humidity) is the main driverof the simulation. The soil parameters including texture, bulk den-sity, saturated conductivity, etc. are needed for each layer of eachsoil type represented on the soil map. The parameters for differ-ent crop types and their corresponding fertilization regimes are ofimportance, especially in case of water quality modelling. For moredetails about the model concept, input data, parameters, equationsas well as the GIS interface, an online SWIM manual is availableunder http://www.pik-potsdam.de/members/valen/swim.

2.2. Evaluation of the model results

In this study the non-dimensional efficiency criterium ofNash–Sutcliffe (1970) (E) and relative deviation in water balanceor load (B) were used to evaluate the quality of simulated modelresults (daily water discharge and NO3–N load).

E is a measure to describe the squared differences between theobserved and simulated values using the following equation:

E = 1 −∑

(Xobs − Xsim)2

∑(Xobs − Xobs av)2

(2)

B describes the long-term differences of the observed values againstthe simulated ones in percent for the whole modelling period:

X − X

B = sim av obs av

Xobs av× 100 (3)

Here Xobs means the observed values while Xsim is the correspond-ing simulated value. The variables Xobs av and Xsim av are the meanvalues of these variables for the whole simulation period.

2546 S. Huang et al. / Ecological Modelling 220 (2009) 2543–2558

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Fig. 2. The Elbe river basin and the locations of the target catc

The Nash–Sutcliffe efficiency can vary from minus infinity to. A value of 1 denotes a perfect match of predicted and measuredalues. A value of 0 for the deviation in balance means no differencen amount between the measured and simulated values.

ig. 3. Spatial patterns of 90 percentile NO3–N concentrations in the German part of theThe 90 percentile concentrations at the stations are assumed to represent the average NO

ts and their outlet gauges (The Saale basin is marked by dots).

2.3. Study areas

The main case study area is the Saale river basin in Germany.The Saale is the second largest tributary of the Elbe river (Reimann

Elbe basin estimated from the records at water quality stations from 1990 to 20053–N concentrations of their corresponding catchments).

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nd Seiert, 2001) with the length of 427 km, the catchment areaf 24.167 km2, and average discharge of 115 m3/s (FGG-Elbe, 2004)see the area marked by dots in Fig. 2). The relief as well as the pre-ipitation are heterogeneous. The altitude is between 15 and 1100 mbove the sea level, and the average annual precipitation amountaries between 450 and 1600 mm/year, strongly depending on theelief (FGG-Elbe, 2004). Wide loess areas and low mountain rangesharacterize the Saale catchment. Due to very fertile loess soilsore than two-thirds of the catchment area is used for agriculture.

he land use in the catchment area is represented by agriculture69%), forests and natural areas (23%), settlements (6%), industrynd mining 2% (FGG-Elbe, 2004). The Saale basin covers large partsf two German federal states Thuringia and Saxony-Anhalt, and aart of Saxony.

There is a distinct seasonality within the annual course of theischarge cycle. Snowmelt causes high discharges in March andpril. Then the discharge steadily decreases to its lowest values

n August and September. In the lower reach, weirs and locks haveeen constructed to store the water and to make the river navigable

n the drier summer months. Additionally, the river’s morphologys modified with a series of five reservoirs in the upper course for

ater harvest, flood protection and the salt-load control system. Allhese regulatory measures have a large impact on the hydrologicalegime of the river.

At the time of the German reunification the Saale was a veryutrophied and polluted water body. The region has been sub-ect to rapid economic and social changes since then. Although the

ater quality in the Saale has been progressively improving afterhe reunification due to improvement of sewage treatment, closuref some industries and changes in agriculture practices, the river

s still heavily loaded with nutrients mainly from diffuse sourcesBehrendt et al., 2001). According to the published data, the totalitrogen concentration is not expected to decrease significantly innear future (Theile, 2001) as a result of the relative long reten-

ion time of nutrients in the catchment, and the high proportionf agricultural areas in the basin (FGG-Elbe, 2004). There is stilln increasing trend in nutrient concentrations in the flow direc-ion. This trend is attributed more to the emissions in the middleaale, and less to the emissions in the lower course of the riverLindenschmidt, 2005).

Fig. 3 shows spatial patterns of 90 percentile NO3–N concen-rations in the German part of the Elbe basin, classified accordingo the German Regional Water Association (LAWA, 1998). The 90ercentile concentrations are estimated from the records at wateruality stations from 1990s to 2005, and they are assumed to rep-esent the average NO3–N concentrations of their correspondingatchments. The 90 percentile concentrations below 2.5 mg/L indi-ate small to moderate level of NO3–N in the rivers, and the valuesbove 5 mg/L mean high to very high level. This figure shows howeavily the Saale basin is polluted currently. The lowland catch-ents are notably less polluted.

In addition, seven meso-scale catchments located in the Elbeasin were included in the modelling study, partly to investigate theodel uncertainties and the physical representation of retention

arameters in SWIM, and partly to simulate land use change scenar-os. Their location is shown in Fig. 2 and the general characteristicsre listed in Table 1. They are: the Stepenitz, gauge Wolfshagen;he Nuthe, gauge Babelsberg; the Wipper, gauge Hachelbich; thenstrut, gauge Oldisleben; the upper Saale, gauge Blankenstein;

he Weiße Elster, gauge Greiz; and the Mulde, gauge Meinsberg.he Nuthe is located in the lowland part of the Elbe basin, and

as the lowest average annual precipitation. The upper Saale andulde are located in the mountainous and loess sub-regions with

he highest average annual precipitation compared to other catch-ents. The Stepenitz, Unstrut, Wipper and Weiße Elster have an

ntermediate position between mountains and loess areas, and also Tab

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2548 S. Huang et al. / Ecological Modelling 220 (2009) 2543–2558

Table 2Sequential scheme of the study.

Study procedures Saale (macro-scale basin) Subbasins of the Saale(intermediate gauges)

Meso-scale catchments

Calibration/validation Done Done Done for allUncertainty analysis Done for the Weiße Elster

catchment

Hypothesis(1) Plausibilitytest

Done for the six selectedcatchments

(2) Test ofhypothesis inthe Saale basin

Done Done

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L Done for the Unstrut andWeisse Elster catchments

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Table 3Fertilization data for seven major crops used in the study (except potato and maize,all the crops have two fertilization stages in one year) (Source Voss, 2007).

Crop Nitrogen fertilization

Fertilization dates in one year (julian day) Amount (kg N/ha)

Potato 110 120Silage maize 180 150Summer barley 85, 120 60,20Winter rye 293, 95 40,60

earch for optimal parameter ranges

and use change scenarios

nclude lowlands, and their average annual precipitation is also athe medium level—from 620 to 763 mm/year. The land use charac-eristics are different in the catchments, though agriculture areasccupy significant share of drainage areas in all of them: from 36%o 67%.

.4. Study scheme and data preparation

Table 2 shows the sequential scheme of the study performedn both the macro-scale basin and the meso-scale catchments.irst, the calibration and validation were carried out for all of thetudy areas. The meso-scale Weiße Elster catchment was used forncertainty analysis since it is located in one federal state, and thegricultural management could be identified in accordance withhe state’s statistical data.

The differences in retention parameters obtained after cali-ration for the six selected meso-scale catchments indicate thathe physical representation of these parameters corresponds tohe denitrification conditions in soils and groundwater, i.e. higherecomposition rates are generally found in catchments with a highhare of poorly drained soils like gleysols and histosols (a plausibil-ty test). When the plausibility of the parameters was proved, theistributed parameters were applied in the Saale basin aiming in

mprovement of the simulation results at the last and intermediateauges. After that a search for optimal parameter ranges was doneor the six meso-scale catchments.

Land use change scenarios were applied for the Unstrut (Old-sleben) located in the German federal state Thuringia, and the

eiße Elster (Greiz) located in the federal state Saxony. The landse change scenarios were based on the current and planned agri-ultural practices, which are proved to be major factor affectingitrate input from diffuse sources to surface water (Wade et al.,002).

To setup the model, four raster maps are required. The digi-al elevation model (DEM) provided by the NASA Shuttle Radaropographic Mission (SRTM), the general soil map of the Federalepublic of Germany BÜK 1000 from the Federal Institute for Geo-ciences and Natural Resources (BGR), the land use map (Corine000), and the subbasin map which was generated from the DEMap were used. The Saale drainage basin includes 39 soil types

mong the 72 soil types classified in BÜK 1000 for Germany, andhe chernozems and cambisols are the main soil types in the region.

Climate data (temperature, precipitation, solar radiation andir humidity) were provided by the German Weather Service, and

nterpolated to centriods of the subbasins. Fertilization data foreven major crops in the region (winter wheat, silage maize, pota-oes, summer barley, winter barley, winter rape and winter rye)ere identified taking into account the regional recommendations

see Table 3).

Winter barley 283, 95 40,60Winter wheat 300, 95 40,80Winter rape 257, 95 60, 120

The daily water discharge and fortnightly measurements ofNO3–N concentrations at the last gauge of each catchment obtainedfrom the State Office of Flood Protection and Water ManagementSaxony-Anhalt, Thuringia State Office of Environment and Geol-ogy, Federal Environmental Agency of Brandenburg and SaxonianState Ministry of the Environment and Agriculture were used forthe model calibration and validation.

3. Calibration, validation and uncertainty analysis

The model calibration and validation is a basis for its fur-ther application for environmental and water quality assessment.Usually, hydrological validation is performed first, and then themodel is validated for nutrient dynamics. The simulation peri-ods for each basin are not identical because they are based onthe observed data available for the respective basins. The fort-nightly measurements of nitrate nitrogen are available for severalgauges in the Saale basin during the period 1997–2002, for theNuthe basin between 1990 and 1994 and for the Stepenitz inthe 1980s. In the Mulde basin, only monthly data is available forthe period 1987–1992. For each basin, the period of observationswas divided into two equal sub-periods, and then the first halfperiod was used for the calibration and the second half periodwas used for the validation. The following parameters were usedfor hydrological calibration: the correction factors for evapotran-spitation, curve numbers and saturated conductivity as well asthe baseflow factor, alpha factor for groundwater and two routingcoefficients. For nitrogen load, the residence time and the decompo-sition rates in subsurface and groundwater were used as calibrationparameters.

3.1. Hydrological calibration and validation

To simulate the dynamics of nitrogen load and concentrations,a sufficiently good simulation of water discharge is a prerequi-site. Therefore, calibration and validation of SWIM for all eight

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780.

700.

780.

820.

800.

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S. Huang et al. / Ecological M

atchments was performed firstly for water discharge, and thenor concentrations and loads of nitrogen. Table 4 includes theash–Sutcliffe efficiencies (from 0.7 to 0.84) and the deviations

n water discharge (±3%) for each of the eight basins for the val-dation period. Fig. 4 shows the comparisons of the observed andimulated daily and average monthly discharges at the last waterischarge gauges for three meso-scale catchments (the Nuthe, theeiße Elster and the Unstrut) and the macro-scale basin of the

aale (gauge Calbe-Grizehne). All simulated results have a goodgreement with the observed values. Only the hydrograph for theuthe was reproduced not as well as the others. This is most proba-ly due to the extensive anthropogenic water regulation measures

n this lowland catchment, which transform the natural behaviourf water fluxes. Therefore, the calibration and validation for thisatchment was more difficult. Generally, the efficiencies are sat-sfactory and the deviations in water discharge are low for allatchments.

.2. Calibration and validation for nitrogen dynamics

The calibration and validation of SWIM for nitrogen dynamicsas done for the Saale basin, checking also several intermediate

auges, and in addition separately for seven meso-scale catchmentsfor locations see Fig. 2, and for characteristics see Table 1). Allhe eight basins were firstly calibrated with the “global” (uniqueor the basin) retention parameters: residence times in subsurfacend groundwater (Ksub and Kgrw) and the decomposition rates inubsurface and groundwater (�sub and �grw).

As crop rotation data for the basins are not available, andnly average annual crop shares can be estimated from statis-ical data for the federal states, winter wheat was applied foralibration and validation. It is justified by the facts that it issually one of the dominant crops in the study areas, and itehaves similar to most other winter crops. Based on the rangesf the amount of fertilizers recommended by the local govern-ents (Landwirtschaft und Landschaftspflege in Thüringen, 2001;inisterium für Landwirtschaft und Umwelt in Sachsen-Anhalt,

004; Sächsische Landesanstalt für Landwirtschaft, 2007 and Mlur-randenburg, 2000), the mean rates of fertilizer applications werehosen as input data (Table 3). Input from point sources (wasteater treatment plants and industries) was taken into account

ssuming constant daily rates estimated from the average annualata (source: IKSE, 2005).

Table 4 includes the model validation results: the Nash–Sutcliffefficiencies and the deviations in N load for the Saale and seveneso-scale catchments. Only the last gauge of the Saale basinas calibrated and validated, and results for several intermediate

auges (not calibrated) were also compared. The efficiencies for sev-ral intermediate subbasins of the Saale are listed in Table 5. As anxample, Fig. 5 shows the comparison between the observed andimulated nitrate N loads and concentrations for the Saale basinnd four meso-scale catchments.

One can see that the observed seasonal dynamics: higher con-entrations and loads in winter season and lower in summer areeproduced by SWIM quite well. However, the high peaks in concen-ration are often underestimated, and in general the load dynamicss reproduced better than concentrations.

The efficiency at the last gauge of the Saale basin is 0.70, indi-ating a good simulation result for this macro-scale basin. Theimulated nitrate loads in seven meso-scale catchments (Table 4)

re in a good agreement with the observed data, with theash–Sutcliffe efficiencies varying from 0.52 to 0.72. The efficien-ies at the intermediate (not calibrated) gauges of the Saale areower: only in 50% of all cases the efficiency is higher or equal 0.49Table 5). Ta

ble

4Th

eN

ash

–Su

tcl

Wat

erd

isch

arge

NO

3–N

load

2550 S. Huang et al. / Ecological Modelling 220 (2009) 2543–2558

Fig. 4. Comparison of the observed and simulated water discharges (left) at gauges Babelsberg (River Nuthe), Oldisleben (River Unstrut), Greiz (River Weiße Elster) andCalbe-Grizehne (River Saale) for calibration and validation periods, and the comparisons of the average monthly discharges for the whole period (right).

Table 5Comparison of the efficiencies for the simulated NO3–N load at the main gauge and intermediate gauges in the Saale basin using global and distributed parameters.

Rivers Gauges Global parameters efficiency Distributed parameters efficiency

Saale Groß-Rosenburg 0.7 0.68Saale Halle-Trotha 0.52 0.51Saale Naumburg 0.53 0.58Saale Rudolstadt 0.42 0.51Ilm Niedertrebra 0.32 0.24Unstrut Freyburg 0.67 0.63Weiße Elster Halle-Ammendorf 0.49 0.47Wipper Hachelbich 0.38 0.35Saale Blankenstein 0.44 0.39Schwarza Schwarzburg 0.24 0.33Weiße Elster Greiz 0.13 0.15Unstrut Oldisleben 0.58 0.54

S. Huang et al. / Ecological Modelling 220 (2009) 2543–2558 2551

F tionsW comp

ddwq

ig. 5. Comparison of the observed and simulated NO3–N loads (left) and concentraeiße Elster), Oldisleben (River Unstrut), and Calbe-Grizehne (River Saale) (a), and

The uncertainty in crop type and fertilization rates and scheduleefinitely influence the simulation results, and therefore it is moreifficult to reproduce the observed nutrient dynamics as good asater discharge, and the calibration and validation results for water

uality in terms of Nash–Sutcliffe efficiency are lower than that for

(right) at gauges Babelsberg (River Nuthe), Hachelbich (River Wipper), Greiz (Riverarison of the average monthly dynamics for the Calbe-Grizehne (River Saale) (b).

water discharge. It is difficult to reproduce better the high valuesduring extreme events for all the cases. The underestimation of thenutrient load may be due to unrealistic input data, improper settingof the model parameters, or incorrect simulation of nitrogen trans-fer processes during the extreme events like floods. Therefore, an

2552 S. Huang et al. / Ecological Modelling 220 (2009) 2543–2558

Table 6Parameter ranges used in the Monte-Carlo simulations for the uncertainty analysis and search for the retention parameters ranges.

Parameters Parameter description Parameter rangesa for random number generation

Minimum Maximum

Ksub (day) Residence time of nitrate in subsurface 1 500Kgrw (day) Residence time of nitrate in groundwater 30 5000�� ter

s used

uih

3

lcpgam

mwt

FS

sub (1/day) Decomposition rate of nitrate in subsurfacegrw (1/day) Decomposition rate of nitrate in groundwa

a The parameters ranges were obtained from extending all the parameter setting

ncertainty analysis related to model parameters and input data ismportant in order to quantify these sources of uncertainty. It canelp to find ways to improve the model performance.

.3. Uncertainty analysis

As mentioned above, the assumption of monoculture on arableand and the fertilization schedule as in the recommended tablesannot fully represent the reality. A number of sensitive modelarameters are also taken from literature, where they are often sug-ested as ranges. Therefore, the uncertainty analysis was performedimed at the investigation of the separate effects of input data and

odel parameters on the resulting simulated nitrogen dynamics.

The uncertainty analysis was done for the Weiße Elster catch-ent. It considered all major crops in the region: winter barley,inter rye, winter rape, maize, summer barley and potatoes with

heir specific fertilization schemes. In accordance with regional

ig. 6. The uncertainty corridors for NO3–N loads related to (a) input data (different cropWIM; and (d) input data and parameters.

0.0001 0.50.0001 0.3

for the calibration and validation for the eight basins.

data (Table 3), winter crops are fertilized twice a year, once inautumn and the second time in spring, while the summer cropsare mainly fertilized shortly after plantation. The simulated resultsfrom validation (only with winter wheat) were treated as the baserun for the uncertainty analysis. Then the crop type was changedto one of the major crops for each single run with all the otherinput data and parameter fixed. Apart from different crop types,the uncertainty related to fertilization activities is also important,as the time and the amount of fertilizer applications are recom-mended as ranges by the local authorities. Hence, several singleruns with variation of fertilization rates 50% higher or 50% lowerthan the recommended ones, and time of fertilizer application two

weeks earlier or later for winter wheat (all other parameters arefixed), were also included in the uncertainty analysis.

Regarding the parameters, the simulated nitrogen dynamics ismost sensitive to the retention times and decomposition rates insubsurface and groundwater, therefore they are the main calibra-

s); (b) input data (fertilization regime for winter wheat); (c) six parameters used in

S. Huang et al. / Ecological Modelling 220 (2009) 2543–2558 2553

Table 7Soil classification for denitrification properties according to Wendland et al. (1993) (“−”: poor denitrification condition; “o”: neutral denitrification condition; “+”: gooddenitrification condition).

Soil type Soil water Nutrients Temperature PH Total evaluation

Podsol − − − − −Dystric Cambisol o − − − −Spodic Luvisoils o − − − −Cambisol (insufficient base nutrients) −o o − − −Lithic Leptosols − − − − −Calcaric Regosols o o+ o+ + oRendzina o o+ o + oCambisol (sufficient base nutrients) o o+ o o+ oHaplic Luvisols o o+ o −o+ oStagnic Gleysols + − o− o− +Haplic Chernozems o+ + + + +Stagnic Gleysols + −o o− o− +GFED

ttntiwwlaclacdbba

ttfcttmtfbtotsattr

FcvrwMtb

t

leysols + +luvisols + +utric Histosols + +ystric Histosols + +

ion parameters in SWIM for water quality, and uncertainty relatedo them was quantified by Monte-Carlo simulations. The randomumbers for these four parameters were generated by triangle dis-ributions within the pre-defined ranges (Table 6). Keeping othernput data and parameters the same, the model was run 500 times

ith different combination of the random numbers, and the resultsith the Nash–Sutcliffe efficiency above 0.5 and deviation in load

ess than 10% were included in the resulting uncertainty bound-ries. In addition, the mineralization rate (cmn), and soil waterontent as a threshold for denitrification (deth), which occurs underow oxygen conditions caused by high water content in soil, werelso taken into account. Four single runs were carried out withhanged cmn (50% higher or lower than the original setting) oreth (10% higher or lower than the original setting). The uncertaintyoundaries related to parameters include all the results producedy the Monte-Carlo simulation for the four retention parametersnd the single runs for the two additional parameters.

Fig. 6 shows the uncertainty boundaries, which are formed byhe maximum and minimum results of the single runs relatedo input data: different crops (Fig. 6(a)) and fertilization regimeor winter wheat (Fig. 6(b)). The uncertainty related to crops wasaptured by modelling all main crops in this basin and assuminghat half of the cropland is taken by one of the major crops, andhe rest—by winter wheat. Most of the crops, such as potatoes,

aize, winter barley, summer barley and winter rye, do not changehe simulation results in terms of nitrate concentrations and loadrom that with winter wheat significantly. However, winter rapeehaves quite different from the other crops. It can lead not onlyo higher nitrate N load in the river, but also increases the numberf extremely high load values. The upper boundary of the uncer-ainty is mostly resulted from the simulation with winter rape. Iteems like the major reason of this distinct behaviour is the highermount of fertilizers applied to this crop. The uncertainty relatedo the fertilization activities of winter wheat shows that the varia-ions in fertilization rates and timing do not change the simulationesults significantly, at least for the assumed changes.

The uncertainty related to the model parameters is shown inig. 6(c). Unlike the uncertainty related to crops, the uncertaintyorridor is narrower, and it includes all the low measured NO3–Nalues. The simulated nitrogen dynamics is more sensitive to theetention times and decomposition rates in subsurface and ground-ater, as the uncertainty corridor is mostly resulted from the

onte-Carlo simulation. These results also show that the calibra-

ion of parameters can partly compensate the uncertainties causedy input data.

When the uncertainty of parameters and input data (fertiliza-ion and crops) are accounted together (see Fig. 6(d)), the wide

− o +o + +o + +o − +

boundaries contain almost all of the measured NO3–N loads andconcentrations. The results of the uncertainty analysis indicate thatagricultural activities have significant influence on simulated nitro-gen dynamics, especially on the extremely high loads. Hence, morerealistic input data would definitely help to improve the results.

4. Testing the hypothesis about distributed retentionparameters

It was proven that the set of global retention parameters, whichare the main calibration parameters in SWIM for water quality, aresufficient to model water quality in meso-scale catchments. Theresults for the Saale basin show that SWIM with the global (uniquefor a basin) parameter settings is capable to reproduce the nitratedynamics at the last gauge of the basin, but some improvement forintermediate gauges is still needed. Therefore, it was decided to testthe hypothesis about distributed retention parameters in macro-scale basins. The idea was to apply distributed retention parameterscorresponding to real denitrification conditions in soils and ground-water instead of global retention parameters, and to check how thisinfluences simulation results in the last and intermediate gauges ina large basin. The potential improvement of results in the interme-diate gauges was supposed to improve the result at the last gaugeas well.

Wendland et al. (1993) evaluated the denitrification conditionsin soils and groundwater in Germany. They analyzed differentsoil properties, classified soils into three denitrification conditionsclasses (Table 7), and estimated the residence time in groundwa-ter based on the hydrogeological conditions. Their results indicatethat in large river basins the denitrification condition in soil andgroundwater can vary from very poor (e.g. in mountainous areas)to very good conditions (e.g. in lowland).

According to the classification of denitrification conditions intothree classes by Wendland et al. (1993), the four nitrogen retentionparameters in SWIM (residence times Ksub and Kgrw and decom-position rates �sub and �grw) were increased to 12 to representthe denitrification process in different soil and groundwater con-ditions. For example, the denitrification rate in groundwater canbe described by three parameters: �grw1 in poor conditions, �grw2in neutral conditions and �grw3 in good conditions instead of oneglobal parameter �grw. To verify the hypothesis, the physical repre-

sentation of retention parameters in SWIM should be firstly verifiedfor each denitrification condition (plausibility test). Unfortunately,it was impossible to find three sub-catchments in the Saale belong-ing each to one of the three classes and having hydrological andwater quality gauges. Therefore, the six meso-scale catchments

2554 S. Huang et al. / Ecological Modelling 220 (2009) 2543–2558

Table 8Characteristics of the denitrification conditions in six meso-scale catchments (derived by overlaying maps using classification from Wendland et al., 1993).

SaaleBlankenstein

StepenitzWolfshagen

NutheBabelsberg

WipperHachelbich

WeißeElster Greiz

MuldeMeinsberg

Denitrificationconditions insoils

Soil Class 1 (poor condition) 78% 7% 14% 29% 77% 85%

Soil Class 2 (neutral condition) 18% 85% 49% 36% 19% 3%Soil Class 3 (good condition) 4% 8% 37% 35% 4% 12%

Residence timein groundwater

Minimum residence time (year) – 1–5 5–10 0.1–1 – –

Maximum residence time (year) – 5–60 30–400 1 – –

Decompositionrate

In groundwater Limited tonegligible

Unlimited tolimited

Unlimited Negli. Limited Limited tonegligible

Table 9Retention parameter values obtained for six meso-scale catchments (plausibility test).

Saale Blankenstein Stepenitz Wolfshagen Nuthe Baberlsberg Wipper Hachelbich Weiße Elster Greiz Mulde Meinsberg

KK��

wwOac

4

bthcdgsifmsW

shcctpol

4

vtmitcfid

part of the Saale basin also cause difficulties and may influencethe results. Moreover, there could be uncertainties in the denitrifi-cation map (Wendland et al., 1993) used for the parameter setting.Finally, a lack of management information is also a problem in thisbasin which includes parts of three federal states. Different agricul-

sub (day) 28 10 230grw (day) 50 141 1100sub (1/day) 0.0001 0.1 0.14grw (1/day) 0.0001 0.006 0.04

ith relatively homogenous or mixed denitrification characteristicsere selected to test the plausibility of the retention parameters.nly when the global parameters were proved to comply with thectual conditions in six catchments, the distributed parametersould be applied for the Saale basin to test the hypothesis.

.1. Testing the plausibility of the retention parameters

Table 8 lists the denitrification characteristics in these subbasinsased on the estimations of Wendland et al. (1993). The basins inhe mountainous area (Mulde, upper Saale and Weiße Elster) have aigh share of areas with denitrification class 1 (poor denitrificaitononditions). The Nuthe in lowland has the best denitrification con-itions, and the Stepenitz has neutral condition in both soil androundwater. The Wipper has mixed denitrification conditions inoil, but very poor condition in groundwater. The residence timen groundwater estimated by Wendland et al. (1993) was availableor part of the study areas, and shows an increasing trend from

ountains to lowland. Generally, the decomposition conditions inoil and groundwater are consistent in the study cases, except the

ipper.The obtained values of retention parameters for the six meso-

cale catchments (see Table 9) show that parameters used in SWIMave an adequate physical representation of the actual retentiononditions. For example, the parameters obtained for the Nuthean be considered as typical values for good denitrification condi-ion, and the lower parameters for the mountainous area representoor denitrification conditions. This result indicates the possibilityf testing the hypothesis about distributed retention parameters in

arge basins.

.2. Testing the hypothesis

Instead of four global retention parameters calibrated in the pre-ious study, 12 distributed parameters were used to indicate thehree-denitrification conditions. Due to lack of more detailed infor-

ation on groundwater in this study, the denitrification conditions

n groundwater were assumed to be the same as in soils, sincehey were generally consistent in most of the meso-scale cases. Thealibration process was based on the assumption that good denitri-cation conditions should have longer residence time and higherecomposition rates than the neutral and poor conditions, and the

4 8 1447 496 108

0.0003 0.019 0.0180.0001 0.0012 0.0001

calibration process became much more complex due to the largernumber of parameters.

After the calibration with distributed parameters, it was foundthat the result at the last gauge is very similar to that with theglobal parameters (see Fig. 7). The results in terms of Nash–Sutcliffeefficiency were improved only for one third of the intermediategauges (see Table 5 for the comparison of efficiencies), while theresults for the rest were the same or even worse. The use of thedistributed method did not improve the results as expected. Theconclusion is that the hypothesis about the usefulness of distributedparameters for macro-scale basins was not confirmed in the Saalebasin.

One reason can be a lack of detailed information on decom-position conditions in groundwater. The assumption of the samedenitrification characteristics in soils and groundwater may be notappropriate, because they are not always identical (e.g. in the Wip-per basin). The retention time from the south mountainous areas tothe north lowland areas in the Saale is hardly distinguishable onlyby the soil class, and elevation and other geographic characteris-tics may also play important roles and influence the result. Besides,SWIM is not suitable for simulating specifically hydrological pro-cesses in the karstic areas. So, the karstic areas in the northern

Fig. 7. Comparison of the NO3–N load at the gauge Calbe-Grizehne (River Saale)using global and distributed parameter settings.

S. Huang et al. / Ecological Modelling 220 (2009) 2543–2558 2555

tory r

ti

iigvmStt

5b

bocptdput

ub

TT

P

KK��

Fig. 8. Results of the Monte-Carlo simulation: parameter ranges giving satisfac

ure policies and activities may influence actual nitrogen wash-offn parts of the basin, and affect the results.

Hence, the distributed parameter setting does not help tomprove the modelling results in the macro-scale basin andts intermediate gauges. The actual denitrification condition inroundwater, which can be set for each hydrotope, may help toerify the hypothesis further in this or another basin. However, asore detailed information for groundwater is not available for the

aale, the global parameters should be still applied in SWIM, sincehey are sufficient to reproduce nitrogen dynamics in most parts ofhe basin.

. Search for retention parameter ranges in meso-scaleasins

Although the hypothesis of the distributed parameters in largeasins was not confirmed, the different retention parameter valuesbtained for the meso-scale basins can still indicate some distinctharacteristics in different sub-regions. Hence, a search for realisticarameter ranges could be helpful for inducting the parameter set-ings in the future modelling studies. As the simulated nitrogenynamics is most sensitive to the retention times and decom-osition rates in subsurface and groundwater (concluded from

ncertainty analysis), only these four parameters were included inhe search.

The same Monte-Carlo simulation procedure as described in thencertainty analysis was carried out for each of six meso-scaleasin: looking for the results with the Nash–Sutcliffe efficiency

able 10he suggested parameter ranges for retention parameters in mountainous and mixed are

arameters Parameter description M

M

sub (day) Residence time of nitrate in subsurface 1grw (day) Residence time of nitrate in groundwater 40sub (1/day) Decomposition rate of nitrate in subsurface 0grw (1/day) Decomposition rate of nitrate in groundwater 0

esults for each of six meso-scale basins (a) Ksub; (b) Kgrw; (c) �sub and (d) �grw.

above 0.5 and deviation in load less than 10%. The parameter rangesused for generating random parameter combinations are also thesame as listed in Table 6. The obtained optimal parameter rangesfor each basin are shown in Fig. 8.

Similar results were obtained as in the plausibility test. TheNuthe catchment located in lowland area has much higherresidence times and decomposition rates than the other five catch-ments. The ranges of residence time in soil have no clear distinctionsfor the catchments located in the mountainous and mixed areas.They seem to be determined not only by the relief of the basin, butalso by soil properties. The ranges of residence time in groundwatercomply with the minimum residence time estimated by Wendlandet al. (1993) (compare with Table 8). The decomposition parametershave a good fit with the soil and groundwater conditions estimatedby Wendland et al. (1993). Namely, higher decomposition valuescorrespond to better denitrification conditions. Since the param-eter ranges for mountainous and mixed catchments are similar,only two sets of optimal parameter ranges can be suggested forfurther studies: one for the lowland areas, and one for all others(Table 10).

6. Land use/land management change

As discussed above, agricultural land use may influence thenitrate concentration and load significantly. Land use and land man-agement scenarios were applied not to the whole Saale basin, whichoverlaps with three federal states with their different policies, butto two of its meso-scale subbasins. One is the Unstrut basin (gauge

as and lowlands.

ountainous and mixed areas Lowland areas

inimum Maximum Minimum Maximum

100 100 300600 1000 4000

.0001 0.1 0.1 0.3

.0001 0.03 0.03 0.05

2556 S. Huang et al. / Ecological Modelling 220 (2009) 2543–2558

cenar

OWotp

crtNwsl7aL

Fig. 9. Deviations in nitrate N loads for different land use s

ldisleben) located almost fully in Thuringia, and the second is theeiße Elster (gauge Greiz) located almost fully in Saxony. The main

bjective was to estimate potential changes in NO3–N load underhe development trend and policies, and to find better managementractices leading to a reduced nitrogen load.

Due to the complexity of crop rotations, only the shares of majorrops were considered in the reference scenario. According to theeport of the Thuringian Ministry for Agriculture, Nature Protec-ion and Environment (Thüringer Ministerium für Landwirtschaft,aturschutz und Umwelt, 2007), the major crops in the region are:inter wheat (42%), winter rape (21%), winter barley (12%) and

ummer barley (11%). The arable land in Saxony includes the fol-owing major crops: 33% of winter wheat, 19% of winter barley,% of summer barley, 27% of winter rape and 8% of winter ryeccording to the statistical data (Staatsministerium für Umwelt undandwirtschaft, 2007). These main crops were used for the refer-

Fig. 10. Deviations in nitrate N loads for different land use scena

ios compared to the reference in the Weiße Elster (Greiz).

ence period considering climate data for the period from 1995 to2002.

According to the information in regional reports, the land usechange scenarios were based on the development trend of the worldenergy market and environmental protection targets. In the reportof the Thuringian Ministry (2007) the biogas production from win-ter rape is highlighted, and the agricultural area for winter rape isgrowing since 2001. Gömann et al. (2007) estimated that the energymaize will occupy more than 10% of agricultural land in Saxony-Anhalt, Thuringia and Saxony by the end of 2020. Therefore, anincrease of areas under maize and winter rape was included in the

scenarios. On another hand, various measures are applied in theregion aimed in environmental protection, namely, in reduction ofnitrogen input to surface water and groundwater. These measuresinclude ecological agriculture, optimal fertilization schemes, etc.Therefore, an increase and decrease of organic or mineral fertilizers

rios compared to the reference in the Unstrut (Oldisleben).

odell

wii

crichawsaiCliwpTttnasapN

7

qIrimtmhtotsfdatt

fd

-

-

-

-

rt

S. Huang et al. / Ecological M

ere analyzed in the study. In addition, the role of cover crops andncrement and reduction in agricultural land were also consideredn the scenarios.

Fig. 9 (Weiße Elster,) and Fig. 10 (Unstrut) show the simulatedhanges in nitrate nitrogen loads for each scenario compared to theeference one. According to the results, such measures as increas-ng areas under winter rape, higher fertilizer rates, excluding coverrops and converting pasture to agriculture land would lead toigher nitrogen loads, whereas lower fertilizer rates, set-aside ofgricultural land and planting more maize instead of winter rapeould reduce the nitrogen load. Mineral fertilizers have a much

tronger effect on the nitrate load than organic fertilizers, so thedjustment of mineral fertilizer rates and timing to the plant needss essential for the control of nitrogen input into the surface waters.over crops, which play an important role in reduction of nitrate

osses from fields, should be maintained in cropland, and the plant-ng area of winter rape should not be enlarged significantly in areas,

here environmental targets are important. As another energylant, maize has a moderate effect on the water environment.his advantage should be accounted considering biofuel produc-ion and environmental quality plans in future. In the Unstrut basin,he changes associated with the land use scenarios are more pro-ounced than in the Weiße Elster basin. This is because of the largerrea of cropland in the Unstrut, and the different crop types. Thecenario studies revealed how important an optimal agriculturalctivity is. A good planning based on the modelling results can com-ile with the development priorities, and significantly reduce theO3–N loads in the future.

. Conclusions

The study was focused on the upscaling of dynamical wateruality modelling from meso-scale to macro-scale river basins.

t included uncertainty analysis, search for retention parameteranges depending on the catchment characteristics, and test-ng of the hypothesis about distributed retention parameters in

acro-scale basins. The study has demonstrated that the reten-ion parameters reflect the actual denitrification conditions in the

eso-scale subbasins. The uncertainty analysis revealed that someighly fertilized crops as winter rape could be the main driver ofhe high nitrate loads in the river system, and that the calibrationf parameters can compensate for the uncertainty in the input datao a certain extent. The results indicate the importance of the con-ideration of real crop rotations and parameter uncertainties in theuture studies. Two sets of optimal parameter ranges for differentenitrification conditions were obtained; one for the lowland areasnd one for all others. These ranges have an appropriate representa-ion of the actual conditions, and they provide suggestions on howo set and calibrate parameters for each condition.

The hypothesis about the usefulness of distributed parametersor macro-scale basins was not confirmed. This could be due toifferent possible reasons, like:

uncertainties in the denitrification maps in Wendland et al. (1993)used for parameter settings,an assumption that denitrification conditions in soil and ground-water belong to the same class,karstic areas in a part of the Saale basin notably influencing hydro-logical conditions, anda lack of detailed information on agricultural and water manage-

ment.

In any case, though the hypothesis was not confirmed, theesults for the macro-scale basin of the Saale with the global reten-ion parameters were sufficiently good, and comparable with the

ing 220 (2009) 2543–2558 2557

results for meso-scale basins. The calibration of global retentionparameters should be considered as appropriate for meso-scale andmacro-scale basins.

The land use change scenario study shows how important is theinfluence of agricultural policy on water quality in the rivers. Theplanting area of winter rape should not be enlarged significantly inareas, where environmental targets are important. Another energyplants, like maize, have a moderate effect on the water environ-ment, and should be considered as an alternative of winter rape forbio-energy production. Rates and timing of mineral fertilizers appli-cation are very important factors influencing the nitrate loads in therivers. Hence, the optimal agricultural land use and managementare essential for the reduction in nutrient loads and improvementof water quality.

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