From meso- to macro-scale dynamic water quality modelling for the assessment of land use change scenarios

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<ul><li><p>Ecological Modelling 220 (2009) 25432558</p><p>Contents lists available at ScienceDirect</p><p>Ecological Modelling</p><p>journa l homepage: www.e lsev ier .com/</p><p>From m alitthe ass</p><p>Shaochu ed HPotsdam Institu any</p><p>a r t i c l</p><p>Article history:Received 23 JaReceived in reAccepted 19 JuAvailable onlin</p><p>Keywords:Water quality modellingSWIMMeso-scale river basinMacro-scale river basinUncertainty analysisRetention paraLand use chan</p><p>Watausedresu</p><p>he Sa(So</p><p>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 rstly proved to have a reasonable representation</p><p>1. Introduc</p><p>The incrby point soand municiatmosphereity and canof pollutedtreatment ias the mainal., 2002). Ilem and thEuropean Waiming to rriver system</p><p> CorresponE-mail add</p><p>0304-3800/$ doi:10.1016/j.emetersge scenarios</p><p>of the denitrication conditions in six meso-scale catchments. The area of the Saale region was classieddepending on denitrication conditions in soil and groundwater into three classes (poor, neutral andgood), and the distributed parameters were applied. However, the hypothesis about the usefulness ofdistributed retention parameters for macro-scale basins was not conrmed. 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 developmentplans for future.</p><p> 2009 Elsevier B.V. All rights reserved.</p><p>tion</p><p>easing input of nutrients in rivers induced or causedurces (sewage treatment plants, industrial enterprisespal wastewater) and diffuse sources (agricultural land,) often leads to pollution and degradation of water qual-</p><p>cause ecological changes in freshwater. As the dischargewastewater has been notably reduced by wastewater</p><p>n the recent decades, agriculture is generally perceivedsource of nutrient input to rivers in Europe (De Wit et</p><p>n recognition of the importance of water quality prob-e need for integrated management in river basins, theater Framework Directive (WFD) was adopted in 2000,</p><p>estore the good ecological status at the scale of larges until 2015 (EC, 2000).</p><p>ding author. Tel.: +49 331 288 2406.ress: (S. Huang).</p><p>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 fullling 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 simplied conceptual modelshave limited applicability, and are not appropriate for simulation ofsome essential spatially distributed processes. The physically basedmodels are by denition 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</p><p>see front matter 2009 Elsevier B.V. All rights reserved.colmodel.2009.06.043eso- to macro-scale dynamic water quessment of land use change scenarios</p><p>n Huang , Cornelia Hesse, Valentina Krysanova, Frte for Climate Impact Research, P.O. Box 601203, Telegrafenberg, 14412 Potsdam, Germ</p><p>e i n f o</p><p>nuary 2009vised form 5 June 2009ne 2009e 21 July 2009</p><p>a b s t r a c t</p><p>The implementation of the Europeanwater quality situations in streams criver basins. This paper presents thetion within the macro-scale basin of tecohydrological dynamic model SWIMlocate /eco lmodel</p><p>y modelling for</p><p>attermann</p><p>er Framework Directive requires reliable tools to predict theby planned land use changes at the scale of large regional</p><p>lts of modelling the in-stream nitrogen load and concentra-ale river (24,167 km2) using a semi-distributed process-basedil and Water Integrated Model). The simulated load and con-</p></li><li><p>2544 S. Huang et al. / Ecological Modelling 220 (2009) 25432558</p><p>uded i</p><p>input dataparticulateet al., 1996(Krysanovascale waterpromising.models aremajor hydrcesses at thal., 2004; Kret al., 2006)et al., 1997)2004) belonHowever, informed usininformation</p><p>Besides,dynamic prcatchmentsal., 2004; Eibasins withal., 2007; Samodelling astatistical aOne exampconditions ideterministent stationsbasin scale,of describin</p><p>As all netion scheduusually canconsiderablover, the prproblem of</p><p>imilam oublito p</p><p>s lanpouris allbehae waent sFig. 1. An overview of processes incl</p><p>and computation resources for such models is also aconcern, especially for large basin simulations (Lunn). This indicates that using the process-based modelset al., 2005a) of intermediate complexity for the basin-quality assessment may be sufcient and even more</p><p>Numerous studies have proven that the process-basedspatially explicit and sufciently adequate to representological, biogeochemical and vegetation growth pro-e catchment scale (Arnold and Allen, 1996; Chaplot etysanova et al., 2005b; Stewart et al., 2006; Hattermann</p><p>very s(problemany prelatedsuch a(Abbasanalysmodelreliabl</p><p>Rec</p><p>. The models SWAT (Arnold et al., 1998), HSPF (Bicknell, SWIM (Krysanova et al., 1998) and DWSM (Borah et al.,g to the process-based modelling tools for river basins.the most of the published studies the validation is per-g the catchment outlet data only, which provides noon how they perform spatially (Cherry et al., 2008).most of the current catchment scale studies withocess-based models focus on micro-scale and smallranging from 1 to 100 km2 (Du et al., 2006; Chaplot et</p><p>sele et al., 2001 and Bogena et al., 2003) and meso-scalethe drainage area from 100 to 5000 km2 (Abbaspour etleh et al., 2000 and Volk et al., 2008). The process-basedt the macro-scale is very rare in literature, where the</p><p>nd conceptual riverine load models are usually applied.le is by Even et al. (2007), who simulated water qualityn the Seine River basin (78650 km2) using four coupledic models with satisfactory seasonal results at differ-. Since the WFD requires the assessment on large rivermore efforts are needed on improving the capabilitiesg nutrient uxes in large basins.cessary input data, such as crop rotations and fertiliza-le, and parameters of the current process-based modelsnot be precisely dened at the basin scale, there aree uncertainties in the water quality modelling. More-ocess-based water quality models often confront theover-parameterization, which results in the equal or</p><p>that the prosimulate theither evalucurrent magate the oprivers (ZamHowever, thin land usewater qualiios are baseand includedevelopme</p><p>Hence, t</p><p>- to test thmodel SWbasin contion point</p><p>- to investparametr</p><p>- to test thetion paramand to estcatchmenn SWIM.</p><p>r model result using various parameter combinationsf equinality) (Oreskes et al., 1994). There are alreadyshed studies on uncertainty in water quality modellinghysical data such as rainfall (Bertoni, 2001), spatial datad use maps (Payraudeau et al., 2004), and parameterset al., 2007; Hattermann et al., 2006). The uncertainty</p><p>ows to nd the most important factors controlling theviour, and to represent the modelling results in a more</p><p>y.tudies on land use change impacts have demonstrated</p><p>cess-based water quality models are effective tools toe hypothetic land use scenarios. They are helpful toate the long-term impacts of implementation of the</p><p>nagement practices (Santhi et al., 2005), or to investi-timal solutions in order to reduce the nutrients loads inmit et al., 2005; Hesse et al., 2008 and Volk et al., 2008).ese studies did not account for some new tendencies</p><p>, like the inuence of introducing energy plants on thety aspect in the future. In this study the land use scenar-d on various measures of controlling nutrient emissions,</p><p>the potential agricultural practice changes due to thent of the energy market.he objectives of the study were:</p><p>e applicability of the process-based ecohydrologicalIM for simulating nitrogen dynamics in a macro-scale</p><p>sidering the outlet and intermediate gauges as valida-s,igate the uncertainties related to input data andization,</p><p>hypothesis about the usefulness of distributed reten-eters for nitrogen simulation in a macro-scale basin,</p><p>ablish ranges of retention parameters depending on thet characteristics, and</p></li><li><p>S. Huang et al. / Ecological Modelling 220 (2009) 25432558 2545</p><p>- to evaluate impacts on nitrate nitrogen load under the potentialland use changes.</p><p>In this study, both input data and a set of most sensitive param-eters for nanalysis. Thexplain thein a large bimproveme</p><p>2. Method</p><p>2.1. Model S</p><p>The dynand Watermodels SWal., 1989). Uapplied forbasins, and1998; Hatteof processe</p><p>SWIM sisubbasins amentary untypes. Up toIt is assumelogical procin the modesubbasins, a</p><p>Water oevery hydrothe river nereaching thriver netwo</p><p>The simuumes: the sThe waterprecipitatioand percolagroundwateand percola</p><p>Surfaceitation andcontent, lantion Servicesubsurfacepercolationeld capacihaving impmeable onemethod of Pmethod ofActual evapcalculated s</p><p>The modimportant ied EPIC afor simulattoes) and aforest, mixcrop/vegetaspecied fothe model. Vthe cover-sp</p><p>and indirectly controlling transpiration, which is simulated as afunction of potential evapotranspiration and leaf area index (LAI).</p><p>The nitrogen and phosphorus modules include the followingpools in the soil layers: nitrate nitrogen, active and stable organic</p><p>n, orandorus</p><p>chanon, inptakw an</p><p>ountbsurvoluon in</p><p>tranrepreed isubjnd Kwate6):</p><p>Nt,i</p><p>Nt,ouaram</p><p>he deon pre, a</p><p>or thrmanr main m</p><p>genucturlimaitatiosimuturatpe rep typanceabou</p><p>l as thttp:</p><p>alua</p><p>thisSutcl</p><p>(B)(daia meed an</p><p>ibesulat</p><p>im av</p><p>Xo</p><p>obs mulatof thitrogen dynamics were included in the uncertaintye most effective factors revealed in the analysis coulddifculties in simulating nutrient loads simultaneouslyasin and its subbasins, and to suggest the ways for</p><p>nt.</p><p>, study area and data</p><p>WIM</p><p>amic process-based ecohydrological model SWIM (SoilIntegrated Model) was developed on the basis of theAT (Arnold et al., 1993) and MATSALU (Krysanova etntil now, SWIM was intensively tested, validated andsimulating water discharge in meso- to macro-scalewater quality in meso-scale basins (Krysanova et al.,rmann et al., 2006 and Hesse et al., 2008). An overviews included in SWIM is shown in Fig. 1.mulates all processes by disaggregating the basins tond hydrotopes, where the hydrotopes are sets of ele-its in a subbasin with homogeneous soil and land use10 vertical soil layers can be considered for hydrotopes.d that a hydrotope behaves uniformly regarding hydro-esses and nutrient cycles. Spatial disaggregation schemel is exible, e.g. grid cells can be considered instead ofnd hydrotope units instead of sets of, nutrient cycling and plant growth are calculated fortope and then lateral uxes of water and nutrients to</p><p>twork are simulated taking into account retention. Aftere river system, water and nutrients are routed along therk to the outlet of the simulated basin.lated hydrological system consists of three control vol-</p><p>oil surface, the root zone of soil and the shallow aquifer.balance for the soil surface and soil column includesn, surface runoff, evapotranspiration, subsurface runofftion. The water balance for the shallow aquifer includesr recharge, capillary rise to the soil prole, lateral ow,tion to the deep aquifer.runoff is estimated as a non-linear function of precip-a retention coefcient, which depends on soil waterd use and soil type (modication of the Soil Conserva-(SCS) curve number method, Arnold et al., 1990). Lateralow (or interow) is calculated simultaneously with. It appears when the storage in any soil layer exceedsty after percolation and is especially important for soilsermeable or less permeable layer below several per-s. Potential evapotranspiration is estimated using theriestleyTaylor (Priestley and Taylor, 1972), though the</p><p>PenmanMonteith (Monteith, 1965) can also be used.oration from soil and actual transpiration by plants areeparately.ule representing crops and natural vegetation is an</p><p>nterface between hydrology and nutrients. A simpli-pproach (Williams et al., 1984) is included in SWIMing arable crops (like wheat, barley, rye, maize, pota-ggregated vegetation types (like pasture, evergreened forest), using specic parameter values for eachtion type. A number of plant-related parameters arer 74 crop/vegetation types in the database attached toegetation in the model affects the hydrological cycle byecic retention coefcient, inuencing surface runoff,</p><p>nitrogeactivephosphsoil, extilizatiplant uintero</p><p>Ameral suof theretentisurfaceand isdescribperiod(Ksub agroundal., 200</p><p>Nt,out =</p><p>wheret, the p(d1) tretentiliteratuoped f(Hatte</p><p>Fousubbasused toing strdaily cprecipof thesity, sasoil tyent croimportdetailsas welunder</p><p>2.2. Ev</p><p>InNashor loadresults</p><p>E isobserv</p><p>E = 1 </p><p>B descrthe sim</p><p>B = Xs</p><p>Here Xing simvaluesganic nitrogen in the plant residue, labile phosphorus,stable mineral phosphorus, organic phosphorus, and</p><p>in the plant residue, and the uxes (inows to theges between the pools, and outows from the soil): fer-put with precipitation, mineralization, denitrication,e, leaching to groundwater, losses with surface runoff,d erosion.</p><p>s of soluble nutrients (N and P) in surface runoff, lat-face ow and percolation are estimated as the productsme of water and the average concentration. Nitrogen</p><p>subsurface and groundwater occurs during the sub-sport of nitrogen from soil column to rivers and lakes,sented mainly by denitrication. Nitrogen retention is</p><p>n SWIM by four parameters: residence times (the timeect to denitrication) in subsurface and groundwatergrw) and the decomposition rates in subsurface andr (sub and grw) using the equation (Hattermann et</p><p>n1</p><p>1 + K (1 e((1/K)+)t) + Nt1,out e((1/K)+)t, (1)</p><p>t is nitrogen output and Nt,in is nitrogen input at timeeter K represents the residence time in days (d), and composition rate. The term t is...</p></li></ul>


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