challenges in modeling hydrologic and water...

ABSTRACT: This paper presents key challenges in modeling waterquality processes of riparian ecosystems: How can the spatial andtemporal extent of water and solute mixing in the riparian zone bemodeled? What level of model complexity is justified? How can pro-cesses at the riparian scale be quantified? How can the impact ofriparian ecosystems be determined at the watershed scale? Flexiblemodels need to be introduced that can simulate varying levels of hillslope-riparian mixing dictated by topography, upland and ripariandepths, and moisture conditions. Model simulations need to accountfor storm event peak flow conditions when upland solute loadings mayeither bypass or overwhelm the riparian zone. Model complexityshould be dictated by the level of detail in measured data. Model algo-rithms need to be developed using new macro-scale and meso-scaleexperiments that capture process dynamics at the hillslope or land-scape scales. Monte Carlo simulations should be an integral part ofmodel simulations and rigorous tests that go beyond simple timeseries, and point-output comparisons need to be introduced. Theimpact of riparian zones on watershed-scale water quality can beassessed by performing simulations for representative hillslope-riparian scenarios.(KEY TERMS: ecosystem models; riparian zones; nonpoint source pol-lution; best management practices (BMPs); water quality models;hydrology models; buffers.)

Inamdar, Shreeram, 2006. Challenges in Modeling Hydrologic andWater Quality Processes in Riparian Zones. Journal of the AmericanWater Resources Association (JAWRA) 42(1):5-14.

INTRODUCTION

Research over the past two decades has shown thatunder specific hydrologic conditions, riparian zonesare effective in filtering sediment and nutrients fromupland runoff (Lowrance et al., 1984, 1997; Peterjohnand Correll, 1984; Hill, 1996; Burt, 1997; Cirmo andMcDonnell, 1997; Correll, 1997; Lowrance, 1998).

This recognition has led to widespread use of riparianzones as best management practices (BMPs) for pollu-tion control (Welsch, 1991). The use of riparian zonesas BMPs in turn has led to the need for a quantitativemodel that could be used to determine the size andwidth of riparian zones for nutrient reduction. Inresponse to this need, the U.S. Department of Agricul-ture-Agricultural Research Service (USDA-ARS)developed the Riparian Ecosystem ManagementModel (REMM) (Lowrance et al., 2000; Altier et al.,2002) as a tool to design riparian buffers. The repre-sentation of the riparian system in REMM was guidedby the riparian transect studies in Georgia and else-where and the three-zone buffer scheme proposed byUSDA (Welsch, 1991) for riparian buffers. Theassumptions, algorithms, and water and solute mix-ing schemes used in REMM were essentially based onthe technology that has been in use in hydrologic andwater quality models for the past two or threedecades.

However, in recent years the understanding of themovement and fate of water and solutes in water-sheds has seen tremendous advances, especially fromthe combined use of hydrometric, chemical, and iso-topic methods and more innovative application of fieldtechniques (Hill, 1993; Bohlke and Denver, 1995; Ver-chot et al., 1997a,b; Devito et al., 2000; McGlynn andMcDonnell 2003a,b; Vidon and Hill, 2004). Insightsfrom these studies suggest that the numeric modelingschemes in use are antiquated and do not match newunderstandings of solute mixing and transport. It isclear that there is a need to reevaluate models in lightof the new data and develop new modeling approach-es that better fit the improved understanding of

1Paper No. 04159 of the Journal of the American Water Resources Association (JAWRA) (Copyright © 2006). Discussions are open untilAugust 1, 2006.

2Assistant Professor, Great Lakes Center and Geography, SUNY College at Buffalo, 1300 Elmwood Avenue, Buffalo, New York 14222 (E-Mail: [email protected]).

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 5 JAWRA

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATIONFEBRUARY AMERICAN WATER RESOURCES ASSOCIATION 2006

CHALLENGES IN MODELING HYDROLOGIC AND WATERQUALITY PROCESSES IN RIPARIAN ZONES1

Shreeram Inamdar2

solute transport processes. This paper explores thechallenges in modeling riparian ecosystems for thepurpose of estimating the size and width of riparianbuffers required to meet water quality objectives. Keyquestions addressed in this paper include the follow-ing. How can the spatial and temporal extent of waterand solute mixing in the riparian zone be modeled?What level of model complexity is justified? How canprocesses at the riparian scale be quantified? How canthe impact of riparian ecosystems be determined atthe watershed scale?

SPATIAL CHARACTERIZATIONAND MIXING REGIME

One of the first questions in riparian modeling is –how can the riparian store and its individual compart-ments be defined or characterized? In REMM, a hillslope-scale model, the riparian zone was charac-terized using a sequence of three reservoirs extendingfrom the upland contributing field to the stream edge(Figure 1). This reservoir scheme followed the three-zone BMP scheme recommended by USDA (Welsch,1991), and each reservoir was assumed to represent adifferent vegetative zone. Each reservoir was furtherdivided in the vertical direction into three soil layers.Upland runoff is sequentially routed through each ofthese reservoirs at a daily time step with the assump-tion of complete water and solute mixing and equili-bration within each reservoir and soil layer. Thisscheme followed the classic completely mixed reser-voir approach so prevalent in most hillslope andwatershed scale models. This scheme may have beenappropriate for the mildly sloping, sandy, coastalplains of Georgia where upland water moves slowlythrough the riparian zone but may not reflect themixing possibilities in steeper terrain. Results fromisotopic and chemical tracer studies from moderate tosteep landscapes suggest that upland and riparianwaters may not mix completely. Recent work in aforested watershed near Buffalo, New York, withsteep hillslopes and narrow valley bottom riparianareas indicated that surface runoff from the hillslopesmoved rapidly over the moist surface of the riparianareas with little interaction with subsurface riparianwaters (Inamdar and Mitchell, 2006). The valley bot-tom riparian areas were maintained at elevated mois-ture conditions by upwelling regional ground waters.Local ground water was released at hillslope seepsthat moved downslope rapidly due to the steep slopegradients. The seep waters then traversed the ripari-an zone along streamlets with some infiltration loss in the riparian zones. The quick transfer andlarge contributions of seep waters to the streams

essentially resulted in decoupling of riparian groundwaters from the stream, especially during elevatedsoil moisture conditions. Similar observations haveearlier been reported by Hill (1993), McGlynn andMcDonnell (2003 a,b), and Vidon and Hill (2004).McGlynn and McDonnell (2003b) found that for smallevents and during early parts of the event, streamwater was composed predominantly of pre-eventriparian waters, but the proportion of hillslope andupland runoff contributions increased for largeevents.

These studies highlight the importance of the avail-able storage in the riparian reservoir vis-à-vis uplandloadings in determining the mixing regime for waterand solutes and their eventual expression in stream-flow. Recently, Vidon and Hill (2004) characterized thepotential for mixing of upland and riparian watersand consequent nitrogen removal capacity in terms ofthe upland slope gradients and the depth of the per-meable sediments in the upland and riparian zones(Figure 2). Mixing scenarios were proposed by Hill(2000) that were similar but in the graphical form ofupland-riparian transects. Vidon and Hill (2004) clas-sified the upland riparian regime into seven cate-gories representing varying levels of mixing of uplandand riparian waters. Such classifications can be quitevaluable and can help characterize the mixing scenar-ios that can be introduced in a riparian model to sim-ulate the various combinations of upland and riparianwater mixing. However, the modeling schemes need tobe flexible so that further adjustments to the mixingproportions can be made based on field observations.One such modeling approach was recently presented

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Figure 1. Characterization of the Riparian Zone in the RiparianEcosystem Management Model (REMM) Into Three Zones

and Soil Layers. This scheme was based on thethree-zone buffer model (Welsch, 1991).

by Seibert et al. (2003) in a model that allowed for theearly expression of riparian water and also allowedhillslope water to bypass the riparian store once aspecified threshold was reached.

Seibert et al.’s (2003) model essentially representedthe simplest scenarios for hillslope riparian mixing –a clear distinction could be made between the hills-lope and riparian areas, the riparian zone was repre-sented by a single reservoir, and the model wasconcerned only with mixing of water types (old/new)and not solutes (such as carbon, nitrogen, and phos-phorus). For models in which both hydrologic and bio-geochemical processes are being simulated and theriparian zone is composed of multiple vegetative

zones (grass and or trees), the task of characterizingthe riparian zone(s) would likely become more compli-cated. For such situations the initial characterizationof the zones could be based on the topography andhydrology [following some of the scenarios suggestedby Hill (2000) and Vidon and Hill (2004)]. The hydro-logic zones or stores could then be further subdividedto account for the variations in vegetation or soil fea-tures. Clearly this modeling scheme (as opposed tothe completely mixed approach) will require moremeasured data or field observations but will very like-ly produce a more realistic picture of the water andsolute mixing occurring in the riparian zone.


CHALLENGES IN MODELING HYDROLOGIC AND WATER QUALITY PROCESSES IN RIPARIAN ZONES

Figure 2. Scenarios of Upland Riparian Solute (nitrate in this case) Mixing Based on Topographyand Upland and Riparian Depths of Permeable Sediments (from Vidon and Hill, 2004).

TEMPORAL CHARACTERIZATIONOF RIPARIAN PROCESSES

In REMM all hydrologic, nutrient fate, and vegeta-tive growth processes were simulated at a daily timestep. Since REMM was a management oriented modeldesigned for long term simulations (100 years ormore), the daily time step was the shortest time stepthat could be selected without overly increasing thecomputational burden. The choice of the time step canhave a significant impact on model predictions. Forbiogeochemical and vegetative growth processes, adaily time step may be appropriate, but whether peaksaturation conditions and solute fluxes in riparianzones associated with large events can be adequatelycharacterized at a daily time step is open to question.Recent observations during storm events (over fewhours or minutes) showed that nitrate behaved con-servatively as it moved in saturation overland flowover valley bottom riparian areas (Inamdar andMitchell, 2006). Similar observations were earlierreported by Hill (1993). This suggests that duringtransient peak moisture conditions, upland soluteloadings may bypass the riparian zone. Verchot et al.(1997a,b) observed that large storm events accountedfor most of the annual total water and nutrient fluxesfrom the riparian zone. Furthermore, they found thatthe riparian system switched from a sink for nitrogenduring small and moderate events to a source of nitro-gen during large storm events. These switches inriparian response to moisture thresholds are critical,and it is not known whether daily time-step simula-tions can reproduce these situations. The importanceof these transient and peak hydrologic conditions forbiogeochemical processes has also recently beenemphasized by McClain et al. (2003), who havereferred to them as hot moments. The potential forelevated solute exports from riparian areas duringlarge storm events have also been highlighted innumerous watershed-scale studies (Hinton et al.,1997; Butturini and Sabater, 2000; Buffam et al.,2001; Inamdar et al., 2006).

In daily time step simulations, the transfer ofwater through riparian reservoirs is typically volumecontrolled, that is, the movement of water betweenthe reservoirs is dictated by the volume of water inputand the storage available in the receiving reservoir.However, the transient and peak hydrologic condi-tions or hot moments typically occur during high-intensity storm events, in which the movement ofwater is intensity- or rate controlled; for example, therate of water input (such as from rainfall) exceeds thehydraulic conductivity of the reservoir. These situa-tions cannot typically be represented in daily-scalevolume based models. Such situations can only be

accounted for if rate controls are introduced in modelsor if hydrologic simulations are performed at a small-er time step (hours). For riparian hydrobiogeochemi-cal models, transient hydrologic conditions andassociated solute fluxes can be simulated by perform-ing model simulations at subdaily time steps (hours)for storm events exceeding a selected threshold, whilesolute cycling simulations (such as nitrification, min-eralization, plant uptake) could still be performed atthe daily time steps. For sites where detailed storm-event data are not available or for ungaged ripariansites, simulations could be performed for designstorms. These design storms should reflect the inten-sity and duration associated with the largest stormsassociated with the location. Such a differential timestep scheme would allow the model to maintain com-putational efficiency and yet allow for a more realisticrepresentation of any shifts in riparian processes dur-ing large storm events.

HOW MUCH PROCESS DETAIL ANDMODEL COMPLEXITY IS WARRANTED?

How much process detail and complexity should beintroduced in ecosystem models is a question thatcontinues to generate much debate (DeAngelis andMooij, 2003; Pace, 2003). The issue is important sinceit directly determines the computational size of themodel, the amount of input data requirements, andthe uncertainty associated with model predictions.Addressing the question is difficult since it requiresdetailed field data and a thorough understanding ofthe important processes that determine the ecosystemresponse. Addressing this question is especially diffi-cult for riparian ecosystems where complex interac-tions of hydrologic, chemical, and biological conditionsproduce a mosaic of responses that vary with chang-ing site conditions.

Ideally, the inclusion of processes in models shouldbe strictly dictated by the understanding of these pro-cesses based on field data and confidence in testingthese processes via field measurements. However, thishas not been the case with respect to many physicallybased ecosystem and water quality models. Considerthe example of REMM. Although originally slated tobe a water quality model (Lowrance and Shirmoham-madi, 1985), the model gradually evolved into a full-fledged ecosystem process model. One of themotivations behind this evolution was the need toaddress a growing number of riparian buffer manage-ment scenarios that were of interest to the naturalresource agencies (the eventual beneficiaries from themodel). These scenarios included the impact of vege-tation harvesting on water quality and the effect of


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growing versus mature forested buffers on nitrogenand phosphorus cycling. To allow the model to addresssuch complex scenarios, additional process algorithmswere included, many of them being derived fromexisting complex models. As more and more algo-rithms were included, the model complexity increasedand the number of model parameters that had to beinitialized increased dramatically. Furthermore, someof the process algorithms were so detailed it was high-ly unlikely that the typical REMM users (water quali-ty scientists and engineers) would ever have the datato test these process components.

One example of such overly detailed model compo-nents was the vegetative growth model in REMM. Itwas derived from the TREGRO model (Weinstein andYanai, 1994) and simulated growth and maintenanceof five vegetative components – roots, stems, branch-es, leaves, and buds. A sophisticated algorithm wasimplemented for carbon allocation among these vege-tative pools. The key question that needs to be posedhere is – how important is the detailed simulation offive vegetative pools and detailed carbon allocationbetween these pools to the water quality functions(transport and fate of N and P) of riparian buffers(the primary focus of the model) and if such detail isreally required? Furthermore, can such detailedmodel components be realistically evaluated? Admit-tedly, a vegetation model was needed to address theimpacts of vegetation type, growth stage, and harvest-ing on water quality, but could a simpler model havesufficed? Could a model have been used that simulat-ed two biomass components – above ground and belowground – as opposed to five separate vegetative pools?It would have been far easier for modelers to estimateand test two riparian biomass pools than to accountfor five separate ones. One immediate benefit of usinga simpler scheme would have been a reduction in thenumber of parameters to be initialized.

Another example is the soil carbon model inREMM. The carbon model in REMM was based on theCENTURY model, which was developed in Colorado(Parton et al., 1988) to simulate long term (100 to1,000 years) impacts of climate change on carbonpools in grassland soils. The CENTURY model waschosen since it was the most well documented andtested soil dynamics model available. The model par-titions the soil mass into five pools (structural,metabolic, active, slow, and passive), and first-orderdecay coefficients are used to simulate the decomposi-tion and transfer among the pools. The first-ordercoefficients simulating the decay of various carbonpools required more than 50 to 100 years of simula-tion time to produce numerically stable carbon pools.This was a major limitation, since it required thatREMM be executed for more than 50 to 100 yearsbefore reasonable predictions could be generated.

Furthermore, first-order carbon decay coefficientsthat were developed for grassland soils had to be usedto simulate riparian conditions. The question thatneeds to be answered here is whether simulating thenumerous carbon pools and their decay through timeis necessary for the nitrogen and phosphorus dynam-ics in riparian ecosystems. Carbon plays an importantrole for nitrogen processes like denitrification (Groff-man and Tiedje, 1989; Gold et al., 1998), but it isunclear whether a full fledged carbon model is justi-fied. Would a simpler model have sufficed? These areimportant questions and can only be answered bystudying measured data on the variability of carbonpools in riparian soils and their relative importance tonitrogen and phosphorus cycling. If measured dataindicate that the labile carbon pool (the fraction thatinfluences microbial nitrogen and phosphorus pro-cesses) can be determined based on the total soilorganic matter content and a few other factors (suchas moisture, temperature), a simple model could beused to replace the CENTURY soil carbon model.

To address model complexity, modelers need tohave a good look at the available data on riparianecosystems. Modelers need to clearly lay out the cen-tral purpose of the model during model development.If estimating the riparian buffer dimension necessaryto accomplish water quality objectives is the mainobjective, then only processes that directly fulfill thatobjective need to be included. At every step of themodel development process, it is necessary to checkwhether measured data can be used to evaluate themodel component. A simple model that can be rigor-ously evaluated provides much more insight into sys-tem functioning than a sophisticated, complex modelthat cannot be constrained.

SCALE ISSUES FOR MODELALGORITHMS AND PARAMETERS

In REMM, riparian processes were simulated bybringing together algorithms from various other mod-els; for example, the carbon decomposition algorithmwas derived from the CENTURY model. Thisapproach has been used for most water quality mod-els (e.g., Soil Water Assessment Tool, SWAT; Arnoldand Fohrer, 2005) where component algorithms areaggregated within the main model structure. Theproblem with this methodology is that componentequations and their rate constants that were original-ly derived for a particular process scale are now beingscaled up or down in the new model framework. Thisproblem of scaling up or down of process algorithmshas been voiced before (Beven, 1995, 2001). Can these rate constants and the relationships with their



modifiers hold at scales other than those at whichthey were derived? The answer to this question isvery likely “No.” The algorithms appear to workbecause of the use of effective values for the rate con-stants. These effective values are either calibratedusing measured data or based on best guesses of themodelers.

A way to resolve this problem of scaling may be tofollow Beven’s (1995, 2001) suggestion and developalgorithms and parameters at the scale of interest –in this case the riparian scale. Some leading riparianresearchers likewise have come to this realization andinitiated work in this direction (Groffman and Tiedje,1989; Groffman et al., 1992; Gold et al., 2001).Instead of focusing on point-scale values of soil mois-ture and other variables, Groffman and Tiedje (1989)looked at landscape-scale attributes of soil textureand drainage as regulators for denitrification. Theyfound that soil texture and drainage could explain 80percent of the variation of annual denitrification flux,in contrast to previous studies in which less than 50percent of the variance in hourly and daily denitrifica-tion rates could be explained by point-scale measure-ments of soil moisture and other variables. Usingrelationships between denitrification and soil textureand drainage, Groffman et al. (1992) then producedlandscape-scale and regional-scale estimates of deni-trification flux. Although the work of Groffman andTiedje (1989) and Groffman et al. (1992) is promising,static attributes like soil texture and drainage cannotbe used directly in dynamic modeling. These resultscan, however, be used indirectly to develop parameterestimates for simulation models.

One approach to developing scale relevant parame-ters is to use in situ macro-scale or mesocosm-scaleexperiments (Verchot et al., 1997a,b; Addy et al.,2002) in combination with hydrologic and chemicalmass balances and isotope-based mixing models forriparian zones. Verchot et al. (1997a,b) used thesequential core technique to measure in situ rates ofnitrogen mineralization and immobilization, nitrifica-tion, and plant uptake in riparian zones. Addy et al.(2002) recently proposed an in situ push-pull methodto determine ground water denitrification in riparianzones. The advantage of the push-pull method is thatit aggregates the response of denitrificationmicrosites, thereby providing great advantages overmicrocosm-based methods. Burns and Kendall (2002)and Campbell et al. (2002) made use of δ15N andδ18O to differentiate between nitrate derived fromprecipitation and nitrate generated in the watershedvia microbial nitrification. Such macro-scale or meso-scale approaches and isotopic data will provide someidea of the relative proportion of process rates (e.g.,the ratio of denitrification to vegetative uptake with

respect to consumption of N). The validity of theseprocess ratios can then be verified by comparing sim-ulated chemical mass balances against measuredtotals for the riparian zone.

EVALUATION OF MODEL PREDICTIONS

Rigorous evaluations of ecosystem models not onlyrequire a large amount of data but also require mea-surements that are carefully selected to test the mul-tiple components of the models. This recognition wasespecially driven home during REMM evaluations(Inamdar et al., 1999a,b). For example, REMM simu-lations for nitrogen transport and fate were evaluatedby comparing simulated values of nitrate concentra-tions in ground water along the riparian transect tomeasured data from ground water wells. Such com-parisons are often conducted for water quality models.However, the total nitrate concentrations in groundwater for REMM were influenced by processes likenitrification, vegetative uptake, and denitrification.By comparing the total nitrate, the sum result of thethree processes was evaluated, but not the individualprocess components. For rigorous model testing, eval-uation of individual process rates is equally essential.Innovative methods to measure process rates (such asthe sequential core technique for nitrification andvegetative uptake) are available and should be adopt-ed. In addition, multiple data such as concentrationson various solutes and isotopes can provide an inde-pendent test for various model components. Isotopicdata can be used to determine whether the mixingregime for event and pre-event waters is being cor-rectly simulated in the model. Solutes that can pro-vide estimates on the residence times for water(Burns et al., 2003) in the riparian zone can be espe-cially valuable. Particularly beneficial are measuredobservations that test different model parameters andthat can evaluate processes that are activated at vari-ous model thresholds.

Another aspect that needs attention is the types oftests or criteria that are adopted for comparing simu-lated and measured data. Time series comparisons ofobserved and simulated values with an equation likeNash-Sutcliffe efficiency to determine the fit of thepoint values are fairly popular. However, Kirchner etal. (1996) showed that time series comparisons havelittle diagnostic power because multiple factors couldinfluence the outcome variable(s) being evaluated.Rather, Kirchner et al. (1996) recommended that westatistically isolate the relationships between the forc-ing factor and the outcome variable while correctingfor other confounding factors. An example of this for


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riparian models could be the influence of nitrate con-centrations on denitrification rates. The interest herewould be to determine whether the model can repli-cate the controls of nitrate concentrations on denitrifi-cation rates measured in the field. However, thiswould require that denitrification rates be correctedfor influences of carbon availability (the likely con-founding factor). Such process evaluations provide amore rigorous evaluation of the model than simplycomparing time series of predicted and observed data.Another example of statistical analysis is the use ofsignature plots such as flow duration curves (Jyoth-ityangkoon et al., 2001).

In addition to the hard data, Seibert and McDon-nell (2002) have voiced support for the use of soft datafor model evaluations. Ecosystem studies generallyproduce large amounts of soft information, which canbe invaluable in constraining parameter ranges andverifying model behavior. Examples of such soft datafor riparian evaluations could be the areal extent ofsurface saturation along the riparian transect and therelative rates of solute uptake among the vegetationzones estimated from plant age and maturity. TheC:N:P ratios of soils in various riparian subzones canbe used to check the simulated mass balances forthese solutes, or the ratios could also be used to pro-vide relative estimates on decomposition or nitrifica-tion rates, which can then be compared to modelsimulated values. By establishing some specific rulesor tolerances, model predictions can be categorized asacceptable or not acceptable following comparisonswith soft data (Seibert and McDonnell, 2002).

Ecosystem models like REMM are generally highlyparameterized, which means considerable uncertaintyis associated with model predictions. One of the waysto assess model uncertainty is to perform multipleruns in a Monte Carlo type analysis. This analysiscan help ascertain the sensitive parameters (Freer etal., 1996) and can generate the confidence boundsassociated with model outputs. However, ecosystemmodels (e.g., REMM) and water quality models (e.g.,SWAT) are typically executed in a single run mode.Such single runs do not provide any idea of the influ-ence of parameter choice on model outputs. Ecosystemand water quality models need to be simplified (com-putationally) as far as possible so that multiple MonteCarlo simulations can be generated with a reasonablyshort execution time. A solution might be to performMonte Carlo sampling for only a select few of themost sensitive parameters. Sensitive parameterscould be identified using procedures such as the Gen-eralized Sensitivity Analysis proposed by Hornbergerand Spear (1981).

ASSESSING THE IMPACTS OF RIPARIANECOSYSTEMS AT THE WATERSHED SCALE

The emphasis by the U.S. Environmental Protec-tion Agency (USEPA) on addressing environmentalproblems at the watershed scale has led to the inter-est in determining the impacts of riparian zones atthe watershed scale (USEPA, 1996a,b). Models ifapplied correctly can address this need. The key ques-tion then is how to implement landscape-scale or hillslope-scale models like REMM to get answers atthe watershed level. What is needed is the selection ofrepresentative riparian transects or upland riparianscenarios. These selected scenarios should representall the likely combinations of upland loadings, soilprofile, vegetation type, riparian zone size, and hydro-logic conditions along the upland-riparian continuum.The fraction of the catchment area associated witheach of these upland riparian scenarios needs to beidentified. Simulations can then be performed foreach of these scenarios, and the results can beaggregated using the catchment area associated witheach scenario.

One of the key watershed parameters that willinfluence the riparian effectiveness at the watershedscale will be the size of the riparian reservoir vis-à-vis the upland loadings (Vidon and Hill, 2004). If thesize of the riparian reservoir is large compared to theupland loadings, it will provide a longer residencetime for water within the riparian zone and thus agreater opportunity for degradation or transformationof solutes. It would be interesting to investigate howthe accumulated volume of the riparian reservoir atvarious points along the main drainage of the water-shed correlates with the corresponding water qualityconditions at these points. A method for computingthe riparian area along the main drainage has beenproposed by McGlynn and Seibert (2003).

Transferability of models to riparian ecosystemsacross various ecoregions or physiographic regions(e.g., the U.S. Coastal Plain, Piedmont, Ridge and Val-ley, and Western) is also an issue of concern. Obvious-ly the mixing regimes and parameter values forriparian ecosystems will vary across the regions.Parameters describing the mixing regime and the pro-cess rates for riparian zones in the Coastal Plain willlikely not be valid for riparian areas in the Ridge andValley. One systematic approach to address this prob-lem would be to first identify specific watershed orriparian types or classes within each of these physio-graphic or ecoregions. Such organization could initial-ly be guided by the mixing regimes proposed by Hill(2000), Vidon and Hill (2004), and/or the functionalclassifications proposed by Lowrance et al. (1997).Experimental riparian studies, past and current,



could be identified and categorized into these ripari-an/watershed classes. Measured data from these siteswould provide the starting point for developingparameter types and values that could be used to sim-ulate riparian zones within a specific watershed/riparian classes. It is very likely that some additionalexperimentation will be required at these sites as themodel is fine-tuned and calibrated for each of theselocations.

SUMMARY AND CONCLUSIONS

Four important challenges in modeling water quali-ty processes in riparian zones were addressed in thispaper: How can the spatial and temporal extent ofwater and solute mixing in the riparian zone be mod-eled? What level of model complexity is justified? Howcan processes at the riparian scale be quantified? Howcan the impact of riparian ecosystems be determinedat the watershed scale? Currently, riparian modelsassume complete mixing and equilibration of hillslopeand riparian waters. Results from isotope and chemi-cal tracer studies show that hillslope and riparianwaters may not mix completely, and furthermore, hill-slope fluxes may even bypass the riparian zone underelevated catchment moisture conditions. Consideringthe depth of upland and riparian sediments and theslope gradients, Hill (2000) and Vidon and Hill (2004)proposed a number of scenarios describing the poten-tial for mixing of hillslope and riparian waters. Suchclassifications could provide the first attempt atdeveloping flexible frameworks that can simulatewater and solute mixing in riparian models. To allowlong term simulations, riparian simulations have typ-ically been performed at the daily scale. Howeverstorm event data indicate that upland solute loadingsmay either bypass or overwhelm the riparian zoneduring transient peak flow conditions. The riparianzone may shift from being a sink of solutes to a sourceof solutes during such large storm events. Clearly,such shifts need to be taken into account for realisticmodel simulations. One approach to addressing thisproblem would be to simulate nutrient cycling andvegetative growth processes at the daily scale andsimulate large storms at the hourly time step. Anoth-er way to account for these conditions would be to per-form simulations at hourly time steps for selecteddesign storms that represent these high flow episodes.

Existing models of riparian ecosystems are overlycomplex, leading to excessive parameterization andmodel uncertainty. Algorithms need to be simplifiedconsidering the level of detail and data available infield measurements. The scope and limits of themodel need to be defined prior to development and

adhered to during model development. Model algo-rithms need to be developed using new macro-scaleand meso-scale experiments that capture processdynamics at the hillslope or landscape scales. Use ofscale relevant algorithms will result in more reliableparameter values and greater confidence in modelresults. Single model runs need to be replaced byMonte Carlo simulations that provide a better esti-mate of model uncertainty and parameter sensitivity.In addition, it is necessary to move beyond simpletime series and point-output comparisons of observedand simulated data. A more rigorous evaluation ofmodel performance can be accomplished by extractingand comparing statistical responses in observed andsimulated data. The impact of riparian ecosystems onwater quality at the watershed scale is especially ofinterest. One approach to address this need is to per-form simulations for representative hillslope-ripariantransects that account for all likely combinations ofupland loadings, soils, vegetation, riparian width, andhydrology. The discussion in this paper clearly sug-gests a need to embrace a new set of paradigms indeveloping more reliable and robust models of ripari-an ecosystems.

LITERATURE CITED

Addy, K, D.Q. Kellogg, A.J. Gold, P.M. Groffman, Gina Ferendo, andC. Sawyer, 2002. In Situ Push-Pull Method to DetermineGround Water Denitrification in Riparian Zones. Journal ofEnvironmental Quality 31:1017-1024.

Altier, L.S., R.R. Lowrance, R.G. Williams, S.P. Inamdar, D.D.Bosch, J.M. Sheridan, R.K. Hubbard, and D.L. Thomas, 2002.Riparian Ecosystem Management Model (REMM): Documenta-tion. USDA Conservation Research Report 46, USDA, Washing-ton D.C.

Arnold, J.G. and N. Fohrer, 2005. SWAT2000: Current Capabilitiesand Research Opportunities in Applied Watershed Modeling.Hydrological Processes 19:563-572.

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