GIS-BASED HYIROLOGIC MODELING OF RIPARIAN AREAS ...

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATIONVOL. 37, NO. 6 AMERICAN WATER RESOURCES ASSOCIATION DECEMBER 2001

GIS-BASED HYIROLOGIC MODELING OF RIPARIAN AREAS:IMPLICATIONS FOR STREAM WATER QUALITY'

Matthew E. Baker, Michael J. Wiley, and Paul W Seelbach2

ABSTRACT: Riparian buffers have potential for reducing excessnutrient levels in surface water. Spatial variation in riparian buffereffectiveness is well recogrnzed, yet researchers and managers stilllack effective general tools for understanding the relevance of dif-ferent hydrologic settings. We present several terrain-based GISmodels to predict spatial patterns of shallow, subsurface hydroiogicflux and riparian hydrology. We then link predictions of riparianhydrology to patterns of nutrient export in order to demonstratepotential for augmenting the predictive power of land use/landcover (LU/LC) maps. Using predicted hydrology in addition toLUILC, we observed increases in the explained variation of nutri-ent exports from 290 sites across Lower Michigan. The results sug-gest that our hydrologic predictions relate more strongly topatterns of nutrient export than the presence or absence of wetlandvegetation, and that in fact the influence of vegetative structurelargely depends on its hydrologic context. Such GIS models are use-ful and complimentary tools for exploring the role of hydrologicrouting in riparian ecosystem function and stream water quality.Modeling efforts that take a similar GIS approach to materialtransport might be used to further explore the causal implicationsof riparian buffers in heterogeneous watersheds.(KEY TERMS: aquatic ecosystems; GIS; ground water hydrology;modeling; watershed management; wetlands.)

INTRODUCTION

Human land use is a major factor influencing non-point sources of N and P in watersheds and the deliv-ery of excess nutrients to surface waters (Correll,1997; O'Neill et al., 1997). At regional scales, manyresearchers have related patterns of watershed landuse/land cover (LU/LC) to patterns of nutrient exportin streams (Omernik et al., 1981; Osborne and Wiley,1988; Hunsaker and Levine, 1995; Tompkins et al.,

1997; Johnson et al., 1997; Castillo et al., 2000).Much of this work has incorporated mapped wetlandvegetation to characterize riparian or catchmenthydrology Weller et al. (1996) used vegetative charac-terizations and spatial descriptions of wetlands toimprove predictions of instream nutrient chemistry inwestern Vermont. Mixed-forested wetlands, the over-all aerial extent of wetlands, and their proximity tostreams greatly influenced multiple linear regressions(MLR) with P concentrations. Tompkins et al. (1997)related nutrient export to varying degrees and typesof wetland cover in a mid-Michigan basin.

At local scales, a number of transect studies haveshown that riparian forest "buffers" can be effective atremoving excess nutrients from surface water andground water (Peverly, 1982; Lowrance et al., 1984;Peterjohn and Correll, 1984; Jacobs and Gilliam,1985; Haycock and Pinay, 1993; Jordan et al., 1993;Lowrance et al., 1997). However, reviews of ripariannutrient dynamics make it clear that riparian areasexhibit a wide range of effects on stream water quali-ty within and among regions (Cirmo and McDonnell,1996; Hill, 1996; Correll, 1997). Even at very localscales, considerable spatial and temporal heterogene-ity has been observed in rates of nutrient flux inriparian areas (Peverly, 1982; Elder, 1985; Pinay et al.1989; Groffman et al., 1992; Pinay et al., 1992; Jordanet al., 1993; Haycock and Pinay, 1993). Several stud-ies examining the role of plants in N removal indicatethat vegetation character (composition, structure, orsuccessional stage) alters buffer effectiveness byaffecting uptake rates or providing a source of C forsoil denitrification (Correll et al., 1992; Jordan et al.,

'Paper No. 01026 of the Journal of the A,nerican Water Resources Association. Discussions are open until August 1, 2002.2Respectively, Doctoral Candidate and Associate Professor, School of Natural Resources and Environment, University of Michigan, G170

Dana Bldg., 430 East University, Ann Arbor, Michigan 48109-1115; and Research Biologist, Michigan Department of Natural Resources, Insti-tute for Fisheries Research, 1109 North University Ave., 212 Museums Annex Bldg. 1084, Ann Arbor, Michigan 48109-1084 (E-MailJ Baker:[email protected]).

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Baker, Wiley, and Seelbach

1993; Osborne and Kovacic, 1993; Pinay et al., 1993).A number of investigators have suggested that thelocal hydrologic setting ultimately controls the extentto which riparian vegetation and soil denitrificationcan affect significant nutrient removal (Pinay andDecamps, 1988; Hill, 1990; Bohlke and Denver, 1995;Cirmo and McDonnell, 1996; Hill, 1996).

Sharpening predictions of surface and subsurfaceflow paths across broad heterogeneous landscapes isprecisely the challenge facing ecologists seeking tounderstand both watershed and riparian nutrientdynamics (Osborne and Wiley, 1988; Phillips, 1989;Levine and Jones, 1990; O'Neill et al., 1997; Weller etal., 1998). The idea that variation in the hydrology ofriparian areas is linked to variations in their abilityto act as nutrient sinks is not new (Hynes, 1983;Elder, 1985; Howard-Williams, 1985; Pinay andDecamps, 1988; Cooper, 1990; Hill, 1990; Groffman etal., 1992; Brinson, 1993a, 1993b; Bohlke and Denver,1995). Since water is responsible for much of thematerial transport between riparian and other ecosys-tems, understanding riparian hydrology is both a logi-cal and practical approach to understanding spatialvariation in riparian buffer potential. Hydrologictransport of P is typically associated with the sedi-ment in surface runoff, thus general watershed mod-eling concepts have been applied with some success(Phillips, 1989; Levine and Jones, 1990; Hunsakerand Levine, 1995; Soranno et al., 1996). In contrast,ground water is considered an important vehicle for Ntransport from uplands to streams, but because Nremoval is accomplished via both root uptake andmicrobially mediated geochemical processes, itsdynamics are somewhat more complex (Cirmo andMcDonnell, 1996; Hill, 1996; Brunke and Gonser,1997).

Accounting for ground water movement has beenthe focus of considerable work in hydrology (Freezeand Cherry, 1979). Common approaches includefinite-difference, finite-element, or distributed groundwater models using Darcy's Law (e.g., MODFLOW:McDonald and Harbaugh, 1988; Hill, 1992; Harbaughand McDonald, 1996). Parameterization of such mod-els can involve considerable investment in detailedlocal information (e.g., well pumping, well logs,piezometer/lysimeter studies). In the absence ofdetailed information or when working across broadspatial scales, hydrologists frequently rely upon pre-existing data and proximal predictors. Increasingly,both researchers and natural resource managersrequire site-specific information over larger geograph-ic areas (e.g., whole river basins, states, ecoregions).Understanding riparian contributions to the mainte-nance of stream water quality requires relative esti-mates of subsurface flux with respect to bodies ofwater, wetlands, and soils. Yet most ground water

models have been concerned with the dynamics ofspecific supply aquifers, baseflow recession, individu-al wetlands and lakes, or contaminant plume tracking(Freeze and Cherry, 1979; Holtschlag and Crosky,1984; Mandle and Westjohn, 1989; Molson and Frind,1995; Christensen et al., 1998; Martin and Frind,1998). Ground water flux and recharge rates can bemodeled at broader scales, but due to the lack ofparameter input, the resulting information isfrequently too coarse for use in riparian studies.

In analyses across very broad landscapes wherecomputationally intensive distributed models areunwieldy and/or impractical, terrain-based hydrologicmodeling represents an attractive alternative. Ter-rain-based hydrologic models such as TOPMODEL(Beven and Kirkby, 1979) utilize digital terrain anddigital elevation data (DTMs and DEMs) to computeflow direction and accumulation across a landscape.Although they do not generate the detailed flux esti-mates of more intensive ground water models, ter-rain-based models have been used to estimate runoff,areas of soil saturation, and water tables in smallcatchments (e.g., O'Loughlin, 1981; Band, 1989; Bandet al., 1991; Quinn et al., 1991; Grayson et al., 1992;Wolock and Price, 1994).

Here we used GIS information derived from readilyavailable digital maps and a novel approach to ter-rain-based ground water modeling to predict spatialpatterns of hydrologic routing and riparian hydrology.We then used summaries of these predictions to pre-dict patterns of nutrient export across broad, hetero-geneous landscapes. Our objectives were to comparethe relative utility of simple GIS-based models ofriparian hydrology with LU/LC map classes and toillustrate the importance of incorporating explicithydrologic information into landscape-scale nutrientexport models.

MODELS AND METHODS

A Varied Landscape

The Lower Peninsula of Michigan provided an idealnatural laboratory for our study of variation in ripari-an hydrology. Michigan has a tremendous variety oflocal landscapes due to an exceptionally diverse arrayof glacial drift, pro-glacial deposits, and glacio-fluvialvalleys (Farrand and Bell, 1982). A variable geology iscomplemented by ecologically relevant climaticgradients from north to south and east to west (Albertet al., 1986). These features compliment a patchworkof LU/LC and result in a broad range of hydrologicconditions for our study.

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GIS-Based Hydrologic Modeling of Riparian Areas: Implications for Stream Water Quality

MRI-DARCY Models

The Michigan Rivers Inventory (MRI; Seelbach andWiley, 1997) ground water models, like other topo-graphically based hydrologic models, used a DEM andother mapped information to estimate the spatial pat-tern of physical constraints on hydrologic flow paths.Instead of actual hydraulic heads or in situ conductiv-ity measurements, the MRI ground water modelsrelied upon a topographic approximation of head froma 1:24,000, 30-m DEM (1 m vertical resolution), andestimated hydrologic conductivity from a 1:250,000surficial geology map in a GIS-application of Darcy'sLaw (MRI-DARCY; Baker et al., 2001). Model esti-mates were interpreted as potential ground waterflow velocities under the assumptions that topograph-ic slopes are roughly proportional to ground waterheads and that subsurface hydrologic conductivity ispredictable from surficial deposits. Researchers andmanagers have used the models as a broad-scale(catchment, regional) and local-scale (— 1 ha) quanti-tative index, as well as for qualitative visualization,of potential ground water deliveries across theLower Peninsula. Various iterations of the MRI-DARCY model have been used successfully to predictstream baseflow yields, discharge accrual, water

temperatures, fish community assemblages, andstream habitat conditions in Lower Michigan (Seel-bach et al., 1997; Wiley et al., 1997; Wehrly et al.,1998; Baker et al., 2001; Seelbach et al., 2001; Zorn etal., 2002).

The MRI-DARCY model used a "moving landscapewindow" as its sampling template, incorporating a4 kin radial "snapshot" of the surrounding landscapein the generation of distinct delivery index values forevery 30 x 30 m cell in a raster grid the size of LowerMichigan. The template consisted of 12 transects ori-ented 30 degrees apart (like the hands of a clock) for4 km away from the centroid of a given focal grid cell(Figure 1A). Along each transect, elevation and hydro-logic conductivity were sampled at 100-m intervals,and transect slope was multiplied by mean conductiv-ity to determine a delivery estimate. As one proceedsaway from the focal grid cell, transect profiles thatincreased in elevation were assumed to have thepotential to contribute water to the focal cell (Figure1B). Transect profiles that dropped below the eleva-tion of the focal cell were assumed to have the poten-tial to draw water away from the focal cell (Figure1C). If a particular transect profile first rose above,then dropped below the elevation of the focal cell, themodel assumed the potential for both contributionsand withdrawals (Figure 1B). If a particular transect

Figure 1. Diagram Showing the Transect Template Used to Combine Topographic and Geologic Information in theMRI-DARCY Models of Potential Ground Water Delivery (A), Transect Profiles Assumed to Contribute to the Focal

Cell (B), and Transect Profiles Assumed to Withdraw From the Focal Cell (C). In 1A, black dots identify thelocation of template samples over a landscape grid depicting slope and aspect. White arrows indicate

the direction and magnitude of transect contributions relative to the central, focal grid cell.

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profile first dropped below, then rose above the eleva-tion of the focal cell, the model assumed the resultingdepression was a sink with the potential to withdrawfrom, but not to contribute to, the focal cell (Figure1C). Unlike distributed hydrologic models that gener-ate estimates via cell-to-cell flow, any of the 12 tran-sects could contribute to or withdraw from a givenfocal cell from any distance along their length. Initial-ly, only contributing transects were summed to deter-mine potential delivery rate estimates for each gridcell (MRI-DARCYv3; Baker et al., 2001). Anothervariation of the model consisted of summing all tran-sects, regardless of their potential to contribute orwithdraw, in an effort to estimate a two-dimensionalmass-balance of ground water movement (MRI-DARCYJO; Baker et al., 2001).

Hydrologic Classification

In order to evaluate the importance of spatial vari-ation in hydrologic routing to streamside landscapes,we identified three general types of hydrologic regime:(1) perennially saturated, (2) seasonally inundated,and (3) dry or rarely inundated. Our rationale wasthat these broad categories were likely to provide use-ful information in predictive models of nutrientexport. We used the MRI-DARCYIO to identify areasof "perennially saturated" soils having a positive flowaccumulation (inflow [mlday'] — outflow [mlday-1})greater than one standard deviation (SD) above themean for Lower Michigan. This particular cut-offvalue was identified after examining a number ofexamples of saturated wetlands from across theLower Peninsula. Areas of positive flow accumulationless than this threshold were characterized as "unsat-urated" and further subdivided into seasonally inun-dated and dryland categories.

"Seasonally inundated" riparian areas were identi-fied using vertical proximity (< 1 m) to an interpolat-ed phreatic surface. This interpolation assumed thatany stream channel or lake is a representation of thewater table surface. Our phreatic estimates werederived using MRI 1:100,000 hydrography maps andtheir DEM values as input for a surface interpolationoperation in Arc/Info. The resulting estimate of thewater table surface was subtracted from the DEM togenerate a "depth to water table" map likely to beincreasingly inaccurate with increasing distance fromany water body. Our rationale was that root uptakeand prolonged seasonal inundation of riparian soilswas often dependent upon a shallow water table lessthan 1 m (the vertical resolution of our DEM) fromthe surface. "Dryland" areas were those with neitherhigh rates of predicted ground water delivery (< 1 SD)nor close proximity to the water table (> 1 m). Such

areas are not necessarily dry — they may includeperched, run-off fed wetlands in addition to well-drained soils. Thus, the above classification thresh-olds are relatively arbitrary but we assumed theresulting groups would have fairly distinct averageconditions. Application of these categories provided acontext-dependent prediction of riparian hydrologiccharacter across broad, heterogeneous landscapes.

Nutrient Export Analysis

Nutrient data for 290 sites across Lower Michiganwere obtained from the MRI regional database (Fig-ure 2). This data set included water quality samplescollected by a combination of state, federal, and aca-demic laboratories between 1975 and 1994, for whichsimultaneous stream discharge measurements wereavailable. Proportional summaries of LU/LC classesand modeled hydrologic classes were generated usingcontiguous 500-rn buffers of the river networkupstream of each site. Although other GIS analyseshave used narrower buffers (e.g., Osborne and Wiley,1988; Hunsaker and Levine, 1995; Johnson et al.,1997), we chose a wider 500-rn buffer to accommodatethe potential for variable source-area loading (e.g.,Levine and Jones, 1990; Soranno et al., 1996) andbroad streamside wetlands observed in Lower Michi-gan (e.g., Tompkins et al., 1997; Baker and Barnes,1998; Crow et al., 2000). LU/LC data were obtainedfrom the Michigan Resource Information System(MIRIS; Michigan Department of Land and MineralServices, Lansing) database based on 1978 aerial pho-tography. Mapped resolution was approximately 1 ha,with a minimum lateral dimension of 61 m.

Our approach was to evaluate the effect of aug-menting a standard "base nutrient export model"(hereafter "base model") with various combinations ofvariables that might be used to descr:ibe riparian con-ditions. We were primarily interested in the ability ofour GIS-derived hydrologic classes to predict hydro-logic function both with and without the vegetativestructure or wetland information available fromLU/LC maps. LU/LC class proportions were thereforeaggregated in two ways so as to emphasize wetlandsor vegetative structure: (1) agriculture, forest (conifer-ous, deciduous, and mixed), nonforest (range, barren),and wetland (forested and nonforested); (2) agricul-ture, forest (coniferous, deciduous, mixed, and forest-ed wetland) and nonforest/herbaceous (barren,nonforested wetland, range). Urban and open waterclasses were summarized but not included in eitheranalysis for reasons of parsimony. Because the pur-pose of these analyses was illustrative comparisonrather than finding the best fitting MLR model, weutilized a standard set of independent: variables in the



various predictions of N and P concentrations andloads.

Figure 2. Map of Michigan Rivers Inventory Site Locationsfor Simultaneous Nutrient Concentration and

Discharge Measurements Across the MajorRiver Basins of Lower Michigan.

A conceptual diagram of our initial MLR model andthe five augmented models used in the empiricallsta-tistical comparison is presented in Figure 3. Our basemodel utilized drainage area and percent agriculturewithin a 500-rn upstream network buffer as indepen-dent predictors of nutrient levels. The following modelvariations were compared using the r-squares andstandard errors of the MLR estimates. A "mappedwetland" model, consistent with the idea that riparianwetlands can influence nutrient export, used LU/LCwetlands to augment the base model. In the "vegeta-tion structure" model, wetlands were replaced withforest and non-forest/herbaceous cover classes to eval-uate the effect of augmenting the base model withvegetation structure information. The "hydrologicclass" model used GIS-derived hydrologic predictions

instead of LU/LC information with the implicithypothesis that terrain-based hydrologic inferenceswould better augment the base model than the infor-mation derived from LU/LC maps. However, becauseeach of the above models used a different number ofindependent predictors, we included two additionalmodels to evaluate the influence of differences indegrees of freedom. The "wetland hydrology" modeluses the GIS-derived hydrologic classes to furthercharacterize areas mapped as LU/LC wetlands,whereas the "vegetation hydrology" model uses thehydrologic classes to further characterize forested andherbaceous cover. In all models, dependent interceptswere forced through the origin because we assumedthat nutrient levels at sites without any watershed orland cover source area were necessarily equal to zero.

Figure 3. Diagrammatic Representation of Six MultipleLinear Regression Analyses Compared in the Case Study.Boxes indicate empirical variables, ovals indicate predictedhydrologic variables, solid arrows indicate implied effects ofindependent predictors on dependent variables, and dashedarrows indicate subdivision of empirical variables withpredicted hydrologic classes. N P = NO2 + NO3 or SRP, DA =Drainage Area, AG = Percent Agriculture, For = PercentForest, Hrb = Percent Nonforested/Herbaceous, Wet =Percent Wetlands, sat = predicted saturated areas, seas =predicted seasonally inundated areas, dry = predicteddryland areas. Note that the base model shown in 3A,remains in gray in each of the remaining analyses.


100 km


RESULTS

Ground Water Models and HydrologicCharacterization

Predicted ground water delivery clearly variedacross broad regions in the state as well as withinspecific local landscapes (Figure 4). At the scale ofLower Michigan, what we see is that many low-lyingareas such as major river valleys are predicted toexperience subsurface delivery, and that this is partic-ularly true within the sand hills in northern LowerMichigan (Figure 4A). At the scale of a small valleysegment, steep valley walls along meander bends aremore likely to have seeps than more gently sloping

valley walls, and sandy valley alluvium is predicted toconduct water more effectively than the till of adja-cent moraines (Figure 4B). The MRI-DARCYIO modelin Figure 4C illustrates the spatial variability in local"hotspots" of potential net ground water accumulationalong the river valley bottom, as well as expanses ofpotential net ground water loss along the valley wallsand into the upland. The white areas indicate adja-cent uplands that have relatively steep subsurfacegradients to the valley bottom. These locations have aproportionately high potential to deliver water anddissolved substances directly to the dark areas on themap simply because these areas are nearby, they arerelatively conductive, and because they are situatedabove the darker areas. Across Lower Michigan, we

Figure 4. MRI-DARCY Model of Potential Ground Water Loading Across Lower Michigan (A), a Local River Valley Landscape NearAnn Arbor, Michigan (B), and the Same Landscape Viewed With the MRI-DARCY JO Model (C). Higher rates of potential

flux or accumulation are shown in darker shade variations corresponding to 1/2 SD (mday1) relative to the mean forLower Michigan. In the JO version, darker shades indicate higher net accumulation and lighter shades indicatenet withdrawals. Values are relative to a net accumulation of zero as indicated by the shade marked with an X.



observed a variety of narrow to broad river valleybottoms with a range of predicted net groundwateraccumulation estimates from 0 to >18 rn/day-1 (> 3SD). Concurrently, we observed adjacent landscapeswith predicted ground water net loss estimates rang-ing from 0 to> 20 mlday' (> 3 SD). Broad and fine-scale variation in these predicted delivery patternscontributed to similar patterns of riparian hydrologicregime. Across Lower Michigan, we observed xarn-pies of valley bottoms occupied with uniform values ofeach of the three hydrologic categories, as well asexamples of patchy combinations of classes as shownin Figure 5.

Nutrient Export Analyses

The MLR that comprised our base and augmentednutrient export models are compared in Figure 6.

Predictions of N02+N03 export using drainage areaand percent agriculture had higher squared-multiple-r values and lower standard errors than similar pre-dictions of SRP export, and our base model accountedfor a greater proportion of the observed variance innutrient loads versus nutrient concentrations.Accordingly, it is worth noting that the increases insquared-multiple-r values and relative decreases instandard errors of the augmented models are smallerfor N02+N03 than for SRP (Figures 6B and 6C). Inour comparison of augmented nutrient export models,the mapped wetland model showed greater predictivepower for SRP than the vegetation structure modeldespite differences in degrees of freedom, but this wasnot the case for N02+N03. In addition, for bothN02+N03 and SRP, models using GIS-based hydro-logic classes improved predictions of nutrient exportrelative to the base model more than models witheither mapped wetlands or vegetation structurealone.

Figure 5. Maps of Potential Ground Water Loading (A) and Water Table Depth (B) Used to Estimate the Extent ofPerennially Saturated and Seasonally Inundated Wetlands (C) in the Kalamazoo River Valley Near Augusta,

Michigan. In 5A and 5C, black areas indicate high rates of ground water delivery (>1 SD mday') relativeto the Lower Peninsula mean (X). In 5B and 5C, dark gray areas indicate seasonally inundated

wetlands that are close (<1 m in elevation) to an estimated water table surface.



Figure 6. Comparison of Various Characterizations of Riparian Landscapes Showing Squared-Multiple-rValues and Standard Errors of Estimated Nutrient Levels Using the Base Model (A), Increases inSquare-Multiple-r Values Over the Base Model (B), and Proportional Decreases of Standard Errors

Relative to the Base Model (C). Categories in 6B and 6C correspond to the analyses in Figure 3.

The wetland hydrology model utilized the samenumber of independent variables as the hydrologicclass model, yet restricted the GIS-derived predictionsto locations already mapped as wetlands. As indepen-dent predictors, the derived hydrologic classes werenot strongly correlated with mapped wetlands (Pear-son product-moment r of 0.210, 0.621, and 0.620 forsaturated, seasonal, and dryland classes versusmapped LUILC wetlands). Characterizing LUILC wet-lands by hydrologic class seemed to improve predic-tive relationships with nutrient export over themapped wetland model (Figures 6B and 6C). Howev-er, adding the initial LUTLC wetland classification didnot improve predictive relationships with nutrientlevels over the hydrologic class model. Similarly, thevegetation hydrology model used derived hydrologicclasses to characterize the hydroperiod of LU/LC for-est and herbaceous cover. Unlike the wetland hydrolo-gy model, augmenting vegetative structure withhydrologic predictions improved observed relation-ships with nutrient levels over either the vegetativestructure or hydrologic class models in three out of

the four comparisons (Figures 6B and 6(J). In general,all models using predicted hydrologic classes showedstronger relationships with patterns of nutrientexport than models based solely on mapped LC/LUinformation.

DISCUSSION

The Utility of Hydrologic Models

With riparian areas under ever-increasing develop-ment pressure in many landscapes, there is a verypractical need for understanding their role in main-taining the ecological integrity of streams. Accountingfor spatial patterns of surface and subsurface hydro-logic transport is key to understanding the ecologicalsignificance of riparian processes. Given the complexi-ty of hydrologic processes relevant to riparian func-tion, there is a very practical need for thesimplification and abstraction inherent in modeling.

Base model r2E Base model std. error

4.0 3.673

A. — —

3.02.4292.5 — _______ — —

2 1662.0

1.2201.5

0 7971.0

0638OO1J0.5

N02+N03 N02+N03 SRP SRPconc load conc load

B. C.

—I-. a)0 Ca

a)a)UCa a)a)I-0 —Ca

200I-

wet veg hydro wt+hyd vg+hyd wet veg hydro wt+hyd vg+hyd



In this context, GIS approaches can be a cost-effectiveand eminently practical alternative for understandingcomplex spatial phenomena (Levine and Jones, 1990,O'Neill et al., 1997). Our GIS-based hydrologic classi-fications were not designed to make precise predic-tions about water table elevation or ground waterflow rate, but to predict the spatial location of differ-ent hydrologic settings and improve our understand-ing of how patterns of hydrologic routing influenceecological character at specific sites.

Although the MRI-DARCY models provide esti-mates of the relative magnitude of potential subsur-face flow paths involved in nutrient flux, they do notactually account for water movement. Nor, in fact, dothe models account for deeper, regional ground waterflows or overbank river flooding. Instead, the modelsare based on the idea that low-lying areas in conduc-tive landscapes are more likely to receive shallowground water from their surroundings than areas inless conductive or elevated landscape settings. Unlikeother terrain-based hydrologic models, the MRI-DARCY models do not derive their ground water esti-mates as a consequence of surface flow patterns; onlythe elevation differentials between several often wide-ly spaced points on the transect template are used.The significance of the transect template is its sam-pling and integration of a relatively broad surround-ing landscape to compute the delivery value for arelatively small, yet distinct, receiving locale on thetopographic surface. Rather than providing absoluteestimates of flux, the utility of the MRI-DARCY mod-els lies in their ability to call our attention to placeswhere subsurface connectivity is likely to exist andfrom which direction shallow subsurface flux is likelyto be important. According to O'Neill et al. (1997), it isthe ability to account for spatial variation in hydrolog-ic processes leading to soil saturation, vegetativecharacter, and material transport that will result in agreater understanding of the water quality benefits ofriparian buffers in different landscape settings.

The spatially complex delivery maps in Figure 4illustrate the ability of the MRI-DARCY models tohighlight areas where ground water is likely to occurat or near the surface, as well as the direction fromwhich it is likely to come, given the character of thesurrounding landscape. The significance of using the30 m cell resolution of the MRI-DARCY model andthe phreatic DEM for hydrologic predictions — as com-pared to the 100 m cells of the LU/LC map — is thatwe were able to generate hydrologic estimates forboth broad valley bottoms and fairly narrow tribu-taries (Figure 5). Hence, not only were the hydrologicmodels able to distinguish between broad expanses ofvarious wetland types and more patchy landscapepatterns, they were also able to predict relatively

narrow bands of streamside wetlands that larger cellsmight miss.

Although many riparian investigators acknowledgethat hydrologic setting is an important determinant ofriparian function, there is still an urgent need toaccumulate, synthesize, and communicate the extentand implications of hydrologic variation toresearchers and resource managers. Progress in riverand riparian conservation and management may behampered by generalized application of riparian con-cepts derived from a single hydrologic setting. Recog-nition of the potential for hydrologic variation needsto- occur during study designs, in the analysis andinterpretation of results, and during the developmentof management recommendations. Prior to the devel-opment of effective models of riparian function, thereis a need to develop a conceptual framework thatexplicitly accounts for hydrologic variation and itsimplications for stream water quality.

A-number of studies have described a range of dis-tinct hydrologic settings and their potential influenceon riparian buffer function. In the context of theseexamples, we present a summary of six general ripar-ian hydrologic types we observe repeatedly through-out Lower Michigan (Figure 7). The first exampledescribes those settings where riparian buffers areineffective because subsurface flow paths bypassriparian soils and plant roots (Hill, 1990; Bohike andDenver, 1995; Hill, 1996; Brunke and Gonser, 1997).The second type includes riparian areas that arebypassed by drainage tiles, ditches, sewer systems, orwhere nutrient sources occur immediately adjacent tothe stream channel. The third type occurs in arunoff-dominated landscape where riparian soils andvegetation may contribute to P removal but limited Nremoval (Phillips, 1989; Soranno et al., 1996). Thefourth type of riparian area refers to those that expe-rience seasonal inundation from phreatic fluctuation.Although plant roots may remain in contact with thewater table for much of the growing season, thepotential for root uptake relative to denitrificationremains dependent upon spatial and seasonal varia-tion in water levels (Pinay and Decamps, 1988; Cirmoand McDonnell, 1996). The fifth type includes ripari-an surfaces whose soils are in continuous or near-con-tinuous contact with ground water. These areas oftenexhibit saturated soils and anaerobic conditionsthroughout much of the year. As a result, these areasare locations with relatively high denitrificationpotential and fairly limited root activity (Pinay andDecamps, 1988; Cirmo and McDonnell, 1996). It isimportant to remember that in any of these types,water may reach the river without being influencedsignificantly by riparian processes. All of these exam-ples refer to hydrologic settings where the process of



Figure 7. Six Types of Hydrologic Function in Riparian Areas: (1) Riparian Buffer Bypassed by Ground Water,(2) Riparian Buffer Bypassed by Sewer, (3) Runoff Buffer, (4) Seasonally Inundated Buffer,

(5) Perennially Saturated Buffer, and (6) Overbank Flooding and Hyporheic Exchange.

nutrient removal is dependent upon hydrologic trans-port of nutrients from the landscape to a river. Inaddition, the sixth riparian type serves as a reminderthat under any of these settings, lateral flux from theriver as overbank flow or hyporheic exchange mayaugment riparian water levels and alter nutrient con-ditions (Elder, 1985; Brunet et al., 1994; Tremolierset al., 1994; Brunke and Gonser, 1997).

In an ideal model of riparian nutrient dynamics,some estimate of the spatial complexity of all relevantprocesses would be included at an appropriate spatialand temporal resolution. Both surface runoff andsubsurface flow would be estimated, including theflow paths that bypass riparian influence. Riparian

inundation would include runoff detention, andphreatic saturation, as well as overbank flooding atthe recurrence interval appropriate for each. To theextent that long-term hydrologic constraints influencesoil development and plant assemblages within aregion, spatial variation in soil processes and vegeta-tion character would be included. The spatial patternof vegetative cover would also be used to improve pre-dictions of nutrient removal from surface and subsur-face sources, and all of these processes would bereactive to potential human activity in both the ripar-ian buffer and the source catchment.

As an early step in the iterative modeling process,we chose to focus on identifying Types 4 and 5 (Figure

JAWRA 1 624 JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION


7) since these are the scenarios under which riparianareas may have the greatest influence on groundwater nutrient levels. Our GIS models are presentlytoo general to predict precise riparian function, yetthey do indicate locations where conditions necessaryfor significant denitrification and plant uptake arelikely to occur. Saturated wetlands (Type 5) createdby ground water seeps are often areas of high denitri-fication potential and may serve as a source, ratherthan a sink, for P (Cirmo and McDonnell, 1996; Tomp-kins et al., 1997). Nutrient removal by plant roots inseasonally inundated wetlands (Type 4) requires asource of N and aerobic soils which are more likely tooccur together where water table levels fluctuate yetremain within the reach of many plant roots. Broadexpanses of such shallow riparian water tables mayalso indicate areas of poor drainage with prolongedinundation and additional denitrification potential inthe late winter or spring.

Including Hydrologic Variation in Nutrient ExportModels

Using general relationships between buffer landuse, simple estimates of subsurface flux, and mea-surements of instream nutrient levels, we have illus-trated the importance of including hydrologicinformation in spatial models of water quality. Ourpurpose here was not necessarily to develop the bestpossible prediction of nutrient export per Se, but toillustrate that a simple hydrologic model — because itmakes explicit simplifications of complex hydrologicprocesses — can improve our overall understandingand better address questions of riparian function.Our nutrient measurements were obtained during awide variety of seasonal flow conditions, and manyresearchers describe strong seasonal and flow depen-dencies in nutrient data (Peverly, 1982; Hill, 1996;Cirmo and McDonnell, 1996; Johnson et al., 1997;Castillo et al., 2000). Nevertheless, a two-variablebase model accounted for over half of the observedvariation in nutrient exports (Figure 6). Our modelsclearly exhibited stronger relationships with loadthan concentration. Because drainage area wasincluded in the analyses and water load generation isproportional to catchment area, this result was notsurprising. Other researchers have found strong rela-tionships between watershed size and patterns ofnutrient export (Osborne and Wiley, 1988; Johnson etal., 1997; Castillo et al., 2000). The primary focus ofour analysis was riparian interaction with agricultur-ally derived pollutants, and our base model relatedmore strongly with N02+N03 than with SRP (Figure6). This result is consistent with other nutrient export

studies where agricultural areas are the predominantnutrient sources in catchment or channel buffer sum-maries (Johnson et al., 1997; Castillo et al., 2000;Jones et al., 2001).

Our results showed that using predicted hydrologicconditions to augment our base model consistentlyimproved our ability to explain nutrient exports overusing LU/LC information alone (Figure 6). Althoughdifferences in degrees of freedom for our multipleregressions make statistical comparisons of allsquared-multiple-r values somewhat inappropriate,adding more variables to the base model did notalways improve the squared-multiple-r of the regres-sion or the standard error of the estimate. Further-more, where degrees of freedom were identical,modeled hydrologic classes alone generally performedas well or better than LU/LC wetlands augmented bythe hydrologic predictions. Thus, these resultsindicated that some estimation of hydrologic functionis desirable in spatial models of nutrient exportbeyond that provided by the presence or absence ofmapped wetlands.

Although they identify location and aerial extent ofwetlands, LUILC maps do not capture the hydrologicprocesses responsible for their existence or character-ize their function. The implicit assumption of aLU/LC wetland summary in a nutrient export modelis that all wetlands have the same magnitude anddirection of influence on nutrient export, regardless oftheir hydroperiod or hydrologic source. For example,in our mapped wetland model of N02+N03 load, theregression coefficients for mapped wetlands were sig-nificant and negative as we might expect given theirpotential for assimilation, denitrification, and inter-ruption of nitrification (Cirmo and McDonnell, 1996;Tompkins et al., 1997). Yet in our hydrologic classmodel, as well as in individual augmentations of thebase model for NO2+N03 load, the seasonally inun-dated class coefficient was significant and negativewhereas the saturated class coefficient was signifi-cant and positive. Although assigning causality on thebasis of a MLR coefficient would be inappropriate, itis clear that the influence of saturated and seasonallyinundated areas on NO2+NO3 levels is distinctly dif-ferent. Without the ability to account for the hydrolog-ic processes and landscape characteristics that giverise to such phenomena, LU1LC indices may be unre-liable predictors of ecological function in differentlandscape settings (Tischendorf, 2001). It is thehydroperiod in riparian areas and hydrologic routingfrom surrounding landscapes that ultimately deter-mine the degree to which riparian buffers are effec-tive at intercepting watershed-derived pollutants.These hydrologic processes are not simply a functionof LU/LC patterns, but the result of underlying physi-cal constraints and a functioning riparian ecosystem.



Making use of underlying geology and geomorphologycan further inform us about the hydrologic setting inwhich we interpret LC/LU patterns, as well as alertus to the potential for LU/LC, geology, and geomor-phology to be spatially auto-correlated.

When hydrologic classes were used to characterizevegetation structure, our ability to explain patterns ofnutrient export increased somewhat (Figure 6). How-ever, the observed increases over the hydrologic classmodel were less consistent and often relatively smallcompared to the increases we observed by addinghydrologic information to the base model or the vege-tative structure model. The magnitude of theseresults suggested that the influence of forest versusgrassy buffers or the composition of riparian vegeta-tion on nutrient dynamics (as described by Correll etal., 1992; Osborne and Kovacic, 1993; and Haycockand Pinay 1993) might well depend upon the hydro-logic context of the observation. Moreover, becausedistinct riparian site conditions can result in distinctforest community composition (e.g., Hupp andOsterkamp, 1985; Pautou and Decamps, 1985; Bakerand Barnes, 1998), it is likely that there is often co-variation between the character of riparian hydrologyand character of riparian vegetation. Therefore, someestimate of relevant hydrologic processes seemsimportant for assessing the role of vegetation in ripar-ian buffer dynamics. Indeed, adding more explicithydrologic information is consistent with the work ofmany other researchers, who have suggested that sat-urated, seasonally flooded, and other kinds of riparianhydrology have distinct buffer functions (Elder, 1985;Howard-Williams, 1985; Pinay and Decamps, 1988;Chauvet and Decamps, 1989; Brinson, 1993b; Cirmoand McDonnell, 1996; Hill, 1996).

Overall, despite the inclusion of hydrologic classes,the added predictive power of riparian buffer charac-teristics above our base model was variable and mod-est, especially for N02+N03 (Figure 6). Given theorder-of-magnitude increases in nutrient concentra-tions generated across heterogeneous source land-scapes, it seems unlikely that riparian factors alonecan regulate observed rates of nutrient flux. However,the use of our hydrologic models did improve our esti-mates of SRP concentration and load by 13 and 10percent, respectively. In the context of this range ofvariation, the integrated application of models reflect-ing rapid advective transport of river channels andstorm runoff with relatively slow transport of subsur-face flow paths holds much promise for future causalunderstanding of watershed and riparian function.

Implications of Hydrologic Variation

Given the spatial complexity of ecological functionin riparian areas and widespread managementpolicies geared towards riparian preservation andrestoration, the need for spatially explicit informationacross large landscapes is apparent. It is our inten-tion to encourage researchers to develop location-specific models of riparian hydrology and tools thataid in the adaptive implementation of riparian andwatershed management guidelines. our results sug-gest that using GIS models to characterize hydrologicprocesses can significantly increase cur understand-ing of riparian nutrient dynamics. To date, much ofour understanding of riparian nutrient dynamics hasbeen based on transect level studies or broad-scaleempirical relationships. Local studies are oftenunable to address the role of hydrologic context intheir results, whereas many broad-scale analyseshave tended to assume consistent underlying linkagesbetween rivers, riparian areas, and surrounding land-scapes and typically focus on spatial patterns ofLCILU. Moreover, there appears to be a trend in theapplied literature to focus on vegetation width andcontinuity as a measure of ecological integrity andbuffer protection. In contrast, our models predict con-siderable variation in the aerial extent of hydrologicconditions likely to facilitate nutrient removal at thescale of a stream reach, lakeshore, or wetland (Fig-ures 4 and 5). Our models also suggest that the aerialextent of surrounding landscapes with the potentialto contribute ground water relatively rapidly to valleybottoms varies along a river, and among rivers andlandscapes. Whereas standard vegetated bufferwidths of 10 to 150 m have been advocated to achievevarious physical and biotic benefits (Barton et al.,1985; Crow et al., 2000), our models suggest the widthof hydrologically connected riparian zones may rangewidely and extend up to 500 m or more in certainlandscapes.

There is now ample evidence to suggest that thehydrology and ecological function of all riparian areasare not the same. Rather than relying on a singlebuffer dimension (e.g., vegetated width), a context-specific, place-based approach to understanding ripar-ian nutrient dynamics and buffer delineation in thecontext of catchment sources and sinks seems neces-sary. While vegetative structure and spatial patternare undoubtedly important aspects of riparianbuffers, we advocate a shift in focus to the hydrologicprocesses ultimately responsible for material trans-port in and across streamside riparian zones.


GIS-Based Hydrologic Modelmg of Riparian Areas: Implications for Stream Water Quality

ACKNOWLEDGMENTS

We would like to thank J. D. Allan, K. E. Wehrly, R. L. Cifaldi,and three anonymous reviewers for their helpful comments on ear-lier drafts of this manuscript. This work was supported through aresearch grant from the Michigan Department of NaturalResources, Virtual Geographic Information Laboratory (ViGIL) Pro-gram.

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