SENSITIVITY ANALYSIS,CALIBRATION, AND VALIDATIONS FOR A MULTISITE AND MULTIVARIABLE SWAT MODEL1

The ability of a watershed model to mimic specified watershed processes is assessed through the calibration and validation process. The Soil and Water Assessment Tool (SWAT) watershed model was implemented in the Beaver Reservoir Watershed of Northwest Arkansas. The objectives were to: (1) provide detailed information on calibrating and applying a multisite and multivariable SWAT model; (2) conduct sensitivity analysis; and (3) perform calibration and validation at three different sites for flow, sediment, total phosphorus (TP), and nitrate‐nitrogen (NO₃‐N) plus nitrite‐nitrogen (NO₂‐N). Relative sensitivity analysis was conducted to identify parameters that most influenced predicted flow, sediment, and nutrient model outputs. A multi objective function was defined that consisted of optimizing three statistics: percent relative error (RE), Nash‐Sutcliffe Coefficient (R_NS²), and coefficient of determination (R²). This function was used to successfully calibrate and validate a SWAT model of Beaver Reservoir Watershed at multi‐sites while considering multivariables. Calibration and validation of the model is a key factor in reducing uncertainty and increasing user confidence in its predictive abilities, which makes the application of the model effective. Information on calibration and validation of multisite, multivariable SWAT models has been provided to assist watershed modelers in developing their models to achieve watershed management goals.     

REGRESSION MODELS FOR ESTIMATING HERBICIDE CONCENTRATIONS IN U.S. STREAMS FROM WATERSHED CHARACTERISTICS1

ABSTRACT: Regression models were developed for estimating stream concentrations of the herbicides alachlor, atrazine, cyanazine, metolachior, and trilluralin from use‐intensity data and watershed characteristics. Concentrations were determined from samples collected from 45 streams throughout the United States during 1993 to 1995 as part of the U.S. Geological Survey's National Water‐Quality Assessment (NAWQA). Separate regression models were developed for each of six percentiles (10th, 25th, 50th, 75th, 90th, 95th) of the annual distribution of stream concentrations and for the annual time‐weighted mean concentration. Estimates for the individual percentiles can be combined to provide an estimate of the annual distribution of concentrations for a given stream. Agricultural use of the herbicide in the watershed was a significant predictor in nearly all of the models. Several hydrologic and soil parameters also were useful in explaining the variability in concentrations of herbicides among the streams. Most of the regression models developed for estimation of concentration percentiles and annual mean concentrations accounted for 50 percent to 90 percent of the variability among streams. Predicted concentrations were nearly always within an order of magnitude of the measured concentrations for the model‐development streams, and predicted concentration distributions reasonably matched the actual distributions in most cases. Results from application of the models to streams not included in the model development data set are encouraging, but further validation of the regression approach described in this paper is needed.     

Herbicide Transport in Goodwater Creek ExperimentalWatershed: I. Long‐Term Research on Atrazine1

Lerch, R.N., E.J. Sadler, K.A. Sudduth, C. Baffaut, and N.R. Kitchen, 2010. Herbicide Transport in Goodwater Creek Experimental Watershed: I. Long‐Term Research on Atrazine. Journal of the American Water Resources Association (JAWRA) 1‐15. DOI: 10.1111/j.1752‐1688.2010.00503.x Abstract: Atrazine continues to be the herbicide of greatest concern relative to contamination of surface waters in the United States (U.S.). The objectives of this study were to analyze trends in atrazine concentration and load in Goodwater Creek Experimental Watershed (GCEW) from 1992 to 2006, and to conduct a retrospective assessment of the potential aquatic ecosystem impacts caused by atrazine contamination. Located within the Central Claypan Region of northeastern Missouri, GCEW encompasses 72.5 km² of predominantly agricultural land uses, with an average of 21% of the watershed in corn and sorghum. Flow‐weighted runoff and weekly base‐flow grab samples were collected at the outlet to GCEW and analyzed for atrazine. Cumulative frequency diagrams and linear regression analyses generally showed no significant time trends for atrazine concentration or load. Relative annual loads varied from 0.56 to 14% of the applied atrazine, with a median of 5.9%. A cumulative vulnerability index, which takes into account the interactions between herbicide application, surface runoff events, and atrazine dissipation kinetics, explained 63% of the variation in annual atrazine loads. Based on criteria established by the U.S. Environmental Protection Agency, atrazine reached concentrations considered harmful to aquatic ecosystems in 10 of 15 years. Because of its vulnerability, atrazine registrants will be required to work with farmers in GCEW to implement practices that reduce atrazine transport.     

Sources and Delivery of Nutrients to the Northwestern Gulf of Mexico from Streams in the South‐Central United States1

Rebich, Richard A., Natalie A. Houston, Scott V. Mize, Daniel K. Pearson, Patricia B. Ging, and C. Evan Hornig, 2011. Sources and Delivery of Nutrients to the Northwestern Gulf of Mexico From Streams in the South‐Central United States. Journal of the American Water Resources Association (JAWRA) 47(5):1061‐1086. DOI: 10.1111/j.1752‐1688.2011.00583.x Abstract: SPAtially Referenced Regressions On Watershed attributes (SPARROW) models were developed to estimate nutrient inputs [total nitrogen (TN) and total phosphorus (TP)] to the northwestern part of the Gulf of Mexico from streams in the South‐Central United States (U.S.). This area included drainages of the Lower Mississippi, Arkansas‐White‐Red, and Texas‐Gulf hydrologic regions. The models were standardized to reflect nutrient sources and stream conditions during 2002. Model predictions of nutrient loads (mass per time) and yields (mass per area per time) generally were greatest in streams in the eastern part of the region and along reaches near the Texas and Louisiana shoreline. The Mississippi River and Atchafalaya River watersheds, which drain nearly two‐thirds of the conterminous U.S., delivered the largest nutrient loads to the Gulf of Mexico, as expected. However, the three largest delivered TN yields were from the Trinity River/Galveston Bay, Calcasieu River, and Aransas River watersheds, while the three largest delivered TP yields were from the Calcasieu River, Mermentau River, and Trinity River/Galveston Bay watersheds. Model output indicated that the three largest sources of nitrogen from the region were atmospheric deposition (42%), commercial fertilizer (20%), and livestock manure (unconfined, 17%). The three largest sources of phosphorus were commercial fertilizer (28%), urban runoff (23%), and livestock manure (confined and unconfined, 23%).     

Spatial-Scale Effects on Relative Importance of Physical Habitat Predictors of Stream Health

A common theme in recent landscape studies is the comparison of riparian and watershed land use as predictors of stream health. The objective of this study was to compare the performance of reach-scale habitat and remotely assessed watershed-scale habitat as predictors of stream health over varying spatial extents. Stream health was measured with scores on a fish index of biotic integrity (IBI) using data from 95 stream reaches in the Eastern Corn Belt Plain (ECBP) ecoregion of Indiana. Watersheds hierarchically nested within the ecoregion were used to regroup sampling locations to represent varying spatial extents. Reach habitat was represented by metrics of a qualitative habitat evaluation index, whereas watershed variables were represented by riparian forest, geomorphology, and hydrologic indices. The importance of reach- versus watershed-scale variables was measured by multiple regression model adjusted-R² and best subset comparisons in the general linear statistical framework. Watershed models had adjusted-R² ranging from 0.25 to 0.93 and reach models had adjusted-R² ranging from 0.09 to 0.86. Better-fitting models were associated with smaller spatial extents. Watershed models explained about 15% more variation in IBI scores than reach models on average. Variety of surficial geology contributed to decline in model predictive power. Results should be interpreted bearing in mind that reach habitat was qualitatively measured and only fish assemblages were used to measure stream health. Riparian forest and length-slope (LS) factor were the most important watershed-scale variables and mostly positively correlated with IBI scores, whereas substrate and riffle-pool quality were the important reach-scale variables in the ECBP.     

SPARROW Modeling of Nitrogen Sources and Transport in Rivers and Streams of California and Adjacent States,U.S.

The SPARROW (SPAtially Referenced Regressions On Watershed attributes) model was used to evaluate the spatial distribution of total nitrogen (TN) sources, loads, watershed yields, and factors affecting transport and decay in the stream network of California and portions of adjacent states for the year 2002. The two major TN sources to local catchments on a mass basis were fertilizers and manure (51.7%) and wastewater discharge (15.9%). Other sources contributed < 12%. Fertilizer use is widespread in the Central Valley region of California, and also important in several other regions because of the diversity of California agriculture. Precipitation, sand content of surficial soils, wetlands, and tile drains were important for TN movement to stream reaches. Median streamflow in the study area is about 0.04 m³/s. Aquatic losses of nitrogen were found to be most important in intermittent and small to medium sized streams (0.2‐14 m³/s), while larger streams showed less loss, and therefore are important for TN transport. Nitrogen loss in reservoirs was found to be insignificant, possibly because most of the larger ones are located upstream of nitrogen sources. The model was used to show loadings, sources, and tributary inputs to several major rivers. The information provided by the SPARROW model is useful for determining both the major sources contributing nitrogen to streams and the specific tributaries that transport the load.     

Sources of Suspended-Sediment Flux in Streams of the Chesapeake Bay Watershed: A Regional Application of the SPARROW Model1

Brakebill, John W., Scott W. Ator, and Gregory E. Schwarz, 2010. Sources of Suspended-Sediment Flux in Streams of the Chesapeake Bay Watershed: A Regional Application of the SPARROW Model. Journal of the American Water Resources Association (JAWRA) 46(4): 757-776. DOI: 10.1111/j.1752-1688.2010.00450.x Abstract: We describe the sources and transport of fluvial suspended sediment in nontidal streams of the Chesapeake Bay watershed and vicinity. We applied SPAtially Referenced Regressions on Watershed attributes, which spatially correlates estimated mean annual flux of suspended sediment in nontidal streams with sources of suspended sediment and transport factors. According to our model, urban development generates on average the greatest amount of suspended sediment per unit area (3,928 Mg/km²/year), although agriculture is much more widespread and is the greatest overall source of suspended sediment (57 Mg/km²/year). Factors affecting sediment transport from uplands to streams include mean basin slope, reservoirs, physiography, and soil permeability. On average, 59% of upland suspended sediment generated is temporarily stored along large rivers draining the Coastal Plain or in reservoirs throughout the watershed. Applying erosion and sediment controls from agriculture and urban development in areas of the northern Piedmont close to the upper Bay, where the combined effects of watershed characteristics on sediment transport have the greatest influence may be most helpful in mitigating sedimentation in the bay and its tributaries. Stream restoration efforts addressing floodplain and bank stabilization and incision may be more effective in smaller, headwater streams outside of the Coastal Plain.     

A Web‐Based Decision Support System for Assessing Regional Water‐Quality Conditions and Management Actions1

Booth, Nathaniel L., Eric J. Everman, I‐Lin Kuo, Lori Sprague, and Lorraine Murphy, 2011. A Web‐Based Decision Support System for Assessing Regional Water‐Quality Conditions and Management Actions. Journal of the American Water Resources Association (JAWRA) 47(5):1136‐1150. DOI: 10.1111/j.1752‐1688.2011.00573.x Abstract: The U.S. Geological Survey National Water Quality Assessment Program has completed a number of water‐quality prediction models for nitrogen and phosphorus for the conterminous United States as well as for regional areas of the nation. In addition to estimating water‐quality conditions at unmonitored streams, the calibrated SPAtially Referenced Regressions On Watershed attributes (SPARROW) models can be used to produce estimates of yield, flow‐weighted concentration, or load of constituents in water under various land‐use condition, change, or resource management scenarios. A web‐based decision support infrastructure has been developed to provide access to SPARROW simulation results on stream water‐quality conditions and to offer sophisticated scenario testing capabilities for research and water‐quality planning via a graphical user interface with familiar controls. The SPARROW decision support system (DSS) is delivered through a web browser over an Internet connection, making it widely accessible to the public in a format that allows users to easily display water‐quality conditions and to describe, test, and share modeled scenarios of future conditions. SPARROW models currently supported by the DSS are based on the modified digital versions of the 1:500,000‐scale River Reach File (RF1) and 1:100,000‐scale National Hydrography Dataset (medium‐resolution, NHDPlus) stream networks.     

Water quality trends and changing agricultural practices in a midwest U.S. watershed, 1994-2006

Sediment and nutrient concentrations in surface water in agricultural regions are strongly influenced by agricultural activities. In the Corn Belt, recent changes in farm management practices are likely to affect water quality, yet there are few data on these linkages at the landscape scale. We report on trends in concentrations of N as ammonium (NH(4)) and nitrate (NO(3)), soluble reactive phosphorus (SRP), and suspended sediment (SS) in three Corn Belt streams with drainage areas of 12 to 129 km(2) for 1994 through 2006. During this period, there has been an increase in conservation tillage, a decline in fertilizer use, and consolidation of animal feeding operations in our study watersheds and throughout the Corn Belt. We use an autoregressive moving average model to include the effects of discharge and season on concentrations, LOWESS plots, and analyses of changes in the relation between discharge and concentration. We found significant declines in mean monthly concentrations of NH(4) at all three streams over the 13-yr period, declines in SRP and SS in two of the three streams, and a decline in NO(3) in one stream. When trend coefficients are converted to percent per year and weighted by drainage, area changes in concentration are -8.5% for NH(4), -5.9% for SRP, -6.8% for SS, and -0.8% for NO(3). Trends in total N and P are strongly tied to trends in NO(3), SRP, and SS and indicate that total P is declining, whereas total N is not.     

Applications of Explicitly Incorporated/Post‐Processing Measurement Uncertainty in Watershed Modeling

In the field of watershed modeling, the impact of measurement uncertainty (MU) on calibration results indicates the potential issue of inaccurate model predictions. It is important to note that MU refers to the uncertainty in measured data such as flow and nutrient values that are used to evaluate model outputs. The calculation of error statistics assuming measured data are deterministic may not be appropriate as has been frequently stated in literature. Although MU can affect model calibration results, it is rarely incorporated in modeling practice. MU can be incorporated in two schemes: explicitly incorporated (MU‐EI) during model calibration and post‐processed (MU‐PP) after calibration is completed. In this study, both schemes are implemented in a case study of the Arroyo Colorado Watershed, Texas. Unexpectedly, no substantial differences were observed between each scheme for flow predictions. Although MU did not cause dramatic differences in most sediment and NH₄‐N predictions, error statistics were affected in cases with MU greater than 50%, especially for sediment and NH₄‐N. Therefore, it is concluded that MU may not exert a significant impact on model predictions until certain threshold is reached. This study demonstrates that high levels of uncertainty in measured calibration/validation data significantly affect parameter estimation, especially in the auto‐calibration process.     

Reducing Nitrogen Export from the Corn Belt to the Gulf of Mexico: Agricultural Strategies for Remediating Hypoxia

SPAtially Referenced Regression on Watershed models developed for the Upper Midwest were used to help evaluate the nitrogen‐load reductions likely to be achieved by a variety of agricultural conservation practices in the Upper Mississippi‐Ohio River Basin (UMORB) and to compare these reductions to the 45% nitrogen‐load reduction proposed to remediate hypoxia in the Gulf of Mexico (GoM). Our results indicate that nitrogen‐management practices (improved fertilizer management and cover crops) fall short of achieving this goal, even if adopted on all cropland in the region. The goal of a 45% decrease in loads to the GoM can only be achieved through the coupling of nitrogen‐management practices with innovative nitrogen‐removal practices such as tile‐drainage treatment wetlands, drainage–ditch enhancements, stream‐channel restoration, and floodplain reconnection. Combining nitrogen‐management practices with nitrogen‐removal practices can dramatically reduce nutrient export from agricultural landscapes while minimizing impacts to agricultural production. With this approach, it may be possible to meet the 45% nutrient reduction goal while converting less than 1% of cropland in the UMORB to nitrogen‐removal practices. Conservationists, policy makers, and agricultural producers seeking a workable strategy to reduce nitrogen export from the Corn Belt will need to consider a combination of nitrogen‐management practices at the field scale and diverse nitrogen‐removal practices at the landscape scale.     

Incorporating Uncertainty Into the Ranking of SPARROW Model Nutrient Yields From Mississippi/Atchafalaya River Basin Watersheds1

Abstract: Excessive loads of nutrients transported by tributary rivers have been linked to hypoxia in the Gulf of Mexico. Management efforts to reduce the hypoxic zone in the Gulf of Mexico and improve the water quality of rivers and streams could benefit from targeting nutrient reductions toward watersheds with the highest nutrient yields delivered to sensitive downstream waters. One challenge is that most conventional watershed modeling approaches (e.g., mechanistic models) used in these management decisions do not consider uncertainties in the predictions of nutrient yields and their downstream delivery. The increasing use of parameter estimation procedures to statistically estimate model coefficients, however, allows uncertainties in these predictions to be reliably estimated. Here, we use a robust bootstrapping procedure applied to the results of a previous application of the hybrid statistical/mechanistic watershed model SPARROW (Spatially Referenced Regression On Watershed attributes) to develop a statistically reliable method for identifying “high priority” areas for management, based on a probabilistic ranking of delivered nutrient yields from watersheds throughout a basin. The method is designed to be used by managers to prioritize watersheds where additional stream monitoring and evaluations of nutrient‐reduction strategies could be undertaken. Our ranking procedure incorporates information on the confidence intervals of model predictions and the corresponding watershed rankings of the delivered nutrient yields. From this quantified uncertainty, we estimate the probability that individual watersheds are among a collection of watersheds that have the highest delivered nutrient yields. We illustrate the application of the procedure to 818 eight‐digit Hydrologic Unit Code watersheds in the Mississippi/Atchafalaya River basin by identifying 150 watersheds having the highest delivered nutrient yields to the Gulf of Mexico. Highest delivered yields were from watersheds in the Central Mississippi, Ohio, and Lower Mississippi River basins. With 90% confidence, only a few watersheds can be reliably placed into the highest 150 category; however, many more watersheds can be removed from consideration as not belonging to the highest 150 category. Results from this ranking procedure provide robust information on watershed nutrient yields that can benefit management efforts to reduce nutrient loadings to downstream coastal waters, such as the Gulf of Mexico, or to local receiving streams and reservoirs.     

GIS-based spatial regression and prediction of water quality in river networks: A case study in Iowa

Nonpoint source pollution is the leading cause of the U.S.’s water quality problems. One important component of nonpoint source pollution control is an understanding of what and how watershed-scale conditions influence ambient water quality. This paper investigated the use of spatial regression to evaluate the impacts of watershed characteristics on stream NO₃NO₂-N concentration in the Cedar River Watershed, Iowa. An Arc Hydro geodatabase was constructed to organize various datasets on the watershed. Spatial regression models were developed to evaluate the impacts of watershed characteristics on stream NO₃NO₂-N concentration and predict NO₃NO₂-N concentration at unmonitored locations. Unlike the traditional ordinary least square (OLS) method, the spatial regression method incorporates the potential spatial correlation among the observations in its coefficient estimation. Study results show that NO₃NO₂-N observations in the Cedar River Watershed are spatially correlated, and by ignoring the spatial correlation, the OLS method tends to over-estimate the impacts of watershed characteristics on stream NO₃NO₂-N concentration. In conjunction with kriging, the spatial regression method not only makes better stream NO₃NO₂-N concentration predictions than the OLS method, but also gives estimates of the uncertainty of the predictions, which provides useful information for optimizing the design of stream monitoring network. It is a promising tool for better managing and controlling nonpoint source pollution.     

Landscape Character and Fish Assemblage Structure and Function in Western Lake Superior Streams: General Relationships and Identification of Thresholds

As part of a comparative watershed project investigating land-cover/land-use disturbance gradients for streams in the western Lake Superior Basin, we examined general relationships between landscape character and fish assemblage structure and function. We also examined the shape of those relationships to identify discontinuity thresholds where small changes in landscape character were associated with marked shifts in the fish assemblages. After completing a geographic analysis of second- and third-order watersheds in the western Lake Superior drainage, we randomly selected 48 streams along mature forest and watershed storage gradients in 2 hydrogeomorphic regions as our study sites. During the summers of 1997 and 1998, we used electrofishing to sample fish assemblages from each stream. Each of the landscape factors was significantly associated with fish assemblage structure and function based on analysis of covariance. Watershed storage was related to the greatest number of fish assemblage characteristics, but hydrogeopmorphic region and mature forest cover were strongly associated as well. The hydrogeomorphic region also mediated relationships between watershed character and fish assemblages. Discontinuity thresholds for our fish assemblages averaged 11% for watershed storage and 50% for watershed mature forest cover based on piecewise regression analysis. Although many of the landscape–fish relationships might have been manifest through effects on in-stream habitat, our results highlight the importance of management and land-use planning decisions at the watershed and landscape scales.Published online     

Surface‐Water Nutrient Conditions and Sources in the United States Pacific Northwest1

Wise, Daniel R. and Henry M. Johnson, 2011. Surface‐Water Nutrient Conditions and Sources in the United States Pacific Northwest. Journal of the American Water Resources Association (JAWRA) 47(5):1110‐1135. DOI: 10.1111/j.1752‐1688.2011.00580.x Abstract: The SPAtially Referenced Regressions On Watershed attributes (SPARROW) model was used to perform an assessment of surface‐water nutrient conditions and to identify important nutrient sources in watersheds of the Pacific Northwest region of the United States (U.S.) for the year 2002. Our models included variables representing nutrient sources as well as landscape characteristics that affect nutrient delivery to streams. Annual nutrient yields were higher in watersheds on the wetter, west side of the Cascade Range compared to watersheds on the drier, east side. High nutrient enrichment (relative to the U.S. Environmental Protection Agency’s recommended nutrient criteria) was estimated in watersheds throughout the region. Forest land was generally the largest source of total nitrogen stream load and geologic material was generally the largest source of total phosphorus stream load generated within the 12,039 modeled watersheds. These results reflected the prevalence of these two natural sources and the low input from other nutrient sources across the region. However, the combined input from agriculture, point sources, and developed land, rather than natural nutrient sources, was responsible for most of the nutrient load discharged from many of the largest watersheds. Our results provided an understanding of the regional patterns in surface‐water nutrient conditions and should be useful to environmental managers in future water‐quality planning efforts.     

Estimating Contributions of Nitrate and Herbicides From Groundwater to Headwater Streams,Northern Atlantic Coastal Plain,United States1

Abstract: Groundwater transport often complicates understanding of surface‐water contamination. We estimated the regional flux of nitrate and selected herbicides from groundwater to nontidal headwater streams of the Atlantic Coastal Plain (New Jersey through North Carolina) based on late‐winter or spring base‐flow samples from 174 streams. Sampled streams were selected randomly, and flux estimates are based on resulting population estimates rather than on empirical models, which have been used previously for similar estimates. Base‐flow flux in the estimated 8,834 headwater streams of the study area are an estimated 21,200 kg/day of nitrate (as N) and 5.83, 0.565, and 20.7 kg/day of alachlor, atrazine, and metolachlor (and selected degradates), respectively. Base‐flow flux of alachlor and metolachlor is <3% of the total base‐flow flux of those compounds plus degradates. Base‐flow flux of nitrate and herbicides as a percentage of applications is typically highest in well‐drained areas and lowest in areas with abundant poor drainage and anoxic conditions. In Coastal Plain watersheds of Albemarle and Pamlico Sounds, <2% of applied nitrogen reaches headwater streams as base flow. On the Delmarva Peninsula part of the Chesapeake Bay watershed, however, more than 10% of such applications are transported through groundwater to streams, and base‐flow nitrate flux represents 70% of total nitrogen flux in headwater streams.     

A Multi‐Agency Nutrient Dataset Used to Estimate Loads,Improve Monitoring Design,and Calibrate Regional Nutrient SPARROW Models1

Saad, David A., Gregory E. Schwarz, Dale M. Robertson, and Nathaniel L. Booth, 2011. A Multi‐Agency Nutrient Dataset Used to Estimate Loads, Improve Monitoring Design, and Calibrate Regional Nutrient SPARROW Models. Journal of the American Water Resources Association (JAWRA) 47(5):933‐949. DOI: 10.1111/j.1752‐1688. 2011.00575.x Abstract: Stream‐loading information was compiled from federal, state, and local agencies, and selected universities as part of an effort to develop regional SPAtially Referenced Regressions On Watershed attributes (SPARROW) models to help describe the distribution, sources, and transport of nutrients in streams throughout much of the United States. After screening, 2,739 sites, sampled by 73 agencies, were identified as having suitable data for calculating long‐term mean annual nutrient loads required for SPARROW model calibration. These sites had a wide range in nutrient concentrations, loads, and yields, and environmental characteristics in their basins. An analysis of the accuracy in load estimates relative to site attributes indicated that accuracy in loads improve with increases in the number of observations, the proportion of uncensored data, and the variability in flow on observation days, whereas accuracy declines with increases in the root mean square error of the water‐quality model, the flow‐bias ratio, the number of days between samples, the variability in daily streamflow for the prediction period, and if the load estimate has been detrended. Based on compiled data, all areas of the country had recent declines in the number of sites with sufficient water‐quality data to compute accurate annual loads and support regional modeling analyses. These declines were caused by decreases in the number of sites being sampled and data not being entered in readily accessible databases.     

GIS‐Based Stream Classification in a Mountain Watershed for Jurisdictional Evaluation

This study analyzed stream characteristics in a mountain watershed in southwestern Colorado and developed a three‐level hierarchical classification scheme using national datasets to demonstrate jurisdictional evaluation as “waters of the United States (U.S.)” under U.S. Clean Water Act Section 404 at the watershed scale. The National Hydrography Dataset and USGS StreamStats were used with field observations to classify streams in the 53 km² Cement Creek Watershed based on flow duration (Level 1), stream order (Level 2), and other biophysical metrics (Level 3). Kruskal‐Wallis tests and discriminant analysis showed significant differences among Level 2 classes. Level 3 classification used cluster analysis for stream length, distance to the downstream traditional navigable water (TNW), and the ratio of mean annual flow from the source stream to the TNW. Results showed all perennial and intermittent streams are jurisdictional relatively permanent waters (RPWs), which include over a third of all streams, 64% are intermittent or ephemeral, and almost half are ephemeral first order. All ephemeral reaches are non‐RPWs requiring significant nexus evaluation to determine jurisdiction. These ephemeral first‐order streams can contribute 5% of the annual flow to the TNW at the confluence, while the Cement Creek main stem contributes 21% of the TNW flow. The study demonstrated that the classification provides key biophysical and regulatory information to aid jurisdictional evaluations in mountain watersheds.     

GIS‐Based Predictive Models of Hillslope Runoff Generation Processes1

Abstract: Successful nonpoint source pollution control using best management practice placement is a complex process that requires in‐depth knowledge of the locations of runoff source areas in a watershed. Currently, very few simulation tools are capable of identifying critical runoff source areas on hillslopes and those available are not directly applicable under all runoff conditions. In this paper, a comparison of two geographic information system (GIS)‐based approaches: a topographic index model and a likelihood indicator model is presented, in predicting likely locations of saturation excess and infiltration excess runoff source areas in a hillslope of the Savoy Experimental Watershed located in northwest Arkansas. Based on intensive data collected from a two‐year field study, the spatial distributions of hydrologic variables were processed using GIS software to develop the models. The likelihood indicator model was used to produce probability surfaces that indicated the likelihood of location of both saturation and infiltration excess runoff mechanisms on the hillslope. Overall accuracies of the likelihood indicator model predictions varied between 81 and 87% for the infiltration excess and saturation excess runoff locations respectively. On the basis of accuracy of prediction, the likelihood indicator models were found to be superior (accuracy 81‐87%) to the predications made by the topographic index model (accuracy 69.5%). By combining statistics with GIS, runoff source areas on a hillslope can be identified by incorporating easily determined hydrologic measurements (such as bulk density, porosity, slope, depth to bed rock, depth to water table) and could serve as a watershed management tool for identifying critical runoff source areas in locations where the topographic index or other similar methods do not provide reliable results.