Characterizing the Extent of Spatially Integrated Floodplain and Wetland Systems in the White River,Indiana, USA

Floodplain delineation may inform protection of wetland systems under local, state, or federal laws. Nationally available Federal Emergency Management Agency Flood Insurance Rate Maps (FIRMs, “100‐year floodplain” maps) focus on urban areas and higher‐order river systems, limiting utility at large scales. Few other national‐scale floodplain data are available. We acquired FIRMs for a large watershed and compared FIRMs to floodplain and integrated wetland area mapping methods based on (1) geospatial distance, (2) geomorphic setting, and (3) soil characteristics. We used observed flooding events (OFEs) with recurrence intervals of 25‐50 to >100 years to assess floodplain estimate accuracy. FIRMs accurately reflected floodplain areas based on OFEs and covered 32% of river length, whereas soil‐based mapping was not as accurate as FIRMs but characterized floodplain areas over approximately 65% of stream length. Geomorphic approaches included more areas than indicated by OFE, whereas geospatial approaches tended to cover less area. Overall, soil‐based methods have the highest utility in determining floodplains and their integrated wetland areas at large scales due to the use of nationally available data and flexibility for regional application. These findings will improve floodplain and integrated wetland system extent assessment for better management at local, state, and national scales.     

The Regionalization of National‐Scale SPARROW Models for Stream Nutrients1

Schwarz, Gregory E., Richard B. Alexander, Richard A. Smith, and Stephen D. Preston, 2011. The Regionalization of National‐Scale SPARROW Models for Stream Nutrients. Journal of the American Water Resources Association (JAWRA) 47(5):1151‐1172. DOI: 10.1111/j.1752‐1688.2011.00581.x Abstract: This analysis modifies the parsimonious specification of recently published total nitrogen (TN) and total phosphorus (TP) national‐scale SPAtially Referenced Regressions On Watershed attributes models to allow each model coefficient to vary geographically among three major river basins of the conterminous United States. Regionalization of the national models reduces the standard errors in the prediction of TN and TP loads, expressed as a percentage of the predicted load, by about 6 and 7%. We develop and apply a method for combining national‐scale and regional‐scale information to estimate a hybrid model that imposes cross‐region constraints that limit regional variation in model coefficients, effectively reducing the number of free model parameters as compared to a collection of independent regional models. The hybrid TN and TP regional models have improved model fit relative to the respective national models, reducing the standard error in the prediction of loads, expressed as a percentage of load, by about 5 and 4%. Only 19% of the TN hybrid model coefficients and just 2% of the TP hybrid model coefficients show evidence of substantial regional specificity (more than ±100% deviation from the national model estimate). The hybrid models have much greater precision in the estimated coefficients than do the unconstrained regional models, demonstrating the efficacy of pooling information across regions to improve regional models.     

A Gis‐Based Approach to Watershed Classification for Nebraska Reservoirs1

Abstract: The U.S. Environmental Protection Agency is charged with establishing standards and criteria for assessing lake water quality. It is, however, increasingly evident that a single set of national water quality standards that do not take into account regional hydrogeologic and ecological differences will not be viable as lakes clearly have different inherent capacities to meet such standards. We demonstrate a GIS‐based watershed classification strategy for identifying groups of Nebraska reservoirs that have similar potential capacity to attain a certain level of water quality standard. A preliminary cluster analysis of 78 reservoirs was performed to determine the potential number of Nebraska reservoir groups. Subsequently, a Classification Trees method was used to refine number of classes, describe the structure of reservoir watershed classes, and to develop a predictive model that relates watershed conditions to reservoir classes. Results suggest that Nebraska reservoirs can be represented by nine classes and that soil organic matter content in the watershed is the most important single variable for segregating the reservoirs. The cross‐validation prediction error rate of the Classification Tree model was 26.3%. Because all geospatial data used in this work are available nationally, the method could be adopted throughout the U.S. Hence, this GIS‐based watershed classification approach could provide water resources managers an effective decision‐support tool in managing reservoir water quality.     

A New Open‐Access HUC‐8 Based Downscaled CMIP‐5 Climate Model Forecast Dataset for the Conterminous United States

Watershed‐scale hydrologic simulation models generally require climate data inputs including precipitation and temperature. These climate inputs can be derived from downscaled global climate simulations which have the potential to drive runoff forecasts at the scale of local watersheds. While a simulation designed to drive a local watershed model would ideally be constructed at an appropriate scale, global climate simulations are, by definition, arbitrarily determined large rectangular spatial grids. This paper addresses the technical challenge of making climate simulation model results readily available in the form of downscaled datasets that can be used for watershed scale models. Specifically, we present the development and deployment of a new Coupled Model Intercomparison Project phase 5 (CMIP5) based database which has been prepared through a scaling and weighted averaging process for use at the level of U.S. Geological Survey (USGS) Hydrologic Unit Code (HUC)‐8 watersheds. The resulting dataset includes 2,106 virtual observation sites (watershed centroids) each with 698 associated time series datasets representing average monthly temperature and precipitation between 1950 and 2099 based on 234 unique climate model simulations. The new dataset is deployed on a HydroServer and distributed using WaterOneFlow web services in the WaterML format. These methods can be adapted for downscaled General Circulation Model (GCM) results for specific drainage areas smaller than HUC‐8. Two example use cases for the dataset also are presented.     

Spatiotemporal Variability of Snow Depletion Curves Derived from SNODAS for the Conterminous United States, 2004‐2013

Assessment of water resources at a national scale is critical for understanding their vulnerability to future change in policy and climate. Representation of the spatiotemporal variability in snowmelt processes in continental‐scale hydrologic models is critical for assessment of water resource response to continued climate change. Continental‐extent hydrologic models such as the U.S. Geological Survey National Hydrologic Model (NHM) represent snowmelt processes through the application of snow depletion curves (SDCs). SDCs relate normalized snow water equivalent (SWE) to normalized snow covered area (SCA) over a snowmelt season for a given modeling unit. SDCs were derived using output from the operational Snow Data Assimilation System (SNODAS) snow model as daily 1‐km gridded SWE over the conterminous United States. Daily SNODAS output were aggregated to a predefined watershed‐scale geospatial fabric and used to also calculate SCA from October 1, 2004 to September 30, 2013. The spatiotemporal variability in SNODAS output at the watershed scale was evaluated through the spatial distribution of the median and standard deviation for the time period. Representative SDCs for each watershed‐scale modeling unit over the conterminous United States (n = 54,104) were selected using a consistent methodology and used to create categories of snowmelt based on SDC shape. The relation of SDC categories to the topographic and climatic variables allow for national‐scale categorization of snowmelt processes.     

Evaluation of Three Watershed‐Scale Pesticide Environmental Transport and Fate Models1

Abstract: The U.S. Environmental Protection Agency (USEPA) Office of Pesticide Programs (OPP) has completed an evaluation of three watershed‐scale simulation models for potential use in Food Quality Protection Act pesticide drinking water exposure assessments. The evaluation may also guide OPP in identifying computer simulation tools that can be used in performing aquatic ecological exposure assessments. Models selected for evaluation were the Soil Water Assessment Tool (SWAT), the Nonpoint Source Model (NPSM), a modified version of the Hydrologic Simulation Program‐Fortran (HSPF), and the Pesticide Root Zone Model‐Riverine Water Quality (PRZM‐RIVWQ) model. Simulated concentrations of the pesticides atrazine, metolachlor, and trifluralin in surface water were compared with field data monitored in the Sugar Creek watershed of Indiana’s White River basin by the National Water Quality Assessment (NAWQA) program. The evaluation not only provided USEPA with experience in using watershed models for estimating pesticide concentration in flowing water but also led to the development of improved statistical techniques for assessing model accuracy. Further, it demonstrated the difficulty of representing spatially and temporally variable soil, weather, and pesticide applications with relatively infrequent, spatially fixed, point estimates. It also demonstrated the value of using monitoring and modeling as mutually supporting tools and pointed to the need to design monitoring programs that support modeling.     

Evaluation of Methods for Delineating Riparian Zones in a Semi‐Arid Montane Watershed

Riparian zones in semi‐arid, mountainous regions provide a disproportionate amount of the available wildlife habitat and ecosystem services. Despite their importance, there is little guidance on the best way to map riparian zones for broad spatial extents (e.g., large watersheds) when detailed maps from field data or high‐resolution imagery and terrain data are not available. Using well‐established accuracy metrics (e.g., kappa, precision, computational complexity), we evaluated eight methods commonly used to map riparian zones. Focusing on a semi‐arid, mountainous watershed, we found that the most accurate and robust method for mapping riparian zones combines data on upstream drainage area and valley topography. That method performed best regardless of stream order, and was most effective when implemented with fine resolution topographic and stream line data. Other commonly used methods to model riparian zones, such as those based on fixed‐width buffers, yielded inaccurate results. We recommend that until very‐high resolution (<1 m) elevation data are available at broad extents, models of riparian zones for semi‐arid mountainous regions should incorporate drainage area, valley topography, and quantify uncertainty.     

INTERFACING GIS WITH WATER RESOURCE MODELS: A STATE‐OF‐THE‐ART REVIEW1

Two distinctive, independently developed technologies, geographic information systems (GIS) and predictive water resource models, are being interfaced with varying degrees of sophistication in efforts to simultaneously examine spatial and temporal phenomena. Neither technology was initially developed to interact with the other, and as a result, multiple approaches to interface GIS with water resource models exist. Additionally, continued model enhancements and the development of graphical user interfaces (GUIs) have encouraged the development of application “suites” for evaluation and visualization of engineering problems. Currently, disparities in spatial scales, data accessibility, modeling software preferences, and computer resources availability prevent application of a universal interfacing approach. This paper provides a state‐of‐the‐art critical review of current trends in interfacing GIS with predictive water resource models. Emphasis is placed on discussing limitations to efficient interfacing and potential future directions, including recommendations for overcoming many current challenges.     

GIS‐Based Spatial Precipitation Estimation: A Comparison of Geostatistical Approaches1

Abstract: As one of the primary inputs that drive watershed dynamics, the estimation of spatial variability of precipitation has been shown to be crucial for accurate distributed hydrologic modeling. In this study, a Geographic Information System program, which incorporates Nearest Neighborhood (NN), Inverse Distance Weighted (IDW), Simple Kriging (SK), Ordinary Kriging (OK), Simple Kriging with Local Means (SKlm), and Kriging with External Drift (KED), was developed to facilitate automatic spatial precipitation estimation. Elevation and spatial coordinate information were used as auxiliary variables in SKlm and KED methods. The above spatial interpolation methods were applied in the Luohe watershed with an area of 5,239 km², which is located downstream of the Yellow River basin, for estimating 10 years’ (1991‐2000) daily spatial precipitation using 41 rain gauges. The results obtained in this study show that the spatial precipitation maps estimated by different interpolation methods have similar areal mean precipitation depth, but significantly different values of maximum precipitation, minimum precipitation, and coefficient of variation. The accuracy of the spatial precipitation estimated by different interpolation methods was evaluated using a correlation coefficient, Nash‐Sutcliffe efficiency, and relative mean absolute error. Compared with NN and IDW methods that are widely used in distributed hydrologic modeling systems, the geostatistical methods incorporated in this GIS program can provide more accurate spatial precipitation estimation. Overall, the SKlm_EL_X and KED_EL_X, which incorporate both elevation and spatial coordinate as auxiliary into SKlm and KED, respectively, obtained higher correlation coefficient and Nash‐Sutcliffe efficiency, and lower relative mean absolute error than other methods tested. The GIS program developed in this study can serve as an effective and efficient tool to implement advanced geostatistics methods that incorporate auxiliary information to improve spatial precipitation estimation for hydrologic models.     

WATER QUALITY MODEL OUTPUT UNCERTAINTY AS AFFECTED BY SPATIAL RESOLUTION OF INPUT DATA1

ABSTRACT: Resolution of the input GIS data used to parameterize distributed‐parameter hydrologic/water quality models may affect uncertainty in model outputs and impact the subsequent application of model results in watershed management. In this study we evaluated the impact of varying spatial resolutions of DEM, land use, and soil data (30 × 30 m, 100 × 100 m, 150 × 150 m, 200 × 200 m, 300 × 300 m, 500 × 500 m, and 1,000 × 1,000 m) on the uncertainty of SWAT predicted flow, sediment, NO₃‐N, and TP transport. Inputs included measured hydrologic, meteorological, and watershed characteristics as well as water quality data from the Moores Creek watershed in Washington County, Arkansas. The SWAT model output was most affected by input DEM data resolution. A coarser DEM data resolution resulted in decreased representation of watershed area and slope and increased slope length. Distribution of pasture, forest, and urban areas within the watershed was significantly affected at coarser resolution of land use and resulted in significant uncertainty in predicted sediment, NO₃‐N, and TP output. Soils data resolution had no significant effect on flow and NO₃‐N predictions; however, sediment was overpredicted by 26 percent, and TP was underpredicted by 26 percent at 1,000 m resolution. This may be due to change in relative distribution of various hydrologic soils groups (HSGs) in the watershed. Minimum resolution for input GIS data to achieve less than 10 percent model output error depended upon the output variable of interest. For flow, sediment, NO₃‐N, and TP predictions, minimum DEM data resolution should range from 30 to 300 m, whereas minimum land use and soils data resolution should range from 300 to 500 m.     

HYDROLOGIC PARAMETERIZATION OF WATERSHEDS FOR RUNOFF PREDICTION USING SWAT1

ABSTRACT: The use of continuous time, distributed parameter hydrologic models like SWAT (Soil and Water Assessment Tool) has opened several opportunities to improve watershed modeling accuracy. However, it has also placed a heavy burden on users with respect to the amount of work involved in parameterizing the watershed in general and in adequately representing the spatial variability of the watershed in particular. Recent developments in Geographical Information Systems (GIS) have alleviated some of the difficulties associated with managing spatial data. However, the user must still choose among various parameterization approaches that are available within the model. This paper describes the important parameterization issues involved when modeling watershed hydrology for runoff prediction using SWAT with emphasis on how to improve model performance without resorting to tedious and arbitrary parameter by parameter calibration. Synthetic and actual watersheds in Indiana and Mississippi were used to illustrate the sensitivity of runoff prediction to spatial variability, watershed decomposition, and spatial and temporal adjustment of curve numbers and return flow contribution. SWAT was also used to predict stream runoff from actual watersheds in Indiana that have extensive subsurface drainage. The results of this study provide useful information for improving SWAT performance in terms of stream runoff prediction in a manner that is particularly useful for modeling ungaged watersheds wherein observed data for calibration is not available.     

Modeling Watershed‐Scale Impacts of Stormwater Management with Traditional versus Low Impact Development Design

Stormwater runoff and associated pollutants from urban areas in the greater Chesapeake Bay Watershed (CBW) impair local streams and downstream ecosystems, despite urbanized land comprising only 7% of the CBW area. More recently, stormwater best management practices (BMPs) have been implemented in a low impact development (LID) manner to treat stormwater runoff closer to its source. This approach included the development of a novel BMP model to compare traditional and LID design, pioneering the use of comprehensively digitized storm sewer infrastructure and BMP design connectivity with spatial patterns in a geographic information system at the watershed scale. The goal was to compare total watershed pollutant removal efficiency in two study watersheds with differing spatial patterns of BMP design (traditional and LID), by quantifying the improved water quality benefit of LID BMP design. An estimate of uncertainty was included in the modeling framework by using ranges for BMP pollutant removal efficiencies that were based on the literature. Our model, using Monte Carlo analysis, predicted that the LID watershed removed approximately 78 kg more nitrogen, 3 kg more phosphorus, and 1,592 kg more sediment per square kilometer as compared with the traditional watershed on an annual basis. Our research provides planners a valuable model to prioritize watersheds for BMP design based on model results or in optimizing BMP selection.     

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.     

Effectiveness of Multi‐Site Weather Generator for Hydrological Modeling1

Khalili, Malika, François Brissette, and Robert Leconte, 2011. Effectiveness of Multi‐site Weather Generator for Hydrological Modeling. Journal of the American Water Resources Association (JAWRA) 1‐12. DOI: 10.1111/j.1752‐1688.2010.00514.x Abstract: A multi‐site weather generator has been developed using the concept of spatial autocorrelation. The multi‐site generation approach reproduces the spatial autocorrelations observed between a set of weather stations as well as the correlations between each pair of stations. Its performance has been assessed in two previous studies using both precipitation and temperature data. The main objective of this paper is to assess the efficiency of this multi‐site weather generator compared to a uni‐site generator with respect to hydrological modeling. A hydrological model, known as Hydrotel, was applied over the Chute du Diable watershed, located in the Canadian province of Quebec. The distributed nature of Hydrotel accounts for the spatial variations throughout the watershed, and thus allows a more in‐depth assessment of the effect of spatially dependent meteorological input on runoff generation. Simulated streamflows using both the multi‐site and uni‐site generated weather data were statistically compared to flows modeled using observed data. Overall, the hydrological modeling using the multi‐site weather generator significantly outperformed that using the uni‐site generator. This latter combined to Hydrotel resulted in a significant underestimation of extreme streamflows in all seasons.     

Examining Modeling Approaches for the Rainfall‐Runoff Process in Wildfire‐Affected Watersheds: Using San Dimas Experimental Forest

Wildfire can significantly change watershed hydrological processes resulting in increased risks for flooding, erosion, and debris flow. The goal of this study was to evaluate the predictive capability of hydrological models in estimating post‐fire runoff using data from the San Dimas Experimental Forest (SDEF), San Dimas, California. Four methods were chosen representing different types of post‐fire runoff prediction methods, including a Rule of Thumb, Modified Rational Method (MODRAT), HEC‐HMS Curve Number, and KINematic Runoff and EROSion Model 2 (KINEROS2). Results showed that simple, empirical peak flow models performed acceptably if calibrated correctly. However, these models do not reflect hydrological mechanisms and may not be applicable for predictions outside the area where they were calibrated. For pre‐fire conditions, the Curve Number approach implemented in HEC‐HMS provided more accurate results than KINEROS2, whereas for post‐fire conditions, the opposite was observed. Such a trend may imply fundamental changes from pre‐ to post‐fire hydrology. Analysis suggests that the runoff generation mechanism in the watershed may have temporarily changed due to fire effects from saturation‐excess runoff or subsurface storm dominated complex mechanisms to an infiltration‐excess dominated mechanism. Infiltration modeling using the Hydrus‐1D model supports this inference. Results of this study indicate that physically‐based approaches may better reflect this trend and have the potential to provide consistent and satisfactory prediction.     

IDW‐Plus: An ArcGIS Toolset for Calculating Spatially Explicit Watershed Attributes for Survey Sites

Watershed characteristics such as land‐use and land‐cover affect stream condition at multiple scales, but it is widely accepted that conditions in close proximity to the stream or survey site tend to have a stronger influence. Although spatially weighted watershed metrics have existed for years, nonspatial lumped landscape metrics (i.e., areal mean or percentage) are still widely used because relatively few technical skills are needed to implement them. The Inverse Distance Weighted Percent Land Use for Streams (IDW‐Plus) custom ArcGIS toolset provides the functionality to efficiently calculate six spatially explicit watershed metrics which account for the Euclidean or flow length distance to the stream or outlet, as well as the probability for overland runoff. These include four distance‐weighted metrics, those being inverse Euclidean distance to the stream or outlet, and the inverse flow length to the stream or outlet. Two tools are also included to generate hydrologically active (i.e., runoff potential), inverse flow length to the stream or outlet metrics. We demonstrate the tools using real data from Southeast Queensland, Australia. We also provide detailed instructions, so readers can recreate the examples themselves before applying the tools to their own data.     

Regional Scale Stressor‐Response Models in Aquatic Ecosystems1

Abstract: A systematic method for identification and estimation of regional scale stressor‐response models in aquatic ecosystems will be useful in monitoring and assessment of aquatic resources, determination of regional nutrient criteria and for increased understanding of the differences between regions. The model response variable is chlorophyll a, a measure of algal density, while the stressors include nutrient concentrations from the USEPA Nutrient Criteria Database (NCD) for lakes/ponds and reservoirs of the continental United States. The NCD has observations for both stressors and biological responses determined using methods that are not consistently available at the continental scale. To link multiple environmental stressors to biological responses and quantify uncertainty in model predictions, we take a multilevel modeling approach to the estimation of a linear model for prediction of log Chlorophyll a using predictors log TP and log TN. The multilevel modeling approach allows us to adjust the impact of covariates at all levels (observation, higher level groups) for the simultaneous operation of contextual and individual variability in the outcome. Here, we wish to allow separate regression coefficients for inference regarding similarities and differences between each of 14 ecoregions, and between the two water‐body types, lakes/ponds and reservoirs. We are also interested in the nuisance effects of the categorical variables indicating the type of nitrogen measurements (three levels) and the type of chlorophyll a measurements (four levels) used. Model‐based determination of nutrient criteria points to an apparent incompatibility of criteria developed for nutrient stressors and eutrophication responses using current Environmental Protection Agency’s guidance.     

The Road to NHDPlus — Advancements in Digital Stream Networks and Associated Catchments

A progression of advancements in Geographic Information Systems techniques for hydrologic network and associated catchment delineation has led to the production of the National Hydrography Dataset Plus (NHDPlus). NHDPlus is a digital stream network for hydrologic modeling with catchments and a suite of related geospatial data. Digital stream networks with associated catchments provide a geospatial framework for linking and integrating water‐related data. Advancements in the development of NHDPlus are expected to continue to improve the capabilities of this national geospatial hydrologic framework. NHDPlus is built upon the medium‐resolution NHD and, like NHD, was developed by the U.S. Environmental Protection Agency and U.S. Geological Survey to support the estimation of streamflow and stream velocity used in fate‐and‐transport modeling. Catchments included with NHDPlus were created by integrating vector information from the NHD and from the Watershed Boundary Dataset with the gridded land surface elevation as represented by the National Elevation Dataset. NHDPlus is an actively used and continually improved dataset. Users recognize the importance of a reliable stream network and associated catchments. The NHDPlus spatial features and associated data tables will continue to be improved to support regional water quality and streamflow models and other user‐defined applications.     

Long‐Term Water Quality and Biological Responses to Multiple Best Management Practices in Rock Creek,Idaho1

Abstract: Water quality and macroinvertebrate assemblage data from 1981 to 2005 were assessed to evaluate the water quality and biological responses of a western trout stream to the implementation of multiple best management practices (BMPs) on irrigated cropland. Data from Rock Creek near Twin Falls, Idaho, a long‐term monitoring site, were assembled from state and federal sources to provide the evaluation. Seasonal loads of the nonpoint source pollutants suspended sediment (SS), total phosphorus (TP), and nitrate‐nitrite (NN) were estimated using a regression model with time‐series streamflow data and constituent concentrations. Trends in the macroinvertebrate assemblages were evaluated using a number of biological metrics and nonmetric multidimensional scaling ordination. Regression analysis found significant annual decreases in TP and SS flow‐adjusted concentrations during the BMP implementation period from 1983 to 1990 of about 7 and 10%, respectively. These results are coincident with the implementation of multiple BMPs on about 75% of the irrigated cropland in the watershed. Macroinvertebrate assemblages during this time also responded with a change in taxa composition resulting in improved biotic index scores. Taxon specific TP and SS optima, empirically derived from a large national dataset, predicted a decrease in SS concentrations of about 37% (52 to 33 mg/l) and a decrease in TP concentrations of about 50% (0.20 to 0.10 mg/l) from 1981 to 1987. Decreasing trends in TP, SS, and NN pollutant loads were primarily the result of naturally low streamflow conditions during the BMP post‐implementation period from 1993 to 2005. Trends in macroinvertebrate responses during 1993 to 2005 were confounded by the introduction of the New Zealand mudsnail (Potamopyrgus  antipodarum), which approached densities of 100,000 per m² in riffle habitat. The occurrence of this invasive species appears to have caused a major shift in composition and function of the macroinvertebrate assemblages.