[1] An optimization algorithm linked with a nonpoint source (NPS) pollution model can be used to optimize NPS pollution control strategies on a field-by-field basis in a watershed by maximizing NPS pollution reduction and net monetary return. In this paper a methodology is described which integrated a genetic algorithm (GA) (an optimization algorithm) with a continuous simulation, watershed-scale, NPS pollution model, Annualized Agricultural Non-Point Source Pollution model (AnnAGNPS) to optimize the selection of best management practices (BMP) on a field-by-field basis for an entire watershed. To test the methodology, optimization analysis was performed for a U.S. Department of Agriculture experimental watershed in Pennsylvania to identify BMPs that minimized long-term (over a 4-year period) water quality degradation and maximized net farm return on an annual basis. Results indicate that the GA was able to identify BMP schemes that reduced pollutant load by as much as 56% and increased net annual return by 109%.

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The performance of the Soil and Water Assessment Tool (SWAT) and artificial neural network (ANN) models in simulating hydrologic response was assessed in an agricultural watershed in southeastern Pennsylvania. All of the performance evaluation measures including Nash‐Sutcliffe coefficient of efficiency (E) and coefficient of determination (R2) suggest that the ANN monthly predictions were closer to the observed flows than the monthly predictions from the SWAT model. More specifically, monthly streamflow E and R2 were 0.54 and 0.57, respectively, for the SWAT model calibration period, and 0.71 and 0.75, respectively, for the ANN model training period. For the validation period, these values were −0.17 and 0.34 for the SWAT and 0.43 and 0.45 for the ANN model. SWAT model performance was affected by snowmelt events during winter months and by the model's inability to adequately simulate base flows. Even though this and other studies using ANN models suggest that these models provide a viable alternative approach for hydrologic and water quality modeling, ANN models in their current form are not spatially distributed watershed modeling systems. However, considering the promising performance of the simple ANN model, this study suggests that the ANN approach warrants further development to explicitly address the spatial distribution of hydrologic/water quality processes within watersheds.

1. The restoration of native, forested riparian habitats is a widely accepted method for improving degraded streams. Little is known, however, about how the width, extent and continuity of forested vegetation along stream networks affect stream ecosystems. 2. To increase the likelihood of achieving restoration goals, restoration practitioners require quantitative tools to guide the development of restoration strategies in different catchment settings. We present an empirically based model that establishes a relationship between a 'stress' imposed at different locations along a stream by the spatial pattern of land cover within catchments, and the response of biologically determined ecosystem characteristics to this stress. The model provides a spatially explicit, quantitative framework for predicting the effects of changes in catchment land cover composition and spatial configuration on specific characteristics of stream ecosystems. 3. We used geospatial datasets and biological data for attached algae and benthic macroinvertebrates in streams to estimate model parameters for 40 sites in 33 distinct catchments within the mid-Atlantic Piedmont region of the eastern U.S. Model parameters were estimated using a genetic optimisation algorithm. R 2 values for the resulting relationships between catchment land cover and biological characteristics of streams were substantially improved over R 2 values for spatially aggregated regression models based on whole-catchment land cover. 4. Using model parameters estimated for the mid-Atlantic Piedmont, we show how the model can be used to guide restoration planning in a case study of a small catchment. The model predicts the quantitative change in biological characteristics of the stream, such as indices of species diversity and species composition, that would occur with the implementation of a hypothetical restoration project.