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Selection and placement of best management practices used to reduce water quality degradation in Lincoln Lake watershed" [1] An increased loss of agricultural nutrients is a growing concern for water quality in Arkansas. Several studies have shown that best management practices (BMPs) are effective in controlling water pollution. However, those affected(More)
Total mercury (THg) and mono-methylmercury (MeHg) levels in water, sediment, and largemouth bass (LMB) (Micropterus salmoides) were investigated at 52 sites draining contrasting land use/land cover and habitat types within the Mobile Alabama River Basin (MARB). Aqueous THg was positively associated with iron-rich suspended particles and highest in(More)
This paper describes the effect of DEM data resolution on predictions from the SWAT model. Measured hydrologic, meteorological, watershed characteristics and water quality data from Moores Creek watershed (near Lincoln, AR, USA) were used in the simulation. The effect of input data resolution was evaluated by running seven scenarios at increasing DEM grid(More)
There is an abundant supply of corn stover in the United States that remains after grain is harvested which could be used to produce cellulosic biofuels mandated by the current Renewable Fuel Standard (RFS). This research integrates the Soil Water Assessment Tool (SWAT) watershed model and the DayCent biogeochemical model to investigate water quality and(More)
Lake water quality monitoring using traditional water sampling and laboratory analyses is very expensive and time consuming. Application of neural networks to predict water quality using satellite imagery data has a potential to make the water quality determination process cost-effective, quick, and feasible. This paper includes an indirect method of(More)
This paper reports on an evaluation of the use of artificial neural network (ANN) models to forecast daily flows at multiple gauging stations in Eucha Watershed, an agricultural watershed located in northwest Arkansas and northeast Oklahoma. Two different neural network models, the multilayer perceptron (MLP) and the radial basis neural network (RBFNN),(More)
There has been a steady shift towards modeling and model-based approaches as primary methods of assessing watershed response to hydrologic inputs and land management, and of quantifying watershed-wide best management practice (BMP) effectiveness. Watershed models often require some degree of calibration and validation to achieve adequate watershed and(More)
The project goal was to loosely couple the SWAT model and the QUAL2E model and compare their combined ability to predict total phosphorus (TP) and NO 3-N plus NO 2-N yields to the ability of the SWAT model with its completely coupled water quality components to predict TP and NO 3-N plus NO 2-N yields from War Eagle Creek watershed in Northwest Arkansas.(More)
[1] One of the principal sources of uncertainty in hydrological models is the absence of understanding of the complex physical processes of the hydrological cycle within the system. This leads to uncertainty in input selection and consequently its associated parameters, and hence evaluation of uncertainty in a model becomes important. While there has been(More)
Tillage information is crucial for environmental modeling as it directly affects evapotranspiration, infiltration, runoff, carbon sequestration, and soil losses due to wind and water erosion from agricultural fields. However, collecting this information can be time consuming and costly. Remote sensing approaches are promising for rapid collection of tillage(More)