Watershed Modeling of BMP Scenarios to Improve Agricultural Water Quality –A Case Study in the Orestimba Creek Watershed, California.

Quantifying the effectiveness of agricultural best management practices (BMPs) at watershed scale is a challenging issue, requiring robust algorithms to simulate not only the agricultural production system but also pollutant transport and fate. The research takes this challenge to simulate and potentially improve the performances of BMPs in reducing runoff of organophosphate (OP) pesticides (diazinon and chlorpyrifos) at the watershed scale. Parameter sensitivities are evaluated using a combined method of Latin Hypercube sampling and One-factor-At-a-Time simulation (LH-OAT sensitivity analysis). The Soil and Water Assessment Tool (SWAT) model is successfully calibrated with Nash-Sutcliffe coefficients over 0.92 and 0.82 for monthly simulation of diazinon and chlorpyrifos, respectively. The calibrated model is then applied in the Orestimba Creek Watershed to simulate BMPs including buffer strips, sediment ponds, vegetated ditches, use reductions, and their combinations. The sensitivity analysis revealed that transport and fate of pesticides at watershed level is greatly impacted by surface runoff and physico-chemical properties. Rainfall intensity and pesticide application timing determines the peaks of pesticide load in surface water. Simulation of BMP scenarios suggested that combining vegetated ditches and buffer strips in addition to pesticide use reduction would decrease by over 94% the dissolved diazinon and chlorpyrifos. The effectiveness in removing both OPs were above 89% and 30% for buffer strips and vegetated ditches, respectively, while those for sediment ponds were only 3-10%. This study has demonstrated that SWAT model can reasonably predict BMP effectiveness at watershed scale. However, the model can be further improved by enhancing the irrigation algorithm to allow water input when soil moisture exceeds field capacity and by including more adjustable parameters for representing BMP mitigation processes. Future research will also include incorporating Monte-Carol simulation to derive probabilistic distribution functions of BMP effectiveness and linking the SWAT model with a field-scale based model. The research findings can be widely applied to facilitate BMP implementation and evaluation through (1) simulating BMP performance under various environmental conditions; (2) estimating annual pollutant removal, and (3) evaluating BMP design options.