Assessing the impact of the MRBI [Mississippi River Basin Healthy Watersheds Initiative] program in a data limited Arkansas Watershed using the SWAT model
2018
Leh, Mansoor | Sharpley, A.N. | Singh, G. | Matlock, M.D.
The USDA Natural Resources Conservation Service (NRCS) developed the Mississippi River Basin Healthy Watersheds Initiative (MRBI) program to improve the health, water quality and wildlife habitat within the Mississippi River Basin. Lake Conway Point Remove (LCPR) watershed was identified as one of the watersheds for the MRBI program implementation. The goal of this paper is to evaluate the effectiveness of the MRBI program in LCPR watershed using a computer simulation model. Seven best management practices (BMPs) (pond, wetland, pond and wetland, cover crops, vegetative filter strips, grassed waterways and forage and biomass planting) were modelled under four placement strategies: random placement in 30% of the watershed, random placement in 30% hydrologic response units (HRUs) of the high priority hydrological unit code (HUCs), placement in the top 30% of the high priority HUCs, and top 30% of the HRUs in the HUCs near the outlet of the watershed. The model was calibrated for flow for the period 1987–2006 and validated for the period 2007–2012. Sediment and nutrients were validated from 2011 to 2012. Out of the BMPs evaluated, grassed waterways proved to be the most effective BMP in reducing sediment and nutrient loads from row crop (soy beans) and pasture fields. Reductions at the watershed outlet ranged 0–1% for flow, 0.28–14% for sediment, 0.3–10% for TP and 0.3–9% for TN. Relatively higher reductions were observed at the subwatershed level, flow reductions ranged 0–51%, sediment reductions -1 to 79%, TP -1 to 65% and TN -0.37 to 66% depending on BMP type, placement scenario, and watershed characteristics. The results from this study provide the data to help prioritize monitoring needs for collecting watershed response data in LCPR and BMP implementation evaluations, which could be used to inform decisions in similar studies.
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