Effectiveness of BMP plans in different land covers, with random, targeted, and optimized allocation

The ability of 5 Best Management Practice (BMP) allocation methods that consider 8 pre-selected BMPs, to control 4 Nonpoint Source (NPS) constituents in 4 watersheds with contrasting land covers, is investigated. The methods range from random selection of BMPs on randomly selected sites, to optimized selection of BMPs at optimized locations, and the land covers range from natural to ultra-urban. The optimization methods rely on Genetic Algorithms (GA), and a method that uses expert systems is also applied. Watershed hydrologic and water quality response models are developed, using the Soil Water Assessment Tool (SWAT), to compute baseline outputs from the 4 study watersheds without BMPs, and to obtain predicted reductions in NPS constituent outputs when BMPs are implemented in accordance with the 5 allocation plans. Methods used to represent BMPs in SWAT and to speed up optimization processes are also presented. Results indicate that the most computationally intensive methods produce the best results across landscape types. Results also show that opportunities exist for less intensive methods, particularly in less-built environments. For these, however, siting BMPs to hotspots remains an important requirement. The need to select the most appropriate BMP for each implementation site is observed to increase with the level of urbanization of the landscape. Results indicate that optimized selection of BMPs, sited at optimized locations, results in the highest-performing BMP allocation plans across landscape types. Also, the focus on hotspots has the advantage of resulting in BMP plans requiring involvement of fewer stakeholders than when BMPs are located in non-hotspot zones. This targeted hotspot approach could help reduce cost and increase efficiency of implementation.