More recently, water quality issues in the United States and Canada, such as the widespread contamination of groundwater with nitrates (e.g. Around the same time came a more general increased awareness surrounding the issue of non-point source (NPS) pollution from agricultural lands, which eventually led to what were, perhaps, some of the first attempts to incentivize the adoption of agricultural best management practices (BMP)-such as conservation tillage-for purposes beyond soil conservation ( Hayes and Young 1982 Logan 1993). The 1960s brought new awareness with the publication of Silent Spring ( Carson 1962), which focused not on the denudation of agricultural lands, but instead on the downstream impacts of agricultural practices-specifically pesticide application-on wildlife. 1 IntroductionĪwareness of the environmental consequences resulting from agricultural production dates back at least to the 1920s with the publication of Soil Erosion: A National Menace ( Bennett and Chapline 1928), which some consider the start of modern soil conservation ( Renschler and Harbor 2002).
Results show general agreement with literature reported nutrient reduction values, but more testing of these capabilities is required.
Three simulation scenarios were run and results are reported for a single model subcatchment. The Storm Water Management Model (SWMM) was originally developed for urban watershed modelling, but its robust hydraulic simulation capabilities have been applied in conjunction with new tools developed within PCSWMM in order to simulate the downstream impacts of a suite of agricultural best management practices (BMPs) in a watershed in rural Ontario, Canada. In rural watersheds with complex stormwater conveyance systems, models designed for agricultural landscapes tend to inadequately represent the spatial and temporal resolution required in the simulation of hydraulic systems. As agricultural production has continued to expand and intensify around the world, many models have been developed in an attempt to accurately predict the downstream impacts on water quality and quantity due to changes in on-farm management practices.