A method coupled with remote sensing data to evaluate non-point source pollution in the Xin'anjiang catchment of China

Non-point source (NPS) pollution has been recognized as the largest threat to water resources throughout the world, and the evaluation of NPS loads is a priority. In China, some models, such as SWAT (Soil and Water Assessment Tools) model, have been widely used at the watershed scale. However, variations in natural and social factors make it difficult to find a proper model to use on NPS pollution management in China. In this study, a Dualistic Structure model is coupled with remote sensing data to capture the spatial and temporal processes of NPS pollution. Land parameters were derived from HJ-1A and HJ-1B satellite data (resolution 30 m), which offered greatly enhanced spatial resolution. This approach offers the advantage of being a rapid estimation system with fairly precise knowledge of the distribution, sources and quantities of NPS pollutants, and it can be used at the country scale, including in areas with insufficient data. The method is used in the Xin'anjiang catchment, an important water source for Hangzhou city, China. The simulation in this study includes the spatial distribution of monthly total nitrogen (TN), total phosphorous (TP), ammonia nitrogen (NH4-N) and chemical oxygen demand (CODcr) loads and the total production of NPS pollutants. The simulations were compared to pollution census (PC) data in 2010 and the results of SWAT model, with an average R-2 larger than 0.7. Additionally, the impacts of soil erosion and human activities on NPS pollution were assessed, indicating that soil and water conservation is very significant factor in the Xin'anjiang catchment. Results indicate that by coupling remote sensing data and parameter retrieval techniques to Dualistic Structure models, estimations of NPS loads on the catchment scale can be improved by spatial pixel-based modeling. This rapid NPS estimation system will offer effective support to policy makers for environmental management in China. (C) 2012 Elsevier B.V. All rights reserved.