A raster-based estimation of watershed phosphorus load and its impacts on surrounding rivers based on process-based modeling

Quantifying phosphorus (P) loads from watersheds at a fine scale is crucial for studying P sources in lake or river ecosystems; however, it is particularly challenging for mountain–lowland mixed watersheds. To address this challenge, we proposed a framework to estimate the P load at the grid scale and assessed its risk to surrounding rivers in a typical mountain–lowland mixed watershed (Huxi Region in Lake Taihu Basin, China). The framework coupled three models: the Phosphorus Dynamic model for lowland Polder systems (PDP), the Soil and Water Assessment Tool (SWAT), and the Export Coefficient Model (ECM). The coupled model performed satisfactory for both hydrological and water quality variables (Nash–Sutcliffe efficiency > 0.5). Our modelling practice revealed that polder, non-polder, and mountainous areas had P loads of 211.4, 437.2, and 149.9 t yr^-1, respectively. P load intensity in lowlands and mountains was 1.75 and 0.60 kg ha^-1 yr^-1, respectively. A higher P load intensity (> 3 kg ha^-1 yr^-1) was mainly observed in the non-polder area. In lowland areas, irrigated cropland, aquaculture ponds and impervious surfaces contributed 36.7, 24.8, and 25.8% of the P load, respectively. In mountainous areas, irrigated croplands, aquaculture ponds, and impervious surfaces contributed 28.6, 27.0, and 16.4% of the P load, respectively. Rivers with relatively high P load risks were mainly observed around big cities during rice season, owing to a large contribution of P loads from the non-point source pollution of urban and agricultural activities. This study demonstrated a raster-based estimation of watershed P loads and their impacts on surrounding rivers using coupled process-based models. It would be useful to identify the hotspots and hot moments of P loads at the grid scale.