Temporal-spatial analysis of water environmental capacity based on the couple of SWAT model and differential evolution algorithm

Abstract

The objective of this study was to advance a new method to dynamically analyze the water environmental capacity (WEC) of non-point source (NPS) pollution. A Soil and Water Assessment Tool (SWAT) model and differential evolution (DE) algorithm were coupled to automatically provide a calculation of the WEC in a daily step. The results showed that the largest ideal WECs occurred in the wet season with values up to 4500 t and 80 t for total nitrogen (TN) and total phosphorus (TP), respectively. The ideal WEC had a positive correlation with the rainfall intensity, while the opposite was true for actual WEC. The smallest value of actual WEC for TN was −150 t in June, and a negative value also occurred in June for TP. This is because NPS pollution discharge was more sensitive to rainfall and flow change than the ideal WEC. Compared to phosphorus, the WEC of nitrogen was more affected by the hydrological and NPS pollution discharge conditions. In addition, the WEC of nitrogen and phosphorus in organic species was more affected by seasonal change than that of nitrogen and phosphorus in inorganic species. Spatially, the channels in the middle and southeast of the study area had the smallest actual WEC and even negative values were observed. The results of remnant WEC showed that the smallest value occurred in July and June with values of −800 t and −140 t for TN and TP, respectively. Thus, significant attention should be paid to controlling NPS pollution during the wet season, and organic nitrogen and phosphorus should be set as the priority pollutants to control. Furthermore, controlling the NPS pollution discharge and enhancing the WEC should be carried out simultaneously. Compared to previously used methods, the calculation method employed in this study had the ability to simulate the transformation and interaction between nitrogen and phosphorus in organic and inorganic species, and better considered dynamic changes in the input conditions.