Multi-objective spatial optimization of pond system for diffuse pollution control of paddy field watershed in the middle reaches of the Yangtze River

Ponds within paddy field watershed have been proved to be effective in controlling runoff, sediment, and nutrient loss. However, the optimal area and distribution of these ponds are not always achieved at watershed scale. To address this, optimizing pond area and allocation is crucial for effective diffuse pollution control in water management. This study presented a Decision Support System (DSS) that integrated a pond measurement database (POND database), the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimization, and the Soil and Water Assessment Tool (SWAT) for simulation. This DSS aims to identify cost-effective conservation strategies to meet desired water quality goals. Results showed that, with constraints on water quality and available area, the implementation of ponds in paddy field watersheds led to reductions of 8.4 % to 36.9 % in diffuse nitrogen (N) and 11.12 % to 45.7 % in phosphorus (P) loss. As the desired reduction rates increased, stricter controls on diffuse pollution necessitated a significant increase in pond system area. Under the optimal allocation scenario identified by the DSS, total nitrogen (TN) and total phosphorus (TP) losses could be reduced by 24.27 % and 29.45 %, respectively. Annual precipitation had a negative impact on the pond system’s efficiency, with effective water quality management achievable with annual rainfall below 1150 mm. These findings evaluated the potential of pond systems in managing diffuse pollution. The proposed DSS framework offered valuable technical support for optimizing pond area and allocation, enhancing the effectiveness of water quality management in paddy field watersheds. This research contributes significantly to understanding and implementing effective conservation strategies for evaluating water quality.