Prioritizing conservation sites for multi-pond systems to maintain protection of water quality in a fragmented agricultural catchment

Precise targeting of conservation practices to the most effective sites in multi-pond systems (MPS) is critical for resource optimization and water quality improvement. Previous studies generally prioritized ponds for conservation practices considering nutrient removal efficiency, but sediment connectivity under the influence of human activities and environmental factors was disregarded. Herein, the present study developed and applied a novel framework for pond prioritization by integrating the Pressure State-Response (PSR) model, graph theory, and K-mean clustering. The framework consists of three components. The indicating system (PSR) represents the nutrient removal performance of any MPS, impacts on catchment sediment connectivity, external threats, and human-initiated conservation. A flow path network considering natural and artificial elements was constructed to calculate indicator values. A cluster analysis conducted on the index values of different ponds, and a hierarchical sorting method were used to prioritize ponds. The framework was applied to the Guilinqiao Catchment, a typical fragmented agricultural catchment in the Yangtze River Basin, China. The study has generally quantified the Pressure, State, and Response indices of different ponds in this catchment, prioritized the ponds, and drawn recommendations for conserving MPSs based on field surveys and remote sensing data. Ponds with higher Pressure index, higher State index, and lower Response index scores should be targeted as conservation priorities. This framework provides an effective method for ensuring management of MPSs to sustainably maximize water cleanup capacity with limited resources.