New framework for dynamic optimization of best management practices to achieve spatiotemporal scale matching

The spatial optimization of best management practices (BMPs) plays a critical role in precise watershed pollution control. However, the effectiveness of BMPs exhibits a complex nonlinear dependence on both configuration unit scale and rainfall intensity, often leading to widespread spatiotemporal mismatches during implementation. To fill this gap, this study proposes a new framework: (a) delineating configuration units based on the implementation scale differences between structural and nonstructural BMPs; (b) incorporating BMP reduction thresholds to enable dynamic adjustment of design scales according to inflow loads; and (c) developing a staged allocation strategy tailored to varying rainfall scenarios. The framework is exemplified by an agricultural catchment in the southeastern Liaohe watershed, China. The results showed that the framework could improve the assessment accuracy and cost-effectiveness of pollution control. Specifically, neglecting BMP reduction thresholds resulted in a 51.35% underestimation of treatment costs. Incorporating these thresholds and dynamically adjusting BMP design scales reduced treatment costs by 62.70%. Furthermore, the framework facilitated more precise localization of structural BMPs (1 km2) and improved optimization efficiency by 95.91%. The proposed staged allocation strategy ensured water quality compliance under varying rainfall intensities. Structural BMPs primarily addressed pollution from light to moderate rainfall in the initial stage, while nonstructural BMPs targeted heavy rainfall pollution in the subsequent stage. The proposed framework may enhance the spatiotemporal adaptability of BMP configuration to respond to the threats posed by climate change and human activities. It can also be extended to other agriculture-dominated watersheds.