A model-data synergy framework for assessing and enhancing runoff control in sponge cities: Sensitivity-driven calibration and multi-tiered monitoring integration

Urban waterlogging and runoff pollution necessitate robust evaluation methodologies for optimizing sponge city development (SCD). This study proposes a systematic evaluation framework that integrates multi-tiered in-situ monitoring with sensitivity-driven mathematical modeling. While the constituent methods (SWMM and Morris analysis) are well-established, the novelty of this work lies in the integrated workflow that bridges the gap between fragmented monitoring and cross-scale performance diagnosis. The approach synergizes a multi-tiered monitoring network (spanning source, process, and end levels) with localized parameter optimization using the Morris screening method, identifying critical parameters (e.g., soil porosity and thickness). Iterative calibration via artificial trial-and-error resulted in good agreement between simulated and observed runoff processes (Nash-Sutcliffe coefficient > 0.85). Applied to Shenzhen’s G pilot area, the framework revealed significant deficiencies under heavy rainfall scenarios (e.g., 62.9% runoff control in Subarea D during R-9 events). Specific enhancements—including increasing soil layer porosity by 20% and thickness by 20% in bioretention cells—were recommended to substantially improve infiltration capacity and runoff pollution retention. This replicable methodology bridges theoretical modeling and practical implementation, providing actionable, data-driven strategies for enhancing SCD in climate-vulnerable urban environments.