Response of Runoff Coefficient to Urban Underlying Surface Change Based on BP Neural Network

[Objective] Against the background of rapid urbanization, changes in the urban underlying surface constitute a significant factor influencing runoff processes, yet their mechanisms remain inadequately studied. [Methods] Taking Qingshan District of Wuhan City as a representative study area, this paper used remote sensing technology, GIS analysis, and a BP neural network model to quantitatively assess urban underlying surface changes during the typical study period and analyze its impact on the runoff coefficient. [Results] (1) Under urban development, land use in the study area from 2002 to 2017 shifted overall from permeable to impermeable surfaces. Vegetation, rooftops, and other land-use types fluctuated, whereas water bodies shrank year by year. Construction of the sponge city demonstration zone in 2015 slowed this trend. (2) The runoff coefficient was jointly affected by underlying surface changes and rainfall. However, urban rainfall changed little over short timescales, the impervious surface ratio was the dominant factor. As the area ratio of high-runoff land use (e.g., hardened ground) increased and that of low-runoff land use (e.g., vegetation, green space) decreased, the runoff coefficient rose yearly—from 0.399 in 2009 to 0.535 in 2017—showing that land-use change directly altered the runoff coefficient to some extent. (3) After sponge city interventions, the annual runoff coefficient showed a decreasing trend; in 2017 it was 0.535, 0.051 lower than in 2014. [Conclusions] Sponge city construction reduces the runoff coefficient by expanding highly permeable surfaces and adding storage volume, thereby mitigating the adverse impacts of urban development on stormwater regulation capacity. The study offers scientific guidance for urban planning and flood-control drainage system design, and technical support for urban hydrological cycles and water-resource management.