An integrated land change modeler and distributed hydrological model approach for quantifying future urban runoff dynamics

Abstract

Climate and land use changes have a significant impact on the runoff generation process in urban environments, and these effects could get worse in the future. The combined contributions of these changes have increased the risk of flooding. Therefore, there is a need for integrated modeling to better understand the runoff variability, especially in small urban catchments. To quantify and separate the effects of land-use changes and climate change on the hydrological response of urban catchments with a distributed hydrological model (Storm Water Management Model, SWMM), this study introduces a new integrated approach based on the Machine Learning based land use change modeler and climate change scenarios under CMIP6. Based on supervised classification and land use change model analysis, accumulated impervious area increase from 22% (in 2023) to 33% (in 2060) was observed in the study area. Furthermore, integrating this projected increase in imperviousness with future climate change into SWMM by considering three different scenarios i.e., S1 (Climate Change), S2 (Combined Land Use and Climate Change), and S3 (Land use Change) resulted that climate change could cause an increase in runoff from 13.2% to 18.3% in peak runoff and the contribution of land use could range from 9.1% to 18.6%. Similarly, in response to the coupled effects of climate and land-use change, the runoff would likely change from 24.53% to 39.66%. Conclusively, the study showed that despite climate change, intensive urban development by the substitution of impervious surfaces could also have a severe impact on the microclimate and hydrology of small catchments. Lastly, this study could provide a way forward for the future planning and management of water resources in small catchments which could be extended to larger catchments.