Quantifying the impacts of land cover change on catchment-scale urban flooding by classifying aerial images

Abstract

Stormwater urban drainage systems are historically designed to mitigate certain recurrence interval flooding events. However, increases in impervious land cover due to urban redevelopment enhance the surface imperviousness and then magnifies urban floods over the design criteria by elevating flooding peaks and volumes. In this paper, the object-based image classification was developed to evaluate the impervious land cover impacts on system peak flooding rate and total flooding volume. The object-based aerial image classification quantified the spatiotemporal changes in impervious surfaces with an average accuracy of 92%. A drainage model, based on a stormwater drainage system in Salt Lake City, Utah, USA, was calibrated and validated under four storm events with an average of Nash-Sutcliffe efficiency of 0.69 and 0.46 for calibration and validation outcomes. Results show that impervious areas increased up to 32% under impacts induced by urban redevelopment from 1950 to 2018. The land cover imperviousness resulted in a maximum of 575% and 753% growth in flooding peak and volume, respectively, under storm events from 10-year to 100-year return periods. Implications of this research seek to inform homeowners and engineers of the flooding risks in human-altered landscapes based on remote sensing image classification.