For the last 5 years, Gurgaon a city in India has been facing an issue of urban flooding due to illicit encroachments over the local waterbodies, poor drainage system and increasing rainfall. In this study, Remote sensing data are employed to find the most flooded areas identified using Partial Least Square Regression and 18 new retention ponds are proposed to build a Sustainable Drainage System (SuDS) in open space and barren lands. In SWMM, the Urban Drainage System (UDS) model is simulated using 24-h rainfall hyetograph from hourly PERSIANN-CSS rainfall data (yearly rainfall events) and 7-h rainfall hyetograph from half-hourly IMERG Global Precipitation Data (extreme rainfall events) from 2000 to 2023. After comparing both UDS and SuDS in SWMM, it is found that the flood volume has decreased significantly from 240 CMS to 180 CMS (for yearly rainfall) and 500 CMS to 350 CMS (7-h rainfall hyetograph). The study also compares the structural resilience of the drainage system under the conditions of no link failure and single link failure scenarios. In no failure situation, 20% more resilience has been achieved for yearly rainfall and 10% more for extreme rainfall events. In single link failure conditions, SuDS is helping to reach 20–47% resilience for yearly rainfall events and 7–30% resilience for extreme rainfall events. Thus, this study helps to achieve SDGs 11 and 13 to build a resilient and climate-adaptive urban drainage in Gurgaon. The study gives significant insights regarding the competency of urban waterbodies to city planners and policymakers.
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