Identification of Critical Hotspots in Urban Drainage Networks using MIKE URBAN

Abstract

In recent years, climate change and urbanization have become major concerns for developing countries, and this will continue to exacerbate in the future. It has triggered abundant challenges, among which urban flooding is becoming one of the most important. In this study, the impact of extreme rainfall on urban drainage systems is analyzed through a case study of Rohtak City in Haryana, India. For the study, a MIKE+ one-dimensional hydrodynamic and rainfall-runoff model was adopted. The monsoon rainfall data, from June to September 2022, was retrieved from India-WRIS and incorporated into MIKE+ as a time series for the simulation of rainfall-runoff. The main objectives of the study were to assess urban flood vulnerability zones and to identify individual hotspot nodes of existing drainage networks. Flooding from extreme rainfall and future rainfall increased due to climate change by 10% (Rainfall; R1), 20% (Rainfall; R2) and 50% (Rainfall; R3) because of the monsoon rainfall. Along with extreme event analysis, predictive analysis was also made. The hydraulic parameter for water level in nodes and pipes was used to determine the hydraulic capacity of the drainage system. The simulation results indicated that the city's drainage system became hydraulically inefficient in dealing with the extreme rainfall event in 2022 that caused urban flooding. For the studied drainage system, 52 overflooding nodes, 57 pressurized links, and 07 critical catchments were found to be vulnerable, which is 9.13% of the total catchment area. Validation of the extreme rainfall event simulated in MIKE+ was done by obtaining a flood extent map using Google Earth Engine with the help of SENTINEL-1 SAR imagery data. The accuracy of the MIKE+ model is analyzed using two parameters, i.e., percentage flooded areas and pixel percentage flooded. The MIKE+ model performed significantly well in determining percentage flooded areas with an accuracy of 79.66%. When using predictive analysis, the MIKE+ model provides a great insight into R3 time series rainfall showing 22.38% of the total sub-catchment area to be flooded when a rainfall intensity of R3 occurs. Remedies to this drainage failure could be either redesigning the drainage system or designing sustainable detention ponds.