Climate uncertainty and vulnerability of urban flooding associated with regional risk using multi-criteria analysis in Mumbai, India

Nilesh Yadav; Jianping Wu; Abhishek Banerjee; Shray Pathak; R D Garg; Shenjun Yao

doi:10.1016/j.envres.2023.117962

Climate uncertainty and vulnerability of urban flooding associated with regional risk using multi-criteria analysis in Mumbai, India

Environ Res. 2024 Mar 1:244:117962. doi: 10.1016/j.envres.2023.117962. Epub 2023 Dec 18.

Authors

Nilesh Yadav¹, Jianping Wu², Abhishek Banerjee³, Shray Pathak⁴, R D Garg⁵, Shenjun Yao¹

Affiliations

¹ Key Laboratory of Geographic Information Science (Ministry of Education) and School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China.
² Key Laboratory of Geographic Information Science (Ministry of Education) and School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China. Electronic address: jpwu@geo.ecnu.edu.cn.
³ State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-environment and Resources, Chinese Academy of Sciences, Donggang, West RD. 318, Lanzhou, 730000, China.
⁴ Department of Civil Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, 140001, India.
⁵ Geomatics Engineering Group, Indian Institute of Technology Roorkee, Roorkee, 247667, India.

PMID: 38123049
DOI: 10.1016/j.envres.2023.117962

Abstract

The study made a comprehensive effort to examine climatic uncertainties at both yearly and monthly scales, along with mapping flood risks based on different land use categories. Recent studies have progressively been engrossed in demonstrating regional climate variations and associated flood probability to maintain the geo-ecological balance at micro to macro-regions. To carry out this investigation, various historical remote sensing record, reanalyzed and in-situ data sets were acquired with a high level of spatial precision using the Google Earth Engine (GEE) web-based remote sensing platform. Non-parametric techniques and multi-layer integration methods were then employed to illustrate the fluctuations in climate factors alongside creating maps indicating the susceptibility to floods. The study reveals an increased pattern in LST (Land Surface Temperature) (0.03 °C/year), albeit marginal declined in southern coastal regions (-0.15 °C/year) along with uneven rainfall patterns (1.42 mm/year). Moreover, long-term LULC change estimation divulges increased trends of urbanization (16.4 km²/year) together with vegetation growth (8.7 km²/year) from 2002 to 2022. Furthermore, this inquiry involves numerous environmental factors that influence the situation (elevation data, topographic wetness index, drainage density, proximity to water bodies, slope, and soil properties) as well as socio-economic attributes (population) to assess flood risk areas through the utilization of Analytical Hierarchy Process and overlay methods with assigned weights. The outcomes reveal nearly 55 percent of urban land is susceptible to flood in 2022, which were 45 and 37 percent in 2012 and 2002 separately. Additionally, 106 km² of urban area is highly susceptible to inundation, whereas vegetation also occupies a significant proportion (52 km²). This thorough exploration offers a significant chance to formulate flood management and mitigation strategies tailored to specific regions during the era of climate change.

Keywords: Climate change; Flood vulnerability; Google earth engine; Mumbai; Non-parametric statistics; Remote sensing.

MeSH terms

Floods*
India
Probability
Uncertainty
Urbanization*