Deopa et al. (2026) Coupled hydrological and public health risks from urban flooding: integrated remote sensing, machine learning, and hydrodynamic–ecological modelling

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-07-10
• Authors: Rahul Deopa, Vaibhav Tripathi, Nandana P.S., Mohit Prakash Mohanty
• DOI: 10.1016/j.jhydrol.2026.135999

Research Groups

Department of Water Resources Development and Management, Indian Institute of Technology, Roorkee

Short Summary

This study develops an integrated remote sensing–machine learning–hydrodynamic modelling framework to quantify coupled flood and microbial health risks in urban areas, with a focus on Delhi (India) as a case study.

Objective

• Investigate the relationship between urban flooding and public health risks, particularly waterborne diseases caused by E. coli contamination.

Study Configuration

• Spatial Scale: Urban scale, focusing on the city of Delhi, India.
• Temporal Scale: Long-term simulation over several years to capture seasonal variations in flood patterns and microbial loads.

Methodology and Data

• Models used:
  • Support Vector Regression (SVR) for predicting E. coli concentration from water surface temperature data.
  • MIKE + Flood–ECO Lab framework for simulating flood inundation, contaminant transport, and exposure pathways.
• Data sources:
  • Landsat thermal infrared imagery for water surface temperature data.
  • Hydro-meteorological forcings (rainfall, streamflow, urban drainage flow) from reanalysis datasets.
  • Spatial inputs (topography, land use–land cover) from remote sensing data.

Main Results

• Estimated annual infection risks surpassed the USEPA benchmark, with children being the most vulnerable to waterborne diseases caused by E. coli contamination.
• ~63% of floodplains faced “high” to “very high” flood hazards, with E. coli levels consistently exceeding safe bathing thresholds (500 MPN/100 mL).

Contributions

• This study provides actionable recommendations for holistic flood management and emphasizes the urgent need to integrate microbial water quality into flood risk assessment and public health planning.
• The proposed framework can be applied to other rapidly urbanizing, climate-sensitive regions to mitigate public health risks associated with urban flooding.

Funding

• This research was funded by [project/program name] (reference code: [insert reference code]).

Citation

@article{Deopa2026Coupled,
  author = {Deopa, Rahul and Tripathi, Vaibhav and P.S., Nandana and Mohanty, Mohit Prakash},
  title = {Coupled hydrological and public health risks from urban flooding: integrated remote sensing, machine learning, and hydrodynamic–ecological modelling},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.135999},
  url = {https://doi.org/10.1016/j.jhydrol.2026.135999}
}