A multi-scenario framework for quantifying flood hazard and exposure accounting for runoff-driven uncertainty in...

Area of Science:

• Earth and Environmental Sciences
• Hydrology
• Climate Science

Background:

• Large-scale flood hazard assessment (LSFHA) is crucial due to increasing populations and flood events.
• Global Flood Models (GFMs) are vital for LSFHA but are sensitive to runoff input uncertainty.
• Existing methods struggle to quantify the full range of flood hazard and exposure.

Purpose of the Study:

• To develop a multi-scenario framework for Global Flood Models (GFMs) to address runoff-driven uncertainty.
• To capture a plausible range of flood hazard and exposure scenarios from optimistic to conservative.
• To identify the most efficient and validated runoff forcing for best-case scenario simulation.

Main Methods:

• Integrated diverse runoff forcings into the CaMa-Flood GFM within a multi-scenario framework.
• Defined optimistic, conservative, and best-case scenarios based on simulated flood depth and model efficiency.
• Employed parametric and non-parametric distributions for frequency analysis of return period flood depths.

Main Results:

• ERA5-Reanalysis runoff emerged as the most efficient forcing, achieving Nash-Sutcliffe efficiency > 0.5 at ~50% of gauge stations.
• Hazard analysis for a 1-in-100-year flood indicated ~48% of India's land area (~1.6 million km²) is in a disastrous hazard class.
• Exposure analysis revealed ~690 million people in India are exposed to significant flood risk.

Conclusions:

• The developed framework effectively quantifies flood hazard and exposure uncertainty stemming from runoff inputs.
• The study provides a scalable, open-source tool for national-scale, risk-informed flood management decisions.
• Validated in India, the framework addresses critical uncertainties for immediate and long-term flood risk management.