Study Shows Flood Insurance Depends on Data Analysis
Satellite image showing flooding in Rio Grande do Sul in Brazil in April 2024
NASA Earth Observatory image by Wanmei Liang, using MODIS data from NASA EOSDIS LANCE and GIBS/Worldview and Landsat data from the US Geological Survey
A University of Arizona-led research team found that using different flood datasets for insurance decision-making can dramatically change when index-based flood insurance is paid out, how fast it is paid, and how predictable program costs are. In a Fall 2025 paper published in Earth’s Future, titled “Sensitivity to Data Choice for Index-Based Flood Insurance,” the researchers tested a simulated insurance program in Bangladesh across twenty monsoon seasons, from 2004 to 2023, comparing five national-and regional-scale flood information sources, including rainfall data, river level measurements, flood model outputs and two satellite-based methods for tracking surface water.
The team discovered that no single dataset consistently produced the best results. Some indices successfully triggered payouts during the most severe flood years, while others failed to do so or activated payouts in less extreme years. For example, river-height and satellite water-extent indices paid out during the two most severe floods from 2004-2007, while a precipitation-based index failed to trigger in 2004. The study also highlighted the promise of new technologies. A machine-learning based satellite method detected flooding earlier than traditional satellite approaches and reduced uncertainty in payout predictions by more than 20%.
These findings show that data choice can shape real world outcomes for communities that rely on insurance as a financial safety net after disasters. Rather than relying on a single dataset, the researchers recommend comparing multiple sources of information to strengthen confidence in flood assessments and improve the design of insurance programs. Their work offers practical guidance for governments, insurers, and organizations seeking to build more effective and equitable disaster risk systems in flood prone regions around the world.