Biondi et al. (2026) A decision-theoretic framework for probabilistic flood warnings

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-07-20
• Authors: Daniela Biondi, E. Todini
• DOI: 10.1016/j.jhydrol.2026.136043

Research Groups

• Department of Civil Protection, Italian National Institute for Geophysics and Volcanology (INGV)
• University of Florence, Department of Information Engineering

Short Summary

This study proposes a decision-theoretic approach to issuing flood warnings in Italy’s Sieve basin by integrating probabilistic precipitation forecasts with a statistical post-processing method to evaluate predictive uncertainty. The proposed framework yields economic benefits and substantial improvements in the Relative Economic Value (REV).

Objective

• Develop an early warning system for weather-related hazards that incorporates a formal assessment of operational and economic impacts associated with chosen actions.

Study Configuration

• Spatial Scale: Local scale, focusing on Italy’s Sieve basin (Tuscany)
• Temporal Scale: Real-time decision-making in the face of uncertainty

Methodology and Data

• Models used: Bayesian decision scheme and statistical post-processing method for probabilistic precipitation forecasts
• Data sources: National Civil Protection system, satellite observations, and reanalysis datasets

Main Results

• Adopting a Bayesian cost-based approach yields economic benefits and substantial improvements in the Relative Economic Value (REV)
• Incorporating precipitation uncertainty drives the system toward more cost-effective warning strategies, reducing false alarms and expected losses

Contributions

• Original value lies in integrating probabilistic precipitation forecasts with a statistical post-processing method to explicitly evaluate predictive uncertainty and adapt to multiple warning levels.

Funding

• This research was funded by the Italian Civil Protection Department (DPC) under project "Early Warning Systems for Weather-Related Hazards" (2018-2020).

Citation

@article{Biondi2026decisiontheoretic,
  author = {Biondi, Daniela and Todini, E.},
  title = {A decision-theoretic framework for probabilistic flood warnings},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.136043},
  url = {https://doi.org/10.1016/j.jhydrol.2026.136043}
}