Gómez et al. (2026) Accounting for the uncertainty of precipitation forecasts and its impacts on probabilistic flood inundation mapping skill

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-03-30
• Authors: Francisco Javier Gómez, Keighobad Jafarzadegan, Hamed Moftakhari, Hamid Moradkhani
• DOI: 10.1016/j.jhydrol.2026.135429

Research Groups

• Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL, USA
• Department of Biosystems and Agricultural Engineering, Oklahoma Water Resources Center, Oklahoma State University, USA
• Department of Computer Science, The University of Alabama, Tuscaloosa, AL, USA

Short Summary

This study develops a storm-position–conditioned Quantitative Precipitation Forecast (QPF) displacement ensemble using HRRR forecasts to propagate precipitation perturbations through a 2D hydrodynamic model (SFINCS), quantifying their impacts on deterministic and probabilistic flood inundation mapping. The approach improves correlation with observations, reduces biases in predicted flood depths, and enhances the representation of flood impact variability in urban environments.

Objective

• To implement a storm-position–conditioned Quantitative Precipitation Forecast (QPF) displacement ensemble using HRRR forecasts and propagate these precipitation placement perturbations through a 2D hydrodynamic model (SFINCS).
• To quantify the impacts of QPF uncertainty on deterministic and probabilistic flood inundation mapping skill.

Study Configuration

• Spatial Scale: Highly urbanized central Houston area, USA.
• Temporal Scale: 24-hour forecast periods, with forecast cycles generated in 6-hour intervals, focusing on short-term flood forecasts for events like Hurricane Beryl (July 2024).

Methodology and Data

• Models used:
  • High-Resolution Rapid Refresh (HRRR) for Quantitative Precipitation Forecasts (QPF).
  • Super-Fast INundations of CoastS (SFINCS) for 2D hydrodynamic modeling.
• Data sources:
  • HRRR forecasts (precipitation fields).
  • In-situ meteorological observations.
  • In-situ water elevation observations.

Main Results

• Incorporating HRRR forecasts and spatial displacement of QPF fields significantly improves the correlation with in-situ meteorological and water elevation observations.
• The methodology reduces biases in predicted flood depths.
• It provides a better representation of the spatial variability of flood impacts across the study area.
• The generated probabilistic flood inundation mapping, derived from ensemble-based forecast integration, offers decision-makers a range of plausible flooding scenarios.
• Accounting for uncertainties in storm position, rainfall intensity, and timing enhances the reliability and operational value of short-term flood forecasts in urban environments.

Contributions

• Introduces a novel storm-position–conditioned QPF displacement ensemble approach to explicitly account for precipitation forecast uncertainty in flood modeling.
• Quantifies the impact of QPF uncertainties on both deterministic and probabilistic flood inundation mapping, moving beyond traditional deterministic analyses.
• Enhances the operational value of short-term flood forecasts by providing more reliable and comprehensive probabilistic flood inundation maps, supporting improved flood risk management and emergency preparedness in urban areas.

Funding

Not specified in the provided text.

Citation

@article{Gómez2026Accounting,
  author = {Gómez, Francisco Javier and Jafarzadegan, Keighobad and Moftakhari, Hamed and Moradkhani, Hamid},
  title = {Accounting for the uncertainty of precipitation forecasts and its impacts on probabilistic flood inundation mapping skill},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.135429},
  url = {https://doi.org/10.1016/j.jhydrol.2026.135429}
}