Published In

Hydrology and Earth System Sciences

Document Type

Article

Publication Date

6-10-2025

Abstract

The Hybrid Ensemble and Variational Data Assimilation framework for Environmental Systems (HEAVEN) is a method developed to enhance hydrologic model predictions while accounting for different sources of uncertainties involved in various layers of model simulations. While the effectiveness of this data assimilation in forecasting streamflow has been proven in previous studies, its potential to improve flood forecasting during extreme events remains unexplored. This study aims to demonstrate this potential by employing HEAVEN to assimilate streamflow data from United States Geological Survey (USGS) stations into a conceptual hydrologic model to enhance its capability to forecast hurricane-induced floods across multiple locations within three watersheds in the southeastern United States. The Sacramento Soil Moisture Accounting (SAC-SMA) hydrologic model is driven by two variables: precipitation and potential evapotranspiration (PET), collected from North American Land Data Assimilation System phase 2 (NLDAS-2) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, respectively. We validated the probabilistic streamflow predictions during five instances of hurricane-induced flooding across three regions. The results show that this data assimilation approach significantly improves the hydrologic model's ability to forecast extreme river flows. By accounting for different sources of uncertainty in model predictions – in particular model structural uncertainty (in addition to model parameter uncertainty) and atmospheric forcing data uncertainty – HEAVEN emerges as a powerful tool for enhancing flood prediction accuracy. The study found that data assimilation improved streamflow forecasting during Hurricane Harvey, enhancing the SAC-SMA model's accuracy across most USGS stations on the peak flow day. However, data assimilation had little effect on streamflow forecasting for Hurricane Rita. In Rita, the streamflow surged dramatically in a single day (from 28 to 566 m3 s−1), causing the model to miss the high-flow event despite accurate initialization the day before. For hurricanes Ivan and Matthew, data assimilation improved peak flow forecasts by 21 % to 46 % in Mobile and 5 % to 46 % in Savanah, with improvements varying by station location.

Rights

© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.

Locate the Document

https://doi.org/10.5194/hess-29-2407-2025

DOI

10.5194/hess-29-2407-2025

Persistent Identifier

https://archives.pdx.edu/ds/psu/43893

Publisher

Copernicus GmbH

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