Preprints
https://doi.org/10.5194/hess-2024-209
https://doi.org/10.5194/hess-2024-209
21 Oct 2024
 | 21 Oct 2024
Status: this preprint is currently under review for the journal HESS.

Towards a Robust Hydrologic Data Assimilation System for Hurricane-induced River Flow Forecasting

Peyman Abbaszadeh, Keyhan Gavahi, and Hamid Moradkhani

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 have 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 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 SAC-SMA hydrologic model is driven by two variables: precipitation and Potential Evapotranspiration (PET), collected from phase 2 of the North American Land Data Assimilation System (NLDAS-2) and MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data, respectively. We have 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 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, the HEAVEN emerges as a powerful tool for enhancing flood prediction accuracy.

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Peyman Abbaszadeh, Keyhan Gavahi, and Hamid Moradkhani

Status: open (until 16 Dec 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2024-209', Anonymous Referee #1, 22 Oct 2024 reply
  • RC2: 'Comment on hess-2024-209', Anonymous Referee #2, 21 Nov 2024 reply
Peyman Abbaszadeh, Keyhan Gavahi, and Hamid Moradkhani
Peyman Abbaszadeh, Keyhan Gavahi, and Hamid Moradkhani

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Short summary
The Hybrid Ensemble and Variational Data Assimilation framework for Environmental System (HEAVEN) enhances flood predictions by refining hydrologic models through improved data integration and uncertainty management. Tested in three Southeastern U.S. watersheds during hurricanes, HEAVEN assimilates real-time USGS streamflow data, boosting forecast accuracy.