Preprints
https://doi.org/10.5194/hess-2023-269
https://doi.org/10.5194/hess-2023-269
25 Jan 2024
 | 25 Jan 2024
Status: this preprint is currently under review for the journal HESS.

Leveraging a Novel Hybrid Ensemble and Optimal Interpolation Approach for Enhanced Streamflow and Flood Prediction

Mohamad El Gharamti, Arezoo RafieeiNasab, and James L. McCreight

Abstract. In the face of escalating instances of inland and flash flooding spurred by intense rainfall and hurricanes, accurately predicting rapid streamflow variations has become imperative. Traditional data assimilation methods face challenges during extreme rainfall events due to numerous sources of error, including model deficiencies, forcing biases and observational uncertainties. This study introduces a cutting-edge hybrid ensemble and optimal interpolation data assimilation scheme tailored to precisely and efficiently estimate streamflow during such critical events. Our hybrid scheme builds upon the ensemble-based framework of El Gharamti et al., integrating the flow-dependent background streamflow covariance with a climatological error covariance derived from historical model simulations. The dynamic interplay (weight) between the static background covariance and the evolving ensemble is adaptively computed both spatially and temporally. By coupling the National Water Model (NWM) configuration of the WRF-Hydro modeling system with the Data Assimilation Research Testbed (DART), we validate the performance of our hybrid prediction system using two impactful test cases: 1. West Virginia's flash flooding event in June 2016, and 2. Florida's inland flooding during Hurricane Ian in September 2022. Our findings reveal that the hybrid scheme significantly outperforms its ensemble counterpart, delivering enhanced streamflow estimates for both low and high flow scenarios, with an improvement of up to 50 %. This heightened accuracy is attributed to the climatological background covariance, mitigating bias and augmenting ensemble variability. The adaptive nature of the hybrid algorithm ensures reliability even with a very small time-varying ensemble. Moreover, this innovative hybrid data assimilation system propels streamflow forecasts up to 18 hours in advance of flood peaks, marking a substantial advancement in flood prediction capabilities.

Mohamad El Gharamti, Arezoo RafieeiNasab, and James L. McCreight

Status: open (until 21 Mar 2024)

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Mohamad El Gharamti, Arezoo RafieeiNasab, and James L. McCreight
Mohamad El Gharamti, Arezoo RafieeiNasab, and James L. McCreight

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Short summary
This study introduces a hybrid data assimilation scheme for precise streamflow predictions during intense rainfall and hurricanes. Tested in real events, it outperforms traditional methods by up to 50 %, utilizing ensemble and climatological background covariances. The adaptive algorithm ensures reliability with a small ensemble, offering improved forecasts up to 18 hours in advance, marking a significant advancement in flood prediction capabilities.