the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Towards a Robust Hydrologic Data Assimilation System for Hurricane-induced River Flow Forecasting
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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Status: open (until 16 Dec 2024)
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RC1: 'Comment on hess-2024-209', Anonymous Referee #1, 22 Oct 2024
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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 results show that this data assimilation approach significantly improves hydrologic model’s ability to forecast extreme river flows.
Overall, this paper is very interesting and well written. It demonstrates the scientific value of HEAVEN in extreme river flow simulation and forecasting. I have some minor comments before publication.
In the abstract, the authors can add some quantitative results to show how much this data assimilation approach significantly improves hydrologic model’s ability to forecast extreme river flows.
How the different sources of uncertainties are considered in HEAVEN should be mentioned.
It would be better to introduce figure 2 in the beginning of section 2.4.
In figure 4, the validation accuracy in terms of R is higher than that during calibration in Savanah, but the validated RMSE is also higher than the latter. Why?
In figures 6-7, Streamflow forecast improved by data assimilation during peak flow conditions across the USGS stations are demonstrated. The POI ranges from 0-100%. Is there any negative value?
It would be better to draw a scatter plot to demonstrate the streamflow forecast skills during peak flow conditions with/without data assimilation.
Citation: https://doi.org/10.5194/hess-2024-209-RC1 -
RC2: 'Comment on hess-2024-209', Anonymous Referee #2, 21 Nov 2024
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The comment was uploaded in the form of a supplement: https://hess.copernicus.org/preprints/hess-2024-209/hess-2024-209-RC2-supplement.pdf
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