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Abstract Floods cause hundreds of fatalities and billions of dollars of economic loss each year in the United States. To mitigate these damages, accurate flood prediction is needed for issuing early warnings to the public. This situation is exacerbated in larger model domains flood prediction, particularly in ungauged basins. To improve flood prediction for both gauged and ungauged basins, we propose a spatio‐temporal hierarchical model (STHM) using above‐normal flow estimation with a 10‐day window of modeled National Water Model (NWM) streamflow and a variety of catchment characteristics as input. The STHM is calibrated (1993–2008) and validated (2009–2018) in controlled, natural, and coastal basins over three broad groups, and shows significant improvement for the first two basin types. A seasonal analysis shows the most influential predictors beyond NWM streamflow reanalysis are the previous 3‐day average streamflow and the aridity index for controlled and natural basins, respectively. To evaluate the STHM in improving above‐normal streamflow in ungauged basins, 20‐fold cross‐validation is performed by leaving 5% of sites. Results show that the STHM increases predictive skill in over 50% of sites' by 0.1 Nash‐Sutcliffe efficiency (NSE) and improves over 65% of sites' streamflow prediction to an NSE > 0.67, which demonstrates that the STHM is one of the first of its kind and could be employed for flood prediction in both gauged and ungauged basins.
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Leveraging synthetic aperture radar (SAR) with the National Water Model (NWM) to improve above-normal flow prediction in ungauged basins
Abstract Effective flood prediction supports developing proactive risk management strategies, but its application in ungauged basins faces tremendous challenges due to limited/no streamflow record. This study investigates the potential for integrating streamflow derived from synthetic aperture radar (SAR) data and U.S. National Water Model (NWM) reanalysis estimates to develop improved predictions of above-normal flow (ANF) over the coterminous US. Leveraging the SAR data from the Global Flood Detection System to estimate the antecedent conditions using principal component regression, we apply the spatial-temporal hierarchical model (STHM) using NWM outputs for improving ANF prediction. Our evaluation shows promising results with the integrated model, STHM-SAR, significantly improving NWE, especially in 60% of the sites in the coastal region. Spatial and temporal validations underscore the model’s robustness, with SAR data contributing to explained variance by 24% on average. This approach not only improves NWM prediction, but also uniquely combines existing remote sensing data with national-scale predictions, showcasing its potential to improve hydrological modeling, particularly in regions with limited stream gauges.
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- Award ID(s):
- 2151651
- PAR ID:
- 10557683
- Publisher / Repository:
- Environmental Research Letters
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 19
- Issue:
- 12
- ISSN:
- 1748-9326
- Page Range / eLocation ID:
- 124002
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/1748-9326/ad8808
