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Improved National-Scale Flood Prediction for Gauged and Ungauged Basins using a Spatio-temporal Hierarchical Model
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  • Shiqi Fang,
  • J. Michael Johnson,
  • Lilit Yeghiazarian,
  • Arumugam Sankarasubramanian
Shiqi Fang
North Carolina State University

Corresponding Author:sfang6@ncsu.edu

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J. Michael Johnson
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Lilit Yeghiazarian
University of Cincinnati
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Arumugam Sankarasubramanian
Department of Civil, Construction, and Environmental Engineering, North Carolina State University
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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 for high flows, particularly in ungauged basins. To improve flood prediction for both gauged and ungauged basins, we propose a spatio-temporal hierarchical model (STHM) to improve high flow estimation using 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 are the previous 3-day average streamflow and the aridity index for controlled and natural basins, respectively. To evaluate the STHM in improving 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.
08 Feb 2023Submitted to ESS Open Archive
09 Feb 2023Published in ESS Open Archive