Preprints
https://doi.org/10.5194/gmd-2022-61
https://doi.org/10.5194/gmd-2022-61
Submitted as: development and technical paper
14 Mar 2022
Submitted as: development and technical paper | 14 Mar 2022
Status: this preprint is currently under review for the journal GMD.

CREST-VEC: A framework towards more accurate and realistic flood simulation across scales

Zhi Li1, Shang Gao1, Mengye Chen1, Jonathan Gourley2, Naoki Mizukami3, and Yang Hong1 Zhi Li et al.
  • 1School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK, 70372, USA
  • 2NOAA/National Severe Storms Laboratory, Norman, OK, 73072, USA
  • 3National Centers for Atmospheric Research, Boulder, CO, 80307, USA

Abstract. Large-scale (i.e., continental and global) hydrologic simulation is an appealing yet challenging topic for the hydrologic community. First and foremost, model efficiency and scalability (flexibility in resolution and discretization) have to be prioritized. Then, sufficient model accuracy and precision are required to provide useful information for water resources applications. This study presents a hydrologic modeling framework – CREST-VEC (Coupled Routing and Excess STorage – VECtor-based routing) that combines a gridded water balance model and a newly developed vector-based routing scheme. First, in contrast to a conventional fully gridded model, this framework can significantly reduce the computational cost by at least ten times, based on experiments at regional (0.07 sec/step vs. 0.002 sec/step) and continental scales (0.35 sec/step vs. 7.2 sec/step). This provides adequate time efficiency for generating operational ensemble streamflow forecasts and even probabilistic estimates across scales. Second, the performance using the new vector-based routing is improved, with the median-aggregated NSE (Nash-Sutcliffe Efficiency) score increased from -0.06 to 0.13 over the United States. Third, with the lake module incorporated, the NSE score is further improved by 62.5 % and the systematic bias is reduced by 36.7 %. Lastly, over 20 % of the false alarms on two-year floods in the US can be mitigated with the lake module enabled, at the expense of only missing 2.3 % more events. This study demonstrated the advantages of the proposed hydrological modeling framework that could provide a solid basis for continental and global scale water modeling at fine resolution. Furthermore, the use of ensemble forecasts can be incorporated into this framework; and thus, optimized streamflow prediction with quantified uncertainty information can be achieved at operational fashion for stakeholders and decision-makers.

Zhi Li et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-61', Daisuke Tokuda, 08 Apr 2022
  • RC2: 'Comment on gmd-2022-61', Anonymous Referee #2, 13 Apr 2022
  • RC3: 'Comment on gmd-2022-61', Anonymous Referee #3, 19 Apr 2022
  • RC4: 'Comment on gmd-2022-61', Anonymous Referee #4, 09 May 2022

Zhi Li et al.

Model code and software

CREST-VEC model code Zhi Li https://doi.org/10.5281/zenodo.6305817

Zhi Li et al.

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Short summary
Operational streamflow prediction at a continental scale is critical for national water resources management. However, limited computational resources often impede such processes with streamflow routing being the most time-consuming part. This study presents a recent development of a hydrologic system that incorporates a vector-based routing scheme along with a lake module that markedly speeds up streamflow prediction. Meanwhile, accuracy is improved and flood false alarms are greatly mitigated.