Preprints
https://doi.org/10.5194/hess-2020-642
https://doi.org/10.5194/hess-2020-642

  25 Mar 2021

25 Mar 2021

Review status: a revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

Ensemble Streamflow Data Assimilation using WRF-Hydro and DART: Hurricane Florence Flooding

Mohamad El Gharamti1, James L. McCreight1, Seong Jin Noh2, Timothy J. Hoar1, Arezoo RafieeiNasab1, and Benjamin K. Johnson1 Mohamad El Gharamti et al.
  • 1National Center for Atmospheric Research, Boulder, CO, USA
  • 2Kumoh National Institute of Technology, Gumi, South Korea

Abstract. Predicting major floods during extreme rainfall events remains an important challenge. Rapid changes in flows over short time-scales combined with multiple sources of model error makes it difficult to accurately simulate intense floods. This study presents a general data assimilation framework that aims to improve flood predictions in channel routing models. Hurricane Florence, which caused catastrophic flooding and damages in the Carolinas in September 2018, is used as a case study. The National Water Model (NWM) configuration of the WRF-Hydro modeling framework is interfaced with the Data Assimilation Research Testbed (DART) to produce ensemble streamflow forecasts and analyses. Hourly streamflow observations from 107 United States Geological Survey (USGS) gauges are assimilated for a period of one month.

The data assimilation (DA) system developed in this paper explores two novel contributions: (1) Along-The-Stream (ATS) covariance localization and (2) spatially and temporally varying adaptive covariance inflation. ATS localization aims to mitigate not only spurious correlations, due to limited ensemble size, but also physically incorrect correlations between unconnected and indirectly connected state variables in the river network. We demonstrate that ATS localization provides improved information propagation during the model update. Adaptive prior inflation is used to tackle errors in the prior, including large model biases. Analysis errors incurred during the update are addressed using posterior inflation. Results show that ATS localization is a crucial ingredient of our hydrologic DA system, providing at least 40% more accurate (RMSE) streamflow estimates than regular, Euclidean distance-based localization. Assessment of hydrographs indicates that adaptive inflation is extremely useful and perhaps indispensable for improving the forecast skill during flooding events with significant model errors. We argue that adaptive prior inflation is able to serve as a vigorous bias correction scheme which varies both spatially and temporally. Major improvements over the model's severely underestimated streamflow estimates are suggested along Pee Dee River in South Carolina and many other locations in the domain, where inflation is able to avoid filter divergence and thereby assimilate significantly more observations.

Mohamad El Gharamti et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2020-642', Anonymous Referee #1, 22 Apr 2021
  • RC2: 'Comment on hess-2020-642', Anonymous Referee #2, 01 Jul 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2020-642', Anonymous Referee #1, 22 Apr 2021
  • RC2: 'Comment on hess-2020-642', Anonymous Referee #2, 01 Jul 2021

Mohamad El Gharamti et al.

Mohamad El Gharamti et al.

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