Abstract
Abstract. Predicting major floods during extreme rainfall events remains an important challenge. Rapid changes in flows over short timescales, 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. Instantaneous streamflow observations from 107 United States Geological Survey (USGS) gauges are assimilated for a period of 1 month. The data assimilation (DA) system developed in this paper explores two novel contributions, namely (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 which often occur in flooding situations. 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 (root mean square error) streamflow estimates than regular, Euclidean distance-based localization. An 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 the 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.
Highlights
Affecting nearly a 100 million people worldwide per year, flooding is the most common natural disaster (Guha-Sapir et al, 2013)
NOAA’s National Water Model (NWM) configuration of the WRF-Hydro framework is coupled to the Data Assimilation Research Testbed (DART) to improve ensemble streamflow forecasts under extreme rainfall conditions during Hurricane Florence in September 2018
Streamflow and bucket head states are simulated using a channel+bucket submodel of the NWM. These states are updated through data assimilation (DA) using streamflow observations collected from 107 United States Geological Survey (USGS) gauges
Summary
Affecting nearly a 100 million people worldwide per year, flooding is the most common natural disaster (Guha-Sapir et al, 2013). This study contributes to improving short-term (hourly) streamflow flooding forecasts by minimizing error in their initial conditions through streamflow data assimilation. Within the context of an operational and spatially distributed hydrologic model, we examine the data assimilation (DA) challenges of dominant errors (bias) arising from the precipitation boundary conditions (forcings) and of improving information propagation from the observations into the model ensemble (background). We explore the use of spatially and temporally varying adaptive covariance inflation (El Gharamti, 2018) as a way to mitigate bias in the context of extreme flood simulations. The impact of inflation on streamflow predictions is not fully understood This is the first study of its kind where spatially and temporally adaptive inflation are applied to streamflow forecasting.
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