Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding

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.

An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space

“…The use of DA within hydrodynamic models has been examined to extend the spatiotemporal utility of these typically narrow swath altimeter measurements. These studies include efforts to assimilate river discharge, velocity, and water level (Andreadis et al., 2007; El Gharamti et al., 2021; Emery et al., 2020; Ercolani & Castelli, 2017; Neal et al., 2007; Paiva et al., 2013; Ricci et al., 2011). The improved surface water characterization over data‐poor transboundary basins from the assimilation of remote sensing data is a highly relevant hydrological modeling challenge not routinely employed in hydrological prediction systems.…”

Interconnected hydrologic extreme drivers and impacts depicted by remote sensing data assimilation

“…This is potentially beneficial for improving physical process understanding through models and for monitoring extreme events for operational stakeholders. Future work could extend the elements of the present study by incorporating surface DA with LSMs to better understand physical processes associated with hydrologic extremes such as floods 30 , 31 , fires 57 , and droughts 13 . Streamflow DA could also help to resolve the impacts of diversions and other human modifications to hydrologic systems.…”

Assimilation of transformed water surface elevation to improve river discharge estimation in a continental-scale river

“…In addition, we used a physically based empirical localization technique (Revel et al, 2019) to reduce erroneous correlations and assimilate observations in significantly correlated areas. It has been found that the physically based empirical localization method performed better than the conventional square-shaped local patches in hydrodynamic DA schemes (El Gharamti et al, 2021;Ishitsuka et al, 2020;Revel et al, 2019;Wongchuig et al, 2019). Hence, the assimilation framework is capable of estimating river discharge at the global scale provided satellite observations are available.…”