Hydro-DART: Ensemble Streamflow Data Assimilation using WRF-Hydro and DART with an application to Hurricane Florence

Abstract:

The Data Assimilation Research Testbed (DART) has been coupled with the community WRF-Hydro modeling system with the intent of providing efficient and flexible support for assimilating a wide range of streamflow and soil moisture observations and delivering an ensemble of model states useful for quantifying streamflow uncertainties. The coupled framework, named Hydro-DART, is used study and assess the flooding consequences of Hurricane Florence over the Carolinas during August-September 2018 period.

Several extensions to earlier versions of Hydro-DART have been explored. These include: (1) a multi-configuration ensemble in which different ensemble members are run with different physical parameters (such as Manning and side slope) in order to create additional ensemble variability, (2) a variable transform, anamorphosis, is introduced such that bounded quantities (e.g., streamflow) are transformed to a Gaussian space prior to the Kalman update as a way to avoid non-physical state updates, (3) spatially-correlated noise is used to create ensembles of overland and subsurface fluxes to force the channel-only configuration of the National Water Model (NWM), (4) adaptive prior and posterior inflation to address model deficiencies and sampling errors and (5) localization along the stream network, rather than physical proximity, is investigated in terms of precipitation correlation length scale. Hourly streamflow gauge data, from the flood-affected area, is used to test the impact of these extensions on the overall prediction accuracy.