Nested Hyper-Resolution Modeling and Assimilation of Water Level Data Using WRF-Hydro, OpenDA and the Community Hydrologic Prediction System

As a part of the National Water Model (NWM) initiative, the NWS has been mandated to provide forecasts at even finer spatiotemporal resolution when and where such information is demanded, e.g., in large urban areas for flooding and inundation, areas of high-value infrastructure, areas susceptible to landslides, or areas impacted by forest fires. In this presentation, we describe implementation of WRF-Hydro at a hyper resolution nested within the NWM. The demonstration domain consists of several urban catchments in the Cities of Arlington and Grand Prairie in the Dallas-Fort Worth Metroplex. This area is susceptible to urban flooding due to hydroclimatology and large impervious cover. The hyper-resolution model (HRM) uses LIDAR-based terrain data to resolve significant land surface features such as streets and large man-made structures and the CASA WX precipitation products for high-resolution quantitative precipitation information. To assimilate water level observations from pressure transducer and ultrasonic sensors, we use ensemble Kalman filter via OpenDA. To control the model runs and display model output, we use the Community Hydrologic Prediction System. The results of this project are expected to provide the forecasters with the operational flexibility necessary to produce forecasts on demand when and where the conditions warrant. This presentation reports progress and preliminary results, and identifies issues and challenges.