Data Assimilation of Soil Moisture in a Distributed Hydrologic Model: A Case Study over the Russian River Basin
Soil moisture assimilation takes place at the daily scale in these experiments to mimic the frequency of space-based retrievals. For hydrologic simulations, the National Weather Service's Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) is used. HL-RDHM includes the conceptually-based Sacramento Heat Transfer with enhanced EvapoTranspiration (SAC-HTET) for rainfall-runoff production, which includes a scheme to transform the native conceptual storages to physically meaningful soil layers. This transformation allows for assimilation of soil moisture at various layers into HL-RDHM. For this study, the model is run at single pixels collocated with observation sites within northern California's Russian River Basin. These observation sites are operated by NOAA's Earth System Research Laboratory and provide point soil moisture profile measurements as well as the air temperature and precipitation measurements needed to force the model. HL-RDHM is run at an hourly scale, and the Ensemble Kalman Filter was selected for assimilation purposes. The experiments were run for the year 2013 for top layer only assimilation as well as multi-layer assimilation and the results were compared to an open (no assimilation) model run.
Results from this work suggest that even assimilation of top layer soil moisture alone may provide an improvement on the open run in recovering the soil moisture state, particularly in the early stages of simulation when unknown initial states are assumed. As the model was run with a-priori parameters derived from soil surveys, this work also indicates that when calibration via streamflow is difficult (as in the case of ungauged basins), data assimilation of soil moisture into a hydrologic model may aid in remediating model deficiencies caused by potentially incomplete soil surveys from which default parameters for HL-RDHM are made.