Estimation of Root Zone Soil Moisture Profile by Reduced-Order Variational Data Assimilation Using Near Surface Soil Moisture Observations

Abstract

Soil moisture plays an important role in the global water cycle and has an important impact on energy fluxes at the land surface. It also defines the initial and boundary condition of terrestrial hydrological processes, including infiltration, runoff, and evapotranspiration. Therefore, accurate estimation of soil moisture pattern is of critical importance. Satellite-based soil moisture can be obtained with well-defined temporal and spatial resolutions and with global coverage. However, they only provide surface soil moisture at the upper few centimeters of the soil column. Soil moisture simulation models can produce estimates of soil moisture profile up to several meters of depth in different time steps. However, uncertainty in model parameters (e.g., unknown initial soil moisture profile) and meteorological forcing can substantially alter the accuracy of the model estimates. In this study, the potential of using surface soil moisture measurements to retrieve the initial soil moisture profile will be explored in a synthetic study, using two proposed reduced-order variational data assimilation (VDA) techniques and a simple 1D-soil moisture model. The accuracy and feasibility of the proposed approaches are confirmed by comparing the initial soil moisture profiles estimated using the proposed reduced-order VDA techniques versus the full-adjoint VDA technique. Results illustrated that the reduced-order VDA techniques can estimate initial soil moisture profile from surface soil moisture observation with the comparable level of accuracy as full-adjoint VDA. The effectiveness of the reduced-order VDA in retrieving the initial soil moisture profile is further demonstrated by assimilating surface soil moisture into HYDRUS-1D, mimicking real-world errors.

A Variational Framework for Coupled Estimation of Evapotranspiration and Recharge Fluxes by Assimilating Land Surface Soil Moisture and Temperature

“…However, these satellites provide SM observation only for the surface layer (∼top 5 cm). Different data assimilation methods have been used to retrieve the root zone SM profile from surface SM observations [27]- [30], [84] and most of these techniques are based on Kalman filtering [27], [32]- [38]. For example, Dunne and Entekhabi [31] used the ensemble Kalman smoother methods to estimate the surface and subsurface SM by assimilating L-band radio brightness temperatures.…”