Continuous data assimilation for downscaling large-footprint soil moisture retrievals

Muhammad U. Altaf; Raghavendra B. Jana; Ibrahim Hoteit; Matthew F. McCabe

doi:10.1117/12.2241042

25 October 2016 Continuous data assimilation for downscaling large-footprint soil moisture retrievals

Muhammad U. Altaf, Raghavendra B. Jana, Ibrahim Hoteit, Matthew F. McCabe

Proceedings Volume 9998, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII; 99981O (2016) https://doi.org/10.1117/12.2241042
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom

Abstract

Soil moisture is a key component of the hydrologic cycle, influencing processes leading to runoff generation, infiltration and groundwater recharge, evaporation and transpiration. Generally, the measurement scale for soil moisture is found to be different from the modeling scales for these processes. Reducing this mismatch between observation and model scales in necessary for improved hydrological modeling. An innovative approach to downscaling coarse resolution soil moisture data by combining continuous data assimilation and physically based modeling is presented. In this approach, we exploit the features of Continuous Data Assimilation (CDA) which was initially designed for general dissipative dynamical systems and later tested numerically on the incompressible Navier-Stokes equation, and the Benard equation. A nudging term, estimated as the misfit between interpolants of the assimilated coarse grid measurements and the fine grid model solution, is added to the model equations to constrain the model’s large scale variability by available measurements. Soil moisture fields generated at a fine resolution by a physically-based vadose zone model (HYDRUS) are subjected to data assimilation conditioned upon coarse resolution observations. This enables nudging of the model outputs towards values that honor the coarse resolution dynamics while still being generated at the fine scale. Results show that the approach is feasible to generate fine scale soil moisture fields across large extents, based on coarse scale observations. Application of this approach is likely in generating fine and intermediate resolution soil moisture fields conditioned on the radiometerbased, coarse resolution products from remote sensing satellites.

Citation Download Citation

Muhammad U. Altaf, Raghavendra B. Jana, Ibrahim Hoteit, and Matthew F. McCabe "Continuous data assimilation for downscaling large-footprint soil moisture retrievals", Proc. SPIE 9998, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, 99981O (25 October 2016); https://doi.org/10.1117/12.2241042

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KEYWORDS

Soil science

Data modeling

Systems modeling

Spatial resolution

Remote sensing

Statistical modeling

Hydrology

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