Abstract:
Recent satellite missions have revolutionized land surface hydrology at the global scale. Observations from the Gravity Recovery and Climate Experiment (GRACE) mission provide monthly global estimates of the vertically integrated terrestrial water storage with about 300–400-km horizontal resolution. The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions observe L-band (1.4 GHz) microwave brightness temperatures, which are sensitive to near-surface soil moisture, with a revisit time of 1–3 days at ~40-km spatial resolution. Through the assimilation of these remote sensing observations into a land surface model, value-added estimates of global land surface hydrology conditions can be obtained with complete spatial and temporal coverage. One example is the SMAP Level-4 Soil Moisture data product, which provides global, 3-hourly, 9-km resolution estimates of surface and root-zone soil moisture and associated land surface variables.

This presentation discusses recent results obtained from the assimilation of GRACE, SMOS, and SMAP observations. As expected, GRACE data assimilation mostly improves estimates of shallow groundwater, whereas SMOS and SMAP data assimilation mainly improves estimates of surface soil moisture, particularly in otherwise data-sparse regions. Better and more consistent soil moisture and groundwater estimates can be achieved when multiple observation types are assimilated. Nevertheless, open questions remain about the synergy of GRACE, SMOS, and SMAP observations and land surface models. An example in northwestern India illustrates that long-term trends in GRACE observations result in erroneous trends in evapotranspiration estimates if irrigation is not considered in the land surface model, thereby emphasizing the importance of representing anthropogenic processes in land surface modeling and data assimilation systems.