Assimilation of remotely sensed hydrological datasets in the North American Land Data Assimilation System (NLDAS)

The North American Land Data Assimilation System (NLDAS) has produced over 34 years (Jan 1979 to present) of hourly land-surface meteorology and surface states, including soil moistures and temperatures, snow cover, runoff, and evapotranspiration. NLDAS uses the best-available observations and reanalyses to create near-surface forcing for land-surface models (LSMs) in “off-line” mode, but to-date has not included the ability to assimilate relevant hydrological remote sensing datasets. Several recent works have independently demonstrated the value of assimilating AMSR-E based soil moisture, AMSR-E based estimates of snow depth; MODIS-based estimates of Snow Covered Area (SCA); GRACE-based terrestrial water storage (TWS) and MODIS-based estimates of irrigation intensity. In this presentation, we will present results of assimilating these datasets in the NLDAS configuration using the NASA Land Information System (LIS), as part of the new phase of the NLDAS project (An evaluation of the model upgrades will also be presented in a companion presentation submitted to this session). The results from the individual assimilation of AMRSR-E based soil moisture and snow depth into the Noah LSM indicate that systematic improvements are obtained not only in soil moisture and snow states, but also on evapotranspiration and streamflow estimates. We will also present results from the combined assimilation of the above-mentioned multi-sensor datasets in NLDAS and an evaluation of the resulting improvements and trends in soil moisture, snowpack, evapotranspiration and streamflow.