The Impact of Soil Moisture and Snow Assimilation on NLDAS Drought Metrics

We will show results from NASA's Land Information System (LIS) configured using the North American Land Data Assimilation System (NLDAS) inputs, to demonstrate the impacts of soil moisture and snow product assimilation on drought assessment. For our soil moisture assimilation experiments, we utilize two different surface soil moisture retrievals (NASA Level-3 product and the Land Parameter Retrieval Model (LPRM) product from VU Amsterdam) from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). For the snow assimilation experiments, we utilize the snow covered area product from the Moderate Resolution Imaging Spectroradiometer (MODIS) and bias-corrected snow depth from AMSR-E.

Recently published results indicate that the assimilation of LPRM soil moisture leads to improvements in evapotranspiration (ET), as quantified via comparisons against gridded tower-based ET estimates from FLUXNET measurements and ET estimates based on MODIS satellite data. More recent results also indicate that LPRM soil moisture assimilation improves streamflow as well. Additionally, assimilation of bias-corrected AMSR-E SWE products is shown to improve both snowpack and streamflow. To quantify the impact of assimilation on drought metrics, we will also show the impact on standard NLDAS drought monitoring products, such as soil moisture percentiles, as well as traditional drought metrics such as the Standardized Runoff Index (SRI) and the Standardized Soil Wetness Index (SSWI).