Assimilation of passive microwave-based soil moisture and snow depth retrievals for drought estimation

The accurate knowledge of soil moisture and snow conditions on the land surface is important for the skillful characterization of agricultural and hydrologic droughts, which are defined as deficits of soil moisture and streamflow, respectively. In this presentation, we examine the influence of passive microwave based soil moisture and snow depth retrievals towards improving estimates of drought through data assimilation. Passive microwave based soil moisture and snow depth retrievals from a variety of sensors are assimilated separately into the Noah land surface model during a period of 1979 to 2011, over the continental United States. Soil moisture data assimilation provides improvements to the soil moisture and streamflow simulations, whereas the improvements noted in the snow depth fields did not consistently translate to improvements in streamflow. A quantitative examination of the percentage drought area from root zone soil moisture and streamflow percentiles was conducted against the U.S. drought monitor data. Our results suggest that soil moisture assimilation is effective in providing improvements at short time scales, both in the magnitude and representation of the spatial patterns of drought estimates, whereas the impact of snow data assimilation was marginal and often disadvantageous.