Assimilation of remotely sensed soil moisture and snow depth retrievals for drought estimation

The accurate knowledge of soil moisture and snow conditions is important for the skillful characterization of agricultural and hydrologic droughts, which are defined as deficits of soilmoisture and streamflow, respectively. This article examines the influence of remotely sensed soilmoisture and snowdepth retrievals toward improving estimates of drought through data assimilation. Soilmoisture and snowdepth retrievals froma variety of sensors (primarily passivemicrowave based) are assimilated separately into the Noah land surfacemodel for the period of 1979-2011 over the continental United States, in the North American Land Data Assimilation System (NLDAS) configuration. Overall, the assimilation of soil moisture and snow datasets was found to provide marginal improvements over the open-loop configuration. Though the improvements in soil moisture fields through soil moisture data assimilation were barely at the statistically significant levels, these small improvementswere found to translate into subsequent small improvements in simulated streamflow. The assimilation of snow depth datasets were found to generally improve the snow fields, but these improvements did not always translate to corresponding improvements in streamflow, including some notable degradations observed in the westernUnited States.Aquantitative examination of the percentage drought area fromroot-zone soilmoisture and streamflow percentiles was conducted against the U.S. DroughtMonitor data. The results suggest that soil moisture assimilation provides 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.