|KUMAR, SUJAAY - National Aeronautics And Space Administration (NASA)|
|PETERS-LIDARD, C - National Aeronautics And Space Administration (NASA)|
|MOCKO, DAVID - National Aeronautics And Space Administration (NASA)|
|REICHLE, R - National Aeronautics And Space Administration (NASA)|
|LIU, YUQIONG - University Of Maryland|
|ARSENAULT, KRISTI - National Aeronautics And Space Administration (NASA)|
|XIA, YOULONG - National Oceanic & Atmospheric Administration (NOAA)|
|EK, M.B. - National Oceanic & Atmospheric Administration (NOAA)|
|RIGGS, GEORGE - Science Systems, Inc|
|LIVNEH, BEN - Collaborator|
Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/26/2014
Publication Date: 12/6/2014
Publication URL: http://handle.nal.usda.gov/10113/60408
Citation: Kumar, S.V., Peters-Lidard, C., Mocko, D., Reichle, R., Liu, Y., Arsenault, K.R., Xia, Y., Ek, M., Riggs, G., Livneh, B., Cosh, M.H. 2014. Assimilation of passive microwave-based soil moisture and snow depth retrievals for drought estimation. Journal of Hydrometeorology. 15:2446-2469.
Interpretive Summary: Agricultural and hydrologic drought monitoring and prediction suffer from a lack of accurate knowledge of soil moisture and snow conditions on the land surface. In this study, passive microwave remote sensing estimates of soil moisture and snow depth are integrated into a land surface model. This model was compared to U.S. drought monitor data and it was determined that soil moisture improves the spatial patterns of drought estimates, but snow data does not significantly improve and may even hinder drought estimates. The results of this study will lead to better drought monitoring and impacts on streamflow estimates. Understanding and predicting drought and streamflow is critical to agriculture planning and natural resources management, especially with respect to water resources in the Midwest and Western U.S.
Technical Abstract: This article examines 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 simulation whereas the improvements noted in the snow dep 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.