USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES
Title: The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates in a land data assimilation system
Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: April 22, 2011
Publication Date: October 1, 2011
Citation: Liu, Q., Reichle, R., Bindlish, R., Cosh, M.H., Crow, W.T., De Jeu, R., De Lannoy, G., Huffman, G., Jackson, T.J. 2011. The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates in a land data assimilation system. Journal of Hydrometeorology. 12:750-765.
Interpretive Summary: Land data assimilation systems are a valuable tool for modeling the hydrologic cycle, and they are improved by incorporating land surface data from in situ instrumentation and remote sensing data. This study analyzed the improvement in skill of a data assimilation system by adding ground data from USDA networks, remote sensing data from NASA’s AMSR-E instrument, and precipitation data from NOAA. Each successive layer of information improved the skill of the system to a reference data set known as MERRA (Modern Era Retrospective-analysis for Research and Applications). The two USDA networks are regional and watershed scale networks which had R values of approximately 0.42 and 0.55 which are reasonable correlations. Additional analysis investigated root zone soil moisture which is accessible to vegetation for growing. Overall, the data assimilation system benefits significantly from inclusion of these data sources.
The contributions of precipitation and soil moisture observations to soil moisture skill in a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). Soil moisture skill (defined as the anomaly time series correlation coefficient R) is assessed using in situ observations in the continental United States at 37 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at four USDA Agricultural Research Service ("CalVal") watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill of AMSR-E retrievals is R=0.42 versus SCAN and R=0.55 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R=0.43 and R=0.47, respectively, versus SCAN measurements. MERRA surface moisture skill is R=0.55 versus CalVal measurements. Adding information from precipitation observations increases (surface and root zone) soil moisture skills by 'R~0.06. Assimilating AMSR-E retrievals increases soil moisture skills by 'R~0.08. Adding information from both sources increases soil moisture skills by 'R~0.13, which demonstrates that precipitation corrections and assimilation of satellite soil moisture retrievals contribute important and largely independent amounts of information.