4.7 Article

Validation of a New Root-Zone Soil Moisture Product: Soil MERGE

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2019.2930946

关键词

European Space Agency Climate Change Initiative (ESA-CCI); exponential filter; North American land data assimilation system (NLDAS) Noah; root-zone soil moisture (RZSM); soil MERGE (SMERGE)

资金

  1. NSF Geoscience Equipment [1636769]
  2. NASA Climate Indicators and Data Products for National Climate Assessments program [NNX16AH30G]
  3. Directorate For Geosciences
  4. Division Of Earth Sciences [1636769] Funding Source: National Science Foundation

向作者/读者索取更多资源

Soil MERGE (SMERGE) is a 0.125 degrees, root-zone soil moisture (RZSM) product (0-40-cm depth) generated within the contiguous United States (CONUS). This product is developed by merging RZSM output from the North American land data assimilation system (NLDAS) with surface satellite retrievals from the European Space Agency Climate Change Initiative. SMERGE, at present, spans four decades (1979-2016). Here, we introduce the SMERGE approach and describe the validation of SMERGE RZSM estimates using three geophysical observations: 1) comparison with sparse in situ soil moisture data acquired from the soil climate analysis network (SCAN) and the U.S. Climate Reference Network (USCRN); 2) ranked correlation analysis against normalized difference vegetation index (NDVI) datasets; and 3) ranked correlation analysis of antecedent RZSM with storm-event streamflow across a range of precipitation intensities (5-45 mm/day). Relative to in situ SCAN and USCRN observations, SMERGE has an average daily correlation of 0.7-0.8 and unbiased root-mean square error close to 0.04 m(3)/m(3)-a level that is commonly applied as a validation target for large-scale soil moisture datasets. NDVI benchmarking allows us to indirectly evaluate SMERGE across CONUS and reveals it can predict near-term vegetation health anomalies with skill comparable to that of RZSM products generated by more complex data assimilation methods. In addition, streamflow-based evaluation results demonstrate that SMERGE antecedent RZSM can be used as a reliable predicator of storm-event runoff efficiency for rainfall events >25 mm/day.

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