The North American Soil Moisture Database (NASMD) was initiated in 2011 to assemble and homogenize in situ soil moisture measurements from 32 observational networks in the United States and Canada encompassing more th...The North American Soil Moisture Database (NASMD) was initiated in 2011 to assemble and homogenize in situ soil moisture measurements from 32 observational networks in the United States and Canada encompassing more than 1800 stations. Although statistical quality control (QC) procedures have been applied in the NASMD, the soil moisture content tends to be systematically underestimated by in situ sensors in frozen soils, and using a single maximum threshold (i.e., 0.6 m3 m-3) may not be sufficient for robust QC because of the diverse soil textures in North America. In this study, based on the in situ soil porosity and North American Land Data Assimilation System phase 2 (NLDAS-2) Noah soil temperature, the simple automated QC method is revised to supplement the existing QC approach. This revised QC method is first validated based on the assessment at 78 of the Soil Climate Analysis Network (SCAN) stations where the manually checked data are available, and is then applied to all stations in the NASMD to produce a more strict quality-controlled dataset. The results show that the revised automated QC procedure can flag the spurious and erroneous soil moisture measurements for the SCAN stations, especially for those located in high altitudes and latitudes. Relative to station measurements in the original NASMD, the quality-controlled data show a slightly better agreement with the manually checked soil moisture content. It should be noted that this quality-controlled dataset may be over-flagged for some valid soil moisture measurements due to potential errors of the soil temperature and soil porosity data, and validation in this study is limited by the availability of benchmark soil moisture data. The updated QC and additional validation will be desirable to boost confidence in the product when high-quality data become available in the future.展开更多
基金Supported by the National Key Research and Development Program of China(2017YFA0604300)National Natural Science Foundation of China(51779278,51379224,and 41671398)NOAA/CPO Modeling,Analyses,Predictions,and Projections(MAP) Program
文摘The North American Soil Moisture Database (NASMD) was initiated in 2011 to assemble and homogenize in situ soil moisture measurements from 32 observational networks in the United States and Canada encompassing more than 1800 stations. Although statistical quality control (QC) procedures have been applied in the NASMD, the soil moisture content tends to be systematically underestimated by in situ sensors in frozen soils, and using a single maximum threshold (i.e., 0.6 m3 m-3) may not be sufficient for robust QC because of the diverse soil textures in North America. In this study, based on the in situ soil porosity and North American Land Data Assimilation System phase 2 (NLDAS-2) Noah soil temperature, the simple automated QC method is revised to supplement the existing QC approach. This revised QC method is first validated based on the assessment at 78 of the Soil Climate Analysis Network (SCAN) stations where the manually checked data are available, and is then applied to all stations in the NASMD to produce a more strict quality-controlled dataset. The results show that the revised automated QC procedure can flag the spurious and erroneous soil moisture measurements for the SCAN stations, especially for those located in high altitudes and latitudes. Relative to station measurements in the original NASMD, the quality-controlled data show a slightly better agreement with the manually checked soil moisture content. It should be noted that this quality-controlled dataset may be over-flagged for some valid soil moisture measurements due to potential errors of the soil temperature and soil porosity data, and validation in this study is limited by the availability of benchmark soil moisture data. The updated QC and additional validation will be desirable to boost confidence in the product when high-quality data become available in the future.
