Abstract
Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model - ESM - simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling and retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970-2016), multilayer (0-10, 10-30, 30-50, and 50-100ĝcm) SM products at monthly 0.5ĝ resolution (available at 10.6084/m9.figshare.13661312.v1; Wang and Mao, 2021) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations (mean bias from -0.044 to 0.033ĝm3ĝm-3, root mean square errors from 0.076 to 0.104ĝm3ĝm-3, Pearson correlations from 0.35 to 0.67) and multiple gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Three of the new SM products, which were produced by applying any of the three merging methods to the source datasets excluding the ESMs, had lower bias and root mean square errors and higher correlations than the ESM-dependent merged products. The ESM-independent products also showed a better ability to capture historical large-scale drought events than the ESM-dependent products. The merged products generally showed reasonable temporal homogeneity and physically plausible global sensitivities to observed meteorological factors, except that the ESM-dependent products underestimated the low-frequency temporal variability in SM and overestimated the high-frequency variability for the 50-100ĝcm depth. Based on these evaluation results, the three ESM-independent products were finally recommended for future applications because of their better performances than the ESM-dependent ones. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.
Original language | English |
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Pages (from-to) | 4385-4405 |
Number of pages | 21 |
Journal | Earth System Science Data |
Volume | 13 |
Issue number | 9 |
DOIs | |
State | Published - Sep 7 2021 |
Funding
This research has been supported by an Oak Ridge National Laboratory (ORNL) subcontract (4000169153) and the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Science Focus Area (RUBISCO SFA) project in the Earth and Environmental Systems Sciences Division (EESSD) of the Biological and Environmental Research (BER) office in the US Department of Energy (DOE) Office of Science. ORNL is managed by UT-BATTELLE, LLC, for the DOE under contract no. DE-AC05-00OR22725.