Forecasting Global Surface Soil Moisture Dataset Using Multi-scenario Integration Methodology (2015-2100)
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DOI:10.3974/geodb.2024.11.10.V1
Published:Nov. 2024
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Key Words：
surface soil moisture,future multi-scenario,global,fusion,
Abstract：
Soil moisture is a key land surface element to express the effects of global climate change. In order to develop a reliable global future multi-scenario surface soil moisture fusion dataset, we firstly utilized the Enhanced Triple Collocation (ETC) to evaluate the accuracy of 22 sets of CMIP6 soil moisture data, and obtained the random error standard deviation (RESD) and correlation coefficient (CC) to select the participating earth system model datasets. Secondly, nine sets of earth system model data were fused based on the normalized weighting of RESD and CC. Finally, the accuracy of the fused data was verified by the evaluation of the measured data at the stations. The datasets include: (1) global monthly 0.5° resolution soil moisture data if SSP1-2.6, SSP2-4.5, and SSP5-8.5. (2) In-situ measurements from four networks, which were from NAQU, REMEDHUS, SMOSMANIA, and TWENTE. The dataset is archived in .tif, .shp and .csv formats, and consists of 3124 data files with data size of 829 MB (Compressed into four files with data size of 770 MB). The analysis findings based on this dataset have been published in the Journal of Hydrology, Vol. 636, 2024.
Foundation Item：
National Natural Science Foundation of China (42101475)；
Data Citation：
1. Forecasting Global Surface Soil Moisture Dataset Using Multi-scenario Integration Methodology (2015-2100)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2024. https://doi.org/10.3974/geodb.2024.11.10.V1.
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