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Data Details

Forecasting Global Surface Soil Moisture Dataset Using Multi-scenario Integration Methodology (2015-2100)


YANG Fen1LIU Yangxiaoyue*1
1 Institute of Geographic Sciences and Natural Resources Research，Chinese Academy of Sciences，Beijing 100101，China

DOI:10.3974/geodb.2024.11.10.V1

Published:Nov. 2024

Visitors：1221       Data Files Downloaded：45      
Data Downloaded：8477.84 MB      Citations：

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：

Data Citation：

YANG Fen, LIU Yangxiaoyue*. 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.

References：


     [1] Zhou, C. H., Yu, J. J. Review and prospect of hydrography research in China [J]. Journal of Geography, 2023, 78(7): 1659-1665.
     [2] Liu, Y. X. Y., Yang, Y. P. A review of the progress in fusing large-scale regional soil moisture datasets with multi-source microwave remote sensing [J]. Frontiers in Data and Computing Development, 2023, 4(6): 24-33. 3.
     [3] Pan, N., Wang, S., Liu, Y. X., et al. Advances in soil moisture remote sensing inversion research [J]. Journal of Ecology, 2019, 39(13): 4615-4626.
     [4] Xie, Z. H., Chen, S., Qin, P. H., et al. Research on climate feedbacks of human water use activities and their impacts on the terrestrial water cycle: Progress and challenges [J]. Advances in Earth Sciences, 2019, 34(8): 801-813.
     [5] Liu, C. M., Liu, S. M. Exploring water cycle research from the perspective of earth system circle mutual feedbacks and geographic synthesis [J]. Journal of Geography, 2023, 78(7): 1593-1598.
     [6] Li, Z., Guo, H. D., Shi, J. C. Integrating active and passive microwave data for monitoring soil moisture changes [J]. Journal of Remote Sensing, 2002, 6(6): 481-484.
     [7] Ma, Z. G., Fu, Z. B., Xie, L., et al. Some issues in the study of soil moisture and climate change relationships [J]. Advances in Earth Sciences, 2001, 16(4): 563-568.
     [8] Liu, Y. X. Y., Yang, Y. P., Song, J. Variations in global soil moisture during the past decades: Climate or human causes? [J]. Water Resources Research, 2023, 59(7): e2023WR034915.
     [9] Zhou, T. J., Zou, L. W., Chen, X. L. Review of the sixth international Coupled Model Intercomparison Program (CMIP6) [J]. Advances in Climate Change Research, 2019, 15(5): 445-456.
     [10] Wang, Y. N., Qiao, L., Zuo, Z. Y. Review of the CMIP6 Land Surface, Snow, and Soil Moisture Model Comparison Program (LS3MIP) [J]. Advances in Climate Change Research, 2022, 18(6): 795-800.
     [11] Jiang, D. B., Wang, N. Interpretation of the IPCC AR6 report: Changes in the water cycle [J]. Progress in Climate Change Research, 2021, 17(6): 699-704.
     [12] Li, Z. L., Leng, P., Zhou, C. H., et al., Soil moisture retrieval from remote sensing measurements: current knowledge and directions for the future [J]. Earth-Science Reviews, 2021, 218(1): 1-24.
     [13] Qiao, L., Zuo, Z. Y., Xiao, D. Evaluation of soil moisture in CMIP6 simulations [J]. Journal of Climate, 2022, 35(2): 779-800.
     

Data Product:

ID Data Name Data Size Operation
1 MonthlyinsituData.rar 38.86KB
2 SSP126_2015-2100.rar 263086.73KB
3 SSP245_2015-2100.rar 263051.28KB
4 SSP585_2015-2100.rar 263026.00KB
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