Dataset List

Vol.|Area

No.10 Vol.12,2025
No.09 Vol.12,2025
No.08 Vol.12,2025
No.07 Vol.12,2025
No.06 Vol.12,2025
No.05 Vol.12,2025
No.04 Vol.12,2025
No.03 Vol.12,2025
No.02 Vol.12,2025
No.01 Vol.12,2025
No.12 Vol.11,2024
No.11 Vol.11,2024
No.10 Vol.11,2024
No.09 Vol.11,2024
No.08 Vol.11,2024
No.07 Vol.11,2024
No.06 Vol.11,2024
No.05 Vol.11,2024
No.04 Vol.11,2024
No.03 Vol.11,2024
No.02 Vol.11,2024
No.01 Vol.11,2024
No.12 Vol.10,2023
No.11 Vol.10,2023
No.10 Vol.10,2023
No.09 Vol.10,2023
No.08 Vol.10,2023
No.07 Vol.10,2023
No.06 Vol.10,2023
No.05 Vol.10,2023
No.04 Vol.10,2023
No.03 Vol.10,2023
No.02 Vol.10,2023
No.01 Vol.10,2023
No.12 Vol.9,2022
No.11 Vol.9,2022
No.10 Vol.9,2022
No.09 Vol.9,2022
No.08 Vol.9,2022
No.07 Vol.9,2022
No.06 Vol.9,2022
No.05 Vol.9,2022
No.04 Vol.9,2022
No.03 Vol.9,2022
No.02 Vol.9,2022
No.01 Vol.9,2022
No.12 Vol.8,2021
No.11 Vol.8,2021
No.10 Vol.8,2021
No.09 Vol.8,2021
No.08 Vol.8,2021
No.07 Vol.8,2021
No.06 Vol.8,2021
No.05 Vol.8,2021
No.04 Vol.8,2021
No.03 Vol.8,2021
No.03 Vol.8,2021
No.02 Vol.8,2021
No.01 Vol.8,2021
No.09 Vol.7,2020
No.08 Vol.7,2020
No.07 Vol.7,2020
No.06 Vol.7,2020
No.05 Vol.7,2020
No.04 Vol.7,2020
No.03 Vol.7,2020
No.02 Vol.7,2020
No.01 Vol.7,2020
No.06 Vol.6,2019
No.05 Vol.6,2019
No.04 Vol.6,2019
No.03 Vol.6,2019
No.02 Vol.6,2019
No.01 Vol.6,2019
No.08 Vol.5,2018
No.07 Vol.5,2018
No.06 Vol.5,2018
No.05 Vol.5,2018
No.04 Vol.5,2018
No.03 Vol.5,2018
No.02 Vol.5,2018
No.01 Vol.5,2018
No.04 Vol.4,2017
No.03 Vol.4,2017
No.02 Vol.4,2017
No.01 Vol.4,2017
No.09 Vol.3,2016
No.08 Vol.3,2016
No.07 Vol.3,2016
No.06 Vol.3,2016
No.05 Vol.3,2016
No.04 Vol.3,2016
No.03 Vol.3,2016
No.02 Vol.3,2016
No.01 Vol.3,2016
No.02 Vol.2,2015
No.01 Vol.2,2015
No.02 Vol.1,2014
No.01 Vol.1,2014
China
Asia
Oceanic region
African
European
North American
South American
Arctic
Global Scale
Data Details

Qinghai-Xizang Plateau Soil Moisture Modeling Dataset (2015-2100)


SONG Qian1LIU Yangxiaoyue*2XU Hongzhao3ZHANG Huifang4ZHU Guili4FU Xiaopeng4
1 Beijing Forestry University，Beijing 100101，China2 Institute of Geographic Sciences and Natural Resources Research，Chinese Academy of Sciences，Beijing 100101，China3 The Second Geological Brigade of the Tibet Autonomous Region Bureau of Geology and Mineral Exploration and Development，Lhasa 850000，China4 Monitoring Center for Ecological Environment of Tibet Autonomous Region，Lhasa 850000，China

DOI:10.3974/geodb.2025.10.05.V1

Published:Oct. 2025

Visitors：21       Data Files Downloaded：0      
Data Downloaded： 无      Citations：

Key Words：

Qinghai-Xizang Plateau,surface soil moisture,future multi-scenario,random forest,fusion

Abstract：

The Qinghai-Xizang Plateau surface monthly soil moisture modeling dataset was developed covering the period from 2015 to 2100 with a spatial resolution of 0.1°x0.1°. Firstly, ground-based observations from the MAQU, NAQU, and NGARI networks were used to evaluate the accuracy of 21 CMIP6 soil moisture datasets, along with SMAP and ERA5-Land products, using bias, correlation coefficient (R), root mean square error (RMSE), and unbiased RMSE (ubRMSE). Meanwhile, the Enhanced Triple Collocation (ETC) method was employed to obtain random error standard deviation (RESD) and correlation coefficient (CC), based on which four Earth system models were selected for integration. Secondly, SMAP and ERA5-Land datasets were fused using differential weighting guided by the ETC evaluation results, and the optimal fusion result was identified. Finally, a Random Forest algorithm was used to integrate multiple sources of explanatory variables for monthly model training, and the model’s prediction accuracy was validated against in-situ observations. The dataset includes: (1) monthly soil moisture data from 2015 to 2100 under four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), with 0.1° spatial resolution; (2) monthly in-situ measurements (0-0.1 m depth) from the MAQU, NAQU, and NGARI networks. The dataset is archived in .mdd, .tif, .shp, and .csv formats, consisting of 4,838 data files with data size of 0.99 GB (compressed into 1 file with data size of 315 MB). The data results show that compared to the original CMIP6 model outputs, the fused product achieves significantly higher accuracy and lower error, demonstrating improved capability in representing soil moisture dynamics over the Qinghai-Xizang Plateau.

Foundation Item：

National Natural Science Foundation of China (42571539)

Data Citation：

SONG Qian, LIU Yangxiaoyue*, XU Hongzhao, ZHANG Huifang, ZHU Guili, FU Xiaopeng.Qinghai-Xizang Plateau Soil Moisture Modeling Dataset (2015-2100)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2025. https://doi.org/10.3974/geodb.2025.10.05.V1.

References：


     [1] Wang, Y. J., Yu, D. Y., Zhou, Z. Y. Progress in hydrological process research and model review in the cold regions of the Qinghai-Xizang Plateau [J]. Glaciers and Frozen Soil, 2024, 46(4): 1312-1328.
     [2] Seneviratne, S. I., Corti, T., Davin, E. L., et al. Investigating soil moisture-climate interactions in a changing climate: a review [J]. Earth-Science Reviews, 2010: 99(3-4): 125-161.
     [3] Cui, J. J., Xin, Z. B., Huang, Y. Z. The spatiotemporal variations in freeze-thaw erosion in 2003-2020 on the Qinghai-Tibet Plateau [J]. Acta Ecologica Sinica, 2023, 43(11): 4515-4526.
     [4] Yang, K., Wu, H., Qin, J., et al. Recent climate changes over the Tibetan Plateau and their impacts on energy and water cycle: a review [J]. Global and Planetary Change, 2014, 112(1): 79-91.
     [5] Li, Z. J., Chen, J. P., Liu, Y. M., et al. Advances in remote sensing inversion of soil moisture [J]. Journal of Beijing Normal University (Natural Science Edition), 2020, 56(3): 474-481.
     [6] Tan, X. D., Pang, Z, G., Jiang, W., et al. Advances and trends in microwave inversion methods for soil moisture [J]. Journal of Earth Information Sciences, 2021, 23(10): 1728-1742.
     [7] Dorigo, W., Wagner, W., Albergel, C., et al. ESA CCI soil moisture for improved Earth system understanding: state-of-the art and future directions [J]. Remote Sensing of Environment, 2017, 203: 185-215.
     [8] Entekhabi, D., Njoku, E. G., O’Neill, P. E., et al. The Soil Moisture Active Passive (SMAP) mission [J]. Proceedings of the IEEE, 2010, 98(5): 704-716.
     [9] Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications [J]. Earth System Science Data, 2021, 13(9): 4349-4383.
     [10] Eyring, V., Bony, S., Meehl, G. A., et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization [J]. Geoscientific Model Development, 2016, 9(5): 1937-1958.
     [11] McColl, K. A., Vogelzang, J., Konings, A. G., et al. Extended triple collocation: estimating errors and correlation coefficients with respect to an unknown target [J]. Geophysical Research Letters, 2014, 41(17): 6229-6236.
     [12] Fu, P. F., Yang, X. J., Jiang, B., et al. Construction and application of a high-resolution soil moisture simulation model integrating multi-source data [J]. Transactions of the Chinese Society of Agricultural Engineering, 2025, 41(5): 96-106.
     [13] Fan, K. K., Zhang, Q., Shi, P. J., et al. Assessment of soil moisture data in the Tibetan Plateau based on satellite remote sensing and reanalysis data [J]. Acta Geographica Sinica, 2018, 73(9): 1778-1791.
     [14] Zhang, Y. L. Integration dataset of Tibet Plateau boundary [DB/OL]. National Tibetan Plateau / Third Pole Environment Data Center, 2019. https://doi.org/10.11888/Geogra.tpdc.270099. https://cstr.cn/18406.11.Geogra.tpdc.270099.

Data Product:

ID Data Name Data Size Operation
1 QZP_RF_SoilMoisture_2015-2100.rar 323040.90KB
Co-Sponsors

Institute of Geographic Sciences and Natural Resources Research，Chinese Academy of Sciences

The Geographical Society of China

Parteners

Committee on Data for Science and Technology (CODATA) Task Group on Preservation of and Access to Scientific and Technical Data in/for/with Developing Countries (PASTD)

Jomo Kenyatta University of Agriculture and Technology

Digital Linchao GeoMuseum