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1-km Monthly Precipitation Climatological Background Field Dataset for the Tarim River Basin (1981-2010)


JIANG Wei1,2WANG Xiaolei1,2WU Jing3GUO Runxiu1,2XIN Zelong1,2SUN Lin1LUO Yi*1,2
1 Key Laboratory of Ecosystem Network Observation and Modeling,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China2 University of Chinese Academy of Sciences,Beijing 100049,China3 Lanzhou Central Meteorological Observatory,Gansu Province,Lanzhou 730020,China

DOI:10.3974/geodb.2026.06.07.V1

Published:June. 2026

Visitors:49       Data Files Downloaded:0      
Data Downloaded: 无      Citations:

Key Words:

Tarim River Basin,CMFD V2,WorldClim,topographic correction,water balance constraint,

Abstract:

The 1-km Monthly Precipitation Climatological Background Field Dataset for the Tarim River Basin (1981-2010) was developed based on the WorldClim 1 km precipitation background field and the multi-year average precipitation from CMFD V2. Firstly, monthly precipitation observations from automatic weather stations within the Tarim River Basin and its surrounding areas were used, combined with terrain similarity weights for spatial residual correction, to improve the representation of topographic precipitation gradients. Subsequently, a water balance method at the headwater regions was employed to estimate the annual total precipitation of the basin, applying a secondary total-amount constraint to the corrected annual precipitation, thereby ensuring consistency between the spatial distribution of precipitation and the regional water balance closure. Validation results indicate that the root mean square error (RMSE) of the corrected annual total precipitation across the entire basin decreased from 107.03 mm to 73.38 mm, the percent bias (PBIAS) decreased from 29.50% to 2.76%, and the correlation coefficient reached 0.82. For the warm season (from April to October), the correlation coefficient for precipitation increased from 0.79 to 0.85, demonstrating significant improvements in both magnitude and spatial distribution compared to the original products. The dataset comprises monthly multi-year average precipitation and annual average precipitation for the period 1981-2010. The data has a spatial resolution of 0.008333° (approximately 1 km) and the unit is mm. The dataset is archived in .tif data format, and consists of 13 data files with data size of 233 MB (compressed into one file with 68.9 MB).

Foundation Item:

Chinese Academy of Sciences (072GJHZ2023086MI); Ministry of Science and Technology of P. R. China (2022xjkk0104);

Data Citation:

JIANG Wei, WANG Xiaolei, WU Jing, GUO Runxiu, XIN Zelong, SUN Lin, LUO Yi*. 1-km Monthly Precipitation Climatological Background Field Dataset for the Tarim River Basin (1981-2010)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2026. https://doi.org/10.3974/geodb.2026.06.07.V1.
.

References:


     [1] Hao, X. M., Li, W. H., Chen Y. N., et al. Discrimination of human activity and climate change factors on annual runoff changes in the mainstream of Tarim River[J]. Progress in Natural Science, 2008, 18(12): 1409-1416.
     [2] He, J., Yang, K., Li X., et al. China Meteorological Forcing Dataset v2.0 (1951–2024)[DS]. National Tibetan Plateau Data Center, 2024[2025-09-16]. https://dx.doi.org/10.11888/Atmos.tpdc.302088.
     [3] Liu, S. Y., Guo, W. Q., Xu J. L. The Second Glacier Inventory Dataset of China (V1.0) (2006–2011) [DS]. National Cryosphere Desert Data Center, 2014[2024-07-13]. https://dx.doi.org/10.3972/glacier.001.2013.db.
     [4] Mu, Z. X.. Study on the Vertical Distribution of Precipitation and Snowmelt Runoff Simulation in High Cold Alpine Areas[D]. Urumqi: Xinjiang Agricultural University, 2010.
     [5] Wu, L. Z., Li, X., Dataset of the first glacier inventory in China [DS]. Cold and Arid Regions Science Data Center at Lanzhou, 2004[2023-08-16]. ftp://ftp.westgis.ac.cn/westdc/china_glacier_map_100k/. (The original FTP service is no longer available and the archived metadata record is available at https://img.data.ac.cn/geores/M00/00/06/n-JvD1N7SSWAbB9EAAOuVaxOLhE170.pdf.)
     [6] Zheng, C. L., Jia, L., Hu, G. C. ETMonitor Global 1-km Resolution Actual Evapotranspiration Dataset over Land Surface[DS]. National Tibetan Plateau Data Center, 2022[2025-10-10]. https://dx.doi.org/10.11888/RemoteSen.tpdc.272831.
     [7] He, J., Yang, K., Tang, W., et al. The first high-resolution meteorological forcing dataset for land process studies over China [J]. Scientific Data, 2020, 7(1): 25.
     [8] Wang, Q., Yi, S., Sun, W. Continuous estimates of glacier mass balance in High Mountain Asia based on ICESat-1,2 and GRACE/GRACE follow-on data [J]. Geophysical Research Letters, 2021, 48(2): e2020GL090954.
     [9] Brun, F., Berthier, E., Wagnon, P., et al. A spatially resolved estimate of High Mountain Asia glacier mass balances from 2000 to 2016 [J]. Nature Geoscience, 2017, 10(9): 668-673.
     [10] Fick, S. E., Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas [J]. International Journal of Climatology, 2017, 37(12): 4302-4315.
     [11] Guo, W., Liu, S., Xu, J., et al. The second Chinese glacier inventory: data, methods and results[J]. Journal of Glaciology, 2015, 61(226): 357-372.
     [12] Hijmans, R. J., Cameron, S. E., Parra, J. L., et al. Very high resolution interpolated climate surfaces for global land areas[J]. International Journal of Climatology, 2005, 25(15): 1965-1978.
     [13] Jiang, Y., Yang, K., Qi, Y., et al. TPHiPr: a long-term (1979–2020) high-accuracy precipitation dataset (1∕30°, daily) for the third pole region based on high-resolution atmospheric modeling and dense observations[J]. Earth System Science Data, 2023, 15(2): 621-638.
     [14] R. Hock, G. Rasul, C. Adler, et al. High Mountain Areas [M] // IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. Cambridge University Press, Cambridge, UK and New York, NY, USA, 2019: 131-202.
     [15] Shao, C., Yang, K., Tang, W., et al. Convolutional neural network-based homogenization for constructing a long-term global surface solar radiation dataset [J]. Renewable and Sustainable Energy Reviews, 2022, 169: 112952.
     [16] Shi, Y., Liu, C., Kang, E. The glacier inventory of China [J]. Annals of Glaciology, 2009, 50(53): 1-4.
     [17] Smith, B., Adusumilli, S., Csathó, B. M., et al. ATLAS/ICESat-2 L3A land ice height, version 7 [DS]. NASA National Snow and Ice Data Center Distributed Active Archive Center, 2025. [2025-10-01]. http://nsidc.org/data/ATL06/versions/7.
     [18] Sobol, I. M. Sensitivity estimates for nonlinear mathematical models [J]. Mathematical Modeling and Computational Experiment, 1993, 1(4): 407.
     [19] Wood, A. W., Leung, L. R., Sridhar, V., et al. Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs [J]. Climatic Change, 2004, 62(1): 189-216.
     [20] Fick, S. E., Hijmans, R. J. WorldClim 2.1 monthly precipitation data for 1970—2000 [DS]. V2.1. WorldClim, 2020. [2026-02-15]. https://geodata.ucdavis.edu/climate/worldclim/2_1/base/wc2.1_30s_prec.zip.
     [21] NASA JPL. NASA Shuttle Radar Topography Mission Global 3 arc second sub-sampled [DS]. Land Processes DAAC (LP DAAC), 2013. [2024-08-20]. https://dwtkns.com/srtm30m/#4.23/42.54/101.29.
     [22] Lin, J. W., Liu, Y. F., Jin, Q., et al. Boundary dataset of Tarim River Basin [J/DB/OL]. Digital Journal of Global Change Data Repository, 2025. https://doi.org/10.3974/geodb.2025.11.02.V1. CSTR: 20146.11.2025.11.02.V1.

Data Product:

ID Data Name Data Size Operation
1 TRB-PREC-BG-CMFDWC-1KM-1981-2010.rar 70568.11KB
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