Articles | Volume 17, issue 3
https://doi.org/10.5194/essd-17-837-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-837-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
05 Mar 2025
Data description paper |
| 05 Mar 2025
| 05 Mar 2025
A new high-resolution multi-drought-index dataset for mainland China
Qi Zhang
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Jiajia Su
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Jiaojiao Gou
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Jinlong Hu
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Xi Zhao
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Ye Xu
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
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Cited articles
Aadhar, S. and Mishra, V.: Increased drought risk in south asia under warming climate: Implications of uncertainty in potential evapotranspiration estimates, J. Hydrometeorol., 21, 2979–2996, https://doi.org/10.1175/JHM-D-19-0224.1, 2020.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration – Guidelines for computing crop water requirements (FAO Irrigation and drainage, paper 56), Food and Agriculture Organizatoin of the United Nations, Rome, 17–28, https://www.fao.org/3/X0490E/X0490E00.htm (last access: 17 October 2024), 1998.
Allen, R. G., Walter, I. A., Elliott, R., Howell, T., Itenfisu, D., and Jensen M.: The ASCE standardized reference evapotranspiration equation, Rep. 0-7844-0805-X, 59 pp., https://ascelibrary.org/doi/epdf/10.1061/9780784408056.fm (last access: 5 February 2025), 2005.
Bai, X., Shen, W., Wu, X., and Wang, P.: Applicability of long-term satellite-based precipitation products for drought indices considering global warming, J. Environ. Manage., 255, 109846, https://doi.org/10.1016/j.jenvman.2019.109846, 2020.
Beguería, S., Vicente-Serrano, S. M., and Angulo-Martínez, M.: A multiscalar global drought dataset: The SPEI base: A new gridded product for the analysis of drought variability and impacts, B. Am. Meteorol. Soc., 91, 1351–1354, https://doi.org/10.1175/2010BAMS2988.1, 2010.
Cheng, Y., Liu, L., Cheng, L., Fa, K., Liu, X., Huo, Z., and Huang, G.: A shift in the dominant role of atmospheric vapor pressure deficit and soil moisture on vegetation greening in China, J. Hydrol., 615, 128680, https://doi.org/10.1016/j.jhydrol.2022.128680, 2022.
Dai, A.: Drought under global warming: a review, WIREs Clim. Change, 2, 45–65, https://doi.org/10.1002/wcc.81, 2011.
Dai, A., Kevin, E. T., and Taotao, Q.: A Global Dataset of Palmer Drought Severity Index for 1870–2002: Relationship with Soil Moisture and Effects of Surface Warming, J. Hydrometeorol., 5, 1117–1130, https://doi.org/10.1175/JHM-386.1, 2004.
Dobson, B., Coxon, G., Freer, J., Gavin, H., Mortazavi-Naeini, M., and Hall, J. W.: The Spatial Dynamics of Droughts and Water Scarcity in England and Wales, Water Resour. Res., 56, e2020WR027187, https://doi.org/10.1029/2020WR027187, 2020.
Dracup, J. A., Lee, K. S., and Paulson, E. G.: On the statistical characteristics of drought events, Water Resour. Res., 16, 289–296, https://doi.org/10.1029/WR016i002p00289, 1980.
Gamelin, B. L., Feinstein, J., Wang, J., Bessac, J., Yan, E., and Kotamarthi, V. R.: Projected U. S. drought extremes through the twenty-first century with vapor pressure deficit, Sci. Rep., 12, 8615, https://doi.org/10.1038/s41598-022-12516-7, 2022.
Gampe, D., Zscheischler, J., Reichstein, M., O'Sullivan, M., Smith, W. K., Sitch, S., and Buermann, W.: Increasing impact of warm droughts on northern ecosystem productivity over recent decades, Nat. Clim. Change, 11, 772–779, https://doi.org/10.1038/s41558-021-01112-8, 2021.
GNDAR: Global Natural Disaster Assessment Report 2020, UN Annu. Rep., 1–80, https://www.gddat.cn/WorldInfoSystem/production/BNU/2020-EN.pdf (last access: 5 February 2025) 2021.
Green, R. M. and Hay, S. I.: The potential of Pathfinder AVHRR data for providing surrogate climatic variables across Africa and Europe for epidemiological applications, Remote Sens. Environ., 79, 166–175, https://doi.org/10.1016/S0034-4257(01)00270-X, 2002.
Han, J., Miao, C., Gou, J., Zheng, H., Zhang, Q., and Guo, X.: A new daily gridded precipitation dataset for the Chinese mainland based on gauge observations, Earth Syst. Sci. Data, 15, 3147–3161, https://doi.org/10.5194/essd-15-3147-2023, 2023.
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset, Sci. Data, 7, 109, https://doi.org/10.1038/s41597-020-0453-3, 2020.
Hashimoto, H., Dungan, J. L., White, M. A., Yang, F., Michaelis, A. R., Running, S. W., and Nemani, R. R.: Satellite-based estimation of surface vapor pressure deficits using MODIS land surface temperature data, Remote Sens. Environ., 112, 142–155, https://doi.org/10.1016/j.rse.2007.04.016, 2008.
He, J., Yang, K., Tang, W., Lu, H., Qin, J., Chen, Y., and Li, X.: The first high-resolution meteorological forcing dataset for land process studies over China, Sci. Data, 7, 25, https://doi.org/10.1038/s41597-020-0369-y, 2020.
Heim, R. R.: A Review of Twentieth-Century Drought Indices Used in the United States, B. Am. Meteorol. Soc., 83, 1149–1166, https://doi.org/10.1175/1520-0477-83.8.1149, 2002.
Hobbins, M. T., Wood, A., McEvoy, D. J., Huntington, J. L., Morton, C., Anderson, M., and Hain, C.: The evaporative demand drought index. Part I: Linking drought evolution to variations in evaporative demand, J. Hydrometeorol., 17, 1745–1761, https://doi.org/10.1175/JHM-D-15-0121.1, 2016.
Huang, S., Huang, Q., Leng, G., and Liu, S.: A nonparametric multivariate standardized drought index for characterizing socioeconomic drought: A case study in the Heihe River Basin, J. Hydrol., 542, 875–883, https://doi.org/10.1016/j.jhydrol.2016.09.059, 2016.
Jin, X., Qiang, H., Zhao, L., Jiang, S., Cui, N., Cao, Y., and Feng, Y.: SPEI-based analysis of spatio-temporal variation characteristics for annual and seasonal drought in the Zoige Wetland, Southwest China from 1961 to 2016, Theor. Appl. Climatol., 139, 711–725, https://doi.org/10.1007/s00704-019-02981-y, 2020.
Lesk, C., Coffel, E., Winter, J., Ray, D., Zscheischler, J., Seneviratne, S. I., and Horton, R.: Stronger temperature–moisture couplings exacerbate the impact of climate warming on global crop yields, Nat. Food, 2, 683–691, https://doi.org/10.1038/s43016-021-00341-6, 2021.
Li, C., Fu, B., Wang, S., Stringer, L. C., Wang, Y., Li, Z., Liu, Y., and Zhou, W.: Drivers and impacts of changes in China's drylands, Nature Reviews Earth & Environment, 2, 858–873, https://doi.org/10.1038/s43017-021-00226-z, 2021.
Li, L., She, D., Zheng, H., Lin, P., and Yang, Z.-L.: Elucidating Diverse Drought Characteristics from Two Meteorological Drought Indices (SPI and SPEI) in China, J. Hydrometeorol., 21, 1513–1530, https://doi.org/10.1175/jhm-d-19-0290.1, 2020.
Li, S., Wang, G., Chai, Y., Miao, L., Fiifi Tawia Hagan, D., Sun, S., Huang, J., Su, B., Jiang, T., Chen, T., Lu, C., and Guan, Z.: Increasing vapor pressure deficit accelerates land drying, J. Hydrol., 625, 130062, https://doi.org/10.1016/j.jhydrol.2023.130062, 2023.
Li, T., He, B., Chen, D., Chen, H. W., Guo, L., Yuan, W., Fang, K., Shi, F., Liu, L., and Zheng, H.: Increasing sensitivity of tree radial growth to precipitation, Geophys. Res. Lett., 51, e2024GL110003, https://doi.org/10.1029/2024GL110003, 2024.
Liu, J., Chen, H., Yang, S., Wang, W., Xiang, Y., and Zhao, C.: Comparison of interpolation methods on annual mean precipitation in Hebei Province, Acta Ecologica Sinica, 29, 3493–3500, 2009 (in Chinese).
Liu, X., Yu, S., Yang, Z., Dong, J., and Peng, J.: The first global multi-timescale daily SPEI dataset from 1982 to 2021, Sci. Data, 11, 223, https://doi.org/10.1038/s41597-024-03047-z, 2024.
Lyu, Z. Z., Gao, H., Gao, R., and Ding, T.: Extreme characteristics and causes of the drought event in the whole Yangtze River Basin in the midsummer of 2022, Advances in Climate Change Research, 14, 642–650, https://doi.org/10.1016/j.accre.2023.09.007, 2023.
Ma, F. and Yuan, X.: When Will the Unprecedented 2022 Summer Heat Waves in Yangtze River Basin Become Normal in a Warming Climate?, Geophys. Res. Lett., 50, e2022GL101946, https://doi.org/10.1029/2022GL101946, 2023.
McKee, T. B., Doesken, N. J., and Kliest, J.: The relationship of drought frequency and duration to time scales, in: Proceedings of the 8th Conference of Applied Climatology, 17–22 January, Anaheim, CA, American Meteorological Society, Boston, MA, 179–184, https://www.droughtmanagement.info/literature/AMS_Relationship_Drought_Frequency_Duration_Time_Scales_1993.pdf (last access: 28 November 2023), 1993.
Miao, C., Immerzeel, W. W., Xu, B., Yang, K., Duan, Q., and Li, X.: Understanding the Asian water tower requires a redesigned precipitation observation strategy, P. Natl. Acad. Sci. USA, 121, e2403557121, https://doi.org/10.1073/pnas.2403557121, 2024.
Mishra, A. K. and Singh, V. P.: A review of drought concepts, J. Hydrol., 391, 202–216, https://doi.org/10.1016/j.jhydrol.2010.07.012, 2010.
Naumann, G., Cammalleri, C., Mentaschi, L., and Feyen, L.: Increased economic drought impacts in Europe with anthropogenic warming, Nat. Clim. Change, 11, 485–491, https://doi.org/10.1038/s41558-021-01044-3, 2021.
Noguera, I., Vicente-Serrano, S. M., Domínguez-Castro, F., and Reig, F.: Assessment of parametric approaches to calculate the Evaporative Demand Drought Index, Int. J. Climatol., 42, 834–849, https://doi.org/10.1002/joc.7275, 2022.
Palmer, W. C.: Meteorological Drought. Office of Climatology Research Paper No. 45, Washington DC: US Weather Bureau, https://www.droughtmanagement.info/literature/USWB_Meteorological_Drought_1965.pdf (last access: 28 November 2023), 1965.
Pyarali, K., Peng, J., Disse, M., and Tuo, Y.: Development and application of high resolution SPEI drought dataset for Central Asia, Sci. Data, 9, 172, https://doi.org/10.1038/s41597-022-01279-5, 2022.
Sadiqi, S. S. J., Hong, E. M., Nam, W. H., and Kim, T.: Review: An integrated framework for understanding ecological drought and drought resistance, Sci. Total Environ., 846, 157477, https://doi.org/10.1016/j.scitotenv.2022.157477, 2022.
Shao, D., Chen, S., Tan, X., and Gu, W.: Drought characteristics over China during 1980–2015, Int. J. Climatol., 38, 3532–3545, https://doi.org/10.1002/joc.5515, 2018.
Shen, F., Xu, C., and Hu, M.: Comparison of approaches to spatiotemporally interpolate land surface air temperature for the Qinghai–Tibet Plateau, Environ. Earth Sci., 82, 452, https://doi.org/10.1007/s12665-023-11151-3, 2023.
Shi, H., Chen, J., Wang, K., and Niu, J.: A new method and a new index for identifying socioeconomic drought events under climate change: A case study of the East River basin in China, Sci. Total Environ., 616–617, 363–375, https://doi.org/10.1016/j.scitotenv.2017.10.321, 2018.
Singer, M. B., Asfaw, D. T., Rosolem, R., Cuthbert, M. O., Miralles, D. G., MacLeod, D., Quichimbo, E. A., and Michaelides, K.: Hourly potential evapotranspiration at 0.1° resolution for the global land surface from 1981–present, Sci. Data, 8, 224, https://doi.org/10.1038/s41597-021-01003-9, 2021.
Su, B., Huang, J., Fischer, T., Wang, Y., Kundzewicz, Z. W., Zhai, J., Sun, H., Wang, A., Zeng, X., Wang, G., Tao, H., Gemmer, M., Li, X., and Jiang, T.: Drought losses in China might double between the 1.5 °C and 2.0 °C warming, P. Natl. Acad. Sci. USA, 115, 10600–10605, https://doi.org/10.1073/pnas.1802129115, 2018.
Svoboda, M. D. and Fuchs, B. A.: Handbook of drought indicators and indices, in: Drought and Water Crises: Integrating Science, Management, and Policy, 2nd edn., World Meteorological Organization Geneva, Switzerland 2016, ISBN 978-92-63-11173-9, https://doi.org/10.1201/b22009, 2017.
Tu, Y., Wang, X., Zhou, J., Wang, X., Jia, Z., Ma, J., Yao, W., Zhang, X., Sun, Z., Luo, P., Feng, X., and Fu, B.: Atmospheric water demand dominates terrestrial ecosystem productivity in China, Agr. Forest Meteorol., 355, 110151, https://doi.org/10.1016/J.AGRFORMET.2024.110151, 2024.
Venkatappa, M. and Sasaki, N.: Datasets of drought and flood impact on croplands in Southeast Asia from 1980 to 2019, Data Brief, 38, 107406, https://doi.org/10.1016/j.dib.2021.107406, 2021.
Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index, J. Climate, 23, 1696–1718, https://doi.org/10.1175/2009JCLI2909.1, 2010a.
Vicente-Serrano, S. M., Beguería, S., López-Moreno, J. I., Angulo, M., and El Kenawy, A.: A new global 0.5° gridded dataset (1901–2006) of a multiscalar drought index: Comparison with current drought index datasets based on the palmer drought severity index, J. Hydrometeorol., 11, 1033–1043, https://doi.org/10.1175/2010JHM1224.1, 2010b.
Vicente-Serrano, S. M., Domínguez-Castro, F., Reig, F., Tomas-Burguera, M., Peña-Angulo, D., Latorre, B., Beguería, S., Rabanaque, I., Noguera, I., Lorenzo-Lacruz, J., and El Kenawy, A.: A global drought monitoring system and dataset based on ERA5 reanalysis: A focus on crop-growing regions, Geosci. Data J., 10, 505–518, https://doi.org/10.1002/gdj3.178, 2023.
Wang, J., Zhang, Q., Zhang, L., Wang, Y., Yue, P., Hu, Y., and Ye, P.: The Global Pattern and Development Trends and Directions on the Drought Monitoring Research from 1983 to 2020 by Using Bibliometric Analysis, B. Am. Meteorol. Soc., 103, E2081–E2107, https://doi.org/10.1175/BAMS-D-21-0324.1, 2022.
Wang, J., Yan, R., Wu, G., Liu, Y., Wang, M., Zeng, N., Jiang, F., Wang, H., He, W., Wu, M., Ju, W., and Chen, J. M.: Unprecedented decline in photosynthesis caused by summer 2022 record-breaking compound drought-heatwave over Yangtze River Basin, Sci. Bull. (Beijing), 68, 2160–2163, https://doi.org/10.1016/j.scib.2023.08.011, 2023.
Wang, K. C. and Dickinson, R. E.: A review on global terrestrial evapotranspiration: observation, modeling, climatology, and Climatic Variability, Rev. Geophys., 50, RG2005, https://doi.org/10.1029/2011RG000373, 2012.
Wang, Q., Zeng, J., Qi, J., Zhang, X., Zeng, Y., Shui, W., Xu, Z., Zhang, R., Wu, X., and Cong, J.: A multi-scale daily SPEI dataset for drought characterization at observation stations over mainland China from 1961 to 2018, Earth Syst. Sci. Data, 13, 331–341, https://doi.org/10.5194/essd-13-331-2021, 2021.
Wells, N., Goddard, S., and Hayes, M. J.: A self-calibrating Palmer Drought Severity Index, J. Climate, 17, 2335–2351, https://doi.org/10.1175/1520-0442(2004)017<2335:ASPDSI>2.0.CO;2, 2004.
WMO: Guide to Meteorological Instruments and Methods of Observation, 8, World Meteorological Organization, http://library.wmo.int/pmb_ged/wmo_8_en-2012.pdf (last access: 1 November 2013), 2012.
Wu, J. and Gao, X. J.: A gridded daily observation dataset over China region and comparison with the other datasets, Acta Geophys. Sinica, 56, 1102–1111, https://doi.org/10.6038/cjg20130406, 2013.
Xiang, K., Li, Y., Horton, R., and Feng, H.: Similarity and difference of potential evapotranspiration and reference crop evapotranspiration – a review, Agr. Water Manage., 232, 106043, https://doi.org/10.1016/j.agwat.2020.106043, 2020.
Xu, X., X. Zhang, and X. Li.: Evaluation of the Applicability of Three Methods for Climatic Spatial Interpolation in the Hengduan Mountains Region, J. Hydrometeorol., 24, 35–51, https://doi.org/10.1175/JHM-D-22-0039.1, 2022.
Xu, Y., Gao, X., Shen, Y., Xu, C., Shi, Y., and Giorgi, F.: A daily temperature dataset over China and its application in validating a RCM simulation, Adv. Atmos. Sci., 26, 763–772, https://doi.org/10.1007/s00376-009-9029-z, 2009.
Yuan, W., Zheng, Y., Piao, S., Ciais, P., Lombardozzi, D., Wang, Y., Ryu, Y., Chen, G., Dong, W., Hu, Z., Jain, A. K., Jiang, C., Kato, E., Li, S., Lienert, S., Liu, S., Nabel, J. E. M. S., Qin, Z., Quine, T., Sitch, S., Smith, W. K., Wang, F., Wu, C., Xiao, Z., and Yang, S.: Increased atmospheric vapor pressure deficit reduces global vegetation growth, Sci. Adv., 5, eaax1396, https://doi.org/10.1126/sciadv.aax1396, 2019.
Zhai, J., Su, B., Krysanova, V., Vetter, T., Gao, C., and Jiang, T.: Spatial variation and trends in PDSI and SPI indices and their relation to streamflow in 10 large regions of china, J. Climate, 23, 649–663, https://doi.org/10.1175/2009JCLI2968.1, 2010.
Zhang, L., Guo, G., Xiong, K., Qin, P., and Wu, Y.: Causes of the high temperature process in the Yangtze River Basin in 2022, Progress in Geography, 42, 971–981, https://doi.org/10.18306/dlkxjz.2023.05.011, 2023a.
Zhang, Q. and Miao, C.: CHM_Drought, Zenodo [data set], https://doi.org/10.5281/zenodo.14634773, 2025.
Zhang, Q., Miao, C., Gou, J., Wu, J., Jiao, W., Song, Y., and Xu, D.: Spatiotemporal characteristics of meteorological to hydrological drought propagation under natural conditions in China, Weather Clim. Extrem., 38, 100505, https://doi.org/10.1016/j.wace.2022.100505, 2022a.
Zhang, R., Bento, V. A., Qi, J., Xu, F., Wu, J., Qiu, J., Li, J., Shui, W., and Wang, Q.: The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018, Big Earth Data, 7, 860–885, https://doi.org/10.1080/20964471.2022.2148331, 2023b.
Zhang, X., Su, Z., Lv, J., Liu, W., Ma, M., Peng, J., and Leng, G.: A set of satellite-based near real-time meteorological drought monitoring data over China, Remote Sens., 11, 453, https://doi.org/10.3390/rs11040453, 2019.
Zhang, X., Hao, Z., Singh, V. P., Zhang, Y., Feng, S., Xu, Y., and Hao, F.: Drought propagation under global warming: Characteristics, approaches, processes, and controlling factors, Sci. Total Environ., 838, 156021, https://doi.org/10.1016/j.scitotenv.2022.156021, 2022b.
Zhao, H., Gao, G., An, W., Zou, X., Li, H., and Hou, M.: Timescale differences between SC-PDSI and SPEI for drought monitoring in China, Phys. Chem. Earth, 102, 48–58, https://doi.org/10.1016/j.pce.2015.10.022, 2017a.
Zhao, M., Geruo, A., Velicogna, I., and Kimball, J. S.: A global gridded dataset of GRACE drought severity index for 2002-14: Comparison with PDSI and SPEI and a case study of the Australia millennium drought, J. Hydrometeorol., 18, 2117–2129, https://doi.org/10.1175/JHM-D-16-0182.1, 2017b.
Zhong, R., Chen, X., Lai, C., Wang, Z., Lian, Y., Yu, H., and Wu, X.: Drought monitoring utility of satellite-based precipitation products across mainland China, J. Hydrol., 568, 343–359, https://doi.org/10.1016/j.jhydrol.2018.10.072, 2019.
Short summary
Our study introduces CHM_Drought, an advanced meteorological drought dataset covering mainland China, offering detailed insights from 1961 to 2022 at a spatial resolution of 0.1°. This dataset incorporates six key drought indices, including multi-scale versions, facilitating early detection and monitoring of droughts. Through the provision of consistent and reliable data, CHM_Drought enhances our understanding of drought patterns, aiding in effective water management and agricultural planning.
Our study introduces CHM_Drought, an advanced meteorological drought dataset covering mainland...
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