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Unraveling Projected Changes in Spatiotemporal Patterns and Drought Events across Mainland China Using CMIP6 Models and an Intensity–Area–Duration Algorithm

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  • Jinping Liu

    (College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
    Key Laboratory of Mine Spatio-Temporal Information and Ecological Restoration, Ministry of Natural Resources, Jiaozuo 454003, China
    Hydraulics and Geotechnics Section, KU Leuven, Kasteelpark Arenberg 40, BE-3001 Leuven, Belgium
    These authors contributed equally to this work.)

  • Junchao Wu

    (Information School of Surveying Mapping and Remote Sensing, Guangdong Polytechnic Industry and Commerce, Guangzhou 510550, China
    These authors contributed equally to this work.)

  • Sk Ajim Ali

    (Department of Geography, Faculty of Science, Aligarh Muslim University, Aligarh 202002, India
    RA (Remote), The University of Manchester, Manchester M13 9PL, UK)

  • Nguyen Thi Thuy Linh

    (Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska Street 60, 41-200 Sosnowiec, Poland)

  • Yanqun Ren

    (College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China)

  • Masoud Jafari Shalamzari

    (Department of Environment, Tabas Branch, Tabas 9791735618, Iran)

Abstract

In the context of global warming, temperature increases have led to frequent drought events and a sharp increase in economic losses and social risks. In this study, five medium- and high-emission scenario models, the SSP245 and SSP585, CMIP6 monthly scale temperature and precipitation datasets under different global warming contexts (1.5 °C and 2 °C), and the 1984–2014 weather station observations were selected. The latter dataset was used to improve the ability of the CMIP6 to simulate surface drought accuracy. A standardized precipitation–evapotranspiration index dataset was generated. The latest intensity–area–duration framework was adopted to identify regional drought events by considering their continuity and spatial dynamic characteristics. The parameters of intensity, area, and duration were used to characterize the dynamic evolution of drought events. Under the medium- to high-emission scenario model, with a continuous increase in global temperature to 1.5 °C, in the southeastern Qinghai–Tibet Plateau (QTP) and southern Xinjiang (XJ) there is a significant increase in intensity, extent, and duration of drought events and some drought exacerbation in northeastern China. Under the high-emission SSP585 scenario model, the severity of these drought events is reduced when compared with the SSP245 scenario model, but this also shows an increasing trend, especially with the 2 °C global warming background. Significant drought aggravation trends were observed in southern XJ, northern QTP, and northern Northwest. In contrast, a small but significant drought-weakening trend was observed in southwestern south China. The results of this study provide a reference for society and government departments to make decisions in response to future drought events.

Suggested Citation

  • Jinping Liu & Junchao Wu & Sk Ajim Ali & Nguyen Thi Thuy Linh & Yanqun Ren & Masoud Jafari Shalamzari, 2024. "Unraveling Projected Changes in Spatiotemporal Patterns and Drought Events across Mainland China Using CMIP6 Models and an Intensity–Area–Duration Algorithm," Land, MDPI, vol. 13(10), pages 1-23, September.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:10:p:1571-:d:1487216
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    References listed on IDEAS

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    1. Ming Li & Guiwen Wang & Shengwei Zong & Xurong Chai, 2023. "Copula-Based Assessment and Regionalization of Drought Risk in China," IJERPH, MDPI, vol. 20(5), pages 1-16, February.
    2. Brian Ayugi & Zablon Weku Shilenje & Hassen Babaousmail & Kenny T. C. Lim Kam Sian & Richard Mumo & Victor Nnamdi Dike & Vedaste Iyakaremye & Abdelghani Chehbouni & Victor Ongoma, 2022. "Projected changes in meteorological drought over East Africa inferred from bias-adjusted CMIP6 models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(2), pages 1151-1176, September.
    3. Liu Changming & Zhang Shifeng, 2002. "Drying up of the yellow river: its impacts and counter-measures," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 7(3), pages 203-214, September.
    4. Gavilan, P. & Lorite, I.J. & Tornero, S. & Berengena, J., 2006. "Regional calibration of Hargreaves equation for estimating reference ET in a semiarid environment," Agricultural Water Management, Elsevier, vol. 81(3), pages 257-281, March.
    5. Ling, Minhua & Han, Hongbao & Hu, Xiaoyue & Xia, Qinyuan & Guo, Xiaomin, 2023. "Drought characteristics and causes during summer maize growth period on Huang-Huai-Hai Plain based on daily scale SPEI," Agricultural Water Management, Elsevier, vol. 280(C).
    6. Yanqun Ren & Jinping Liu & Patrick Willems & Tie Liu & Quoc Bao Pham, 2023. "Detection and Assessment of Changing Drought Events in China in the Context of Climate Change Based on the Intensity–Area–Duration Algorithm," Land, MDPI, vol. 12(10), pages 1-18, September.
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