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Applicability assessment of six drought indices in different maize producing regions of China

Author

Listed:
  • Jie Ma

    (Xiuyan Manchu Autonomous County Meteorological Bureau)

  • Peijuan Wang

    (Chinese Academy of Meteorological Sciences)

  • Rui Feng

    (China Meteorological Administration)

  • Yuanda Zhang

    (Chinese Academy of Meteorological Sciences)

  • Yang Li

    (Chinese Academy of Meteorological Sciences)

  • Qi Wang

    (Chinese Academy of Meteorological Sciences)

  • Dingrong Wu

    (Chinese Academy of Meteorological Sciences)

  • Yuping Ma

    (Chinese Academy of Meteorological Sciences)

Abstract

As the most widely planted food crop in China, maize (Zea mays) often suffers drought stresses during its growing period, which further leads to substantial yield reducing and great economic losses. Therefore, timely and accurate monitoring of drought conditions is essential for maize production. In this paper, six widely used daily drought indices, including Standardized Precipitation Index (SPI10, SPI30) and Standardized Precipitation Evaporation Index (SPEI10, SPEI30) at 10-day and 30-day scales, Meteorological Drought Composite Index (MCI), and Crop Water Deficit Index (CWDI), were calculated by using meteorological data from 1971–2020. Maize drought disaster records were segmented into different maize growing periods in terms of maize phenophase data. The drought identification capacity of each drought index was evaluated by the multi-dimensionality, sensitivity and accuracy in four different maize producing regions of China, i.e., the North SPring maize region (NSP), the Huang-huai-hai SUmmer maize region (HSU), the South SPring maize region (SSP), and the SouthWest SPring maize region (SWSP). The results showed that CWDI demonstrated the highest sensitivity and accuracy in the NSP Region, the HSU Region, and the SWSP Region, and MCI was superior in the SSP Region. From the perspective of maize development stages, the most applicable index during planting — tasseling period (V0–VT) and tasseling — ripening period (V0-R6), as well as the entire growing period from planting to ripening period (V0-R6) was also CWDI, with exceptions for V0-R6 in the SSP region and VT–R6 in the SWSP region. Nevertheless, since CWDI had the highest false negative identification rate among all indices, it was recommended that MCI in conjunction with CWDI to enhance the accuracy of identifying maize droughts in China. This research provides a technical basis for selecting suitable drought indices in different maize producing regions, and also for improving the precision of maize drought identification in China.

Suggested Citation

  • Jie Ma & Peijuan Wang & Rui Feng & Yuanda Zhang & Yang Li & Qi Wang & Dingrong Wu & Yuping Ma, 2025. "Applicability assessment of six drought indices in different maize producing regions of China," 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. 121(11), pages 13067-13092, June.
    • Handle: RePEc:spr:nathaz:v:121:y:2025:i:11:d:10.1007_s11069-025-07310-3
    DOI: 10.1007/s11069-025-07310-3
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    References listed on IDEAS

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    1. Jincai Zhao & Qianqian Liu & Heli Lu & Zheng Wang & Ke Zhang & Pan Wang, 2021. "Future droughts in China using the standardized precipitation evapotranspiration index (SPEI) under multi-spatial scales," 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. 109(1), pages 615-636, October.
    2. Lei Jiang & Yongqin David Chen & Jianfeng Li & Cancan Liu, 2022. "Amplification of soil moisture deficit and high temperature in a drought-heatwave co-occurrence in southwestern China," 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. 111(1), pages 641-660, March.
    3. Gideon Danso-Abbeam & Collins C. Okolie & Temitope O. Ojo & Abiodun A. Ogundeji, 2024. "Understanding drought impacts on livelihoods and risk management strategies: South African smallholder farmers' perspectives," 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. 120(9), pages 8931-8951, July.
    4. Haixia Yu & Dandan Yang & Bingjun Liu & Jianyu Fu & Zhihao Liang, 2023. "Spatio-temporal characteristics of meteorological drought based on the MCI of Penman–Monteith," 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. 119(1), pages 221-241, October.
    5. Xiaoyan Tang & Yongjiu Feng & Chen Gao & Zhenkun Lei & Shurui Chen & Rong Wang & Yanmin Jin & Xiaohua Tong, 2023. "Entropy-weight-based spatiotemporal drought assessment using MODIS products and Sentinel-1A images in Urumqi, China," 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. 119(1), pages 387-408, October.
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