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Drought Risk Assessment Based on Vulnerability Surfaces: A Case Study of Maize

Author

Listed:
  • Hao Guo

    (School of Geography, Beijing Normal University, Beijing 100875, China
    The Key Laboratory of Regional Geography, Beijing Normal University, Beijing 100875, China)

  • Xingming Zhang

    (School of Geography, Beijing Normal University, Beijing 100875, China
    The Key Laboratory of Regional Geography, Beijing Normal University, Beijing 100875, China
    China Insurance Information Technology Management Co., Ltd., Beijing 100144, China)

  • Fang Lian

    (School of Geography, Beijing Normal University, Beijing 100875, China
    The Key Laboratory of Regional Geography, Beijing Normal University, Beijing 100875, China)

  • Yuan Gao

    (School of Geography, Beijing Normal University, Beijing 100875, China
    The Key Laboratory of Regional Geography, Beijing Normal University, Beijing 100875, China)

  • Degen Lin

    (School of Geography, Beijing Normal University, Beijing 100875, China
    The Key Laboratory of Regional Geography, Beijing Normal University, Beijing 100875, China)

  • Jing’ai Wang

    (School of Geography, Beijing Normal University, Beijing 100875, China
    China Insurance Information Technology Management Co., Ltd., Beijing 100144, China
    The State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China)

Abstract

Agriculture is a sector easily affected by meteorological conditions. Crop yield reduction, even regional conflicts, may occur during a drought. It is extremely important to improve the state of our knowledge on agricultural drought risk. This study has proposed a new method (vulnerability surfaces) for assessing vulnerability quantitatively and continuously by including the environmental variable as an additional perspective on exposure and assessed global maize drought risk based on these surfaces. In this research, based on the Environmental Policy Impact Climate (EPIC) model, irrigation scenarios were adopted to fit “Loss rate-Drought index-Environmental indicator (L-D-E)” vulnerability surfaces by constructing a database suitable for risk assessment on a large scale. Global maize drought risk was quantitatively assessed based on its optimal vulnerability surface. The results showed an R 2 for the optimal vulnerability surface of 0.9934, with coarse fragment content as the environmental indicator. The expected global average annual yield loss rate due to drought was 19.18%. The global average yield loss rate due to drought with different return periods (10a, 20a, 50a, and 100a) was 29.18%, 32.76%, 36.89%, and 38.26%, respectively. From a global perspective, Central Asia, the Iberian Peninsula, Eastern Africa, the Midwestern United States, Chile, and Brazil are the areas with the highest maize drought risk. The vulnerability surface is a further development of the vulnerability curve as a continuous expression of vulnerability and considers differences in environmental factors. It can reflect the spatial heterogeneity of crop vulnerability and can be applied in large-scale risk assessment research.

Suggested Citation

  • Hao Guo & Xingming Zhang & Fang Lian & Yuan Gao & Degen Lin & Jing’ai Wang, 2016. "Drought Risk Assessment Based on Vulnerability Surfaces: A Case Study of Maize," Sustainability, MDPI, vol. 8(8), pages 1-22, August.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:8:p:813-:d:76224
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    References listed on IDEAS

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    Cited by:

    1. Beatrice Monteleone & Iolanda Borzí & Brunella Bonaccorso & Mario Martina, 2023. "Quantifying crop vulnerability to weather-related extreme events and climate change through vulnerability curves," 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. 116(3), pages 2761-2796, April.
    2. Hongpeng Guo & Jia Chen & Chulin Pan, 2021. "Assessment on Agricultural Drought Vulnerability and Spatial Heterogeneity Study in China," IJERPH, MDPI, vol. 18(9), pages 1-17, April.
    3. Ying Guo & Rui Wang & Zhijun Tong & Xingpeng Liu & Jiquan Zhang, 2019. "Dynamic Evaluation and Regionalization of Maize Drought Vulnerability in the Midwest of Jilin Province," Sustainability, MDPI, vol. 11(15), pages 1-21, August.
    4. Zhu, Xiufang & Xu, Kun & Liu, Ying & Guo, Rui & Chen, Lingyi, 2021. "Assessing the vulnerability and risk of maize to drought in China based on the AquaCrop model," Agricultural Systems, Elsevier, vol. 189(C).
    5. Zhao, Yunmeng & Na, Mula & Guo, Ying & Liu, Xingping & Tong, Zhijun & Zhang, Jiquan & Zhao, Chunli, 2023. "Dynamic vulnerability assessment of maize under low temperature and drought concurrent stress in Songliao Plain," Agricultural Water Management, Elsevier, vol. 286(C).
    6. Susana T. Leitão & Emanuel Ferreira & M. Catarina Bicho & Mara L. Alves & Duarte Pintado & Daniela Santos & Pedro Mendes-Moreira & Susana S. Araújo & J. Miguel Costa & Maria Carlota Vaz Patto, 2019. "Maize Open-Pollinated Populations Physiological Improvement: Validating Tools for Drought Response Participatory Selection," Sustainability, MDPI, vol. 11(21), pages 1-35, November.
    7. Peng Su & Shiqi Li & Jing’ai Wang & Fenggui Liu, 2021. "Vulnerability Assessment of Maize Yield Affected by Precipitation Fluctuations: A Northeastern United States Case Study," Land, MDPI, vol. 10(11), pages 1-15, November.
    8. Wang, Zhiqiang & Ye, Li & Jiang, Jingyi & Fan, Yida & Zhang, Xiaoran, 2022. "Review of application of EPIC crop growth model," Ecological Modelling, Elsevier, vol. 467(C).
    9. Jiansheng Wu & Xin Lin & Meijuan Wang & Jian Peng & Yuanjie Tu, 2017. "Assessing Agricultural Drought Vulnerability by a VSD Model: A Case Study in Yunnan Province, China," Sustainability, MDPI, vol. 9(6), pages 1-16, May.
    10. Yuan Gao & Anyu Zhang & Yaojie Yue & Jing’ai Wang & Peng Su, 2021. "Predicting Shifts in Land Suitability for Maize Cultivation Worldwide Due to Climate Change: A Modeling Approach," Land, MDPI, vol. 10(3), pages 1-31, March.
    11. Monteleone, Beatrice & Borzí, Iolanda & Arosio, Marcello & Cesarini, Luigi & Bonaccorso, Brunella & Martina, Mario, 2023. "Modelling the response of wheat yield to stage-specific water stress in the Po Plain," Agricultural Water Management, Elsevier, vol. 287(C).
    12. Degen Lin & Chuanqi Hu & Fang Lian & Jing’ai Wang & Xingli Gu & Yingxian Yu, 2023. "Risk Assessment of World Corn Salinization Hazard Factors Based on EPIC Model and Information Diffusion," Land, MDPI, vol. 12(11), pages 1-19, November.
    13. Monteleone, Beatrice & Borzí, Iolanda & Bonaccorso, Brunella & Martina, Mario, 2022. "Developing stage-specific drought vulnerability curves for maize: The case study of the Po River basin," Agricultural Water Management, Elsevier, vol. 269(C).
    14. Wei Pei & Qiang Fu & Dong Liu & Tianxiao Li & Kun Cheng & Song Cui, 2019. "A Novel Method for Agricultural Drought Risk Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 2033-2047, April.

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