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The Study on Compound Drought and Heatwave Events in China Using Complex Networks

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  • Kaiwen Li

    (Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China
    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Ming Wang

    (Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China
    The School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China)

  • Kai Liu

    (Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China
    The School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China)

Abstract

Compound extreme events can severely impact water security, food security, and social and economic development. Compared with single-hazard events, compound extreme events cause greater losses. Therefore, understanding the spatial and temporal variations in compound extreme events is important to prevent the risks they cause. Only a few studies have analyzed the spatial and temporal relations of compound extreme events from the perspective of a complex network. In this study, we define compound drought and heatwave events (CDHEs) using the monthly scale standard precipitation index (SPI), and the definition of a heatwave is based on daily maximum temperature. We evaluate the spatial and temporal variations in CDHEs in China from 1961 to 2018 and discuss the impact of maximum temperature and precipitation changes on the annual frequency and annual magnitude trends of CDHEs. Furthermore, a synchronization strength network is established using the event synchronization method, and the proposed synchronization strength index (SSI) is used to divide the network into eight communities to identify the propagation extent of CDHEs, where each community represents a region with high synchronization strength. Finally, we explore the impact of summer Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO) on CDHEs in different communities. The results show that, at a national scale, the mean frequency of CDHEs takes on a non-significant decreasing trend, and the mean magnitude of CDHEs takes on a non-significant increasing trend. The significant trends in the annual frequency and annual magnitude of CDHEs are attributed to maximum temperature and precipitation changes. AMO positively modulates the mean frequency and mean magnitude of CDHEs within community 1 and 2, and negatively modulates the mean magnitude of CDHEs within community 3. PDO negatively modulates the mean frequency and mean magnitude of CDHEs within community 4. AMO and PDO jointly modulate the mean magnitude of CDHEs within community 6 and 8. Overall, this study provides a new understanding of CDHEs to mitigate their severe effects.

Suggested Citation

  • Kaiwen Li & Ming Wang & Kai Liu, 2021. "The Study on Compound Drought and Heatwave Events in China Using Complex Networks," Sustainability, MDPI, vol. 13(22), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12774-:d:682301
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    References listed on IDEAS

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    1. Niklas Boers & Bedartha Goswami & Aljoscha Rheinwalt & Bodo Bookhagen & Brian Hoskins & Jürgen Kurths, 2019. "Complex networks reveal global pattern of extreme-rainfall teleconnections," Nature, Nature, vol. 566(7744), pages 373-377, February.
    2. Lei Ye & Ke Shi & Zhuohang Xin & Chao Wang & Chi Zhang, 2019. "Compound Droughts and Heat Waves in China," Sustainability, MDPI, vol. 11(12), pages 1-14, June.
    3. Nam, Won-Ho & Hayes, Michael J. & Svoboda, Mark D. & Tadesse, Tsegaye & Wilhite, Donald A., 2015. "Drought hazard assessment in the context of climate change for South Korea," Agricultural Water Management, Elsevier, vol. 160(C), pages 106-117.
    4. N/A, 2004. "Index for 2004," European Union Politics, , vol. 5(4), pages 511-512, December.
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    1. Amuktamalyada Gorlapalli & Supriya Kallakuri & Pagadala Damodaram Sreekanth & Rahul Patil & Nirmala Bandumula & Gabrijel Ondrasek & Meena Admala & Channappa Gireesh & Madhyavenkatapura Siddaiah Ananth, 2022. "Characterization and Prediction of Water Stress Using Time Series and Artificial Intelligence Models," Sustainability, MDPI, vol. 14(11), pages 1-18, May.
    2. Yulong Yao & Wei Zhang & Ben Kirtman, 2023. "Increasing impacts of summer extreme precipitation and heatwaves in eastern China," Climatic Change, Springer, vol. 176(10), pages 1-20, October.

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