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

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.

Linkage Between European and East Asian Heatwaves on Synoptic Scales

“…Compared with a single extreme weather event, compound or multiple events have recently sparked stronger scientific interests because amplified societal impacts are often observed for such concurrent extremes (Zscheischler and Seneviratne [27] ; Sarhadi et al [28] ). Several studies have revealed that recurring concurrences of climate extremes are not in coincidence, including extreme rainfall synchronization (Boers et al [29][30][31] ; Gupta et al [32] ; Su et al [33] ), heatwave-flood teleconnection (Lau and Kim [34] ; Capua et al [35] ), compound drought and heatwave events (Mazdiyasni and AghaKouchak [19] ; Li et al [36] ; Mukherjee and Mishra [37] ), and concurrent heat extremes along the mid-latitude westerly jet streams in the Northern Hemisphere (Kornhuber et al [38,39] ). Kornhuber et al [39] indicated that heat extremes over Eastern Europe and East Asia occurred nearly simultaneously and were associated with the recurrent Rossby wave pattern with wave number 5.…”

Climatic Challenges in the Growth Cycle of Winter Wheat in the Huang-Huai-Hai Plain: New Perspectives on High-Temperature–Drought and Low-Temperature–Drought Compound Events

“…Studies on compound temperature-drought events mostly use a combination of the above definitions of drought and high-temperature heatwave events [33][34][35][36]. Some studies directly characterize compound high-temperature heatwave-drought events by corresponding metrics, including the Dry-Hot Magnitude Index (DHMI) [37], Standardised Compound Event Indicator [38], Standardised Dry-Heat Index [39], etc. These studies indicate that temperature is the primary driver compared to precipitation; the severity of compound high-temperature heatwave-drought events in most regions of China has increased significantly, especially in the northern and southwestern areas of China.…”

Characterization and Prediction of Water Stress Using Time Series and Artificial Intelligence Models

“…Drought conditions for maize in different growing seasons using SPI values in the Adana, Mersin, and Osmaniye regions of Çukurova were determined by [20], whereas ref. [21] applied SPI to calculate drought and heatwaves in China.…”