The application of the analysis framework for compound extreme event dependencies in China

Abstract

The escalation of compound extreme events has resulted in noteworthy economic and property losses. Recognizing the intricate interconnections among these events has become imperative. To tackle this challenge, we have formulated a comprehensive framework for the systematic analysis of their dependencies. This framework consists of three steps. (1) Define extreme events using Mahalanobis distance thresholds. (2) Represent dependencies among multiple extreme events through a point process-based method. (3) Verify dependencies with residual tail coefficients, determining the final dependency structure. Applying this framework to assess the extreme dependence of precipitation on wind speed and temperature in China, revealed four distinct dependency structures. In northern, Jianghuai, and southern China, precipitation heavily relies on wind speed, while temperatures maintain relative independence. In northeastern and northwestern China, precipitation exhibits relative independence, yet a notable dependence exists between temperatures and wind speed. In southwestern China, precipitation strongly depends on temperature, while wind speed remains relatively independent. The Qinghai–Tibet Plateau region displays a significant dependence relationship among precipitation, wind speed, and temperature, with weaker dependence between extreme wind speed and temperature. This framework is instrumental for analyzing dependencies among extreme values in compound events.

Keywords

compound extremes events / extremal dependency structure / extreme precipitation / extreme temperatures / extreme value theory / extreme wind speed

References

[1] Beirlant, J., Goegebeur, Y., Teugels, J., & Segers, J. (2004). Statistics of extremes: Theory and applications. John Wiley & Sons, Ltd.

[2] Brunner, M. I., Swain, D. L., Wood, R. R., Willkofer, F., Done, J. M., Gilleland, E., & Ludwig, R. (2021). An extremeness threshold determines the regional response of floods to changes in precipitation extremes. Communications Earth & Environment, 2(1), 173.

[3] Coles, S. G., & Tawn, J. A. (1991). Modelling extreme multivariate events. Journal of the Royal Statistical Society Series B: Statistical Methodology, 53(2), 377–392.

[4] Copernicus Climate Change Service (C3S). (2017). ERA5: Fifth Generation of ECMWF Atmospheric Reanalyses of the Global Climate, Copernicus Climate Change Service Climate Data Store (CDS). https://cds.climate.copernicus.eu/

[5] Davison, A. C., & Smith, R. L. (1990). Models for exceedances over high thresholds. Journal of the Royal Statistical Society Series B: Statistical Methodology, 52(3), 393–425.

[6] Feng, S., Wu, X., Hao, Z., Hao, Y., Zhang, X., & Hao, F. (2020). A database for characteristics and variations of global compound dry and hot events. Weather and Climate Extremes, 30, 100299.

[7] Fischer, E. M., & Knutti, R. (2016). Observed heavy precipitation increase confirms theory and early models. Nature Climate Change, 6(11), 986–991.

[8] Gao, X., Guo, M., Yang, Z., Zhu, Q., Xu, Z., & Gao, K. (2020). Temperature dependence of extreme precipitation over mainland China. Journal of Hydrology, 583, 124595.

[9] Groisman, P. Y., Knight, R. W., Easterling, D. R., Karl, T. R., Hegerl, G. C., & Razuvaev, V. N. (2005). Trends in intense precipitation in the climate record. Journal of Climate, 18(9), 1326–1350.

[10] Heffernan, J. E., & Tawn, J. A. (2004). A conditional approach for multivariate extreme values. Journal of the Royal Statistical Society Series B: Statistical Methodology, 66(3), 497–546.

[11] Kiran, K. G., & Srinivas, V. V. (2021). A Mahalanobis distance-based automatic threshold selection method for peaks over threshold model. Water Resources Research, 57(1), e2020WR027534.

[12] Kitoh, A., & Endo, H. (2016). Changes in precipitation extremes projected by a 20-km mesh global atmospheric model. Weather and climate extremes, 11, 41–52.

[13] Ledford, A., & Tawn, J. A. (1996). Statistics for near independence in multivariate extreme values. Biometrika, 83(1), 169–187.

[14] Lewis, S. C., & King, A. D. (2017). Evolution of mean, variance and extremes in 21st century temperatures. Weather and climate extremes, 15, 1–10.

[15] Li, S. H., Sifton, V., Lundgren, J., Mcclellan, C., & Gibbons, M. (2021). Extreme wind climate of the Arabian Peninsula characterized by using WRF simulation. Weather and Climate Extremes, 33, 100330.

[16] Nasr, A. A., Wahl, T., Rashid, M. M., Camus, P., & Haigh, I. D. (2021). Assessing the dependence structure between oceanographic, fluvial, and pluvial flooding drivers along the United States coastline. Hydrology and Earth System Sciences, 25(12), 6203–6222.

[17] Osburn, L., Hope, P., & Dowdy, A. (2021). Changes in hourly extreme precipitation in Victoria, Australia, from the observational record. Weather and Climate Extremes, 31, 100294.

[18] Owen, L. E., Catto, J. L., Stephenson, D. B., & Dunstone, N. J. (2021). Compound precipitation and wind extremes over Europe and their relationship to extratropical cyclones. Weather and Climate Extremes, 33, 100342.

[19] Peng, T., Zhao, L., Zhang, L., Shen, X., Ding, Y., Wang, J., Li, Q., Liu, Y., Hu, Y., Ling, J., Li, Z., & Huang, C. (2023). Changes in temperature-precipitation compound extreme events in China during the past 119 years. Earth and Space Science, 10(8), e2022EA002777.

[20] El Rafei, M., Sherwood, S., Evans, J. P., & Ji, F. (2023). Analysis of extreme wind gusts using a high-resolution Australian regional reanalysis. Weather and Climate Extremes, 39, 100537.

[21] Rajulapati, C. R., Abdelmoaty, H. M., Nerantzaki, S. D., & Papalexiou, S. M. (2022). Changes in the risk of extreme temperatures in megacities worldwide. Climate Risk Management, 36, 100433.

[22] Ribeiro, A. F. S., Russo, A., Gouveia, C. M., Páscoa, P., & Zscheischler, J. (2020). Risk of cropfailure due to compound dry and hot extremes estimated with nested copulas. Biogeosciences, 17(19), 4815–4830.

[23] Roy, S. S. (2019). Spatial patterns of trends in seasonal extreme temperatures in India during 1980–2010. Weather and Climate Extremes, 24, 100203.

[24] Sharma, S., & Mujumdar, P. P. (2022). Modeling concurrent hydroclimatic extremes with parametric multivariate extreme value models. Water Resources Research, 58(2), e2021WR031519.

[25] Shi, X., & Xu, X. (2007). Regional characteristics of the interdecadal turning of winter/summer climate modes in Chinese mainland. Chinese Science Bulletin, 52, 101–112.

[26] Tawn, J. A. (1988). Bivariate extreme value theory: Models and estimation. Biometrika, 75(3), 397–415.

[27] Tebaldi, C., Hayhoe, K., Arblaster, J. M., & Meehl, G. A. (2006). Going to the extremes: An intercomparison of model-simulated historical and future changes in extreme events. Climatic Change, 79(3–4), 185–211.

[28] Zhang, Y., Sun, X., & Chen, C. (2021). Characteristics of concurrent precipitation and wind speed extremes in China. Weather and Climate Extremes, 32, 100322.

[29] Zheng, F., Westra, S., Leonard, M., & Sisson, S. A. (2014). Modeling dependence between extreme rainfall and storm surge to estimate coastal flooding risk. Water Resources Research, 50(3), 2050–2071.

[30] Zscheischler, J., Michalak, A. M., Schwalm, C., Mahecha, M. D., Huntzinger, D. N., Reichstein, M., Berthier, G., Ciais, P., Cook, R. B., El-Masri, B., Huang, M., Ito, A., Jain, A., King, A., Lei, H., Lu, C., Mao, J., Peng, S., Poulter, B., … Zeng, N. (2014). Impact of large-scale climate extremes on biospheric carbon fluxes: An intercomparison based on MsTMIP data. Global Biogeochemical Cycles, 28, 585–600.

[31] Zscheischler, J., & Seneviratne, S. I. (2017). Dependence of drivers affects risks associated with compound events. Science Advances, 3(6), e1700263.

[32] Zscheischler, J., Sillmann, J., & Alexander, L. (2022). Introduction to the special issue: Compound weather and climate events. Weather and Climate Extremes, 35, 100381.

RIGHTS & PERMISSIONS

2024 The Author(s). River published by Wiley-VCH GmbH on behalf of China Institute of Water Resources and Hydropower Research (IWHR).