Abstract
Recent years have witnessed climate change characterized by increasingly frequent extreme precipitation events, and the assumption of stationarity in traditional frequency analyses is gradually being questioned. In terms of the current research status in China, there is a lack of thorough investigations on the linkage between extreme precipitation and climate change. This paper aims to determine the dominant climate indices as well as the corresponding significant time scales and periods affecting extreme precipitation over China for dynamic assessments of the upcoming rainstorm risk. Correlations between 15 climate indices and precipitation extremes, as well as the correlations among climate indices, are fully explored to identify potential predictors for non-stationary modeling. Then, 21 non-stationary generalized extreme value (GEV) models are constructed, and the optimal covariates as well as their lag times with extreme precipitation at 769 stations are ascertained in a Bayesian framework. Finally, a complete predictive process is developed, and the national rainstorm risk under non-stationary conditions is assessed. The results indicate that precipitation extremes remain stationary only at 74 stations (less than 10%). WPI is dominant in modeling the variability in precipitation extremes for nearly 22% of the total stations, ranking first among all the climate indices. The predominant time scale affecting extreme precipitation at the majority of stations is 3 months. Ignoring the non-stationarity of extreme precipitation inevitably leads to misperceptions of rainstorm risks, and the spatial distribution of the maximum case of the design rainstorms under non-stationary conditions differs remarkably from that under stationary conditions. Our findings have important implications for the in-depth understanding of the real drivers of extreme precipitation non-stationary and enable advanced predictions of rainstorm risks for mitigating subsequent disasters.
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Data Availability
The data presented in this study is available on request from the corresponding author.
References
Alexander MA, Vimont DJ, Chang P, Scott JD (2010) The impact of extratropical atmospheric variability on ENSO: testing the seasonal footprinting mechanism using coupled model experiments. J Clim 23(11):2885–2901. https://doi.org/10.1175/2010JCLI3205.1
Allan RP, Soden BJ (2008) Atmospheric warming and the amplification of precipitation extremes. Science 321(5895):1481–1484. https://doi.org/10.1126/science.1160787
Arnell NW, Gosling SN (2016) The impacts of climate change on river flood risk at the global scale. Clim Chang 134(3):387–401. https://doi.org/10.1007/s10584-014-1084-5
Blöschl G, Ardoin-Bardin S, Bonell M, Dorninger M, Goodrich D, Gutknecht D, Szolgay J (2007) At what scales do climate variability and land cover change impact on flooding and low flows? Hydrol Process 21(9):1241–1247. https://doi.org/10.1002/hyp.6669
Chen W, Feng J, Wu R (2013) Roles of ENSO and PDO in the link of the East Asian winter monsoon to the following summer monsoon. J Clim 26(2):622–635. https://doi.org/10.1175/JCLI-D-12-00021.1
Cheng L, AghaKouchak A, Gilleland E, Katz RW (2014) Non-stationary extreme value analysis in a changing climate. Clim Change 127(2):353–369. https://doi.org/10.1007/s10584-014-1254-5
Coles S (2001) An introduction to Statistical Modeling of Extreme Values. Springer, London
Du T, Xiong L, Xu CY, Gippel CJ, Guo S, Liu P (2015) Return period and risk analysis of nonstationary low-flow series under climate change. J Hydrol 527:234–250. https://doi.org/10.1016/j.jhydrol.2015.04.041
Gao L, Huang J, Chen X, Chen Y, Liu M (2018) Contributions of natural climate changes and human activities to the trend of extreme precipitation. Atmos Res 205:60–69. https://doi.org/10.1016/j.atmosres.2018.02.006
Gao M, Mo D, Wu X (2016) Nonstationary modeling of extreme precipitation in China. Atmos Res 182:1–9. https://doi.org/10.1016/j.atmosres.2016.07.014
Gersonius B, Ashley R, Pathirana A, Zevenbergen C (2013) Climate change uncertainty: building flexibility into water and flood risk infrastructure. Clim Chang 116(2):411–423. https://doi.org/10.1007/s10584-012-0494-5
Gilleland E, Ribatet M, Stephenson AG (2013) A software review for extreme value analysis. Extremes 16(1):103–119. https://doi.org/10.1007/s10687-012-0155-0
Gilroy KL, McCuen RH (2012) A nonstationary flood frequency analysis method to adjust for future climate change and urbanization. J Hydrol 414:40–48. https://doi.org/10.1016/j.jhydrol.2011.10.009
Gong DY, Wang SW, Zhu JH (2001) East Asian winter monsoon and Arctic oscillation. Geophys Res Lett 28(10):2073–2076. https://doi.org/10.1029/2000GL012311
Goswami BN, Xavier PK (2005) ENSO control on the south Asian monsoon through the length of the rainy season. Geophys Res Lett 32(18). https://doi.org/10.1029/2005GL023216
Gu W, Li C, Li W, Zhou W, Chan JC (2009) Interdecadal unstationary relationship between NAO and east China's summer precipitation patterns. Geophys Res Lett 36(13). https://doi.org/10.1029/2009GL038843
Gu X, Ye L, Xin Q, Zhang C, Zeng F, Nerantzaki SD, Papalexiou SM (2022) Extreme precipitation in China: a review on statistical methods and applications. Adv Water Resour 163:104144. https://doi.org/10.1016/j.advwatres.2022.104144
Hao W, Shao Q, Hao Z, Ju Q, Baima W, Zhang D (2019) Non-stationary modelling of extreme precipitation by climate indices during rainy season in Hanjiang River Basin. China International Journal of Climatology 39(10):4154–4169. https://doi.org/10.1002/joc.6065
He B, Huang X, Ma M, Chang Q, Tu Y, Li Q, Hong Y (2018) Analysis of flash flood disaster characteristics in China from 2011 to 2015. Nat Hazards 90(1):407–420. https://doi.org/10.1007/s11069-017-3052-7
Huang C, Zhang Q, Singh VP, Gu X, Shi P (2017) Spatio-temporal variation of dryness/wetness across the Pearl River basin, China, and relation to climate indices. Int J Climatol 37:318–332. https://doi.org/10.1002/joc.5005
Lau KM, Weng H (2001) Coherent modes of global SST and summer rainfall over China: an assessment of the regional impacts of the 1997–98 El Niño. J Clim 14(6):1294–1308. https://doi.org/10.1175/1520-0442(2001)014%3c1294:CMOGSA%3e2.0.CO;2
Li J, Tan S (2015) Nonstationary flood frequency analysis for annual flood peak series, adopting climate indices and check dam index as covariates. Water Resour Manag 29:5533–5550. https://doi.org/10.1007/s11269-015-1133-5
Liu S, Huang S, Huang Q, Xie Y, Leng G, Luan J, Li X (2017) Identification of the non-stationarity of extreme precipitation events and correlations with large-scale ocean-atmospheric circulation patterns: a case study in the Wei River Basin, China. J Hydrol 548:184–195. https://doi.org/10.1016/j.jhydrol.2017.03.012
Liu S, Huang S, Xie Y, Wang H, Leng G, Huang Q, ..., Wang L (2019) Identification of the non-stationarity of floods: changing patterns, causes, and implications. Water Resour Manag 33:939–953. https://doi.org/10.1007/s11269-018-2150-y
López J, Francés F (2013) Non-stationary flood frequency analysis in continental Spanish rivers, using climate and reservoir indices as external covariates. Hydrol Earth Syst Sci 17(8):3189–3203. https://doi.org/10.5194/hess-17-3189-2013
Lu R, Dong B, Ding H (2006) Impact of the Atlantic Multidecadal Oscillation on the Asian summer monsoon. Geophys Res Lett 33(24). https://doi.org/10.1029/2006GL027655
Milly PC, Betancourt J, Falkenmark M, Hirsch RM, Kundzewicz ZW, Lettenmaier DP, Stouffer RJ (2008) Stationarity is dead: whither water management? Science 319(5863):573–574. https://doi.org/10.1126/science.1151915
Myhre G, Alterskjær K, Stjern CW, Hodnebrog Ø, Marelle L, Samset BH, Stohl A (2019) Frequency of extreme precipitation increases extensively with event rareness under global warming. Sci Rep 9(1):1–10. https://doi.org/10.1038/s41598-019-52277-4
Papalexiou SM, Koutsoyiannis D (2013) Battle of extreme value distributions: a global survey on extreme daily rainfall. Water Resour Res 49(1):187–201. https://doi.org/10.1029/2012WR012557
Papalexiou SM, Montanari A (2019) Global and regional increase of precipitation extremes under global warming. Water Resour Res 55(6):4901–4914. https://doi.org/10.1029/2018WR024067
Rashid MM, Beecham S (2019) Development of a non-stationary Standardized Precipitation Index and its application to a South Australian climate. Sci Total Environ 657:882–892. https://doi.org/10.1016/j.scitotenv.2018.12.052
Razmi A, Mardani-Fard HA, Golian S, Zahmatkesh Z (2022) Time-varying univariate and bivariate frequency analysis of nonstationary extreme sea level for New York City. Environmental Processes 9(1):8. https://doi.org/10.1007/s40710-021-00553-9
Read LK, Vogel RM (2015) Reliability, return periods, and risk under nonstationarity. Water Resour Res 51(8):6381–6398. https://doi.org/10.1002/2015WR017089
Renard B, Sun X, Lang M (2013) Bayesian methods for non-stationary extreme value analysis. In Extremes in a changing climate. Springer, Dordrecht
Rigby RA, Stasinopoulos DM (2005) Generalized additive models for location, scale and shape. J Roy Stat Soc: Ser C (Appl Stat) 54(3):507–554
Serinaldi F, Kilsby CG, Lombardo F (2018) Untenable nonstationarity: an assessment of the fitness for purpose of trend tests in hydrology. Adv Water Resour 111:132–155. https://doi.org/10.1016/j.advwatres.2017.10.015
Shao Y, Mu X, He Y, Sun W, Zhao G, Gao P (2019) Spatiotemporal variations of extreme precipitation events at multi-time scales in the Qinling-Daba mountains region, China. Quatern Int 525:89–102. https://doi.org/10.1016/j.quaint.2019.07.029
Shi P, Yang T, Xu CY, Yong B, Shao Q, Li Z, Li S (2017) How do the multiple large-scale climate oscillations trigger extreme precipitation? Glob Planet Chang 157:48–58. https://doi.org/10.1016/j.gloplacha.2017.08.014
Song X, Zou X, Mo Y, Zhang J, Zhang C, Tian Y (2020) Nonstationary bayesian modeling of precipitation extremes in the Beijing-Tianjin-Hebei Region, China. Atmos Res 242:105006. https://doi.org/10.1016/j.atmosres.2020.105006
Šraj M, Bezak N (2020) Comparison of time trend-and precipitation-informed models for assessing design discharges in variable climate. J Hydrol 589:125374. https://doi.org/10.1016/j.jhydrol.2020.125374
Su C, Chen X (2019) Covariates for nonstationary modeling of extreme precipitation in the Pearl River Basin, China. Atmos Res 229:224–239. https://doi.org/10.1016/j.atmosres.2019.06.017
Villarini G, Smith JA, Napolitano F (2010) Nonstationary modeling of a long record of rainfall and temperature over Rome. Adv Water Resour 33(10):1256–1267. https://doi.org/10.1016/j.advwatres.2010.03.013
Vinnarasi R, Dhanya CT (2022) Time-varying Intensity-Duration-Frequency relationship through climate-informed covariates. J Hydrol 604:127178. https://doi.org/10.1016/j.jhydrol.2021.127178
Vu TM, Mishra AK (2019) Nonstationary frequency analysis of the recent extreme precipitation events in the United States. J Hydrol 575:999–1010. https://doi.org/10.1016/j.jhydrol.2019.05.090
Wang L, Wu Z, He H, Wang F, Du H, Zong S (2017) Changes in summer extreme precipitation in Northeast Asia and their relationships with the East Asian summer monsoon during 1961–2009. Int J Climatol 37(1):25–35. https://doi.org/10.1002/joc.4683
Wang M, Jiang S, Ren L, Xu CY, Wei L, Cui H, ..., Yang X (2022) The development of a nonstationary standardised streamflow index using climate and reservoir indices as covariates. Water Resour Manag 36(4):1377–1392. https://doi.org/10.1007/s11269-022-03088-2
Wang Y, Duan L, Liu T, Li J, Feng P (2020) A non-stationary standardized streamflow index for hydrological drought using climate and human-induced indices as covariates. Sci Total Environ 699:134278. https://doi.org/10.1016/j.scitotenv.2019.134278
Wang Y, Li J, Feng P, Hu R (2015) A time-dependent drought index for non-stationary precipitation series. Water Resour Manage 29:5631–5647. https://doi.org/10.1007/s11269-015-1138-0
Wang Y, Zhou L (2005) Observed trends in extreme precipitation events in China during 1961–2001 and the associated changes in large‐scale circulation. Geophys Res Lett 32(9). https://doi.org/10.1029/2005GL022574
Xiao M, Zhang Q, Singh VP (2015) Influences of ENSO, NAO, IOD and PDO on seasonal precipitation regimes in the Yangtze River basin, China. Int J Climatol 35(12):3556–3567. https://doi.org/10.1002/joc.4228
Xiao M, Zhang Q, Singh VP (2017) Spatiotemporal variations of extreme precipitation regimes during 1961–2010 and possible teleconnections with climate indices across China. Int J Climatol 37(1):468–479. https://doi.org/10.1002/joc.4719
Yang L, Villarini G, Smith JA, Tian F, Hu H (2013) Changes in seasonal maximum daily precipitation in China over the period 1961–2006. Int J Climatol 33(7):1646–1657. https://doi.org/10.1002/joc.3539
Yin Y, Chen H, Xu C (2016) Modeling extreme precipitation in Poyang Lake Basin based on stationary and non-stationary GEV model. J Nat Resour 31(11):1906–1917. https://doi.org/10.11849/zrzyxb.20151336. (Chinese)
Zhang L, Liu Y, Zhan H, Jin M, Liang X (2021) Influence of solar activity and EI Niño-Southern Oscillation on precipitation extremes, streamflow variability and flooding events in an arid-semiarid region of China. J Hydrol 601:126630. https://doi.org/10.1016/j.jhydrol.2021.126630
Zhang Q, Xiao M, Singh VP, Chen YD (2014) Max-stable based evaluation of impacts of climate indices on extreme precipitation processes across the Poyang Lake basin, China. Glob Planet Chang 122:271–281. https://doi.org/10.1016/j.gloplacha.2014.09.005
Zhao JH, Feng GL, Yang J, Zhi R, Wang QG (2012) Analysis of the distribution of the large-scale drought/flood of summer in China under different types of the western Pacific subtropical high. Acta Meteorol Sin 70(05):1021–1031. https://doi.org/10.11676/qxxb2012.085. (Chinese)
Zhou M, Tian F, Lall U, Hu H (2011) Insights from a joint analysis of Indian and Chinese monsoon rainfall data. Hydrol Earth Syst Sci 15(8):2709–2715. https://doi.org/10.5194/hess-15-2709-2011
Acknowledgements
We are grateful for the support received as part of the projects funded by the National Natural Science Foundation of China (nos. 51925902, 52179006). Additionally, we would like to extend sincere appreciation to the editor and reviewers for their constructive comments, which help to improve the quality of the manuscript substantially.
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This work is supported by the National Natural Science Foundation of China (nos. 51925902, 52179006), and the Fundamental Research Funds for the Central Universities (no. DUT22JC28).
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Chi Zhang: Conceptualization, Writing and Funding acquisition. Xuezhi Gu: Methodology, Writing and Editing. Lei Ye: Reviewing and Funding acquisition. Qian Xin: Data analysis and Calculation. Xiaoyang Li: Reviewing. Hairong Zhang: Reviewing. Both authors read and approved the final manuscript.
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Zhang, C., Gu, X., Ye, L. et al. Climate Informed Non-stationary Modeling of Extreme Precipitation in China. Water Resour Manage 37, 3319–3341 (2023). https://doi.org/10.1007/s11269-023-03504-1
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DOI: https://doi.org/10.1007/s11269-023-03504-1