Abstract
The Huaihe River Basin (HRB) is located in the climate transition zone between north and south of china, where cold and warm air flows are easily encountered, resulting in frequent extreme precipitation events occurred in this area. In this study, in order to extract the spatiotemporal distribution characteristics of extreme precipitation in the HRB, the optimal edge distribution functions were used to fit the precipitation series to obtain the extreme precipitation threshold, and then six indicators were used to describe the spatiotemporal distribution characteristics of extreme precipitation. The results show that the number of occurrences and the amount of precipitation in the HRB are generally greater in the southern part than in the northern part, but the intensity of precipitation in the eastern coastal areas is greater than in the inland areas. The Weibull function has the best fitting effect on both the precipitation and precipitation intensity series in the five zones of the HRB. As the cumulative probability increases, the area with the largest precipitation amount is Zone 1, and the area with the largest precipitation intensity is Zone 3. The spatial variation trends of extreme precipitation and intensity-based extreme precipitation in the HRB are roughly the same. The area with more extreme precipitation is in the southwest of the basin, while the area with higher precipitation intensity is on the eastern coast of the basin. The number of extreme precipitation occurrences has a decreasing trend in most of the basin, and the precipitation amount also has a decreasing trend, but the precipitation intensity has an increasing trend in the southern and northern parts of the basin. Both the start date and end date of extreme precipitation have an increasing trend, indicating that the occurrence time of extreme precipitation has a tendency to delay. This study can provide an important reference for the prevention of extreme precipitation disasters in the HRB.
Similar content being viewed by others
Data availability
We declared that materials described in the manuscript, including all relevant raw data, will be freely available to any scientist wishing to use them for non-commercial purposes, without breaching participant confidentiality.
Code availability
The data used to support the findings of this study are available from the corresponding author upon request.
References
Ayantobo OO, Wei J, Wang G (2022) Climatology of landfalling atmospheric rivers and its attribution to extreme precipitation events over Yangtze River Basin. Atmos Res 270:106077. https://doi.org/10.1016/j.atmosres.2022.106077
Burgess CP, Taylor MA, Stephenson T, Mandal A (2015) Frequency analysis, infilling and trends for extreme precipitation for Jamaica (1895–2100). J Hydrol Reg Stud 3:424–443. https://doi.org/10.1016/j.ejrh.2014.10.004
Chen F, Yuan H, Sun R, Yang C (2020) Streamflow simulations using error correction ensembles of satellite rainfall products over the Huaihe river basin. J Hydrol 589:125179. https://doi.org/10.1016/j.jhydrol.2020.125179
Clarke BJ, Otto FEL, Jones RG (2021) Inventories of extreme weather events and impacts: Implications for loss and damage from and adaptation to climate extremes. Clim Risk Manag 32:100285. https://doi.org/10.1016/j.crm.2021.100285
Cotterill D, Stott P, Christidis N, Kendon E (2021) Increase in the frequency of extreme daily precipitation in the United Kingdom in autumn. Weather Clim Extrem 33:100340. https://doi.org/10.1016/j.wace.2021.100340
DeGaetano AT, Castellano CM (2017) Future projections of extreme precipitation intensity-duration-frequency curves for climate adaptation planning in New York State. Clim Serv 5:23–35. https://doi.org/10.1016/j.cliser.2017.03.003
Doherty E, Mellett S, Norton D et al (2021) A discrete choice experiment exploring farmer preferences for insurance against extreme weather events. J Environ Manage 290:112607. https://doi.org/10.1016/j.jenvman.2021.112607
Exum NG, Betanzo E, Schwab KJ et al (2018) Correction to: extreme precipitation, public health emergencies, and safe drinking water in the USA. Curr Environ Heal Rep 5:316. https://doi.org/10.1007/s40572-018-0202-3
Gupta S, Gupta A, Himanshu SK, Singh R (2020) Analysis of the extreme rainfall events over upper catchment of Sabarmati River Basin in Western India using extreme precipitation indices BT - advances in water resources engineering and management. In: Singh RK, Dutta S, Kumari M (eds) AlKhaddar R. Springer Singapore, Singapore, pp 103–111
Hailegeorgis TT, Thorolfsson ST, Alfredsen K (2013) Regional frequency analysis of extreme precipitation with consideration of uncertainties to update IDF curves for the city of Trondheim. J Hydrol 498:305–318. https://doi.org/10.1016/j.jhydrol.2013.06.019
Hosseinzadehtalaei P, Tabari H, Willems P (2020) Climate change impact on short-duration extreme precipitation and intensity–duration–frequency curves over Europe. J Hydrol 590:125249. https://doi.org/10.1016/j.jhydrol.2020.125249
Howe PD, Boudet H, Leiserowitz A, Maibach EW (2014) Mapping the shadow of experience of extreme weather events. Clim Change 127:381–389. https://doi.org/10.1007/s10584-014-1253-6
Hu C, Xia J, She D et al (2019) A modified regional L-moment method for regional extreme precipitation frequency analysis in the Songliao River Basin of China. Atmos Res 230:104629. https://doi.org/10.1016/j.atmosres.2019.104629
Huang H, Cui H, Ge Q (2021) Will a nonstationary change in extreme precipitation affect dam security in China? J Hydrol. https://doi.org/10.1016/j.jhydrol.2021.126859
Huang YF, Mirzaei M, Amin MZM (2016) Uncertainty quantification in rainfall intensity duration frequency curves based on historical extreme precipitation quantiles. Procedia Eng 154:426–432. https://doi.org/10.1016/j.proeng.2016.07.425
Kašpar M (2003) Letter to the editor: reply to comments on the “Objective Frontal Analysis Techniques Applied to Extreme/Non-Extreme Precipitation Events” by M. Kašpar (Stud. Geophys. Geod., 47(2003), 605–631). Stud Geophys Geod 47:639–640. https://doi.org/10.1023/A:1024771820322
Kim H, Shin J-Y, Kim T et al (2020) Regional frequency analysis of extreme precipitation based on a nonstationary population index flood method. Adv Water Resour 146:103757. https://doi.org/10.1016/j.advwatres.2020.103757
Kumar S (2019) Impact of extreme weather events on wheat yield in different agro-ecological zones of middle Indo-Gangetic Plain. Agric Res 8:247–251. https://doi.org/10.1007/s40003-018-0372-0
Li L, Yao N, Liu DL et al (2019) Historical and future projected frequency of extreme precipitation indicators using the optimized cumulative distribution functions in China. J Hydrol 579:124170. https://doi.org/10.1016/j.jhydrol.2019.124170
Li W, Zhai P, Cai J (2011) Research on the relationship of ENSO and the frequency of extreme precipitation events in China. Adv Clim Chang Res 2:101–107. https://doi.org/10.3724/SP.J.1248.2011.00101
Li X, Zhang K, Gu P et al (2021a) Changes in precipitation extremes in the Yangtze River Basin during 1960–2019 and the association with global warming, ENSO, and local effects. Sci Total Environ 760:144244. https://doi.org/10.1016/j.scitotenv.2020.144244
Li Y, Wang W, Chang M, Wang X (2021b) Impacts of urbanization on extreme precipitation in the Guangdong-Hong Kong-Macau Greater Bay Area. Urban Clim 38:100904. https://doi.org/10.1016/j.uclim.2021b.100904
Mesman JP, Ayala AI, Adrian R et al (2020) Performance of one-dimensional hydrodynamic lake models during short-term extreme weather events. Environ Model Softw 133:104852. https://doi.org/10.1016/j.envsoft.2020.104852
Messmer M, Simmonds I (2021) Global analysis of cyclone-induced compound precipitation and wind extreme events. Weather Clim Extrem 32:100324. https://doi.org/10.1016/j.wace.2021.100324
Michel C, Sorteberg A, Eckhardt S et al (2021) Characterization of the atmospheric environment during extreme precipitation events associated with atmospheric rivers in Norway - Seasonal and regional aspects. Weather Clim Extrem 34:100370. https://doi.org/10.1016/j.wace.2021.100370
Miteva R (2020) On extreme space weather events: solar eruptions, energetic protons and geomagnetic storms. Adv Sp Res 66:1977–1991. https://doi.org/10.1016/j.asr.2020.07.006
Mooney S, O’Dwyer J, Lavallee S, Hynds PD (2021) Private groundwater contamination and extreme weather events: the role of demographics, experience and cognitive factors on risk perceptions of Irish private well users. Sci Total Environ 784:147118. https://doi.org/10.1016/j.scitotenv.2021.147118
Mou S, Shi P, Qu S et al (2020) Projected regional responses of precipitation extremes and their joint probabilistic behaviors to climate change in the upper and middle reaches of Huaihe River Basin, China. Atmos Res 240:104942. https://doi.org/10.1016/j.atmosres.2020.104942
Murray-Tortarolo GN, Jaramillo VJ (2019) The impact of extreme weather events on livestock populations: the case of the 2011 drought in Mexico. Clim Change 153:79–89. https://doi.org/10.1007/s10584-019-02373-1
Ossandón Á, Rajagopalan B, Kleiber W (2021) Spatial-temporal multivariate semi-Bayesian hierarchical framework for extreme precipitation frequency analysis. J Hydrol 600:126499. https://doi.org/10.1016/j.jhydrol.2021.126499
Pangapanga-Phiri I, Mungatana ED (2021) Adoption of climate-smart agricultural practices and their influence on the technical efficiency of maize production under extreme weather events. Int J Disaster Risk Reduct 61:102322. https://doi.org/10.1016/j.ijdrr.2021.102322
Peng Y, Yu X, Yan H, Zhang J (2020) Stochastic simulation of daily suspended sediment concentration using multivariate copulas. Water Resour Manag 34:3913–3932. https://doi.org/10.1007/s11269-020-02652-y
Pińskwar I, Choryński A, Graczyk D, Kundzewicz ZW (2019) Correction to: observed changes in extreme precipitation in Poland: 1991–2015 versus 1961–1990. Theor Appl Climatol 135:789. https://doi.org/10.1007/s00704-018-2526-1
Qiu T, Song C, Clark JS et al (2020) Understanding the continuous phenological development at daily time step with a Bayesian hierarchical space-time model: impacts of climate change and extreme weather events. Remote Sens Environ 247:111956. https://doi.org/10.1016/j.rse.2020.111956
Ray K, Giri RK, Ray SS et al (2021) An assessment of long-term changes in mortalities due to extreme weather events in India: a study of 50 years’ data, 1970–2019. Weather Clim Extrem 32:100315. https://doi.org/10.1016/j.wace.2021.100315
Rulfová Z, Buishand A, Roth M, Kyselý J (2016) A two-component generalized extreme value distribution for precipitation frequency analysis. J Hydrol 534:659–668. https://doi.org/10.1016/j.jhydrol.2016.01.032
Rush WD, Kiehl JT, Shields CA, Zachos JC (2021) Increased frequency of extreme precipitation events in the North Atlantic during the PETM: Observations and theory. Palaeogeogr Palaeoclimatol Palaeoecol 568:110289. https://doi.org/10.1016/j.palaeo.2021.110289
Sun J, Ao J (2013) Changes in precipitation and extreme precipitation in a warming environment in China. Chin Sci Bull 58:1395–1401. https://doi.org/10.1007/s11434-012-5542-z
Um M-J, Kim H, Heo J-H (2016) Hybrid approach in statistical bias correction of projected precipitation for the frequency analysis of extreme events. Adv Water Resour 94:278–290. https://doi.org/10.1016/j.advwatres.2016.05.021
Um M-J, Kim Y, Markus M, Wuebbles DJ (2017) Modeling nonstationary extreme value distributions with nonlinear functions: an application using multiple precipitation projections for U.S. cities. J Hydrol 552:396–406. https://doi.org/10.1016/j.jhydrol.2017.07.007
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 H, Gao T, Xie L (2019) Correction to: Extreme precipitation events during 1960–2011 for the Northwest China: space-time changes and possible causes. Theor Appl Climatol 137:997–999. https://doi.org/10.1007/s00704-018-2668-1
Wang L, Chen S, Zhu W et al (2021a) Spatiotemporal variations of extreme precipitation and its potential driving factors in China’s North-South Transition Zone during 1960–2017. Atmos Res 252:105429. https://doi.org/10.1016/j.atmosres.2020.105429
Wang P, Huang Q, Tang Q et al (2021b) Increasing annual and extreme precipitation in permafrost-dominated Siberia during 1959–2018. J Hydrol. https://doi.org/10.1016/j.jhydrol.2021b.126865
Wang Q, Xia J, She D et al (2021c) Assessment of four latest long-term satellite-based precipitation products in capturing the extreme precipitation and streamflow across a humid region of southern China. Atmos Res 257:105554. https://doi.org/10.1016/j.atmosres.2021c.105554
Wang Y (2015) Air pollution or global warming: attribution of extreme precipitation changes in eastern China—Comments on “Trends of extreme precipitation in Eastern China and their possible causes.” Adv Atmos Sci 32:1444–1446. https://doi.org/10.1007/s00376-015-5109-4
Xu L, Wang A, Yu W, Yang S (2021a) Hot spots of extreme precipitation change under 1.5 and 2 °C global warming scenarios. Weather Clim Extrem 33:100357. https://doi.org/10.1016/j.wace.2021a.100357
Xu Y, Sun H, Ji X (2021b) Spatial-temporal evolution and driving forces of rainfall erosivity in a climatic transitional zone: a case in Huaihe River Basin, eastern China. CATENA 198:104993. https://doi.org/10.1016/j.catena.2020.104993
Yao J, Chen Y, Chen J et al (2021) Intensification of extreme precipitation in arid Central Asia. J Hydrol 598:125760. https://doi.org/10.1016/j.jhydrol.2020.125760
Zhang M, Yu H, King AD et al (2020) Correction to: greater probability of extreme precipitation under 1.5 °C and 2 °C warming limits over East-Central Asia. Clim Change 162:621. https://doi.org/10.1007/s10584-020-02792-5
Zhao J-T, Su B-D, Mondal SK et al (2021) Population exposure to precipitation extremes in the Indus River Basin at 1.5 °C, 2.0 °C and 3.0 °C warming levels. Adv Clim Chang Res 12:199–209. https://doi.org/10.1016/j.accre.2021.03.005
Zhong S, Cheng Q, Huang C-R, Wang Z (2021) Establishment and validation of health vulnerability and adaptation indices under extreme weather events on the basis of the 2016 flood in Anhui province, China. Adv Clim Chang Res. https://doi.org/10.1016/j.accre.2021.07.002
Acknowledgements
We thank the China Meteorological Administration (CMA) for providing weather station data.
Funding
The research is financially supported by National Key R&D Program of China (2021YFC3001000) and National Natural Science Foundation of China (Grant No. U1911204, 51861125203).
Author information
Authors and Affiliations
Contributions
HJ contributed to conceptualization and methodology; XC provided software and performed supervision; RZ performed data curation and writing—original draft; YP provided methodology and resources; TZ performed writing—review and editing; ZL carried out validation and formal analysis; XT performed validation and provided software.
Corresponding author
Ethics declarations
Conflicts of interest
We declare that we have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Jin, H., Chen, X., Zhong, R. et al. Spatiotemporal distribution analysis of extreme precipitation in the Huaihe River Basin based on continuity. Nat Hazards 114, 3627–3656 (2022). https://doi.org/10.1007/s11069-022-05534-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-022-05534-1