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Theoretical and Applied Climatology

, Volume 138, Issue 3–4, pp 1795–1808 | Cite as

Intensity and spatial heterogeneity of design rainstorm under nonstationarity and stationarity hypothesis across mainland China

  • Zhaoyang Zeng
  • Chengguang Lai
  • Zhaoli WangEmail author
  • Xiaohong Chen
  • Zhenxing Zhang
  • Xiangju Cheng
Original Paper

Abstract

Understanding the trend characteristics of design rainstorm and spatial heterogeneity of extreme precipitation is of great importance to reduce disasters induced by rare extreme precipitation. Using a high-resolution (0.5° × 0.5°) daily gridded data set of precipitation across mainland China from 1961 to 2013, this study investigated the historical changing trend and spatial heterogeneity of design rainstorm using the 30-year moving window method (30YM). Differences in the quantification of the design rainstorm were compared for the use of the 30YM and the 30-year-based increasing window method (30YBI). The results show that a significant increasing intensity but no spatially uniform trend of design rainstorm can be observed across mainland China based on the 30YM analysis. The south, east, and northeast China mainly showed an increasing trend, but the southwest and north China presented a decreasing trend. The spatial heterogeneity of the design rainstorm was greatly enhanced if the nonstationarity assumption was adopted on the national scale. The heterogeneity showed an increasing trend mainly in southeast, north, northeast, and northwest China, and a decreasing trend in southwest and west China, indicating significant regional variation in spatial heterogeneity. For most areas of mainland China, especially for southeastern, northeastern, and western China, use of the most recent precipitation sub-series to quantify the design rainstorm may weaken the potential nonstationarity and guarantee the safety of infrastructure in these areas where design rainfall increases.

Notes

Funding information

The research is financially supported by the National Key R&D Program of China (2018YFC1508201) and the National Natural Science Foundation of China (Grant Nos. 51879107, 51709117, 51579105, 91547202).

Supplementary material

704_2019_2937_MOESM1_ESM.docx (5.9 mb)
ESM 1 (DOCX 6018 kb)

References

  1. Alexander LV, Zhang X, Peterson TC, Caesar J, Gleason B, Klein Tank AMG, Haylock M, Collins D, Trewin B, Rahimzadeh F, Tagipour A, Rupa Kumar K, Revadekar J, Griffiths G, Vincent L, Stephenson DB, Burn J, Aguilar E, Brunet M, Taylor M, New M, Zhai P, Rusticucci M, Vazquez-Aguirre JL (2006) Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res Atmos 111 (D5)Google Scholar
  2. Asl SJ, Khorshiddoust AM, Dinpashoh Y, Sarafrouzeh F (2013) Frequency analysis of climate extreme events in Zanjan, Iran. Stochastic Environ Res Risk Assess 27(7):1637–1650CrossRefGoogle Scholar
  3. Bao J, Sherwood SC, Alexander LV, Evans JP (2017) Future increases in extreme precipitation exceed observed scaling rates. Nat Clim Chang 7(2):128–132CrossRefGoogle Scholar
  4. Bonnin GM, Todd D, Lin B, Parzybok T, Yekta M, Riley D (2004) Statistics of recent updates to NOAA/NWS rainfall frequency atlases. ASCE/EWRI World Water and Environmental Resources Congress, Salt Lake City, UtahCrossRefGoogle Scholar
  5. Bonsal BR, Zhang X, Vincent LA, Hogg WD (2001) Characteristics of daily and extreme temperatures over Canada. J Clim 14(9):1959–1976CrossRefGoogle Scholar
  6. Burt TP, Howden NJK, Worrall F (2016) The changing water cycle: hydroclimatic extremes in the british isles. Wiley Interdiscip Rev Water 3(6):854–870CrossRefGoogle Scholar
  7. Cancelliere A (2017) Non stationary analysis of extreme events. Water Resour Manag 31(10):3097–3110CrossRefGoogle Scholar
  8. Choi W, Tareghian R, Choi J, Hwang C (2014) Geographically heterogeneous temporal trends of extreme precipitation in Wisconsin, USA, during 1950–2006. Int J Climatol 34(9):2841–2852Google Scholar
  9. Chow VT, Mainment DR, Mays LW (1988) Applied hydrology. McGraw-Hill, New YorkGoogle Scholar
  10. Cong Z, Yang D, Gao B, Yang H, Hu H (2009) Hydrological trend analysis in the Yellow River Basin using a distributed hydrological model. Water Resour Res 45(7):335–345CrossRefGoogle Scholar
  11. Cooley D, Nychka D, Naveau P (2007) Bayesian spatial modeling of extreme precipitation return levels. J Am Stat Assoc 102(479):824–840CrossRefGoogle Scholar
  12. Dai L, Van Rijswick HFMW, Driessen PPJ, Keessen AM (2017) Governance of the sponge city programme in China with Wuhan as a case study. Int J Water Resour Dev 12:1–19Google Scholar
  13. Donat MG, Lowry AL, Alexander LV, O’Gorman PA, Maher N (2017) More extreme precipitation in the world’s dry and wet regions. Nat Clim Chang 7(2):154–158CrossRefGoogle Scholar
  14. 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 527:234–250CrossRefGoogle Scholar
  15. Efron B, Tibshirani RJ (1994) An introduction to the bootstrap. CRC Press, Boca Raton, FlaGoogle Scholar
  16. Evans JP, Argueso D, Olson R, Luca AD (2017) Bias-corrected regional climate projections of extreme rainfall in south-east Australia. Theor Appl Climatol 130(3–4):1085–1098CrossRefGoogle Scholar
  17. Fischer EM, Knutti R (2016) Observed heavy precipitation increase confirms theory and early models. Nat Clim Chang 6(11):986–991CrossRefGoogle Scholar
  18. Gao T, Wang HJ, Zhou T (2017) Changes of extreme precipitation and nonlinear influence of climate variables over monsoon region in China. Atmos Res 197:379–389CrossRefGoogle Scholar
  19. Ghosh S, Das D, Kao SC, Ganguly AR (2012) Lack of uniform trends but increasing spatial variability in observed indian rainfall extremes. Nat Clim Chang 2(2):86–91CrossRefGoogle Scholar
  20. Goswami BN, Venugopal V, Sengupta D, Madhusoodanan MS, Xavier PK (2006) Increasing trend of extreme rain events over India in a warming environment. Science 314(5804):1442–1445CrossRefGoogle Scholar
  21. Gu X, Zhang Q, Singh VP, Chen X, Liu L (2016) Nonstationarity in the occurrence rate of floods in the Tarim River Basin, China, and related impacts of climate indices. Glob Planet Chang 142:1–13CrossRefGoogle Scholar
  22. Gu X, Zhang Q, Singh VP, Liu L, Shi P (2017a) Spatiotemporal patterns of annual and seasonal precipitation extreme distributions across China and potential impact of tropical cyclones. Int J Climatol 37(10):3949–3962CrossRefGoogle Scholar
  23. Gu X, Zhang Q, Singh VP, Shi P (2017b) Nonstationarity in timing of extreme precipitation across China and impact of tropical cyclones. Glob Planet Chang 149:153–165CrossRefGoogle Scholar
  24. Hu D, Saito Y, Kempe S (1998) Sediment and nutrient transport to the coastal zone. In: Galloway JN, Mellilo JM (eds) Asian change in the context of global climate change: impact of natural and anthropogenic changes in Asia on global biogeochemical cycles. IGBP Publ. Series, vol 3. Cambridge University Press, Cambridge, pp 245–270Google Scholar
  25. IPCC (2007) Summary for policymakers of climate change 2007: the physical science basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  26. IPCC (2012) Summary for policymakers. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner G-K, Allen SK, Tignor M, Midgley PM (eds) Managing the risks of extreme events and disasters to advance climate change adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge and New York, pp 3–21Google Scholar
  27. Johnson RW (2001) An introduction to the bootstrap. Teach Stat 23(2):49–54CrossRefGoogle Scholar
  28. Kao SC, Ganguly AR (2011) Intensity, duration, and frequency of precipitation extremes under 21st-century warming scenarios. J Geophys Res Atmos (1984-2012)Google Scholar
  29. Kendon EJ, Roberts NM, Fowler HJ, Roberts MJ, Chan SC, Senior CA (2014) Heavier summer downpours with climate change revealed by weather forecast resolution model. Nat Clim Chang 4(7):570–576CrossRefGoogle Scholar
  30. Kharin VV, Zwiers FW (2005) Estimating extremes in transient climate change simulations. J Clim 18(8):1156–1173CrossRefGoogle Scholar
  31. Kharin VV, Zwiers FW, Zhang XB, Hegerl GC (2007) Changes in precipitation and temperature extremes in the IPCC ensemble of global coupled model simulations. J Clim 20(8):1419–1444CrossRefGoogle Scholar
  32. Knutson TR, McBride JL, Chan J, Emanuel K, Holland G, Landsea C, Held I, Kossin JP, Srivastava AK, Sugi M (2010) Tropical cyclones and climate change. Nat Geosci 3(3):157–163CrossRefGoogle Scholar
  33. Lai C, Shao Q, Chen X, Wang Z, Zhou X, Yang B, Zhang L (2016) Flood risk zoning using a rule mining based on ant colony algorithm. J Hydrol 542:268–280CrossRefGoogle Scholar
  34. Lai C, Zhong R, Wang Z, Wu X, Chen X, Wang P, Lian Y (2019) Monitoring hydrological drought using long-term satellite-based precipitation data. Sci Total Environ 649:1198–1208CrossRefGoogle Scholar
  35. Lima CHR, Kwon HH, Kim JY (2016) A Bayesian beta distribution model for estimating rainfall IDF curves in a changing climate. J Hydrol 540:744–756CrossRefGoogle Scholar
  36. Liu MX, Xu XL, Sun AY, Wang K, Liu W, Zhang X (2014) Is southwestern China experiencing more frequent precipitation extremes? Environ Res Lett 9(6):064002CrossRefGoogle Scholar
  37. Liu MX, Xu XL, Sun A (2015) Decreasing spatial variability in precipitation extremes in southwestern China and the local/large-scale influencing factors. J Geophys Res Atmos 120(13):6480–6488CrossRefGoogle Scholar
  38. Meehl GA, Karl T, Easterling DR, Changnon S, Pielke R, Changnon D, Evans J, Groisman PY, Knutson TR, Kunkel KE, Mearns LO, Parmesan C, Pulwarty R, Root T, Sylves RT, Whetton P, Zwiers F (2000) An introduction to trends in extreme weather and climate events: observations, socioeconomic impacts, terrestrial ecological impacts, and model projections. Bull Am Meteorol Soc 81(3):413–416CrossRefGoogle Scholar
  39. Milly PCD, Betancourt J, Falkenmark M, Hirsch RM, Kundzewicz ZW, Lettenmaier DP, Stouffer RJ (2008) Climate change-stationarity is dead: whither water management? Science 319(5863):573–574CrossRefGoogle Scholar
  40. Min SK, Zhang XB, Zwiers FW, Hegerl GC (2011) Human contribution to more-intense precipitation extremes. Nature 470(7334):378–381CrossRefGoogle Scholar
  41. NMIC (2012) Assessment report of China’s ground precipitation 0.5∘ × 0.5∘ gridded dataset (V2.0). National Meteorological Information Center: BeijingGoogle Scholar
  42. Pall P, Allen MR, Stone DA (2007) Testing the Clausius–Clapeyron constraint on changes in extreme precipitation under CO2 warming. Clim Dyn 28(4):351–363CrossRefGoogle Scholar
  43. Parmesan C (2006) Ecological and evolutionary responses to recent climate change. Annu Rev Ecol Evol Syst 37:637–669CrossRefGoogle Scholar
  44. Pinto I, Lennard C, Tadross M, Hewitson B, Dosio A, Nikulin G, Panitz HJ, Shongwe ME (2016) Evaluation and projections of extreme precipitation over southern Africa from two CORDEX models. Clim Chang 135(3–4):655–668CrossRefGoogle Scholar
  45. Rashid MM, Beecham S, Chowdhury RK (2016) Simulation of extreme rainfall and projection of future changes using the GLIMCLIM model. Theor Appl Climatol 130(1–2):453–466Google Scholar
  46. Ren ZG, Zhang MJ, Wang SJ, Qiang F, Zhu XF, Dong L (2015) Changes in daily extreme precipitation events in South China from 1961 to 2011. J Geogr Sci 25(1):58–68CrossRefGoogle Scholar
  47. Sillmann J, Stjern CW, Myhre G, Forster PM (2017) Slow and fast response of mean and extreme precipitation to different forcing in CMIP5 simulations. Geophys Res Lett 44(12):6383–6390CrossRefGoogle Scholar
  48. Singh V, Goyal MK (2016) Spatio-temporal heterogeneity and changes in extreme precipitation over eastern Himalayan catchments India. Stochastic Environ Res Risk Assess 31(10):2527–2546CrossRefGoogle Scholar
  49. Singh J, Vittal H, Karmakar S, Ghosh S, Niyogi D (2016) Urbanization causes nonstationarity in Indian summer monsoon rainfall extremes. Geophys Res Lett 43(21):11269–11277CrossRefGoogle Scholar
  50. So BJ, Kim JY, Kwon HH, Lima CHR (2017) Stochastic extreme downscaling model for an assessment of changes in rainfall intensity-duration-frequency curves over South Korea using multiple regional climate models. J Hydrol 553:321–337CrossRefGoogle Scholar
  51. Son C, Lee T, Kwon HH (2017) Integrating nonstationary behaviors of typhoon and non-typhoon extreme rainfall events in East Asia. Sci Rep 7:5097CrossRefGoogle Scholar
  52. Sraj M, Viglione A, Parajka J, Bloschl G (2016) The influence of non-stationarity in extreme hydrological events on flood frequency estimation. J Hydrol Hydromech 64(4):426–437CrossRefGoogle Scholar
  53. Stennett-Brown RK, Jones JJP, Stephenson TS, Taylor MA (2017) Future Caribbean temperature and rainfall extremes from statistical downscaling. Int J Climatol 37(14):4828–4845CrossRefGoogle Scholar
  54. Stocker T, Qin D, Plattner G, Tignor M, Allen S, Boschung J, Nauels A, Xia Y, Bex V, Midgley P (2013) IPCC, 2013: climate change 2013 the physical science basis: Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.  https://doi.org/10.1017/CBO9781107415324
  55. Sun J, Zhang FQ (2017) Daily extreme precipitation and trends over China. Sci Chin Earth Sci 60(12):2190–2203CrossRefGoogle Scholar
  56. Sun QH, Miao CY, Qiao YY, Duan QY (2017a) The nonstationary impact of local temperature changes and ENSO on extreme precipitation at the global scale. Clim Dyn 49(11–12):4281–4292CrossRefGoogle Scholar
  57. Sun QH, Miao CY, Duan QY (2017b) Changes in the spatial heterogeneity and annual distribution of observed precipitation across China. J Clim 30(23):9399–9416CrossRefGoogle Scholar
  58. Svensson C, Jones DA (2010) Review of methods for deriving areal reduction factors. J Flood Risk Manag 3(3):232–245CrossRefGoogle Scholar
  59. Um MJ, Kim Y, Markus M, Wuebbles DJ (2017) Modeling nonstationary extreme value distributions with nonlinear functions: an application using multiple precipitation projections for US cities. J Hydrol 552:396–406CrossRefGoogle Scholar
  60. Ummenhofer CC, Meehl GA (2017) Extreme weather and climate events with ecological relevance: a review. Philos Trans R Soc Lond 372(1723):20160135CrossRefGoogle Scholar
  61. Wang WG, Shao QX, Yang T, Peng SZ, Yu ZB, Taylor J, Xing WQ, Zhao CP, Sun FC (2013) Changes in daily temperature and precipitation extremes in the Yellow River Basin, China. Stochastic Environ Res Risk Assess 27(2):401–421CrossRefGoogle Scholar
  62. Wang Z, Lai C, Chen X, Yang B, Zhao S, Bai X (2015) Flood hazard risk assessment model based on random forest. J Hydrol 527:1130–1141CrossRefGoogle Scholar
  63. Wang R, Chen JY, Chen XW, Wang YF (2017a) Variability of precipitation extremes and dryness/wetness over the southeast coastal region of China, 1960-2014. Int J Climatol 37(13):4656–4669CrossRefGoogle Scholar
  64. Wang Z, Xie P, Lai C, Chen X, Zeng Z, Li J (2017b) Spatiotemporal variability of reference evapotranspiration and contributing climatic factors in China during 1961-2013. J Hydrol 544:97–108CrossRefGoogle Scholar
  65. Wang Z, Zeng Z, Lai C, Lin W, Wu X, Chen X (2017c) A regional frequency analysis of precipitation extremes in Mainland China with fuzzy c-means and L-moments approaches. Int J Climatol 37:429–444CrossRefGoogle Scholar
  66. Wang Z, Zhong R, Lai C, Zeng Z, Lian Y, Bai X (2018a) Climate change enhances the severity and variability of drought in the Pearl River Basin in South China in the 21st century. Agric For Meteorol 249:149–162CrossRefGoogle Scholar
  67. Wang Z, Li J, Lai C, Wang RY, Chen X, Lian Y (2018b) Drying tendency dominating the global grain production area. Glob Food Sec 16:138–149CrossRefGoogle Scholar
  68. Westra S, Alexander LV, Zwiers FW (2013) Global increasing trends in annual maximum daily precipitation. J Clim 26(11):3904–3918CrossRefGoogle Scholar
  69. Wong KK, Zhao XB (2001) Living with floods: victims’ perceptions in Beijiang, Guangdong, China. Area 33(2):190–201CrossRefGoogle Scholar
  70. Wu CH, Huang GR (2015) Changes in heavy precipitation and floods in the upstream of the Beijiang River Basin, South China. Int J Climatol 35(10):2978–2992CrossRefGoogle Scholar
  71. Wu XS, Wang ZL, Zhou XW, Lai CG, Lin WX, Chen XH (2016) Observed changes in precipitation extremes across 11 basins in China during 1961-2013. Int J Climatol 36(8):2866–2885CrossRefGoogle Scholar
  72. Yin H, Donat MG, Alexander LV, Sun Y (2015) Multi-dataset comparison of gridded observed temperature and precipitation extremes over China. Int J Climatol 35(10):2809–2827CrossRefGoogle Scholar
  73. You QL, Kang SC, Aguilar E, Pepin N, Flugel WA, Yan YP, Xu YW, Zhang YJ, Huang J (2011) Changes in daily climate extremes in China and their connection to the large scale atmospheric circulation during 1961-2003. Clim Dyn 36(11–12):2399–2417CrossRefGoogle Scholar
  74. Zhang XB, Zwiers FW, Hegerl GC, Lambert FH, Gillett NP, Solomon S, Stott PA, Nozawa T (2007) Detection of human influence on twentieth-century precipitation trends. Nature 448(7152):461–465CrossRefGoogle Scholar
  75. Zhang Q, Singh VP, Li JF, Chen XH (2011a) Analysis of the periods of maximum consecutive wet days in China. J Geophys Res Atmos 116:D23106CrossRefGoogle Scholar
  76. Zhang XB, Alexander L, Hegerl GC, Jones P, Tank AK, Peterson TC, Trewin B, Zwiers FW (2011b) Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdiscip Rev Clim Chang 2(6):851–870CrossRefGoogle Scholar
  77. Zhang DL, Lin YH, Zhao P, Yu XD, Wang SQ, Kang HW, Ding YH (2013) The Beijing extreme rainfall of 21 July 2012: “right results” but for wrong reasons. Geophys Res Lett 40(7):1426–1431CrossRefGoogle Scholar
  78. Zhang ZJ, Zhang CM, Cui QR (2017) Random threshold driven tail dependence measures with application to precipitation data analysis. Stat Sin 27(2):685–709Google Scholar
  79. Zhou BT, Wen QH, Xu Y, Song LC, Zhang XB (2014) Projected changes in temperature and precipitation extremes in China by the CMIP5 multimodel ensembles. J Clim 27(17):6591–6611CrossRefGoogle Scholar
  80. Zhu Q, Xu YP, Gu H (2016) Parameter uncertainty and nonstationarity in regional extreme rainfall frequency analysis in Qu River Basin, East China. J Hydrol Eng 21(5):04016008CrossRefGoogle Scholar
  81. Zong YQ, Chen XQ (2000) The 1998 flood on the Yangtze, China. Nat Hazards 22(2):165–184CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  • Zhaoyang Zeng
    • 1
  • Chengguang Lai
    • 1
    • 2
  • Zhaoli Wang
    • 1
    • 2
    Email author
  • Xiaohong Chen
    • 3
  • Zhenxing Zhang
    • 4
  • Xiangju Cheng
    • 1
    • 2
  1. 1.School of Civil Engineering and TransportationSouth China University of TechnologyGuangzhouChina
  2. 2.State Key Lab of Subtropical Building ScienceSouth China University of TechnologyGuangzhouChina
  3. 3.Center of Water Resources and EnvironmentSun Yat-Sen UniversityGuangzhouChina
  4. 4.The Prairie Research InstituteUniversity of Illinois at Urbana-ChampaignChampaignUSA

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