Skip to main content

Advertisement

SpringerLink
Log in

Probable maximum precipitation in a warming climate over North America in CanRCM4 and CRCM5

  • Published:
Climatic Change Aims and scope Submit manuscript

Abstract

In the context of climate change and projected increase in global temperature, the atmosphere’s water holding capacity is expected to increase at the Clausius-Clapeyron (C-C) rate by about 7% per 1 °C warming. Such an increase may lead to more intense extreme precipitation events and thus directly affect the probable maximum precipitation (PMP), a parameter that is often used for dam safety and civil engineering purposes. We therefore use a statistically motivated approach that quantifies uncertainty and accounts for nonstationarity, which allows us to determine the rate of change of PMP per 1 °C warming. This approach, which is based on a bivariate extreme value model of precipitable water (PW) and precipitation efficiency (PE), provides interpretation of how PW and PE may evolve in a warming climate. Nonstationarity is accounted for in this approach by including temperature as a covariate in the bivariate extreme value model. The approach is demonstrated by evaluating and comparing projected changes to 6-hourly PMP from two Canadian regional climate models (RCMs), CanRCM4 and CRCM5, over North America. The main results suggest that, on the continental scale, PMP increases in these models at a rate of approximately 4% per 1 °C warming, which is somewhat lower than the C-C rate. At the continental scale, PW extremes increase on average at the rate of 5% per 1 °C near surface warming for both RCMs. Most of the PMP increase is caused by the increase in PW extremes with only a minor contribution from changes in PE extremes. Nevertheless, substantial deviations from the average rate of change in PMP rates occur in some areas, and these are mostly caused by sensitivity of PE extremes to near surface warming in these regions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Abbs DJ (1999) A numerical modeling study to investigate the assumptions used in the calculation of probable maximum precipitation. Water Resour Res 35:785–796

    Article  Google Scholar 

  • Bao J, Sherwood SC, Alexander LV, Evans JP (2017) Future increases in extreme precipitation exceed observed scaling rates. Nat Clim Chang 7:128–132

    Article  Google Scholar 

  • Ben Alaya MA, Zwiers FW, Zhang X (2018) Probable maximum precipitation: its estimation and uncertainty quantification using bivariate extreme value analysis. J Hydrometeorol 19:679–694

    Article  Google Scholar 

  • Ben Alaya MA, Zwiers FW, Zhang X (2019) Evaluation and comparison of CanRCM4 and CRCM5 to estimate probable maximum precipitation over North America. J Hydrometeorol 20:2069–2089

  • Benson, A. M., 1973: Thoughts on the design of design floods. Floods and Droughts, Proc. 2nd Intern. Symp. Hydrology, 27-33.

  • Bukovsky, M., 2012: Masks for the Bukovsky regionalization of North America, Regional Integrated Sciences Collective. Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research, Boulder, CO. Downloaded, 07-03.

  • Casas MC, Rodríguez R, Prohom M, Gázquez A, Redaño A (2011) Estimation of the probable maximum precipitation in Barcelona (Spain). Int J Climatol 31:1322–1327

    Article  Google Scholar 

  • Chen LC, Bradley AA (2006) Adequacy of using surface humidity to estimate atmospheric moisture availability for probable maximum precipitation. Water Resour Res 42

  • Chen X, Hossain F (2019) Understanding future safety of dams in a changing climate. Bull Am Meteorol Soc 100:1395–1404

    Article  Google Scholar 

  • Cheng L, AghaKouchak A (2014) Nonstationary precipitation intensity-duration-frequency curves for infrastructure design in a changing climate. Sci Rep 4:7093

    Article  Google Scholar 

  • Clavet-Gaumont, J., Huard D, Frigon A, Koenig K, Slota P, Rousseau A, Klein I,  Thiémonge N, Houdré F, Perdikaris J (2017) Probable maximum flood in a changing climate: An overview for Canadian basins. J Hydrol Reg Stud 13: 11-25

  • Coles, S., J. Bawa, L. Trenner, and P. Dorazio, 2001: An introduction to statistical modeling of extreme values. Vol. 208, Springer.

  • Collier C, Hardaker P (1996) Estimating probable maximum precipitation using a storm model approach. J Hydrol 183:277–306

    Article  Google Scholar 

  • Collins, M., and et al. (2013) Long-term climate change: projections, commitments and irreversibility.

  • Côté J, Gravel S, Méthot A, Patoine A, Roch M, Staniforth A (1998) The operational CMC-MRB global environmental multiscale (GEM) model. Part I: design considerations and formulation. Mon Weather Rev 126:1373–1395

    Article  Google Scholar 

  • Dingman SL (2008) Physical Hydrology, 2nd edn. Waveland Pr Inc.

  • Foufoula-Georgiou E (1989) A probabilistic storm transposition approach for estimating exceedance probabilities of extreme precipitation depths. Water Resour Res 25:799–815

    Article  Google Scholar 

  • Genest C, Favre A-C (2007) Everything you always wanted to know about copula modeling but were afraid to ask. J Hydrol Eng 12:347–368

    Article  Google Scholar 

  • Giorgi F, Jones C, Asrar GR (2009) Addressing climate information needs at the regional level: the CORDEX framework. Bulletin-World Meteorological Organization 58:175-183

  • Hansen EM (1987) Probable maximum precipitation for design floods in the United States. J Hydrol 96:267–278

    Article  Google Scholar 

  • Hansen EM, Schwarz FK, Riedel JT (1977) Probable maximum precipitation estimates, Colorado River and Great Basin drainages. Hydrometeorol Rep 49

  • Hernández-Díaz L, Laprise R, Sushama L, Martynov A, Winger K, Dugas B (2013) Climate simulation over CORDEX Africa domain using the fifth-generation Canadian Regional Climate Model (CRCM5). Clim Dyn 40:1415–1433

    Article  Google Scholar 

  • Hershfield DM (1961) Estimating the probable maximum precipitation. J Hydraul Div 87:99–116

    Google Scholar 

  • Kain JS, Fritsch JM (1990) A one-dimensional entraining/detraining plume model and its application in convective parameterization. J Atmos Sci 47:2784–2802

    Article  Google Scholar 

  • Kite GW (1988) Frequency and risk analyses in hydrology. Water Resources Publication

  • Klein IM, Rousseau AN, Frigon A, Freudiger D, Gagnon P (2016) Evaluation of probable maximum snow accumulation: development of a methodology for climate change studies. J Hydrol 537:74–85

    Article  Google Scholar 

  • Kunkel KE, Karl TR, Easterling DR, Redmond K, Young J, Yin X, Hennon P (2013) Probable maximum precipitation and climate change. Geophys Res Lett 40:1402–1408

    Article  Google Scholar 

  • Laprise R et al (2013) Climate projections over CORDEX Africa domain using the fifth-generation Canadian Regional Climate Model (CRCM5). Clim Dyn 41:3219–3246

    Article  Google Scholar 

  • Lee O, Park Y, Kim ES, Kim S (2016) Projection of Korean probable maximum precipitation under future climate change scenarios. Adv Meteorol 2016:16

    Google Scholar 

  • Leung LR, Qian Y (2009) Atmospheric rivers induced heavy precipitation and flooding in the western US simulated by the WRF regional climate model. Geophys Res Lett:36

  • Micovic Z, Schaefer MG, Taylor GH (2015) Uncertainty analysis for probable maximum precipitation estimates. J Hydrol 521:360–373

    Article  Google Scholar 

  • Ohara N, Kavvas M, Kure S, Chen Z, Jang S, Tan E (2011) Physically based estimation of maximum precipitation over American River watershed, California. J Hydrol Eng 16:351–361

    Article  Google Scholar 

  • Peters GP et al (2013) The challenge to keep global warming below 2 C. Nat Clim Chang 3:4–6

    Article  Google Scholar 

  • Prein AF, Rasmussen RM, Ikeda K, Liu C, Clark MP, Holland GJ (2017) The future intensification of hourly precipitation extremes. Nat Clim Chang 7:48–52

    Article  Google Scholar 

  • Rakhecha P, Kennedy M (1985) A generalised technique for the estimation of probable maximum precipitation in India. J Hydrol 78:345–359

    Article  Google Scholar 

  • Rakhecha P, Clark C (1999) Revised estimates of one-day probable maximum precipitation (PMP) for India. Meteorol Appl 6:343–350

    Article  Google Scholar 

  • Rastogi D et al (2017) Effects of climate change on probable maximum precipitation: a sensitivity study over the Alabama-Coosa-Tallapoosa River Basin. J Geophys Res-Atmos 122:4808–4828

    Article  Google Scholar 

  • Requena A, Mediero Orduña L, Garrote de Marcos L (2013) A bivariate return period based on copulas for hydrologic dam design: accounting for reservoir routing in risk estimation. Hydrol Earth Syst Sci 17:3023–3038

    Article  Google Scholar 

  • Rouhani H, Leconte R (2016) A novel method to estimate the maximization ratio of the Probable Maximum Precipitation (PMP) using regional climate model output. Water Resour Res 52:7347–7365

    Article  Google Scholar 

  • Rousseau AN, Klein IM, Freudiger D, Gagnon P, Frigon A, Ratté-Fortin C (2014) Development of a methodology to evaluate probable maximum precipitation (PMP) under changing climate conditions: application to southern Quebec, Canada. J Hydrol 519:3094–3109

    Article  Google Scholar 

  • Salas JD, Gavilán G, Salas FR, Julien PY, Abdullah J (2014) Uncertainty of the PMP and PMF. CRC Press, Taylor & Francis Group

    Book  Google Scholar 

  • Schreiner LC, Riedel JT (1978) Probable maximum precipitation estimates, United States east of the 105th meridian. Hydrometeorol Rep 51

  • Scinocca J et al (2016) Coordinated global and regional climate modeling*. J Clim 29:17–35

    Article  Google Scholar 

  • Šeparović L et al (2013) Present climate and climate change over North America as simulated by the fifth-generation Canadian regional climate model. Clim Dyn 41:3167–3201

    Article  Google Scholar 

  • Shaw EM, Beven KJ, Chappell NA, Lamb R (2010) Hydrology in practice, 4th edn. Press, CRC

  • Shiau J (2003) Return period of bivariate distributed extreme hydrological events. Stoch Env Res Risk A 17:42–57

    Article  Google Scholar 

  • Sillmann J, Croci-Maspoli M, Kallache M, Katz RW (2011) Extreme cold winter temperatures in Europe under the influence of North Atlantic atmospheric blocking. J Clim 24:5899–5913

    Article  Google Scholar 

  • Tan E (2010) Development of a methodology for probable maximum precipitation estimation over the American River watershed using the WRF model. Vol. 71

  • Wang G, Wang D, Trenberth KE, Erfanian A, Yu M, Bosilovich MG, Parr DT (2017) The peak structure and future changes of the relationships between extreme precipitation and temperature. Nat Clim Chang 7:268–274

    Article  Google Scholar 

  • Whan K, Zwiers F (2016) Evaluation of extreme rainfall and temperature over North America in CanRCM4 and CRCM5. Clim Dyn 46:3821–3843

    Article  Google Scholar 

  • Whan K, Zwiers F, Sillmann J (2016) The influence of atmospheric blocking on extreme winter minimum temperatures in North America. J Clim 29:4361–4381

    Article  Google Scholar 

  • WMO WMO (2009) Manual on estimation of Probable Maximum Precipitation (PMP). WMO-No. 1045

  • Yue S (2001) A bivariate extreme value distribution applied to flood frequency analysis. Hydrol Res 32:49–64

    Article  Google Scholar 

  • Zhang GJ, McFarlane NA (1995) Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmosphere-ocean 33:407–446

    Article  Google Scholar 

  • Zhang X, Wang J, Zwiers FW, Groisman PY (2010) The influence of large-scale climate variability on winter maximum daily precipitation over North America. J Clim 23:2902–2915

    Article  Google Scholar 

  • Zhang X, Zwiers FW, Li G, Wan H, Cannon AJ (2017) Complexity in estimating past and future extreme short-duration rainfall. Nat Geosci 10:255

    Article  Google Scholar 

Download references

Acknowledgments

We thank Dae II Jeong and Megan C. Kirchmeier-Young from Environment and Climate Change Canada for their comments that helped to improve this paper. We thank three anonymous reviewers for their constructive and insightful comments, which helped us to improve this paper.

Author information

Authors and Affiliations

  1. Pacific Climate Impacts Consortium, University of Victoria, PO Box 1700, Stn CSC, Victoria, BC, V8W2Y2, Canada

    M. A. Ben Alaya & F. W. Zwiers

  2. Climate Research Division, Environment and Climate Change, Toronto, ON, M3H 5T4, Canada

    X. Zhang

Authors
  1. M. A. Ben Alaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. F. W. Zwiers
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. X. Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. A. Ben Alaya.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ben Alaya, M.A., Zwiers, F.W. & Zhang, X. Probable maximum precipitation in a warming climate over North America in CanRCM4 and CRCM5. Climatic Change 158, 611–629 (2020). https://doi.org/10.1007/s10584-019-02591-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10584-019-02591-7

Keywords

Search

Navigation