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Safety Design of Water Infrastructures in a Modern Era

  • Xiaodong ChenEmail author
Chapter
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Abstract

Over the past century, numerous water infrastructures have been built to serve the water-related need of people worldwide (Mitchell, 1990). Those larger ones often serve multiple purposes, such as agriculture, navigation, hydropower, and flooding control. Failure of such high-hazard dams, especially those with flooding control purposes, would bring catastrophic ecological and societal loss.

References

  1. Abbs DJ (1999) A numerical modeling study to investigate the assumptions used in the calculation of probable maximum precipitation. Water Resour Res 35(3):785–796.  https://doi.org/10.1029/1998WR900013CrossRefGoogle Scholar
  2. Beauchamp J, Leconte R, Trudel M, Brissette F (2013) Estimation of the summer-fall PMP and PMF of a northern watershed under a changed climate. Water Resour Res 49(6):3852–3862.  https://doi.org/10.1002/wrcr.20336CrossRefGoogle Scholar
  3. Bergeron T (1965) On the low-level redistribution of atmospheric water caused by orography. In: Proceedings of international conference on cloud physics, pp 96–100Google Scholar
  4. Chen LC, Bradley AA (2006) Adequacy of using surface humidity to estimate atmospheric moisture availability for probable maximum precipitation. Water Resour Res 42(9):1–17.  https://doi.org/10.1029/2005WR004469CrossRefGoogle Scholar
  5. Chen X, Hossain F (2018) Understanding model-based probable maximum precipitation estimation as a function of location and seasons from atmospheric reanalysis. J Hydrometeorol  https://doi.org/10.1175/jhm-d-17-0170.1CrossRefGoogle Scholar
  6. Chen X, Hossain F, Leung LR (2017) Probable maximum precipitation in the U.S. Pacific Northwest in a changing climate. Water Resour Res 53(11):9600–9622.  https://doi.org/10.1002/2017wr021094CrossRefGoogle Scholar
  7. Cheng L, AghaKouchak A (2014) Nonstationary precipitation intensity-duration-frequency curves for infrastructure design in a changing climate. Sci Rep 4:7093.  https://doi.org/10.1038/srep07093CrossRefGoogle Scholar
  8. 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/s10584-014-1254-5CrossRefGoogle Scholar
  9. Douglas EM, Barros AP (2003) Probable maximum precipitation estimation using multifractals: application in the eastern United States. J Hydrometeorol 4(6):1012–1024.  https://doi.org/10.1175/1525-7541(2003)004%3c1012:PMPEUM%3e2.0.CO;2CrossRefGoogle Scholar
  10. Frank W (1988) The cause of the Johnstown flood. Civ Eng 58(5):63–66Google Scholar
  11. 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.2016.07.014CrossRefGoogle Scholar
  12. Hayes BD, Kao SC, Kanney JF, Quinlan KR, De Neale ST (2015) Site specific probable maximum precipitation estimates and professional judgement. In: AGU fall meeting abstractsGoogle Scholar
  13. Heim RR, Guttman NB (1997) On computing 1971–2000 climate normals in the ASOS era. In: Proceedings of 10th conference on applications of meteorology, pp 171–175Google Scholar
  14. Hershfield DM (1965) Method for estimating probable maximum rainfall. J Am Water Works Assoc 57(8):965–972CrossRefGoogle Scholar
  15. Hobbs PV (1989) Research on clouds and precipitation: past, present, and future. I. Bull Am Meteorol Soc 70(3):282–285Google Scholar
  16. Hossain F, Degu AM, Yigzaw W, Burian S, Niyogi D, Shepherd JM, Pielke R Sr (2012) Climate feedback-based provisions for dam design, operations, and water management in the 21st century. J Hydrol Eng 17(August):837–850.  https://doi.org/10.1061/(ASCE)HE.1943-5584.0000541CrossRefGoogle Scholar
  17. Hu M, Luo C (1992) Historical floods of China. China Bookstore Publishing HouseGoogle Scholar
  18. International Atomic Energy Agency (2003) Flood hazard for nuclear power plants on coastal and river sitesGoogle Scholar
  19. International Atomic Energy Agency (2009) Hydrological hazards in site evaluation for nuclear installationsGoogle Scholar
  20. Ishida K, Kavvas ML, Asce F, Jang S, Chen Z, Asce AM, Ohara N, Asce AM, Anderson ML, Asce AM (2015) Physically based estimation of maximum precipitation over three watersheds in Northern California: atmospheric boundary condition shifting. J Hydrol Eng 20(4):04014052.  https://doi.org/10.1061/(asce)he.1943-5584.0001026CrossRefGoogle Scholar
  21. Koutsoyiannis D (1999) A probabilistic view of hershfield’s method for estimating probable maximum precipitation. Water Resour Res 35(4):1313–1322CrossRefGoogle Scholar
  22. 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(7):1402–1408.  https://doi.org/10.1002/grl.50334CrossRefGoogle Scholar
  23. Lee J, Choi J, Lee O, Yoon J, Kim S (2017) Estimation of probable maximum precipitation in Korea using a regional climate model. Water 9(4)CrossRefGoogle Scholar
  24. Liu C-C, Yang T-C, Kuo C-M, Chen J-M, Yu P-S (2016) Estimating probable maximum precipitation by considering combined effect of typhoon and southwesterly air flow. Terr Atmos Ocean Sci 27(6)CrossRefGoogle Scholar
  25. Micovic Z, Schaefer MG, Taylor GH (2015) Uncertainty analysis for probable maximum precipitation estimates. J Hydrol 521:360–373.  https://doi.org/10.1016/j.jhydrol.2014.12.033CrossRefGoogle Scholar
  26. Min S-K, Zhang X, Zwiers FW, Hegerl GC (2011) Human contribution to more-intense precipitation extremes. Nature 470(7334):378–381.  https://doi.org/10.1038/nature09763CrossRefGoogle Scholar
  27. Mitchell B (1990) Integrated water management: international experiences and perspectives. Belhaven Press, LondonGoogle Scholar
  28. National Research Council (1994) Estimating bounds on extreme precipitation events: a brief assessment. The National Academies Press, Washington, DCGoogle Scholar
  29. Ohara N, Kavvas M, Kure S, Chen Z, Jang S (2011) Physically based estimation of maximum precipitation over American river watershed, California. J Hydrol Eng 16(4):351–361.  https://doi.org/10.1061/(ASCE)HE.1943-5584.0000324CrossRefGoogle Scholar
  30. Ohara N, Kavvas ML, Anderson ML, Chen ZQ, Ishida K (2017) Characterization of extreme storm events using a numerical model-based precipitation maximization procedure in the Feather, Yuba, and American river watersheds in California. J Hydrometeorol 18(5):1413–1423.  https://doi.org/10.1175/JHM-D-15-0232.1CrossRefGoogle Scholar
  31. Papalexiou SM, Koutsoyiannis D (2006) A probabilistic approach to the concept of probable maximum precipitation. Adv Geosci 7:51–54CrossRefGoogle Scholar
  32. Prasad R, Hibler LF, Coleman AM, Ward DL (2011) Design-basis flood estimation for site characterization at nuclear power plants in the United States of AmericaGoogle Scholar
  33. Rakhecha PR, Kennedy MR (1985) A generalised technique for the estimation of probable maximum precipitation in India. J Hydrol 78(3):345–359.  https://doi.org/10.1016/0022-1694(85)90112-XCrossRefGoogle Scholar
  34. Rakhecha PR, Singh VP (2009) Applied hydrometeorology. Springer Science+Business Media, BerlinCrossRefGoogle Scholar
  35. Rastogi D, Kao S-C, Ashfaq M, Mei R, Kabela ED, Gangrade S, Naz BS, Preston BL, Singh N, Anantharaj VG (2017) Effects of climate change on probable maximum precipitation: a sensitivity study over the Alabama-Coosa-Tallapoosa River Basin. J Geophys Res Atmos 122(9):4808–4828.  https://doi.org/10.1002/2016JD026001CrossRefGoogle Scholar
  36. Rouhani H (2016) Climate change impact on probable maximum precipitatio and probable maximum flood in Quebec. Université de Sherbrooke, QuebecGoogle Scholar
  37. 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.  https://doi.org/10.1016/j.jhydrol.2014.10.053CrossRefGoogle Scholar
  38. Salas JD, Gavilán G, Salas FR, Julien PY, Abdullah J (2014) Uncertainty of the PMP and PMF. Handb Eng Hydrol 2:575–603Google Scholar
  39. Sun X, Barros AP (2010) An evaluation of the statistics of rainfall extremes in rain gauge observations, and satellite-based and reanalysis products using universal multifractals. J Hydrometeorol 11(2):388–404.  https://doi.org/10.1175/2009JHM1142.1CrossRefGoogle Scholar
  40. Tan E (2010) Development of a methodology for probable maximum precipitation estimation over the American river watershed using the WRF model. University of California, DavisGoogle Scholar
  41. Trenberth KE, Dai A, Rasmussen RM, Parsons DB (2003) The changing character of precipitation. Bull Am Meteorol Soc 84(9):1205–1217 + 1161.  https://doi.org/10.1175/bams-84-9-1205CrossRefGoogle Scholar
  42. US Weather Bureau (1961) Interim report, probable maximum precipitation in CaliforniaGoogle Scholar
  43. VandenBerge DR, Duncan JM, Brandon T (2011) Lessons learned from Dam failures. Virginia Polytechnic Institute and State University, BlacksburgGoogle Scholar
  44. Wang J, Fisher BL, Wolff DB (2008) Estimating rain rates from tipping-bucket rain gauge measurements. J Atmos Ocean Technol 25(1):43–56.  https://doi.org/10.1175/2007JTECHA895.1CrossRefGoogle Scholar
  45. Wi S, Valdés JB, Steinschneider S, Kim T-W (2016) Non-stationary frequency analysis of extreme precipitation in South Korea using peaks-over-threshold and annual maxima. Stoch Environ Res Risk Assess 30(2):583–606.  https://doi.org/10.1007/s00477-015-1180-8CrossRefGoogle Scholar
  46. World Meteorological Organization (2009) Manual on estimation of probable maximum precipitation, 3rd edn. GenevaGoogle Scholar
  47. Yang L, Smith JA (2018) Sensitivity of extreme rainfall to atmospheric moisture content in the arid/semi-arid Southwestern US: implications for probable maximum precipitation estimates J Geophys Res Atmos 123:1638–1656. https://doi.org/10.1002/2017JD027850Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of WashingtonSeattleUSA
  2. 2.Pacific Northwest National LaboratoryRichlandUSA

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