A probabilistic approach to the concept of probable maximum precipitation

S.M. Papalexiou, and D. Koutsoyiannis, A probabilistic approach to the concept of probable maximum precipitation, Advances in Geosciences, 7, 51-54, doi:10.5194/adgeo-7-51-2006, 2006.



The concept of probable maximum precipitation (PMP) is based on the assumptions that (a) there exists an upper physical limit of the precipitation depth over a given area at a particular geographical location at a certain time of year, and (b) that this limit can be estimated based on deterministic considerations. The most representative and widespread estimation method of PMP is the so-called moisture maximization method. This method maximizes observed storms assuming that the atmospheric moisture would hypothetically rise up to a high value that is regarded as an upper limit and is estimated from historical records of dew points. In this paper, it is argued that fundamental aspects of the method may be flawed or inconsistent. Furthermore, historical time series of dew points and "constructed" time series of maximized precipitation depths (according to the moisture maximization method) are analyzed. The analyses do not provide any evidence of an upper bound either in atmospheric moisture or maximized precipitation depth. Therefore, it is argued that a probabilistic approach is more consistent to the natural behaviour and provides better grounds for estimating extreme precipitation values for design purposes.

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See also: http://dx.doi.org/10.5194/adgeo-7-51-2006


Our works referenced by this work:

1. D. Koutsoyiannis, A probabilistic view of Hershfield's method for estimating probable maximum precipitation, Water Resources Research, 35 (4), 1313–1322, doi:10.1029/1999WR900002, 1999.
2. S.M. Papalexiou, Probabilistic and conceptual investigation of the probable maximum precipitation, Postgraduate Thesis, 193 pages, Department of Water Resources, Hydraulic and Maritime Engineering – National Technical University of Athens, Athens, September 2005.

Our works that reference this work:

1. D. Koutsoyiannis, Older and modern considerations in the design and management of reservoirs, dams and hydropower plants (Solicited), 1st Hellenic Conference on Large Dams, Larisa, doi:10.13140/RG.2.1.3213.5922, Hellenic Commission on Large Dams, Technical Chamber of Greece, 2008.
2. D. Koutsoyiannis, and A. Langousis, Precipitation, Treatise on Water Science, edited by P. Wilderer and S. Uhlenbrook, 2, 27–78, doi:10.1016/B978-0-444-53199-5.00027-0, Academic Press, Oxford, 2011.
3. D. Koutsoyiannis, and S.M. Papalexiou, Extreme rainfall: Global perspective, Handbook of Applied Hydrology, Second Edition, edited by V.P. Singh, 74.1–74.16, McGraw-Hill, New York, 2017.
4. A. Koskinas, A. Tegos, P. Tsira, P. Dimitriadis, T. Iliopoulou, P. Papanicolaou, D. Koutsoyiannis, and Τ. Williamson, Insights into the Oroville Dam 2017 spillway incident, Geosciences, 9 (37), doi:10.3390/geosciences9010037, 2019.

Works that cite this document: View on Google Scholar or ResearchGate

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1. Clark, C., Uncertainty and the breach of Gasper dam, International Water Power and Dam Construction, 59(11), 24-28, 2007.
2. Deshpande, N.R., B.D. Kulkarni, A.K. Verma and B.N. Mandal, Extreme rainfall analysis and estimation of Probable Maximum Precipitation (PMP) by statistical methods over the Indus river basin in India, Journal of Spatial Hydrology, 8(1), 22-36, 2008
3. Casas, M.C., R. Rodríguez, R. Nieto and A. Redaño, The estimation of probable maximum precipitation: The case of Catalonia, Annals of the New York Academy of Sciences, 1146, 291-302, 2008.
4. Fattahi, E., A. M. Noorian and K. Noohi, Comparison of physical and statistical methods for estimating probable maximum precipitation in southwestern basins of Iran, Desert, 15, 127-132, 2010.
5. Casas, M. C., R. Rodríguez, M. Prohom, A. Gázquez and A. Redaño, Estimation of the probable maximum precipitation in Barcelona (Spain), International Journal of Climatology, 31 (9), 1322-1327, 2011.
6. Ohara, N., M. L. Kavvas, S. Kure, Z. Chen, S. Jang and E. Tan, Physically based estimation of maximum precipitation over American River Watershed, California, Journal of Hydrologic Engineering, 16 (4), 351-361, 2011.
7. Gheidari, M. H. N., A. Telvari, H. Babazadeh and M. Manshouri, Estimating design probable maximum precipitation using multifractal methods and comparison with statistical and synoptically methods - Case study: Basin of Bakhtiari Dam, Water Resources, 38 (4), 484-493, 2011.
8. Bossé, B., B. Bussière, R. Hakkou, A. Maqsoud and M. Benzaazoua, Assessment of phosphate limestone wastes as a component of a store-and-release cover in a semiarid climate, Mine Water and the Environment, 10.1007/s10230-013-0225-9, 2013.
9. Mishra, P. K., D. Khare, A. Mondal, S. Kundu and R. Shukla, Statistical and probability analysis of rainfall for crop planning in a canal command, Agriculture for Sustainable Development, 1, 45-52, 2013.
10. Lagos, M. A. Z., and X. M. Vargas, PMP and PMF estimations in sparsely-gauged Andean basins and climate change projections, Hydrological Sciences Journal, 10.1080/02626667.2013.877588, 2014.
11. Costa, V., W. Fernandes and M. Naghettini, A Bayesian model for stochastic generation of daily precipitation using an upper-bounded distribution function, Stochastic Environmental Research and Risk Assessment, 10.1007/s00477-014-0880-9, 2014.
12. Hassanzadeh, E., A. Nazemi and A. Elshorbagy, Quantile-based downscaling of precipitation using genetic programming: application to idf curves in the city of Saskatoon, Journal of Hydrologic Engineering, 19 (5), 943-955, 2014.
13. Ishida, K., M. Kavvas, S. Jang, Z. Chen, N. Ohara and M. Anderson, Physically based estimation of maximum precipitation over three watersheds in Northern California: Atmospheric boundary condition shifting, J. Hydrol. Eng., 10.1061/(ASCE)HE.1943-5584.0001026, 2014.
14. #Salas, J. D., G. Gavilán, F. R. Salas, P. Y. Julien and J. Abdullah, Uncertainty of the PMP and PMF, Handbook of Engineering Hydrology - Modeling, Climate Change and Variability (ed. by S. Eslamian), Taylor & Francis, Boca Raton, FL, USA, 575-603, 2014.
15. Griffiths, G.A., A. I. McKerchar and C. P. Pearson, Towards prediction of extreme rainfalls in New Zealand, Journal of Hydrology (New Zealand), 53 (1), 41-52, 2014.
16. Rousseau, A. N., I. M. Klein, D. Freudiger, P. Gagnon, A. Frigon and C. Ratté-Fortin, Development of a methodology to evaluate probable maximum precipitation (PMP) under changing climate conditions: Application to southern Quebec, Canada, Journal of Hydrology, 10.1016/j.jhydrol.2014.10.053, 2014.
17. Micovic, Z., M.G. Schaefer and G.H. Taylor, Uncertainty analysis for Probable Maximum Precipitation estimates, Journal of Hydrology, 521, 360-373, 2015.
18. Chavan, S.R., and V.V. Srinivas, Probable maximum precipitation estimation for catchments in Mahanadi river basin, Aquatic Procedia, 4, 892-899, 2015.
19. #Haddad, K., and A. Rahman, Estimation of large to extreme floods using a regionalization model, Landscape Dynamics, Soils and Hydrological Processes in Varied Climates (ed. by A.M. Melesse and W. Abtew, 279-292, 10.1007/978-3-319-18787-7_14, 2016.

Tagged under: Course bibliography: Hydrometeorology, Determinism vs. stochasticity, Extremes, Stochastics