Natural Hazards and Earth System Science www.nat-hazards-earth-syst-sci.net 1561-8633 1684-9981 8 1 2008 10.5194/nhess-8-67-2008 http://www.nat-hazards-earth-syst-sci.net/8/67/2008/ http://www.nat-hazards-earth-syst-sci.net/8/67/2008/nhess-8-67-2008.html http://www.nat-hazards-earth-syst-sci.net/8/67/2008/nhess-8-67-2008.pdf 67 79 2008-02-13 Is there a trend in extremely high river temperature for the next decades? A case study for France F. Huguet frederic-p.huguet@edf.fr S. Parey D. Dacunha-Castelle F. Malek Electricité de France, Recherche & Développement, 6 Quai Watier, BP49, 78401 Chatou Cedex, France Mathématiques, Modélisation Stochastique et Statistique, Université Paris-Sud, 91405 Orsay Cedex, France After 2003's summer heat wave, Electricité de France created a global plan called "heat wave-dryness". In this context, the present study tries to estimate high river temperatures for the next decades, taking into account climatic and anthropogenic evolutions. To do it, a specific methodology based on Extreme Value Theory (EVT) is applied. In particular, a trend analysis of water temperature data is done and included in EVT used. The studied river temperatures consist of mean daily temperatures for 27 years measured near the French power plants (between 1977 and 2003), with four series for the Rhône river, four for the Loire river and a few for other rivers. There are also three series of mean daily temperatures computed by a numerical model. For each series, we have applied statistical extreme value modelling. Because of thermal inertia, the Generalized Extreme Value (GEV) distribution is corrected by the medium cluster length, which represents thermal inertia of water during extremely hot events. The μ and σ parameters of the GEV distributions are taken as polynomial or continuous piecewise linear functions of time. The best functions for μ and σ parameters are chosen using Akaike criterion based on likelihood and some physical checking. For all series, the trend is positive for μ and not significant for σ, over the last 27 years. However, we cannot assign this evolution only to the climatic change for the Rhône river because the river temperature is the resultant of several causes: hydraulic or atmospheric, natural or related to the human activity. For the other rivers, the trend for μ could be assigned to the climatic change more clearly. Furthermore, the sample is too short to provide reliable return levels estimations for return periods exceeding thirty years. Still, quantitative return levels could be compared with physical models for example. Coles, S.: An introduction to statistical modeling of extreme values, Springer series in statistics, Springer, 2001. Davison A. C.: Statistical models, Cambridge series in Statistical and Probabilistic Mathematics, Cambridge University Press, 2003. 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