Advances in Geosciences www.adv-geosci.net 1680-7340 1680-7359 26 11th EGU Plinius Conference on Mediterranean Storms (2009) 2010 10.5194/adgeo-26-25-2010 http://www.adv-geosci.net/26/25/2010/ http://www.adv-geosci.net/26/25/2010/adgeo-26-25-2010.html http://www.adv-geosci.net/26/25/2010/adgeo-26-25-2010.pdf 25 31 2010-06-30 From regional climate simulations to the hydrological information needed for basin scale impact studies I. Portoghese ivan.portoghese@ba.irsa.cnr.it E. Bruno M. Vurro Water Research Institute, National Research Council, Bari, Italy Dept. of Water Engineering and Chemistry, Polytechnic of Bari, Bari, Italy The accuracy of local downscaling of rainfall predictions provided by climate models is crucial for the assessment of climate change impacts on hydrological processes because the presence of bias in downscaled precipitation may produce large bias in the assessment of soil moisture dynamics, river flows, and groundwater recharge. <br><br> In this study, the output of a regional climate model (RCM) is downscaled using a stochastic modelling of the point rainfall process able to adequately reproduce the daily rainfall intermittency which is one of the crucial aspects for the hydrological processes characterizing Mediterranean environments. The historical time-series from a dense rain-gauge network were used for the analysis of the RCM bias in terms of dry and wet daily period and then to investigate the predicted alteration in the local rainfall regime. A Poisson Rectangular Pulse (PRP) model (Rodriguez-Iturbe et al., 1987) was finally adopted for the stochastic generation of local daily rainfall as a continuous-time point process with forcing parameters resulting from the bias correction of the RCM scenario. Burlando, R. and Rosso, R.: Extreme storm rainfall and climate change, Atmos. Res., 27, 71–84, 1991. Burlando R, and Rosso, R.: Effects of transient climate change on basin hydrology. 1. Precipitation scenarios for the Arno River, central Italy, Hydrol. Proc. 16, 1151–1175, 2002. Deidda, R.: Rainfall downscaling in a spacetime multifractal framework, Water Resour. Res., 36(7), 1779–1784, 2000. Deidda, R., Badas, M. G., and Piga, E.: Spacetime Multifractality of Remotely Sensed Rainfall Fields, J. Hydrol., 322, 2–13, 2006. Déqué, M.: Frequency of precipitation and temperature extremes over France in a anthropogenic scenario: model results and statistical correction according to observed values, Global Planet. Change, 54(1–2), 16–26, 2007. Eagleson, P. S.: Climate, Soil, and Vegetation 2. The Distribution of Annual Precipitation Derived From Observed Storm Sequences, Water Resour. Res., 14(5), 713–721, 1978. Gualdi, S., Rajkovic, B., Djurdjevic, V., Castellari, S., Scoccimarro, E., Navarra, A. and Dacic, M.: SINTA, SImulations of climate chaNge in the mediTerranean Area, Final scientific report, INGV, 2008. Kilsby, C. G., Jones, P. D., Burton, A., Ford, A. C., Fowler, H. J., Harpham, C., James, P., Smith and A., Wilby, R. L. A daily weather generator for use in climate change studies, Environ. Model. Softw., 22, 1705–1719, 2007. \ Kottegoda, N. T. and Rosso, R.: Applied Statistics for Civil and Environmental Engineers, second ed., Wiley-Blackwell, Oxford, 517–519, 2008. Rodriguez-Iturbe, I., Cox, D. R., and Isham, V.: Some models for rainfall based on stochastic point processes, Proc. R. Soc. Lond. Ser. A, 410, 269–288, 1987. Salsón, S. and Garcia-Bartual, R.: A space-time rainfall generator for highly convective Mediterranean rainstorms, Nat. Hazards Earth Syst. Sci., 3, 103–114, doi:10.5194/nhess-3-103-2003, 2003.