Statistical Downscaling of Precipitation and Temperature for the Upper Tiber Basin in Central Italy

• Published in: INTERNATIONAL JOURNAL OF WATER SCIENCES Vol. 1; p. 1
• Main Authors: BM, Fiseha, AM, Melesse, E, Romano, E, Volpi, A, Fiori
• Format: Journal Article
• Language: English
• Published: Rijeka InTech 2012
• ISSN: 1848-929X; 1848-929X
• DOI: 10.5772/52890

Precipitation and temperature data are the most frequently used forcing terms in hydrological models. However, the available General Circulation Models (GCMs), which are widely used nowadays to simulate future climate scenarios, do not provide those variables to the need of the models.The purpose of this study is therefore, to apply a statistical downscaling method and assess its strength in reproducing current climate. Two statistical downscaling techniques, namely regression based downscaling and the stochastic weather generator, were used to downscale the HadCM3 GCM predictions of the A2 and B2 scenarios for the Upper Tiber River basin located in central Italy. Four scenario periods, including the current climate (1961 1990), the 2020s,the 2050s and the 2080s, were considered. The Statistical Downscaling Model (SDSM) based downscaling shows an increasing trend in both minimum and maximum temperature as well as precipitation in the study area until the end of the 2080s. Long Ashton Research Station Weather Generator (LARS WG) shows an agreement with SDSM for temperature,however, the precipitation shows a decreasing trend with a pronounced decrease of summer season that goes up to 60% in the time window of the 2080s as compared to the current (1961 1990) climate. Even though the two downscaling models do not provide the same result,both methods reveal that there will be an impact of climate on the selected basin as observed through the time series analysis of precipitation and temperature.The overall result also shows that the performance of the LARS WG resembled the results of previous studies and the IPCC s AR4 projections.