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 LARSWG resembled the results of previous studies and the IPCC’s AR4 projections.

Fiseha, B.m., Melesse, A.m., Romano, E., Volpi, E., Fiori, A. (2012). Statistical Downscaling of Precipitation and Temperature for the Upper Tiber Basin in Central Italy. INTERNATIONAL JOURNAL OF WATER SCIENCES, 1, 1-14 [10.5772/52890].

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

VOLPI, ELENA;FIORI, ALDO
2012-01-01

Abstract

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 LARSWG resembled the results of previous studies and the IPCC’s AR4 projections.
2012
Fiseha, B.m., Melesse, A.m., Romano, E., Volpi, E., Fiori, A. (2012). Statistical Downscaling of Precipitation and Temperature for the Upper Tiber Basin in Central Italy. INTERNATIONAL JOURNAL OF WATER SCIENCES, 1, 1-14 [10.5772/52890].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/134827
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