Evaluation of the Change-Factor and LARS-WG Methods of Downscaling for Simulation of Climatic Variables in the Future (Case study: Herat Azam Watershed, Yazd - Iran)

Authors
E. Goodarzi 1
M. Dastorani 2
A. Massah bavani 3
A. Talebi 4
1 Former M.Sc. Student, Department of Watershed Management, Faculty of Natural Resources, Yazd University, Yazd, Iran
2 Associate Professor, Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad (FUM), Mashhad, Iran
3 Associate Professor, Department of Irrigation and Drainage Engineering, Faculty of Abouraihan, University of Tehran, Pakdasht, Iran
4 Associate Professor, Department of Watershed Management, Faculty of Natural Resources, Yazd University, Yazd, Iran
Abstract
Prediction of climatic variables on a local scale by General Circulation Models of the atmosphere is impossible because the models have large-scale network of resolution. Therefore, downscaling methods are used to solve this problem. Since the climate change phenomenon can affect different systems such as, water resources, agriculture, environment, industry and economy as well, Selection of the most suitable downscaling method is very important. This study aims to evaluate performance of Change-Factor (CF) and LARS-WG downscaling methods in prediction of future climate variability of the Azam River Watershed, located in Yazd Province, Iran, for the period of 2010-2039. For this purpose, the CGCM3-AR4 model under the A2 emission scenario and also two methods of downscaling including statistical (LARS-WG) and proportional (CF) approaches were applied. The results showed increasing of temperature by both downscaling methods in the Azam River watershed in the future. Average temperature difference obtained from the two methods is about 3 to 4 percent. On the other hand, based on the climate condition, the amount of rainfall varied in the whole watershed, in a way that the future maximum precipitation difference calculated by two downscaling methods is about 30 percent.
Keywords
CGCM3-AR4
Change-factor
Climate Change
Downscaling
LARS-WG
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