Assessment Of Uncertainty Associated With Precipitation And Temperature Predictions: Climate Change Impact Assessment In Lenjanat Watershed, Central Iran
Abstract
Afrooz Bagheri, Bahram Malek Mohammadi, Banafsheh Zahraie, Amir Hessam Hasani, Farzam Babaie
The most popular method for predicting future behavior of global climate system under various greenhouse gas emission scenarios is using General Circulation Models (GCM). This study aims at predicting local climate change impacts on average rainfall and temperature in Lenjanat watershed in Iran in the period of 2006-2035. For this purpose, after performing trend analysis using Mann-Kendall test, the output data of general circulation models of atmosphere with two scenarios of climate change, i.e. 8.5 and 4.5, were down scaled with LARS-WG model at the Pol-Kalleh station. The results of that synoptic station were assessed for the fundamental period 1975-2005 and the future period of 2005-2035. To assess the model power, root mean square error and absolute error were calculated between monitored and modeled data. Models were appropriate for Lenjanat watershed station. And finally, data uncertainty was investigated. Results show that rainfall is decreasing and maximum and minimum temperature is increasing in both scenarios. Humid days are decreasing, but arid days are increasing. The maximum decrease in humid days was in November and increase in arid days was in May. More decrease in humid days and more increase in dry days happened in the pessimist scenario. There was a high correlation between rainfall, river flow rate, and underground water level.
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