Climate change is a complex and long-term global atmospheric-oceanic phenomenon which can be influenced by natural factors such as volcanoes, solar, oceans and atmosphere activities which they have interactions between or may be as a result of human activities. Atmospheric general circulation models are developed for simulation of current climate of the earth and are able to predict the earth's future climate change. In this paper, the performance of GFDL CM2.1, CSIRO Mk3 and HadCM3 AOGCMs were assessed and evaluated in the study of the climate change effects on temperature and precipitation in Taleghan basin. The results show that HadCM3 model in comparison with CSIRO Mk3 and GFDL CM2.1 models has indicated the better performance in this region.

According to the importance of climate change, the necessity of develop a fast and accurate tool is undeniable. Although the comparison of a statistical model with specialized models which were designed regard to non-linear complexities of a phenomenon is not common, in this study ARIMA statistical model was analyzed and evaluated with GFDL CM2.1 and CGM3 Atmosphere-Ocean General Circulation Models (AOGCMs) in order to investigate on the effects of climate change on temperature and precipitation in the Taleghan basin. The results showed although GFDL CM2.1 model showed better performance in MAE and R 2 validation criteria and the predicted temperature had similar trend with the observational data, the difference between the model results and observations is significant. The CGM 3 model showed better performance in R 2 for precipitation, temperature and MAE for long term average of precipitation in addition to having similar trend to the observed data. However, for long term average of both temperature and precipitation, the general predicted trend had a considerable distance with the observational values. In contrast, although the statistical ARIMA model predictions had some fluctuations, they had better conformity to the general trend of observations. These results show that contrary to popular belief, in some cases like this investigated case, even cheap statistical models can likely provide acceptable results.

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Climate change is a significant change in weather or in its variability in a long period. This change could be in the average of temperature, rainfall, humidity, weather patterns, wind, and sunlight and so on. Atmosphere-Ocean General Circulation Model (AOGCMs) have been developed to simulate current climate of the planet and are able to predict future climate change of the Earth. In this paper the performance of CGCM3, CSIRO Mk3 and HadCM3 models in estimating the effects of climate change on temperature and precipitation of the Taleghan basin were studied. The results show that despite relatively acceptable performance of all three models in temperature modeling, it seems that the outputs of HadCM3 model for this basin are more desirable when compared with CGCM3 and CSIRO MK3 models and it is can be said that HadCM3 seems to be more reliable for this basin.

Abstract. Floods have caused significant socio-economic damage and are extremely dangerous for human lives as well as infrastructures. The aim of this study is to use machine learning models including regularized random forest (RRF) and Naïve Bayes (NB) algorithms to predict flood susceptibility areas using 410 sample points (205 flood points and 205 non-flood points). Ten flood influencing factors including elevation, topographic wetness index, rainfall, normalized difference vegetation index, curvature, land use, distance to river, slope, lithology, and aspect have been used in the modelling process. For this purpose, 70% of the data was used for training and the rest employed for testing the models. Accuracy (ACC), sensitivity, specificity, negative predictive value (NPV), and the area under the curve (AUC) of the receiver operating characteristic (ROC) were used to validate and compare the performance of the models. The results showed that the RRF model on the testing dataset had the highest performance (AUC = 0.94, ACC = 90%, Sensitivity = 0.89, Specificity = 0.92, NPV = 0.89) compared to that of the NB model (AUC = 0.93, ACC = 89%, Sensitivity = 0.84, Specificity = 0.96, NPV = 0.81). The employed models can be used as an efficient tool for flood susceptibility mapping with the purpose of planning to reduce the damages.