Prediction of Temperature and Precipitation in Damavand Catchment in Iran by Using LARS –WG in Future

Abstract

In recent years the issue of climate change and its effects on various aspects of the environment has become one of the challenges facing planners. It is desirable to analyze and predict the change of critical climatic variables, such as temperature and precipitation, which will provide valuable reference results for future water resources planning and management in the region. The aims of this study are to test the applicability of the Long Ashton Research Station Weather Generator (LARS-WG) model in downscaling daily precipitation and daily maximum (Tmax) and daily minimum (Tmin) temperatures in Damavand catchment in Iran and use it to predict future changes of precipitation and temperature. Future climate of the Damavand catchment is predicted by statistical downscaling outputs from General Circulation Models (GCMs) (HADCM3 for SRES A2 and B2 and A1B scenarios) for the period of 2046–2065.The results showed that the LARS-WG model produces excellent performance in downscaling Tmax and Tmin in the study region but compared to temperature, the model showed more error in downscaling daily precipitation. This issue was confirmed by examining the performance indicators including coefficient of determination, mean absolute error and root-mean square error. Also results showed that precipitation will decrease in future under these scenarios but temperature will increase. Findings of this study will serve as a reference for further studies and planning of future water management strategies in the Damavand catchment.