Prediction of Climatic Parameters Using LARS-WG Model in Ghare-su Basin

A stochastic weather generator can serve as a computationally inexpensive tool to produce multiple-year climate change scenarios at the daily time scale which could incorporate changes both in mean climate and in climate variability as well. In this paper, LARS-WG model was used to downscale GCM outputs and then tp assess the performance for generated daily data of precipitation, minimum and maximum temperature and sunshine hours. Study area was Ghare-su basin in Gorgan and the station is called Gorgan synoptic station. The first step was running the model for the 1970-1999 periods. Then mean of observation and synthetic data were compared. T-test was used in the 99% significance level, and the difference between observation and synthetic data was not significant. Finally monthly mean of observation and synthetic data were compared using statistical parameters such as NA, RMSE & MAE. As a final result, it was found that performance of model was appropriate for generating daily above-listed data in Ghare-su basin. Thus, it was possible to predict the climatic parameters from GCM output using LARS-WG model. Also minimum and maximum temperatures had the highest and sunshine hours involved the lowest correlation. After ensuring performance of model to simulate above-mentioned parameters, this model used to predict future trends (in 2011-2030 and 2080-2099) with A2, A1B and B1 scenarios of the HadCM3 model was. Results showed that future temperature would increase 0.56-4.04 degrees centigrade while precipitation would increase 10.28-23.71%.