Projection of Climate Change Based on Multi-Site Statistical Downscaling over Gilan area, Iran

Introduction: The phenomenon of climate change and its consequences is a familiar topic which is associated with natural disasters such as, flooding, hurricane, drought that cause water crisis and irreparable damages. Studying this phenomenon is a serious warning regarding the earth’s weather change for a long period of time. Materials and Methods: In order to understand and survey the impacts of climate change on water resources, Global Circulation Models, GCMs, are used; their main role is analyzing the current climate and projecting the future climate. Climate change scenarios developing from GCMs are the initial source of information to estimate plausible future climate. For transforming coarse resolution outputs of the GCMs into finer resolutions influenced by local variables, there is a need for reliable downscaling techniques in order to analyze climate changes in a region. The classical statistical methods run the model and generate the future climate just with considering the time variable. Multi-site daily rainfall and temperature time series are the primary inputs in most hydrological analyses such as rainfall-runoff modeling. Water resource management is directly influenced by the spatial and temporal variation of rainfall and temperature. Therefore, spatial-temporal modeling of daily rainfall or temperature including climate change effects is required for sustainable planning of water resources. Results and Discussion: For the first time, in this study by ASD model (Automated regression-based Statistical Downscaling tool) developed by M. Hessami et al., multi-site downscaling of temperature and precipitation was done with CGCM3.1A2 outputs and two synoptic stations (Rasht and Bandar Anzali) simultaneously by considering the correlations of multiple sites. The model can process conditionally on the occurrence of precipitation or unconditionally for temperature. Hence, the modeling of daily precipitation involves two steps: one step, precipitation occurrence and the other step precipitation amounts and the modeling of daily temperature is performed in one step. The choice of predictor variables is one of the most influential steps in the development of statistical downscaling scheme because the decision largely determines the character of the downscaling results. It is essential to remember that predictors relevant to the local predict and should be adequately reproduced by the host climate model at the spatial scales used to condition the downs