عنوان مقاله [English]
Introduction: Uncertainty estimation of climate change impacts has been given a lot of attention in the recent literature, However, uncertainty in downscaling methods have been given less attention. Today many studies have been done about the future impact of climate change on human life and water resources. Urban development, water conflicts, and Green House Gases increasing will intensify this event in future and will alter rivers flow. Basin catchment has faced to flow recession and also runoff decreasing in few last decades. At this field the climate change effects will intensify this conditions in future decades too. The first step of climate change impacts studies is the projection of future climate variables (e.g precipitation and temperature). GCMS models and their outputs are useful tools for this projection. The main problem is the mismatch of spatial scale between the scale of global climate models and the resolution needed for impacts assessments.
Materials and Methods: The Gharesou River Basin is located in the west of Iran. Its area is approximately equal to 5793km2, and the maximum and minimum of its heights are 1237 and 3350 m, respectively. The average of annual rainfall varies from 300 to 800mm. This study focuses on various climate models from IPCC fourth and fifth reports and has been used two downscaling methods including the statistical and proportional downscaling methods and also scenarios and different climate models for considering different uncertainty. The new scenarios as Representative Concentration Pathways (RCPs) of greenhouse gasses have been used in fifth assessment reports (AR5) of IPCC. The Representative Concentration Pathways describe four different 21st-century pathways of greenhouse gas (GHG) emissions and atmospheric concentrations, air pollutant emissions and land use. The RCPs represent the range of GHG emissions. Different kinds of downscaling method include 1) Proportional downscaling that is adding coarse-scale climate changes to higher resolution observations (the delta approach); 2) Statistical method (eg SDSM model; CLIGEN; GEM; LARS-WG and etc); 3) Dynamical method that is application of regional climate model using global climate model boundary conditions (e.g, RegCM3; MM5 and PRECIS). statistical downscaling method processes establish relating large scale climate features (e.g., 500 MB heights), predictors, to local climate (e.g, daily, monthly temperature at a point), predictands. The SDSM software reduces the task of statistically downscaling daily weather series into seven discrete processes that are consist of quality control and data transformation; screening of predictor variables; model calibration; weather generation (observed predictors); statistical analyses; graphing model output and scenario generation (climate model predictors). HEC-HMS (Hydrologic Modeling System) has been designed by HEC (Hydrologic Engineering Center) for simulation of precipitation-runoff processes in a drainage basin. The HEC-HMS simulation methods represent - Watershed precipitation and evaporation: These describe the spatial and temporal distribution of rainfall on and evaporation from a watershed. - Runoff volume: These address questions about the volume of precipitation that falls on the watershed: How much infiltrates on pervious surfaces? How much runoff of the impervious surfaces? When does it run off? - Direct runoff: including overland flow and interflow. These methods describe what happens as water that has not infiltrated or been stored on the watershed moves over or just beneath the watershed surface. Baseflow: simulate the slow subsurface drainage of water from a hydrologic system into the watershed’s channels.- Channel flow: These so-called routing methods simulate one-dimensional open channel flow, thus predicting time series of downstream flow, stage, or velocity, given upstream hydrographs. HEC-HMS includes several models for calculation of cumulative precipitation losses but only the SMA module is continuous (a module that simulates the losses for both wet and dry weather conditions). Other loss models are event based.
Results and Discussion: The results of criteria and models weighting show that CANESM2 and HADCM3 are better than other models for future temperature and precipitation projection for statistical downscaling and HADCM3 for future precipitation and HADGEM for future temperature assessment for Proportional downscaling. According to various scenarios, future temperature and precipitation projection (2040-2069 period for the statistical and 2040-2052 period for Proportional downscaling) have downscaled and have given to HEC-HMS model for future flow projection. Already the rainfall-runoff model has calibrated and validated base on observed flow data in reference period that daily coefficient of determine was 0.7 for calibrated period and 0.6 for validated period. Finally, flow variation has investigated that Most of GCMS represent increases in winter flows and reductions in other season flows.