Uncertainty analysis of the statistically downscaling Precipitation and Temptation on the Qorantalar

One consequence of a significant increase in the man-made greenhouse gases in recent decades has been a global rise in air temperature with the commensurate rise in the atmospheric heat energy, which in turn affects the hydrologic cycle. Thus a drastic change in the amount, distribution and timing of the hydrologic events is logical. Therefore, preparation for the future water-related events dictates an implication of detailed studies on the prediction of the future rise in temperature and the resultant change in precipitation. The Atmosphere-Ocean General Circulation Model (AOGCM) is considered to be the most reliable software for the predicting the weather-related events. The statistical downscaling method (SDSM) and the Artificial Neural Networks (ANN) were tested to remove the uncertainty related to the AOGCM. Of a few software used for downscaling, SDSM was proved to be the most reliable for predicting the 2011-2040 changes in air temperatures and precipitation under the A2, B2 scenarios of the HadCM3 for the Qorantalar Watershed. Results indicated that there would be an increase of 7% and 6% in the precipitation amount, and 0.34 and 0.86 degrees Celsius in temperature using the A2 and B2 scenarios, respectively.