Determination the optimal prediction scenario for the future precipitation rates in the Al-Najaf Governorate in Iraq

Abstract

The world suffers from the effect of climate change which complicates the prediction of future events. Precipitation, temperature, andgeological properties of soil such as drought and relative humidity are among the most important transactions affected by climate change. Statistical downscaling models are recently used to predict future events of precipitation and temperature. In the current study, astatistical downscaling model is built for Al-Najaf Governorate, Iraq, depending on the daily record of observed rainfall data from 2010 to 2020 with the aim of predicting the future rainfall's projections of ​​the decades from 2030 to 2100. Identification the best, ideal and precise scenario that fits the study area to predict the future rainfall values is done. Calibration the statistical downscaling model of Al-Najaf Governorate has given good results comparable to the observations. Overall the three scenariosCanESM2_rcp2.6, CanESM2_rcp4.5 and CanESM2_rcp8.5, CanESM2_rcp8.5 scenario results the best values ​​of standard deviations. Therefore, this scenario is qualified to be the best scenario for anticipating the future rainfall's events. Results indicate a slight increase in rainfall for the decades 2030-2070. In Autumn season (December 2080), the rainfall will increase by 18mm, while rainfall records a 23mm increment in Winter season (Januarys 2080, 2090, and 2100). The implicit years of the predicted decades in Winter and Autumn seasons, rainfall ranges (10-18)mm (Autumn) and (15-23)mm (Winter).