Future predictions of precipitation and temperature in Iraq using the statistical downscaling model

Abstract

Iraq is facing a critical water crisis that has ever experienced. This necessitates a wise management for present and future water resources. Future water availability is mainly influenced by the impacts of climate changes and to dams in Turkey, Syria, Iran, and northern Iraq. The meteorological parameters obtained from global circulation models (GCM) cannot be used to assess the impacts of future climate changes on the water resources availability at catchment scale. The dynamical or statistical downscaling is employed to transfer the coarse resolution of GCM into a finer. In this study, the future maximum/minimum temperature and precipitation for 12 stations of Iraq were projected for three future periods 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2100) from the Canadian GCM model (CanESM2) under different scenarios (RCP2.5, RCP4.5, and RCP8.5) using statistical downscaling model (SDSM). The model was set up utilizing partial correlation and significance level of 0.05 between National Center for Environmental Prediction/Atmospheric Research (NCEP/NCAR) parameters as predictors and the local station data as predictand. Subsequently, the model was calibrated and validated against daily data by using 70% of the data for calibration and the remaining 30% for the validation. Thereafter, the calibrated model was applied to downscale future scenarios of CanESM2 predictors. The study proved a satisfactory performance of SDSM for simulation of maximum-minimum temperatures and precipitation for future periods. All considered stations and the scenarios were consistent in predicting increasing trend of maximum-minimum temperature and decreasing trend of precipitations. RCP8.5 scenario shows the worst trend of precipitation and temperature.