DOWNSCALING CLIMATIC VARIABLES AT A RIVER BASIN SCALE: STATISTICAL VALIDATION AND ENSEMBLE PROJECTION UNDER CLIMATE CHANGE SCENARIOS

Abstract

The application of climatic statistical downscaling in arid and topographically complex river basins remains relatively lacking. To address this gap, climatic variables derived from a GCM ensemble were downscaled from a grid resolution of 2.5⁰x2.5⁰ down to the station level. For this purpose, a combination of multiple linear and logistic regressions were developed, calibrated and validated with regards to their predictions of monthly precipitation and daily temperature at 41 stations in the Jordan River Basin. Seasonal standardized predictors were selected using a backward stepwise regression. The validated models were used to examine future scenarios based on GCM simulations under two Representative Concentration Pathways (RCP4.5 and RCP8.5) for the period 2006-2050. The results showed a cumulative near-surface air temperature increase of 1.54 ⁰C (0.02 and 0.09 oC/year) and 2.11 ⁰C (0.034 and 0.09 oC/year) and a cumulative precipitation decrease of 100 mm (0.26 and 7.17 mm/year) and 135mm (1.10 and 9.53 mm/year) under the RCP4.5 and RCP8.5, respectively or the equivalent reduction of 10 to 15% in water availability by 2050. This pattern will inevitably add stress on water resources, increasing management challenges in the semi-arid to arid regions of the basin. Moreover, the current application highlights the potential of adopting regression-based models to downscale GCM predictions and inform future water resources management in poorly monitored arid regions at the river basin scale.