Statistical Downscaling of Climate Data To Estimate Streamflow in a Semi-Arid Catchment

Abstract

Linear and non-linear statistical 'downscaling' study is done to relate large-scale cli-mate information from a general circulation model (GCM) to local-scale river flows in west Iran. This study aims to investigate and evaluate the more promising downscaling techniques, and provides a through inter comparison study using the Karkheh catch-5 ment as an experimental site in a semi arid region for the years of 2040 to 2069. A hybrid conceptual hydrological model was used in conjunction with modeled out-comes from a General Circulation Model (GCM), HadCM3, along with two downscal-ing techniques, Statistical Downscaling Model (SDSM) and Artificial Neural Network (ANN), to determine how future streamflow may change in a semi arid catchment. 10 The results show that the choice of a downscaling algorithm having a significant im-pact on the streamflow estimations for a semi-arid catchment, which are mainly, influ-enced, respectively, by atmospheric precipitation and temperature projections. Accord-ing to the SDSM and ANN projections, daily temperature will increase up to +0.58 • (+3.90 %) and +0.48 • (+3.48 %) and daily precipitation will decrease up to −0.1mm 15 (−2.56 %) and −0.4 mm (−2.82 %) respectively. Moreover streamflow changes corre-sponding to downscaled future projections presented a reduction in mean annual flow of −3.7 m 3 s −1 and −9.47 m 3 s −1 using SDSM and ANN outputs respectively. The re-sults suggest a significant decrease of streamflow in both downscaling projections, par-ticularly in winter. The discussion considers the performance of each statistical method 20 for downscaling future flow at catchment scale as well as the relationship between atmospheric processes and flow variability and changes.