Statistical downscaling of climate data to estimate streamflow in a semi-arid catchment

Abstract

Abstract. Linear and non-linear statistical 'downscaling' study is done to relate large-scale climate 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 catchment 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 outcomes from a General Circulation Model (GCM), HadCM3, along with two downscaling techniques, Statistical Downscaling Model (SDSM) and Artificial Neural Network (ANN), to determine how future streamflow may change in a semi arid catchment. The results show that the choice of a downscaling algorithm having a significant impact on the streamflow estimations for a semi-arid catchment, which are mainly, influenced, respectively, by atmospheric precipitation and temperature projections. According 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 (−2.56%) and −0.4 mm (−2.82%) respectively. Moreover streamflow changes corresponding to downscaled future projections presented a reduction in mean annual flow of −3.7 m3 s−1 and −9.47 m3 s−1 using SDSM and ANN outputs respectively. The results suggest a significant decrease of streamflow in both downscaling projections, particularly in winter. The discussion considers the performance of each statistical method for downscaling future flow at catchment scale as well as the relationship between atmospheric processes and flow variability and changes.