Quantification of Uncertainty Propagation Effects during Statistical Downscaling of Precipitation and Temperature to Hydrological Modeling

To understand the water balance and other environmental impacts under climate change condition, hydrological models are used to simulate the hydrological cycle and predict future scenarios by using general circulation models (GCMs) outputs. Due to the mismatch of the spatial resolution, different downscaling techniques are usually applied to GCMs outputs to generate higher resolution data for the use with the hydrological models. It is known that there are many uncertainties with hydrological models which lead to inaccuracy and unreliability of the predictions. The uncertainty associated with climate change has been described as irreducible and persistent, and downscaling GCM outputs using downscaling methods also lead to considerable uncertainties. The purpose of this study is to propose a method to quantify the propagation effects of uncertainties from statistical downscaling to hydrological modeling. A case study has been provided in this study to demonstrate the feasibility of the proposed method. Statistical downscaling model (SDSM) was applied to downscale H3A2a (A2 emission scenario in Hadley Centre Coupled Model 3) outputs, and the downscaled results were used as inputs to a distributed hydrological model - the soil and water assessment tool (SWAT). The surface runoff prediction has been made for 2016 similar to 2020 by using downscaled precipitation and temperature. The uncertainty associated with statistical downscaling has been quantified through the evaluation of surface runoff simulation from the application of the hydrological modeling study.