Uncertainty analysis for propagation effects from statistical downscaling to hydrological modelingUncertainty analysis for propagation effects from statistical downscaling to hydrological modeling
To understand the water balance and environmental effects under climate change condition, hydrological models are always used to simulate the hydrological cycle and predict future scenarios by using global climate models (GCMs) outputs. Due to the mismatch of the spatial resolution problem, different downscaling techniques are usually applied to GCMs outputs to generate the high resolution data for fitting the data requirement of hydrological models. As it is known, hydrological modeling always suffers from a number of uncertainties and leads to inaccuracy and unreliability of prediction. Uncertainties associated with climate change have 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 quantify the propagation effects of uncertainties from statistical downscaling to hydrological modeling for improving the accuracy and reliability of hydrological prediction. A real-world 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 for uncertainties evaluation during hydrological modeling when the GCM outputs are used as inputs of a distributed hydrological model.