Uncertainty Impacts of Climate Change and Downscaling Methods on Future Runoff Projections in the Biliu River Basin

Abstract

This paper assesses the uncertainties in the projected future runoff resulting from climate change and downscaling methods in the Biliu River basin (Liaoning province, Northeast China). One widely used hydrological model SWAT, 11 Global Climate Models (GCMs), two statistical downscaling methods, four dynamical downscaling datasets, and two Representative Concentration Pathways (RCP4.5 and RCP8.5) are applied to construct 22 scenarios to project runoff. Hydrology variables in historical and future periods are compared to investigate their variations, and the uncertainties associated with climate change and downscaling methods are also analyzed. The results show that future temperatures will increase under all scenarios and will increase more under RCP8.5 than RCP4.5, while future precipitation will increase under 16 scenarios. Future runoff tends to decrease under 13 out of the 22 scenarios. We also found that the mean runoff changes ranging from −38.38% to 33.98%. Future monthly runoff increases in May, June, September, and October and decreases in all the other months. Different downscaling methods have little impact on the lower envelope of runoff, and they mainly impact the upper envelope of the runoff. The impact of climate change can be regarded as the main source of the runoff uncertainty during the flood period (from May to September), while the impact of downscaling methods can be regarded as the main source during the non-flood season (from October to April). This study separated the uncertainty impact of different factors, and the results could provide very important information for water resource management.

Evaluating the impact of climate change on extreme temperature and precipitation events over the Kashmir Himalaya

“…Historical and future simulated data on the predictors required for the SDSM were derived from the second-generation Canadian Earth System Model (CanESM2) developed by the Canadian center for climate modeling and analysis (CCCma) (von Salzen et al 2013;Khadka and Pathak 2016;Huang et al 2016;Zhu et al 2019). The model is a part of the fifth Coupled Model Inter Comparison Project (CMIP5) and is currently the only CMIP5 model for which readyto-use SDSM predictors are available.…”

“…Statistical downscaling on the other hand relies upon the empirical relationships between the observed data and the large-scale predictors and has the advantages of being simple in application and also computationally less demanding (Samadi et al 2013). Among the different Statistical downscaling approaches, Statistical Downscaling Model (SDSM) is a popular method used globally for climate change assessment and impact studies (Wilby et al 2002;Gagnon et al 2005;Chu et al 2010;Huang et al 2011;Mahmood and Babel 2014;Zhu et al 2019). Numerous comparative studies have pointed out the ability of SDSM to perform better than other downscaling techniques in simulating the current and future climate variability with confidence (Coulibaly et al 2005;Khan et al 2006;Chen et al2011;Teutschbein et al2011;Samadi et al2013).…”

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“…R e values are −5.38% in the validation period. More details about the calibration and validation were introduced in Zhu et al [54].…”

New Zealand River Hydrology under Late 21st Century Climate Change

“…In contrast with the global climate modelling, the downscaling and bias-correction steps used in the driving climate data did not use alternative models or methods. The choice of downscaling can have an impact on hydrological projections [65], particularly if the method does not account for orographic effects [66], and the use of just a single climate model in the dynamic downscaling here is a particular weakness of the climate projections [23,67]. Similarly, the global study by Iizumi, et al [68], comparing two bias-correction methods, showed that while the choice of methods contributed little to the uncertainties in temperature projections, the choice of methods was a major contributor to precipitation uncertainties.…”