Abstract
Abstract. Projections of streamflow, particularly of extreme flows under climate change, are essential for future water resources management and the development of adaptation strategies to floods and droughts. However, these projections are subject to uncertainties originating from different sources. In this study, we explored the possible changes in future streamflow, particularly for high and low flows, under climate change in the Qu River basin, eastern China. ANOVA (analysis of variance) was employed to quantify the contribution of different uncertainty sources from RCPs (representative concentration pathways), GCMs (global climate models) and internal climate variability, using an ensemble of 4 RCP scenarios, 9 GCMs and 1000 simulated realizations of each model–scenario combination by SDRM-MCREM (a stochastic daily rainfall model coupling a Markov chain model with a rainfall event model). The results show that annual mean flow and high flows are projected to increase and that low flows will probably decrease in 2041–2070 (2050s) and 2071–2100 (2080s) relative to the historical period of 1971–2000, suggesting a higher risk of floods and droughts in the future in the Qu River basin, especially for the late 21st century. Uncertainty in mean flows is mostly attributed to GCM uncertainty. For high flows and low flows, internal climate variability and GCM uncertainty are two major uncertainty sources for the 2050s and 2080s, while for the 2080s, the effect of RCP uncertainty becomes more pronounced, particularly for low flows. The findings in this study can help water managers to become more knowledgeable about and get a better understanding of streamflow projections and support decision making regarding adaptations to a changing climate under uncertainty in the Qu River basin.
Highlights
Climate change has been demonstrated to produce profound impacts on hydrological processes all over the world, with its effects lasting throughout the whole of the 21st century (Bosshard et al, 2013; Addor et al, 2014)
Considering that the distribution mapping (DM) method usually shows a comprehensive skill for mean bias correction, standard deviation, various frequency-based indices and even the correction of unobserved extreme values compared with other existing bias correction approaches like power transformation (PT), local intensity scaling (LOCI), linear scaling (LS), delta change (DC) and quantile mapping (QM; Fang et al, 2015; Teutschbein and Seibert, 2012; Ji et al, 2020), the DM method was selected to correct GCM-simulated climate variables based on observations in this study
All rainfall and temperature bias corrections significantly improve the raw GCM simulations, and currently the bias-corrected GCM simulations are very close to the observations, which is indicated by the better matching empirical cumulative distribution functions (ECDFs) and monthly means between observations and bias-corrected GCM simulations
Summary
Climate change has been demonstrated to produce profound impacts on hydrological processes all over the world, with its effects lasting throughout the whole of the 21st century (Bosshard et al, 2013; Addor et al, 2014). Future streamflow projections offer a valuable basis for the assessment of various hydrological extremes including floods and droughts (Giuntoli et al, 2018), which is beneficial for decision makers to plan effective countermeasures for a changing climate (Addor et al, 2014). These climate change projections are usually subject to high uncertainty, making it difficult to identify robust adaptation strategies in the decision process (Whateley and Brown, 2016). Gao et al.: Assessment of extreme flows and uncertainty under climate change
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