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- W3005698975 abstract "Abstract. Projections of streamflow, particularly of extreme flows under climate change are essential for future water resources management and development of adaptation strategies to floods and droughts. However, these projections are subject to uncertainties originating from different sources. In this study, we explore the possible changes in future streamflow, particularly for high and low flows, under climate change in the Qu River basin, East China. ANOVA (Analysis of Variance) is 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 four RCP scenarios, nine GCMs and 1,000 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 low flows will probably decrease in 2041–2070 (2050s) and 2071–2100 (2080s) relative to the historical period 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 is becoming more pronounced, particularly for low flows. The findings in this study can help water managers to get a better knowledge and understanding of streamflow projections and support decision making on adaptions to changing climate under uncertainty in the Qu River basin." @default.
- W3005698975 created "2020-02-24" @default.
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- W3005698975 date "2020-02-14" @default.
- W3005698975 modified "2023-09-23" @default.
- W3005698975 title "Assessment of extreme flows and uncertainty under climate change: disentangling the contribution of RCPs, GCMs and internal climate variability" @default.
- W3005698975 doi "https://doi.org/10.5194/hess-2020-25" @default.
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