Assessment of extreme flows and uncertainty under climate change: disentangling the contribution of RCPs, GCMs and internal climate variability

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.

Climate change impact study on 100-year floods of selected Slovak catchments

“…The parameters of the hydrological models, which were calibrated on the current climate conditions, are then used for simulations of the period of the assumed changed climate with the bias corrected forecasted meteorological data. Despite great efforts to adjust the outputs of RCM models by bias correction and downscaling, several meteorological variables from RCM models are still not suitable for their use in hydrological impact studies (Teutschbein and Seibert, 2012;Dakhlaoui et al, 2019;Gao et al, 2020). This can be resolved using e.g.…”

“…This can be resolved using e.g. a multiensemble approach, which uses an ensemble of climatic outputs from RCM models (precipitation and temperatures), downscaled so that when used in the hydrological model they correspond to the measured hydrological data as much as possible (usually comparing average monthly flows or peak flow exceedance curves according to the type of analysis) (Teutschbein and Seibert, 2010;Hakala et al, 2019;Gao et al, 2020). Another solution is to use an ensemble of climate and hydrological models (Donnelly et al, 2017;Hakala et al, 2019) without further correction of previously corrected climate data (IMPACT2C, 2015).…”