2020
DOI: 10.5194/hess-2020-25
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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… Show more

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Cited by 2 publications
(2 citation statements)
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“…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.…”
Section: Bias Correction Of Hydrological Datamentioning
confidence: 99%
See 1 more Smart Citation
“…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.…”
Section: Bias Correction Of Hydrological Datamentioning
confidence: 99%
“…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).…”
Section: Bias Correction Of Hydrological Datamentioning
confidence: 99%