Abstract. The outputs of four global climate models (GFDL-ESM2M, HadGEM2-ES,
IPSL-CM5A-LR and MIROC5), which were statistically downscaled and bias
corrected, were used to drive four hydrological models (Hydrologiska Byråns, HBV; Soil and Water Assessment Tool, SWAT; Soil and Water Integrated Model, SWIM; and
Variable Infiltration Capacity, VIC) to simulate the daily discharge at the Cuntan hydrological station in
the upper Yangtze River from 1861 to 2299. As the performances of
hydrological models in various climate conditions could be different, the
models were first calibrated in the period from 1979 to 1990. Then, the
models were validated in the comparatively wet period, 1967–1978, and in
the comparatively dry period, 1991–2002. A multi-objective automatic
calibration programme using a univariate search technique was applied to
find the optimal parameter set for each of the four hydrological models. The Nash–Sutcliffe efficiency (NSE) of daily discharge and the weighted least-squares function (WLS) of extreme discharge events, represented by high flow (Q10) and low flow (Q90), were included in the objective functions of the
parameterization process. In addition, the simulated evapotranspiration
results were compared with the GLEAM evapotranspiration data for the upper
Yangtze River basin. For evaluating the performances of the hydrological
models, the NSE, modified Kling–Gupta efficiency (KGE), ratio of the root-mean-square error to the standard deviation of the measured data (RSR) and
Pearson's correlation coefficient (r) were used. The four hydrological
models reach satisfactory simulation results in both the calibration and
validation periods. In this study, the daily discharge is simulated for the
upper Yangtze River under the preindustrial control (piControl) scenario
without anthropogenic climate change from 1861 to 2299 and for the
historical period 1861–2005 and for 2006 to 2299 under the RCP2.6, RCP4.5,
RCP6.0 and RCP8.5 scenarios. The long-term daily discharge dataset can be
used in the international context and water management, e.g. in the
framework of Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) by
providing clues to what extent human-induced climate change could impact
streamflow and streamflow trend in the future. The datasets are available at:
https://doi.org/10.4121/uuid:8658b22a-8f98-4043-9f8f-d77684d58cbc (Gao et
al., 2019).
A 439-year simulated daily discharge dataset (1861–2299) for the upper Yangtze River, China
