Abstract
River flows would be influenced greatly by climate change, which may cause further stress on water resources management by altering the quantity and distribution of runoff. In this paper, taking the Hanjiang River basin for instance, projections of precipitation and temperature are generated from two GCMs under RCP8.5 scenario, an extreme condition. Then the outputs are statistically downscaled and corrected by the daily bias correction method, a hybrid method of combining the daily translation and the local intensity scaling method. The VIC distributed hydrological model is used for the runoff simulation. Results show that the projections of two GCMs consistent with each other. There is a general increase in the annual mean precipitation and temperature in the Hanjiang River Basin in the future period (2021-2099), and the annual mean runoff of the Danjiangkou reservoir increases significantly compared with historical period (1980-2010). However, the annual runoff variability would increase the flood control pressure in wet season, aggravate the conflict between power generation and water supply in dry season despite increasing the water supply capacity in storage season.
Highlights
For the local intensity scaling method (LOCI) method: (1) A wet-day threshold is determined from the daily General Circulation Models (GCMs) precipitation for a specific month, so that the threshold exceedance equals the observed series; (2) A scaling factor is calculated to ensure that the mean of the observed precipitation is equal to that of GCM precipitation at the reference period for each month; and (3) The monthly thresholds and factors determined in the reference climate are used to adjust monthly precipitation for the future period [6]
This study investigated and assessed the impact of water resources management under extreme climate change
Projections of precipitation and temperature from two GCMs under RCP8.5 extreme scenario are statistically downscaled by the daily bias correction method
Summary
Recent decades have witnessed an increase in global average temperature, as documented by the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPPC) in 2013, the changes in climate system would substantially affect global hydrological cycle by the end of the 21st century, which may impose a significant impact on river flows and intensify the pressure on water resources management by altering the temporal and spatial distribution of the water availability [1]. To assess climate change impacts on hydrology, General Circulation Models (GCMs) are used to simulate the present climate and to project the future climate change, results are downscaled by dynamical or statistical downscaling methods for the resolution of GCM outputs are often too coarse and biased, and in final the corrected meteorological elements are driven by hydrological models for impact assessment. Many research groups have done lots of investigations and have proposed many different methods and models to improve the accuracy because of the existence of various sources of uncertainty, especially the uncertainty related to GCM and downscaling technique [2]. It is crucial to choose suitable GCMs and downscaling techniques
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