Abstract
Abstract. The occurrence of climate warming is unequivocal, and is expected to be experienced through increases in the magnitude and frequency of extreme events, including flooding. This paper presents an analysis of the implications of climate change on the future flood hazard in the Beijiang River basin in South China, using a variable infiltration capacity (VIC) model. Uncertainty is considered by employing five global climate models (GCMs), three emission scenarios (representative concentration pathway (RCP) 2.6, RCP4.5, and RCP8.5), 10 downscaling simulations for each emission scenario, and two stages of future periods (2020–2050, 2050–2080). Credibility of the projected changes in floods is described using an uncertainty expression approach, as recommended by the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). The results suggest that the VIC model shows a good performance in simulating extreme floods, with a daily runoff Nash–Sutcliffe efficiency coefficient (NSE) of 0.91. The GCMs and emission scenarios are a large source of uncertainty in predictions of future floods over the study region, although the overall uncertainty range for changes in historical extreme precipitation and flood magnitudes are well represented by the five GCMs. During the periods 2020–2050 and 2050–2080, annual maximum 1-day discharges (AMX1d) and annual maximum 7-day flood volumes (AMX7fv) are expected to show very similar trends, with the largest possibility of increasing trends occurring under the RCP2.6 scenario, and the smallest possibility of increasing trends under the RCP4.5 scenario. The projected ranges of AMX1d and AMX7fv show relatively large variability under different future scenarios in the five GCMs, but most project an increase during the two future periods (relative to the baseline period 1970–2000).
Highlights
Recent research indicates that extreme precipitation is very likely to become more intense and more frequent over most of the mid-latitude land masses and wet tropical regions (IPCC, 2013)
Dynamical downscaling is performed through regional climate models (RCMs) or limitedarea models (LAMs) (Fowler et al, 2007), whereas statistical downscaling defines the empirical relationships between large-scale variable fields
These results indicate that the model has a good performance in simulating both daily streamflow and extreme floods in the selected catchment, and can be used to estimate the potential impacts of climate change on floods
Summary
Recent research indicates that extreme precipitation is very likely (greater than 90 % probability) to become more intense and more frequent over most of the mid-latitude land masses and wet tropical regions (IPCC, 2013). Increases in extreme precipitation are expected to trigger floods, and the associated impacts will cause probable loss of life and economic damage. The most useful tool for investigating the impacts of climate change on floods is a hydrological model driven by outputs from global climate models (GCMs). GCMs are considered to be the most essential and feasible tools for use in supplying useful climate information on global or large scales. Downscaling techniques (e.g. dynamical downscaling and statistical downscaling) are normally used to link coarse resolution GCM outputs with catchment-scale climatic variables (Sachindra et al, 2014b). Dynamical downscaling is performed through regional climate models (RCMs) or limitedarea models (LAMs) (Fowler et al, 2007), whereas statistical downscaling defines the empirical relationships between large-scale variable fields (e.g. climate model outputs)
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