Quantifying uncertainty in the impacts of climate change on river discharge in sub-catchments of the Yangtze and Yellow River Basins, China

Abstract

Abstract. Quantitative evaluations of the impacts of climate change on water resources are primarily constrained by uncertainty in climate projections from GCMs. In this study we assess uncertainty in the impacts of climate change on river discharge in two catchments of the Yangtze and Yellow River Basins that feature contrasting climate regimes (humid and semi-arid). Specifically we quantify uncertainty associated with GCM structure from a subset of CMIP3 AR4 GCMs (HadCM3, HadGEM1, CCSM3.0, IPSL, ECHAM5, CSIRO, CGCM3.1), SRES emissions scenarios (A1B, A2, B1, B2) and prescribed increases in global mean air temperature (1 • C to 6 • C). Climate projections, applied to semi-distributed hydrological models (SWAT 2005) in both catchments, indicate trends toward warmer and wetter conditions. For prescribed warming scenarios of 1 • C to 6 • C, linear increases in mean annual river discharge, relative to baseline , for the River Xiangxi and River Huangfuchuan are +9% and 11% per +1 • C respectively. Intra-annual changes include increases in flood (Q05) discharges for both rivers as well as a shift in the timing of flood discharges from summer to autumn and a rise (24 to 93%) in dry season (Q95) discharge for the River Xiangxi. Differences in projections of mean annual river discharge between SRES emission scenarios using HadCM3 are comparatively minor for the River Xiangxi (13 to 17% rise from baseline) but substantial (73 to 121%) for the River Huangfuchuan. With one minor exception of a slight (−2%) decrease in river discharge projected using HadGEM1 for the River Xiangxi, mean annual river Correspondence to: H. Xu (xuhm@cma.gov.cn) discharge is projected to increase in both catchments under both the SRES A1B emission scenario and 2 • rise in global mean air temperature using all AR4 GCMs on the CMIP3 subset. For the River Xiangxi, there is substantial uncertainty associated with GCM structure in the magnitude of the rise in flood (Q05) discharges (−1 to 41% under SRES A1B and −3 to 41% under 2 • global warming) and dry season (Q95) discharges (2 to 55% under SRES A1B and 2 to 39% under 2 • global warming). For the River Huangfuchuan, all GCMs project a rise in the Q05 flow but there is substantial uncertainty in the magnitude of this rise (7 to 70% under SRES A1B and 2 to 57% under 2 • global warming). Differences in the projected hydrological changes are associated with GCM structure in both catchments exceed uncertainty in emission scenarios. Critically, estimated uncertainty in projections of mean annual flows is less than that calculated for extreme (Q05, Q95) flows. The common approach of reporting of climate change impacts on river in terms of mean annual flows masks the magnitude of uncertainty in flows that are of most importance to water management.

Analysis of projected hydrological behavior of catchments based on signature indices

“…The FDC summarizes a catchment's ability to produce discharge values of different magnitudes, and is therefore strongly sensitive to the vertical distribution of soil moisture within a basin (Yilmaz et al, 2008). Additionally, a steep slope of the FDC indicates flashiness of the stream flow response to precipitation input whereas a flatter curve indicates a relatively damped response and a higher storage (Yadav et al, 2007).…”

“…This makes it necessary to include hydrologically-based metrics in model evaluation. These metrics provide dynamic aspects of the watershed system or hydrological model (Yadav et al, 2007;Zhang et al, 2008) and may therefore allow for a quantitative evaluation of hydrological behavior . Examples for hydrologically-based metrics are signature indices derived from flow duration curves (Yilmaz et al, 2008) or from distributions of runoff event coefficients (Merz et al, 2006;Ley et al, 2011).…”

The global-scale impacts of climate change: the QUEST-GSI project

“…One way to resolve this is to construct coherent spatially-variable, multi-sectoral stories from individual climate model scenarios (as in Arnell et al ( this issue a)), but in order to represent uncertainty it is necessary to build many stories. One major lesson learnt in the QUEST-GSI project from Water Arnell et al (2011), Gosling et al (2010Haddeland et al (2011);Arnell and Gosling (2013), Arnell and Gosling (this issue), Gosling and Arnell (this issue) Catchment-scale ), Arnell (2011), Hughes et al (2011, Kingston and Taylor (2010); Kingston et al (2011);Nobrega et al (2011);Singh et al (2010); Thorne (2011), Xu et al (2011 Agriculture and food Fraser et al (2008;; Osborne et al (2012); Simelton et al (2009;; Dawson et al (2014) Coastal zone Nicholls et al (2011);Brown et al (this issue) Terrestrial ecosystems Gottschalk et al (2012) Human health Lloyd et al (2011);Lloyd et al (2015) presentations to different audiences is that different audiences have different requirements so results need to be presented in a wide variety of ways. Some audiences are more concerned with the ranges of potential impacts and are not concerned with whether the extremes can occur at the same time; others are more concerned with synchronous impacts in different places or sectors.…”

Responses of runoff to historical and future climate variability over China

“…The impacts of climate change on water resources have been investigated in many regions of China, such as the Hanjiang 10 basin (Chen et al, 2007;Guo et al, 2009), the catchment of the Loess Plateau (Wang et al, 2013), the Qingjiang River basin (Chen et al, 2012), the Qiantang River basin (Xu et al, 2013b), the Songhuajiang River basin (Su et al, 2015), the southeastern Tibetan Plateau (Li et al, 2013b), the Pearl River basin (Yan et al, 2015), the Xin River basin (Zhang et al, 2016), the sub-catchments of the Yangtze and Yellow River basins (Xu et al, 2011), the Huang-Huai-Hai region (Lu et al, 2012), and ten major river basins in China . There is a large uncertainty involved in these impact studies,…”

“…The basin number is consistent with that given in Figure 1. Southeast Drainage;2,Pearl River;3,Yangtze River;4,Southwest Drainage;5,Huaihe River;6,Heilongjiang River;7,Liaohe River;8,Yellow River;9,Haihe River;10,Qiangtang River;11,Inner Mongolia River;12,Qinghai River;13,Hexi River; 14, Xinjiang River.…”