Impact of Climate Change on Reservoir Flood Control in the Upstream Area of the Beijiang River Basin, South China

Wu, Chuanhao; Huang, Guoru; Yu, Haoping; Chen, Zhijing; Ma, Jingguang

doi:10.1175/jhm-d-13-0181.1

Cited by 45 publications

(34 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the data provided by GCMs, numerous studies have investigated the effects of climate change on regional floods over the world, including in Europe (Feyen et al, 2012), Germany (Huang et al, 2013), Bangladesh (Mirza et al, 2003), Britain (Kay and Jones, 2012), and China (e.g. Liu et al, 2013;Wu et al, 2014b;Xiao et al, 2013;Xu et al, 2013).…”

Section: H Wu Et Al: Prediction Of Extreme Floods Based On Cmip5mentioning

confidence: 99%

Prediction of extreme floods based on CMIP5 climate models: a case study in the Beijiang River basin, South China

Huang

2015

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

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 . 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 NashSutcliffe 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 .

show abstract

Section: H Wu Et Al: Prediction Of Extreme Floods Based On Cmip5mentioning

confidence: 99%

Prediction of extreme floods based on CMIP5 climate models: a case study in the Beijiang River basin, South China

Huang

2015

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…This highlights the impact of potential 5 misleading conclusions if only one climate model were to be used for the impact assessments. The large uncertainty driven by the GCMs in relation to the hydrological impacts of climate change has been reported in many previous studies (Kay et al, 2009;Prudhomme and Davies, 2009;Chen et al, 2011;Teng et al, 2012;Wu et al, 2014. It is worth noting that although the projected ranges of P and PET show large variability in various GCMs, most project a consistent change (i.e.…”

Section: Uncertaintiesmentioning

confidence: 73%

“…There is a great deal of previous work exploring the impact of climate variations on R, with the motivation stemming from the region's vast resources (Christensen et al, 2004;Guo et al, 2009, Piao et al, 2010Chen et 10 al., 2012;Harding et al, 2012;Xu et al, 2013b), dangers of flooding (Kay et al, 2006(Kay et al, , 2009(Kay et al, , 2012Raff et al, 2009;Xiao et al, 2013;Wang et al, 2013;Smith et al, 2014;Wu et al, 2014, and agricultural water uses (Vano et al, 2010). The most common practices in these previous studies are to use the hydrological models driven by the output from Global Climate Models (GCMs) to simulate the hydrological process (e.g., R) under future climate change scenarios.…”

Section: Introductionmentioning

confidence: 99%

Responses of runoff to historical and future climate variability over China

Wu¹,

Hu²,

Huang³

et al. 2017

Preprint

Self Cite

View full text Add to dashboard Cite

China has suffered some of the effects of global warming, and one of the potential implications of climate warming is the 10 alteration of the temporal-spatial patterns of water resources. Based on the long-term water budget data and climate projections from 28 Global Climate Models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5), this study investigated the responses of runoff (R) to historical and future climate variability in China at both grid and catchment scales using the Budyko-based elasticity method. Results show that there is a large spatial variation in precipitation (P) elasticity (from 1.2 to 3.3) and potential evaporation (PET) elasticity (from -2.3 to -0.2) across China. The P 15 elasticity is larger in northeast and western China than in southern China, while the opposite occurs for PET elasticity. The catchment properties elasticity of R appears to have a strong non-linear relationship with the mean annual aridity index and tends to be more significant in more arid regions. For the period 1960-2012, the climate contribution to R ranges from -2.4 % a -1 to 3.3 % a -1 across China, with the negative contribution in the North China plain and the positive contribution in western China and some parts of the southwest. The results of climate projections indicate that although there is large uncertainty 20 involved in the 28 GCMs, most project a consistent change in P (or PET) in China at the annual scale. For the period 2071-2100, the mean annual P will likely increase in most parts of China, especially the western regions, while the mean annual PET will likely increase in all of China, particularly the southern regions. Furthermore, greater increases are projected for higher emission scenarios. Overall, due to climate change, the arid regions and humid regions of China will likely become wetter and drier in the period 2071-2100, respectively (relative to the baseline 1971-2000).25

show abstract

“…This highlights the impact of potential misleading conclusions if only one climate model were to be used for the impact assessments. The large uncertainty driven by the GCMs in relation to the hydrological impacts of climate change has been reported in many previous studies (Kay et al, 2009;Prudhomme and Davies, 2009;Chen et al, 2011;Teng et al, 2012;Liu et al, 2013;Wu et al, 2014. It is worth noting that although the projected ranges of P and PET show large variability in various GCMs, most project a consistent change (i.e.…”

Section: Uncertaintiesmentioning

confidence: 74%

“…There is a great deal of previous work exploring the impact of climate variations on R, with the motivation stemming from the region's vast resources (Christensen et al, 2004;Guo et al, 2009;Piao et al, 2010;Chen et al, 2012;Harding et al, 2012;Wang et al, 2012; Y. P. , dangers of flooding (Kay et al, 2006, Kay and Jones, 2012Raff et al, 2009;Liu et al, 2013;Xiao et al, 2013;Wang et al, 2013;Smith et al, 2014;Wu et al, 2014, and agricultural water uses (Vano et al, 2010). The most common practices in these previous studies are to use the hydrological models driven by the output from global climate models (GCMs) to simulate the hydrological process (e.g.…”

Section: Introductionmentioning

confidence: 99%

Responses of runoff to historical and future climate variability over China

Huang

et al. 2018

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. China has suffered some of the effects of global warming, and one of the potential implications of climate warming is the alteration of the temporal-spatial patterns of water resources. Based on the long-term water budget data and climate projections from 28 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5), this study investigated the responses of runoff (R) to historical and future climate variability in China at both grid and catchment scales using the Budyko-based elasticity method. Results show that there is a large spatial variation in precipitation (P ) elasticity (from 1.1 to 3.2) and potential evaporation (PET) elasticity (from −2.2 to −0.1) across China. The P elasticity is larger in northeastern and western China than in southern China, while the opposite occurs for PET elasticity. The catchment properties' elasticity of R appears to have a strong non-linear relationship with the mean annual aridity index and tends to be more significant in more arid regions. For the period 1960-2008, the climate contribution to R ranges from −2.4 to 3.6 % yr −1 across China, with the negative contribution in north-eastern China and the positive contribution in western China and some parts of the south-west. The results of climate projections indicate that although there is large uncertainty involved in the 28 GCMs, most project a consistent change in P (or PET) in China at the annual scale. For the period 2071-2100, the mean annual P is projected to increase in most parts of China, especially the western regions, while the mean annual PET is projected to increase in all of China, particularly the southern regions. Furthermore, greater increases are projected for higher emission scenarios.

show abstract

Impact of Climate Change on Reservoir Flood Control in the Upstream Area of the Beijiang River Basin, South China

Cited by 45 publications

References 58 publications

Prediction of extreme floods based on CMIP5 climate models: a case study in the Beijiang River basin, South China

Prediction of extreme floods based on CMIP5 climate models: a case study in the Beijiang River basin, South China

Responses of runoff to historical and future climate variability over China

Responses of runoff to historical and future climate variability over China

Contact Info

Product

Resources

About