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

2015 ◽  
Vol 19 (3) ◽  
pp. 1385-1399 ◽  
Author(s):  
C. H. Wu ◽  
G. R. Huang ◽  
H. J. Yu
Keyword(s):  
Climate Change ◽  
River Basin ◽  
South China ◽  
Climate Models ◽  
Annual Maximum ◽  
Emission Scenarios ◽  
Vic Model ◽  
Extreme Floods ◽  
Beijiang River ◽  
Increasing Trends

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).

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

2014 ◽  
Vol 11 (8) ◽  
pp. 9643-9669 ◽  
Author(s):  
C. H. Wu ◽  
G. R. Huang ◽  
H. J. Yu
Keyword(s):  
Climate Change ◽  
River Basin ◽  
South China ◽  
Climate Models ◽  
Annual Maximum ◽  
Emission Scenarios ◽  
Vic Model ◽  
Extreme Floods ◽  
Beijiang River ◽  
Increasing Trends

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 (RCP2.6, RCP4.5, and RCP8.5), ten 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 and 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 projected 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).

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

2014 ◽  
Vol 15 (6) ◽  
pp. 2203-2218 ◽  
Author(s):  
Chuanhao Wu ◽  
Guoru Huang ◽  
Haijun Yu ◽  
Zhijing Chen ◽  
Jingguang Ma
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Extreme Precipitation ◽  
Flood Control ◽  
Flood Frequency ◽  
Vic Model ◽  
Extreme Floods ◽  
Impact Of Climate Change ◽  
Beijiang River ◽  
The Impact

Abstract One of the potential impacts of global warming is likely to be experienced through changes in flood frequency and magnitude, which poses a potential threat to the downstream reservoir flood control system. In this paper, the downscaling results of the multimodel dataset from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5, respectively) were coupled with the Variable Infiltration Capacity (VIC) model to evaluate the impact of climate change on the Feilaixia reservoir flood control in the Beijiang River basin for the first time. Four emissions scenarios [A1B and representative concentration pathway (RCP) scenarios RCP2.6, RCP4.5, and RCP8.5] were chosen. Results indicate that annual distribution and interannual variability of temperature and precipitation are well simulated by the downscaling results of the CMIP3 and CMIP5 multimodel dataset. The VIC model, which performs reasonably well in simulating runoff processes with high model efficiency and low relative error, is suitable for the study area. Overall, annual maximum 1-day precipitation in 2020–50 would increase under all the scenarios (relative to the baseline period 1970–2000). However, the spatial distribution patterns of changes in projected extreme precipitation are uneven under different scenarios. Extreme precipitation is most closely associated with extreme floods in the study area. There is a gradual increase in extreme floods in 2020–50 under any of the different emission scenarios. The increases in 500-yr return period daily discharge of the Feilaixia reservoir have been found to be from 4.35% to 9.18% in 2020–50. The reservoir would be likely to undergo more flooding in 2020–50.

scholarly journals Impact of climate change on monthly streamflow in the Verde River Basin using two hydrological models

2021 ◽  
Vol 16 (4) ◽  
pp. 1
Author(s):  
Vinícius Siqueira Oliveira Carvalho ◽  
Lívia Alves Alvarenga ◽  
Conceição De Maria Marques de Oliveira ◽  
Javier Tomasella ◽  
Alberto Colombo ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Models ◽  
Swat Model ◽  
Time Slice ◽  
Hydrological Models ◽  
Vic Model ◽  
Impact Of Climate Change ◽  
Monthly Streamflow ◽  
Annual Reduction

This study assessed the impact of climate change on monthly streamflow in the Verde River Basin, located in the Grande River Basin headwater. For this purpose, the SWAT and VIC hydrological models were used to simulate the monthly streamflow under RCP4.5 and RCP8.5 scenarios, obtained by Regional Climate Models Eta-HadGEM2-ES, Eta-CanESM2 and Eta-MIROC5 in the baseline period (1961-2005) and three time-slice (2011-2040, 2041-2070, and 2071-2099) inputs. At the end of the century, the Eta-HadGEM2-ES showed larger decrease of precipitation in both radiative scenarios, with an annual reduction of 17.4 (RCP4.5) and 32.3% (RCP8.5), while the Eta-CanESM2 indicated major warming, with an annual increase of 4.7 and 10.2°C under RCP4.5 and RCP8.5, respectively. As well as precipitation changes, the Eta-HadGEM2-ES also showed greater impacts on streamflow under RCP4.5 for the first time-slice (2011-2040), with an annual decrease of 58.0% for both hydrological models, and for the RCP8.5 scenario by the end the century (2071-2099), with an annual reduction of 54.0 (VIC model) and 56.8% (SWAT model). Regarding monthly streamflow, the Eta-HadGEM2-ES and Eta-CanESM2 inputs indicated decrease under the RCP8.5 scenario by the end the century, varying from 7.2 to 66.3 % (VIC model) and 37.0 to 64.7% (SWAT model). In general, Eta-MIROC5 presented the opposite in terms of direction in the simulations with both hydrological models at the end of the century.  Combined effects of climate models, hydrological model structures and scenarios of climate change should be considered in assessments of uncertainties of climate change impacts.

scholarly journals Long-Term Rainfall Trends and Future Projections over Xijiang River Basin, China

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Muhammad Touseef ◽  
Lihua Chen ◽  
Kaipeng Yang ◽  
Yunyao Chen
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Future Scenarios ◽  
Emission Scenarios ◽  
Xijiang River ◽  
The Xijiang River

Precipitation trend detection is vital for water resources development and decision support systems. This study predicts the climate change impacts on long-term precipitation trends. It deals with the analysis of observed historical (1960–2010) and arithmetic mean method in assembling precipitation from CMIP5 Global Climate Models (GCMs) datasets for a future period (2020–2099) under four emission scenarios. Daily precipitation data of 32 weather stations in the Xijiang River Basin were provided by National Meteorological Information Centre (NMIC) of the China Meteorological Administration (CMA) and Global Climate Models (GCMs) with all four emission scenarios statistically downscaled using Bias Correction Special Disaggregation (BCSD) and applied for bias correction via Climate Change Toolkit (CCT). Nonparametric Mann–Kendall test was applied for statistical significance trend analysis while the magnitude of the trends was determined by nonparametric Sen’s estimator method on a monthly scale to detect monotonic trends in annual and seasonal precipitation time series. The results showed a declined trend was observed for the past 50 years over the basin with negative values of MK test (Z) and Sen’s slope Q. Historical GCMs precipitation detected decreasing trends except for NoerESM1-M which observed slightly increasing trends. The results are further validated by historical precipitation recorded by the Climate Research Unit (CRU-TS-3.1). The future scenarios will likely be positive trends for annual rainfall. Significant positive trends were observed in monsoon and winter seasons while premonsoon and postmonsoon seasons will likely be slightly downward trends. The 2040s will likely observe the lowest increase of 6.6% while the 2050s will observe the highest increase of 11.5% over the 21st century under future scenarios. However, due to the uncertainties in CMIP5, the future precipitation projections should be interpreted with caution. Thus, it could be concluded that the trend of change in precipitation around the Xijiang River Basin is on the increase under future scenarios. The results can be valuable to water resources and agriculture management policies, as well as the approach for managing floods and droughts under the perspective of global climate change.

scholarly journals Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part II: Climate Change Impact Assessment)

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1548
Author(s):  
Suresh Marahatta ◽  
Deepak Aryal ◽  
Laxmi Prasad Devkota ◽  
Utsav Bhattarai ◽  
Dibesh Shrestha
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Models ◽  
Assessment Tool ◽  
Swat Model ◽  
Global Climate Models ◽  
Climate Data ◽  
The Future ◽  
The Impact ◽  
Mountainous River

This study aims at analysing the impact of climate change (CC) on the river hydrology of a complex mountainous river basin—the Budhigandaki River Basin (BRB)—using the Soil and Water Assessment Tool (SWAT) hydrological model that was calibrated and validated in Part I of this research. A relatively new approach of selecting global climate models (GCMs) for each of the two selected RCPs, 4.5 (stabilization scenario) and 8.5 (high emission scenario), representing four extreme cases (warm-wet, cold-wet, warm-dry, and cold-dry conditions), was applied. Future climate data was bias corrected using a quantile mapping method. The bias-corrected GCM data were forced into the SWAT model one at a time to simulate the future flows of BRB for three 30-year time windows: Immediate Future (2021–2050), Mid Future (2046–2075), and Far Future (2070–2099). The projected flows were compared with the corresponding monthly, seasonal, annual, and fractional differences of extreme flows of the simulated baseline period (1983–2012). The results showed that future long-term average annual flows are expected to increase in all climatic conditions for both RCPs compared to the baseline. The range of predicted changes in future monthly, seasonal, and annual flows shows high uncertainty. The comparative frequency analysis of the annual one-day-maximum and -minimum flows shows increased high flows and decreased low flows in the future. These results imply the necessity for design modifications in hydraulic structures as well as the preference of storage over run-of-river water resources development projects in the study basin from the perspective of climate resilience.

scholarly journals An evaluation of the effect of future climate on runoff in the Dongjiang River basin, South China

2015 ◽  
Vol 368 ◽  
pp. 257-262
Author(s):  
K. Lin ◽  
W. Zhai ◽  
S. Huang ◽  
Z. Liu
Keyword(s):  
Climate Change ◽  
River Basin ◽  
South China ◽  
Annual Runoff ◽  
Future Climate ◽  
Weather Data ◽  
Future Climate Change ◽  
Dongjiang River ◽  
The Future ◽  
The Impact

Abstract. The impact of future climate change on the runoff for the Dongjiang River basin, South China, has been investigated with the Soil and Water Assessment Tool (SWAT). First, the SWAT model was applied in the three sub-basins of the Dongjiang River basin, and calibrated for the period of 1970–1975, and validated for the period of 1976–1985. Then the hydrological response under climate change and land use scenario in the next 40 years (2011–2050) was studied. The future weather data was generated by using the weather generators of SWAT, based on the trend of the observed data series (1966–2005). The results showed that under the future climate change and LUCC scenario, the annual runoff of the three sub-basins all decreased. Its impacts on annual runoff were –6.87%, –6.54%, and –18.16% for the Shuntian, Lantang, and Yuecheng sub-basins respectively, compared with the baseline period 1966–2005. The results of this study could be a reference for regional water resources management since Dongjiang River provides crucial water supplies to Guangdong Province and the District of Hong Kong in China.

scholarly journals Assessment of extreme flows and uncertainty under climate change: disentangling the uncertainty contribution of representative concentration pathways, global climate models and internal climate variability

2020 ◽  
Vol 24 (6) ◽  
pp. 3251-3269 ◽  
Author(s):  
Chao Gao ◽  
Martijn J. Booij ◽  
Yue-Ping Xu
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
River Basin ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Internal Climate Variability ◽  
Low Flows ◽  
Uncertainty Sources ◽  
High Flows

Abstract. Projections of streamflow, particularly of extreme flows under climate change, are essential for future water resources management and the development of adaptation strategies to floods and droughts. However, these projections are subject to uncertainties originating from different sources. In this study, we explored the possible changes in future streamflow, particularly for high and low flows, under climate change in the Qu River basin, eastern China. ANOVA (analysis of variance) was employed to quantify the contribution of different uncertainty sources from RCPs (representative concentration pathways), GCMs (global climate models) and internal climate variability, using an ensemble of 4 RCP scenarios, 9 GCMs and 1000 simulated realizations of each model–scenario combination by SDRM-MCREM (a stochastic daily rainfall model coupling a Markov chain model with a rainfall event model). The results show that annual mean flow and high flows are projected to increase and that low flows will probably decrease in 2041–2070 (2050s) and 2071–2100 (2080s) relative to the historical period of 1971–2000, suggesting a higher risk of floods and droughts in the future in the Qu River basin, especially for the late 21st century. Uncertainty in mean flows is mostly attributed to GCM uncertainty. For high flows and low flows, internal climate variability and GCM uncertainty are two major uncertainty sources for the 2050s and 2080s, while for the 2080s, the effect of RCP uncertainty becomes more pronounced, particularly for low flows. The findings in this study can help water managers to become more knowledgeable about and get a better understanding of streamflow projections and support decision making regarding adaptations to a changing climate under uncertainty in the Qu River basin.

scholarly journals Analysis of Long-Term Trends of Annual and Seasonal Rainfall in the Awash River Basin, Ethiopia

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1498 ◽  
Author(s):  
Solomon Mulugeta ◽  
Clifford Fedler ◽  
Mekonen Ayana
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Summer Rainfall ◽  
Seasonal Rainfall ◽  
Winter Rainfall ◽  
Adaptation Measures ◽  
Awash River Basin ◽  
Long Term Trends ◽  
Increasing Trends

With climate change prevailing around the world, understanding the changes in long-term annual and seasonal rainfall at local scales is very important in planning for required adaptation measures. This is especially true for areas such as the Awash River basin where there is very high dependence on rain- fed agriculture characterized by frequent droughts and subsequent famines. The aim of the study is to analyze long-term trends of annual and seasonal rainfall in the Awash River Basin, Ethiopia. Monthly rainfall data extracted from Climatic Research Unit (CRU 4.01) dataset for 54 grid points representing the entire basin were aggregated to find the respective areal annual and seasonal rainfall time series for the entire basin and its seven sub-basins. The Mann-Kendall (MK) test and Sen Slope estimator were applied to the time series for detecting the trends and for estimating the rate of change, respectively. The Statistical software package R version 3.5.2 was used for data extraction, data analyses, and plotting. Geographic information system (GIS) package was also used for grid making, site selection, and mapping. The results showed that no significant trend (at α = 0.05) was identified in annual rainfall in all sub-basins and over the entire basin in the period (1902 to 2016). However, the results for seasonal rainfall are mixed across the study areas. The summer rainfall (June through September) showed significant decreasing trend (at α ≤ 0.1) over five of the seven sub-basins at a rate varying from 4 to 7.4 mm per decade but it showed no trend over the two sub-basins. The autumn rainfall (October through January) showed no significant trends over four of the seven sub-basins but showed increasing trends over three sub-basins at a rate varying from 2 to 5 mm per decade. The winter rainfall (February through May) showed no significant trends over four sub-basins but showed significant increasing trends (at α ≤ 0.1) over three sub-basins at a rate varying from 0.6 to 2.7 mm per decade. At the basin level, the summer rainfall showed a significant decreasing trend (at α = 0.05) while the autumn and winter rainfall showed no significant trends. In addition, shift in some amount of summer rainfall to winter and autumn season was noticed. It is evident that climate change has shown pronounced effects on the trends and patterns of seasonal rainfall. Thus, the study contribute to better understanding of climate change in the basin and the information from the study can be used in planning for adaptation measures against a changing climate.

scholarly journals Uncertainty Impacts of Climate Change and Downscaling Methods on Future Runoff Projections in the Biliu River Basin

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2130 ◽  
Author(s):  
Zhu ◽  
Zhang ◽  
Wu ◽  
Qi ◽  
Fu ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Water Resource Management ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Models ◽  
Liaoning Province ◽  
Lower Envelope ◽  
The Impact

This paper assesses the uncertainties in the projected future runoff resulting from climate change and downscaling methods in the Biliu River basin (Liaoning province, Northeast China). One widely used hydrological model SWAT, 11 Global Climate Models (GCMs), two statistical downscaling methods, four dynamical downscaling datasets, and two Representative Concentration Pathways (RCP4.5 and RCP8.5) are applied to construct 22 scenarios to project runoff. Hydrology variables in historical and future periods are compared to investigate their variations, and the uncertainties associated with climate change and downscaling methods are also analyzed. The results show that future temperatures will increase under all scenarios and will increase more under RCP8.5 than RCP4.5, while future precipitation will increase under 16 scenarios. Future runoff tends to decrease under 13 out of the 22 scenarios. We also found that the mean runoff changes ranging from −38.38% to 33.98%. Future monthly runoff increases in May, June, September, and October and decreases in all the other months. Different downscaling methods have little impact on the lower envelope of runoff, and they mainly impact the upper envelope of the runoff. The impact of climate change can be regarded as the main source of the runoff uncertainty during the flood period (from May to September), while the impact of downscaling methods can be regarded as the main source during the non-flood season (from October to April). This study separated the uncertainty impact of different factors, and the results could provide very important information for water resource management.

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