Skip to main content

Advertisement

Log in

Impacts of climate and reservoirs on the downstream design flood hydrograph: a case study of Yichang Station

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

The Upper Yangtze River (above Yichang) in China has constructed the world's largest reservoir group with the Three Gorges Reservoir (TGR) as the core, the operation of these reservoirs and future climate change will no doubt alter the downstream hydrological processes and pose a challenge to the downstream flood design. As Yichang Hydrologic Station is 44 km downstream of TGR, how the design flood at Yichang Station would be impacted in the future by climate and upstream reservoirs has rarely been investigated. In this study, the climate and upstream reservoirs effects on design flood at Yichang Station are evaluated under six future climate and reservoir scenarios (S1, S2, S3, S4, S5 and S6) with different combinations of summer precipitation anomaly (SPA) and reservoir index (RI), in which SPA is obtained from global climate models under the three emission scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) of CMIP6 and RI is calculated under the two reservoir conditions (RI at current level and RI at planning level). The SPA and RI of S1, S2, S3, S4, S5 and S6 are, respectively, substituted into the optimal nonstationary GEV probability model, and the corresponding 1000-year design floods are estimated by using average annual reliability method. Under the same future reservoir condition, the flood peak discharge, 3-day, 7-day, 15-day and 30-day flood volume (denoted as Qm, W3, W7, W15 and W30, respectively) under SSP2-4.5 and SSP5-8.5 are 0.2% ~ 2.5% larger than those under SSP1-2.6. The change rates of Qm, W3, W7, W15 and W30 under six scenarios relative to the stationary design flood values calculated by Changjiang Water Resources Commission range from −11.4% to −23.9%, and the reduction amount of Qm is more than 16,000 m3/s even under SSP5-8.5. Therefore, reservoirs impact on the design flood of Yichang Station is quite prominent.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Availability of data material

Data are available from the corresponding author.

Code availability

Not applicable.

References

  • Almazroui M, Saeed F, Saeed S, Islam MN, Ismail M, Klutse NAB, Siddiqui MH (2020) Projected change in temperature and precipitation over Africa from CMIP6. Earth Syst Environ 4:455–475

    Article  Google Scholar 

  • Changjiang Water Resources Commission (CWRC) (1996) Hydrologic inscription cultural relics in Three Gorges Reservoir area. Science Press, Beijing (in Chinese)

    Google Scholar 

  • Changjiang Water Resources Commission (CWRC) (1997) Hydrology research of the Three Gorges Reservoir. Hubei Science & Technology Press, Wuhan (in Chinese)

    Google Scholar 

  • Chen J, Brissette FP, Chaumont D, Braun M (2013) Performance and uncertainty evaluation of empirical downscaling methods in quantifying the climate change impacts on hydrology over two North American river basins. J Hydrol 479:200–214

    Article  Google Scholar 

  • Cheng Y, Sang Y, Wang Z, Guo Y, Tang Y (2021) Effects of rainfall and underlying surface on flood recession—the upper Huaihe River basin case. Int J Disaster Risk Sci 12:111–120

    Article  Google Scholar 

  • Chivers C (2012) MHadaptive: General Markov chain Monte Carlo for Bayesian inference using adaptive Metropolis-Hastings sampling. R package version 1.1–8. https://CRAN.R-project.org/package=MHadaptive.

  • Davie T, Quinn NW (2019) Fundamentals of Hydrology (Third Edition). Routledge, New York

    Book  Google Scholar 

  • De Paola F, Giugni M, Pugliese F, Annis A, Nardi F (2018) GEV parameter estimation and stationary vs non-stationary analysis of extreme rainfall in African test cities. Hydrology. 5(2):28

    Article  Google Scholar 

  • Dingman SL (2015) Physical Hydrology (Third Edition). Waveland Press, Long Grove

    Google Scholar 

  • El Adlouni S, Ouarda TB, Zhang X, Roy R, Bobée B (2007) Generalized maximum likelihood estimators for the nonstationary generalized extreme value model. Water Resour Res 43:W03410

    Article  Google Scholar 

  • François B, Schlef KE, Wi S, Brown CM (2019) Design considerations for riverine floods in a changing climate—a review. J Hydrol 574:557–573

    Article  Google Scholar 

  • Gu L, Yin J, Zhang H, Wang H, Yang G, Wu X (2020) On future flood magnitudes and estimation uncertainty across 151 catchments in mainland China. Int J Climatol 41(S1):1–22

    Google Scholar 

  • Guo S, Xiong F, Wang J, Zhong Y, Tian J, Yin J (2019) Preliminary exploration of design flood and control water level of three gorges reservoir in operation period. J Hydraul Eng 50(11):1311–1317 (in Chinese)

    Google Scholar 

  • Isensee LJ, Pinheiro A, Detzel DHM (2021) Dam hydrological risk and the design flood under non-stationary conditions. Water Resour Manag 35:1499–1512

    Article  Google Scholar 

  • Jiang C, Xiong L, Xu C, Guo S (2015) Bivariate frequency analysis of nonstationary low-flow series based on the time-varying copula. Hydrol Process 29:1521–1534

    Article  Google Scholar 

  • Jiang C, Xiong L, Yan L, Dong J, Xu C (2019) Multivariate hydrologic design methods under nonstationary conditions and application to engineering practice. Hydrol Earth Syst Sc 23(3):1683–1704

    Article  Google Scholar 

  • Jiang C, Xiong L, Xu C, Yan L (2021) A river network-based hierarchical model for deriving flood frequency distributions and its application to the Upper Yangtze basin. Water Resour Res 57:e2020WR029374

    Google Scholar 

  • Kendall MG (1975) Rank correlation methods. Charles Griffin, London

    Google Scholar 

  • Li T, Guo S, Chen L, Guo J (2013) Bivariate flood frequency analysis with historical information based on copula. J Hydraul Eng 18(8):1018–1030

    Google Scholar 

  • Li B, Shi X, Lian L, Chen Y, Chen Z, Sun X (2020a) Quantifying the effects of climate variability, direct and indirect land use change, and human activities on runoff. J Hydrol 584:124684

    Article  Google Scholar 

  • Li H, Liu P, Guo S, Cheng L, Yin J (2020b) Climatic control of upper yangtze river flood hazard diminished by reservoir groups. Environ Res Lett 15:124013

    Article  Google Scholar 

  • Li Y, Yan D, Peng H, Xiao S (2021) Evaluation of precipitation in CMIP6 over the Yangtze River Basin. Atmos Res 253:105406

    Article  Google Scholar 

  • Liang Z, Hu Y, Huang H, Wang J, Li B (2016) Study on the estimation of design value under non-stationary environment. South-to-North Water Transfers Water Sci Technol 14(1):50–53 (in Chinese)

    Google Scholar 

  • Liu J, Yuan D, Zhang L, Zou X, Song X (2016) Comparison of three statistical downscaling methods and ensemble downscaling method based on Bayesian model averaging in upper Hanjiang River basin. China Adv Meteorol 2016:7463963

    Google Scholar 

  • López J, Francés F (2013) Non-stationary flood frequency analysis in continental Spanish rivers, using climate and reservoir indices as external covariates. Hydrol Earth Syst Sc 17(8):3189–3203

    Article  Google Scholar 

  • Lu F, Song X, Xiao W, Zhu K, Xie Z (2020) Detecting the impact of climate and reservoirs on extreme floods using nonstationary frequency models. Stoch Env Res Risk A 34(1):169–182

    Article  Google Scholar 

  • Mann HB (1945) Nonparametric tests against trend. Econometrica 13:245–259

    Article  Google Scholar 

  • Martins ES, Stedinger JR (2000) Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data. Water Resour Res 36(3):737–744

    Article  Google Scholar 

  • Massey EJ (1951) The Kolmogorov-Smirnov test of goodness of fit. J Am Stat Assoc 46(253):68–78

    Article  Google Scholar 

  • Milly PCD, Wetherald RT, Dunne KA, Delworth TL (2002) Increasing risk of great floods in a changing climate. Nature 415(6871):514–517

    Article  Google Scholar 

  • Ministry of Water Resources of the People’s Republic of China (MWRPRC) (2006) Regulation for calculating design flood of water resources and hydropower projects: SL 44–2006. China Water & Power Press, Beijing (in Chinese)

    Google Scholar 

  • Naima Z, Meddi M, LaVanchy GT, Remaoun M (2020) The impact of human activities on flood trends in the semi-arid climate of Cheliff Basin, Algeria. Water Resour 47:409–420

    Article  Google Scholar 

  • Olsen JR, Lambert JH, Haimes YY (1998) Risk of extreme events under nonstationarity conditions. Risk Anal 18:497–510

    Article  Google Scholar 

  • Parey S, Hoang TTH, Dacunha-Castelle D (2010) Different ways to compute temperature return levels in the climate change context. Environmetrics 21:698–718

    Article  Google Scholar 

  • Read LK, Vogel RM (2015) Reliability, return periods, and risk under nonstationarity. Water Resour Res 51(8):6381–6398

    Article  Google Scholar 

  • Rigby RA, Stasinopoulos DM (2005) Generalized additive models for location, scale and shape. J R Stat Soc C54:507–554

    Article  Google Scholar 

  • Rootzén H, Katz RW (2013) Design life level: quantifying risk in a changing climate. Water Resour Res 49:5964–5972

    Article  Google Scholar 

  • Salas JD, Obeysekera J (2014) Revisiting the concepts of return period and risk for nonstationary hydrologic extreme events. J Hydrol Eng 19:554–568

    Article  Google Scholar 

  • Salvadori G, De Michele C, Durante F (2011) On the return period and design in a multivariate framework. Hydrol Earth Syst Sc 15:3293–3305

    Article  Google Scholar 

  • Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2):461–464

    Article  Google Scholar 

  • Shi F, Zhao S, Guo Z, Goosse H, Yin Q (2017) Multi-proxy reconstructions of May-September precipitation field in China over the past 500 years. Clim past 13(12):1919–1938

    Article  Google Scholar 

  • Shroder J, Paron P, Baldassarre GD (2014) Hydro-meteorological hazards, risks, and disasters. Elsevier, New York

    Google Scholar 

  • Shuster WD, Zhang Y, Roy AH, Daniel FB, Troyer M (2008) Characterizing storm hydrograph rise and fall dynamics with stream stage data. J Am Water Resour as 44(6):1431–1440

    Article  Google Scholar 

  • Slater L, Villarini G, Archfield S, Faulkner D, Lamb R, Khouakhi A, Yin J (2021) Global changes in 20, 50, and 100-year river floods. Geophys Res. Lett 48(6):e2020GL091824

    Article  Google Scholar 

  • Tang Y, Wu J, Li P, Zhang L, Chen X, Lin K (2019) Quantifying flood frequency modification caused by multi-reservoir regulation. Water Resour Manag 33:4451–4470

    Article  Google Scholar 

  • Viglione A, Merz R, Salinas JL, Blöschl G (2013) Flood frequency hydrology: 3 a Bayesian analysis. Water Resour Res 49:675–692

    Article  Google Scholar 

  • Volpi E, Di Lazzaro M, Bertola M, Viglione A, Fiori A (2018) Reservoir effects on flood peak discharge at the catchment scale. Water Resour Res 54(11):9623–9636

    Article  Google Scholar 

  • Wan X, Hua L, Yang S, Gupta H, Zhong P (2018) Evaluating the impacts of a large-scale multi-reservoir system on flooding: case of the Huai River in China. Water Resour Manag 32:1013–1033

    Article  Google Scholar 

  • Wang Q, Xu Y, Cai X, Tang J, Yang L (2021) Role of underlying surface, rainstorm and antecedent wetness condition on flood responses in small and medium sized watersheds in the Yangtze River Delta region. China Catena 206:105489

    Article  Google Scholar 

  • Wu J, Shi Y, Xu Y (2020) Evaluation and projection of surface wind speed over China based on CMIP6 GCMs. J Geophys Res-Atmos 125:e2020JD033611

    Google Scholar 

  • Xiong L, Jiang C, Xu C, Yu K, Guo S (2015) A framework of change-point detection for multivariate hydrological series. Water Resour Res 51:8198–8217

    Article  Google Scholar 

  • Xiong F, Guo S, Liu P, Xu C, Zhong Y, Yin J, He S (2019a) A general framework of design flood estimation for cascade reservoirs in operation period. J Hydrol 577:124003

    Article  Google Scholar 

  • Xiong B, Xiong L, Xia J, Xu C-Y, Jiang C, Du T (2019b) Assessing the impacts of reservoirs on downstream flood frequency by coupling the effect of scheduling-related multivariate rainfall with an indicator of reservoir effects. Hydrol Earth Syst Sc 23(11):4453–4470

    Article  Google Scholar 

  • Xiong B, Xiong L, Guo S, Xu C, Xia J, Zhong Y, Yang H (2020) Nonstationary frequency analysis of censored data: a case study of the floods in the Yangtze River from. Water Resour Res. 56(8):e2020WR027112

    Article  Google Scholar 

  • Xu C, Zhang D (2018) Impact of the operation of cascade reservoirs in upper Yangtze River on hydrological variability of the mainstream. Proc IAHS 379:421–432

    Article  Google Scholar 

  • Yan L, Xiong L, Guo S, Xu C, Xia J, Du T (2017) Comparison of four nonstationary hydrologic design methods for changing environment. J Hydrol 551:132–150

    Article  Google Scholar 

  • Yang H, Zhong X, Deng S, Xu H (2021) Assessment of the impact of LUCC on NPP and its influencing factors in the Yangtze River basin. China Catena 206:105542

    Article  Google Scholar 

  • Yin H, Li C (2001) Human impact on floods and flood disasters on the Yangtze River. Geomorphology 41(2–3):105–109

    Article  Google Scholar 

  • Yin J, Guo S, Liu Z, Chen K, Chang F, Xiong F (2017) Bivariate seasonal design flood estimation based on copulas. J Hydraul Eng 22:05017028

    Google Scholar 

  • Zhang H, Dou Y, Ye L et al (2022) Realizing the full reservoir operation potential during the 2020 Yangtze river floods. Sci Rep 12:2822

    Article  Google Scholar 

  • Zhong W, Guo J, Chen L, Zhou J, Zhang J, Wang D (2020) Future hydropower generation prediction of large-scale reservoirs in the upper Yangtze River basin under climate change. J Hydrol 588:125013

    Article  Google Scholar 

Download references

Funding

This research is financially supported jointly by the National Natural Science Foundation of China (NSFC Grants U2240201 and 41890822), China Three Gorges Corporation Research Grant (0799254), and the Ministry of Education “Plan 111” Fund of China (B18037), all of which are greatly appreciated. No conflict of interest exists in the submission of the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

R. Li: Methodology; Investigation; Formal analysis; Writing-Original Draft. L. Xiong: Resources; Project administration; Writing—Review & Editing; Funding acquisition. X. Zha: data collection and processing; Writing—Review & Editing. B. Xiong: Writing—Review & Editing. H. Liu: data collection and processing. J. Chen: Writing—Review & Editing. L. Zeng: data collection. W. Li: data collection.

Corresponding author

Correspondence to Lihua Xiong.

Ethics declarations

Conflicts of interest

The authors declare no competing interests.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publish

Authors give their permission.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, R., Xiong, L., Zha, X. et al. Impacts of climate and reservoirs on the downstream design flood hydrograph: a case study of Yichang Station. Nat Hazards 113, 1803–1831 (2022). https://doi.org/10.1007/s11069-022-05370-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-022-05370-3

Keywords

Navigation