Advertisement

Quantifying the Contributions of Climate Change and Human Activities to Drought Extremes, Using an Improved Evaluation Framework

  • Shuang Zhu
  • Zhanya Xu
  • Xiangang LuoEmail author
  • Chao Wang
  • Hairong Zhang
Article
First Online:
  • 45 Downloads

Abstract

An increasing amount of studies have emphasized that more frequent and extensive extreme events have occurred around the world. The effects of climate change and anthropogenic activities on the variation in runoff have been studied extensively. However, the effects of variation in hydrological extremes have rarely been studied. In this study, a modeling framework was developed to quantify the time-varying probability of extreme hydrological drought events under a changing environment, and the framework includes standardized runoff index construction, a change point test, generalized extreme value modeling, a return years analysis and an evaluation of the impact of climate change and human activities. Importantly, unlike the common change point test in the individual runoff series, the copula method was introduced to determine the change in the precipitation-runoff dependence structure. Generalized extreme value models were developed to make inferences about the return probability of the extreme standardized runoff index. The new method was applied to the Jinshajiang River Basin (JSJR). The results show that a change point in the relationship between precipitation and runoff occurred in 1995. Even though the climate became slightly drier, extreme drought was alleviated in the JSJR, and human activities were the main contributors to drought mitigation. The copula multivariate change point detection was accurate. Studying the impacts of climate change and human activities on general runoff and hydrological drought extremes is important to better understanding complex water resource variations.

Keywords

Hydrological extremes variation Copulas Climate change Human activities 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (51809242).

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Shuang Zhu, Zhanya Xu, Xiangang Luo, Chao Wang and Hairong Zhang. All authors read and approved the final manuscript.

Compliance with Ethical Standards

Conflict of Interest

There is no conflict of interest.

References

  1. Ahmed Y, Al-Faraj F, Scholz M, Soliman A (2019) Assessment of upstream human intervention coupled with climate change impact for a transboundary river flow regime: Nile River Basin. Water Resour Manag 33:2485–2500.  https://doi.org/10.1007/s11269-019-02256-1 CrossRefGoogle Scholar
  2. Bazrafshan J, Hejabi S (2018) A non-stationary reconnaissance drought index (NRDI) for drought monitoring in a changing climate. Water Resour Manag 32:2611–2624CrossRefGoogle Scholar
  3. Berg D (2009) Copula goodness-of-fit testing: an overview and power comparison. Eur J Financ 15:675–701CrossRefGoogle Scholar
  4. Bouzebda S, Keziou A (2013) A semiparametric maximum likelihood ratio test for the change point in copula models. Stat Methodol 14:39–61CrossRefGoogle Scholar
  5. Budyko, Mikhail Ivanovich, David Hewitt Miller, and David Hewitt Miller. Climate and life. Vol. 508. New York: Academic press, 1974. Costa Dias, Alexandra da. Copula inference for finance and insurance. Doctoral thesis. ETH Zurich, 2004.Google Scholar
  6. Chen L, Singh VP, Guo S, Zhou J, Zhang J (2015) Copula-based method for multisite monthly and daily streamflow simulation. J Hydrol 528:369–384CrossRefGoogle Scholar
  7. Coles S, Bawa J, Trenner L, Dorazio P (2001) An introduction to statistical modeling of extreme values, vol 208. Springer, New YorkCrossRefGoogle Scholar
  8. Dias ADC (2004) Copula inference for finance and insurance. Doctoral thesis, ETH ZurichGoogle Scholar
  9. Fu W, Wang K, Li C, Tan J (2019a) Multi-step short-term wind speed forecasting approach based on multi-scale dominant ingredient chaotic analysis, improved hybrid GWO-SCA optimization and ELM. Energy Convers Manag 187:356–377CrossRefGoogle Scholar
  10. Fu W, Wang K, Zhang C, Tan J (2019b) A hybrid approach for measuring the vibrational trend of hydroelectric unit with enhanced multi-scale chaotic series analysis and optimized least squares support vector machine. Trans Inst Meas Control 41:4436–4449CrossRefGoogle Scholar
  11. Gao G, Fu B, Wang S, Liang W, Jiang X (2016) Determining the hydrological responses to climate variability and land use/cover change in the Loess Plateau with the Budyko framework. Sci Total Environ 557-558:331CrossRefGoogle Scholar
  12. Guo A, Chang J, Liu D, Wang Y, Huang Q, Li Y (2016) Variations in the precipitation–runoff relationship of the Weihe River Basin. Hydrol Res 48:295–310CrossRefGoogle Scholar
  13. Huang K, Chen L, Zhou J, Zhang J, Singh VP (2018) Flood hydrograph coincidence analysis for mainstream and its tributaries. J Hydrol 565:341–353CrossRefGoogle Scholar
  14. Jia X, Fu B, Feng X, Hou G, Liu Y, Wang X (2014) The tradeoff and synergy between ecosystem services in the grain-for-green areas in northern Shaanxi, China. Ecol Indic 43:103–113CrossRefGoogle Scholar
  15. Jiang C, Li D, Gao Y, Liu W, Zhang L (2016) Impact of climate variability and anthropogenic activity on streamflow in the three Rivers headwater region, Tibetan Plateau, China. Theor Appl Climatol 129:1–15Google Scholar
  16. Liu J, Zhang Q, Singh VP, Shi P (2016) Contribution of multiple climatic variables and human activities to streamflow changes across China. J Hydrol 545:145–162CrossRefGoogle Scholar
  17. Liu W, Sun F, Lim WH, Zhang J, Wang H, Shiogama H, Zhang Y (2018) Global drought and severe drought-affected populations in 1.5 and 2° C warmer worlds. Earth System Dynamics 9:267CrossRefGoogle Scholar
  18. Luan XB, Wu PT, Sun SK, Li XL, Wang YB, Gao XR (2018) Impact of land use change on hydrologic processes in a large Plain Irrigation District. Water Resour Manag 32:3203–3217CrossRefGoogle Scholar
  19. Mwangi HM, Julich S, Patil SD, Mcdonald MA, Feger KH (2016) Relative contribution of land use change and climate variability on discharge of upper Mara River, Kenya. J Hydro: Reg Stud 5:244–260Google Scholar
  20. Nelsen RB (2007) An introduction to copulas. Springer Science & Business Media, BerlinGoogle Scholar
  21. Pan Z, Ruan X, Qian M, Hua J, Shan N, Xu J (2018) Spatio-temporal variability of streamflow in the Huaihe River basin, China: climate variability or human activities? Hydrol Res 49:177–193CrossRefGoogle Scholar
  22. Pettitt AN (1979) A non-parametric approach to the change-point problem. J R Stat Soc 28:126–135Google Scholar
  23. Salvadori G, Michele CD (2010) Multivariate multiparameter extreme value models and return periods: a copula approach. Water Resour Res 46:219–233CrossRefGoogle Scholar
  24. Shahid M, Cong Z, Zhang D (2018) Understanding the impacts of climate change and human activities on streamflow: a case study of the Soan River basin, Pakistan. Theor Appl Climatol 134:205–219CrossRefGoogle Scholar
  25. Sklar A (1973) Random variables, joint distribution functions, and copulas. Kybernetika 9:449–460Google Scholar
  26. Sneyers R (1977) R Sneyers - Sur l'analyse statistique des séries d'observations. Ciel Et Terre 93:186Google Scholar
  27. Tabari H, Somee BS, Zadeh MR (2011) Testing for long-term trends in climatic variables in Iran. Atmos Res 100:132–140CrossRefGoogle Scholar
  28. Wu J, Miao C, Yang T, Duan Q, Zhang X (2018) Modeling streamflow and sediment responses to climate change and human activities in the Yanhe River, China. Hydrol Res 49:150–162CrossRefGoogle Scholar
  29. Wu C, Ji C, Shi B, Wang Y, Gao J, Yang Y, Mu J (2019) The impact of climate change and human activities on streamflow and sediment load in the Pearl River basin. Int J Sediment Res 34:307–321.  https://doi.org/10.1016/j.ijsrc.2019.01.002 CrossRefGoogle Scholar
  30. Xiong L, Jiang C, Xu CY, Yu KX, Guo S (2016) A framework of change-point detection for multivariate hydrological series. Water Resour Res 51:8198–8217CrossRefGoogle Scholar
  31. Yang J, Ding Y, Chen R (2006) Spatial and temporal of variations of alpine vegetation cover in the source regions of the Yangtze and yellow Rivers of the Tibetan plateau from 1982 to 2001. Environ Geol 50:313–322.  https://doi.org/10.1007/s00254-006-0210-8 CrossRefGoogle Scholar
  32. Yin J, Guo S, He S, Guo J, Hong X, Liu Z (2018) A copula-based analysis of projected climate changes to bivariate flood quantiles. J Hydrol 566:23–42CrossRefGoogle Scholar
  33. Zeng S, Zhan C, Sun F, Du H, Wang F (2015) Effects of climate change and human activities on surface runoff in the Luan River Basin. Adv Meteorol 2015:1–12CrossRefGoogle Scholar
  34. Zhang Q, Liu J, Singh VP, Gu X, Chen X (2016) Evaluation of impacts of climate change and human activities on streamflow in the Poyang Lake basin, China. Hydrol Process 30:2562–2576CrossRefGoogle Scholar
  35. Zhang L, Nan Z, Wang W, Ren D, Zhao Y, Wu X (2019) Separating climate change and human contributions to variations in streamflow and its components using eight time-trend methods. Hydrol Process 33:383–394.  https://doi.org/10.1002/hyp.13331 CrossRefGoogle Scholar
  36. Zou L, Xia J, She D (2018) Analysis of impacts of climate change and human activities on hydrological drought: a case study in the Wei River basin, China. Water Resour Manag 32:1421–1438CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of Geography and Information EngineeringChina University of GeosciencesWuhanChina
  2. 2.State Key Laboratory of Simulation and Regulation of Water Cycle in River BasinChina Institute of Water Resources and Hydropower ResearchBeijingChina
  3. 3.Department of Water Resources ManagementChina Yangtze Power Company LimitedYichangChina

Personalised recommendations

Cite article

Buy options