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A new multiple integral model for water shortage risk assessment and its application in Beijing, China

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Abstract

In terms of drought years, the assessment of water shortage risk is a significant precondition for taking effective measures to reduce the potential losses. This paper proposes a new multiple integral model for evaluating the risk of water shortage. First, the probability density function for water shortage was simulated. Second, a nonlinear function between vulnerability and its indicators was developed based on projection pursuit. Third, a function of consequence was proposed from the perspective of water-use benefit, and data envelopment analysis was applied to compute the water-use benefit coefficients. Fourth, risk was defined as a double integral in monetary units. Risks in Beijing, used as a case study, are assessed under different inflow scenarios (1956–2012) by using the model. The findings of the study were as follows: In 2020, the vulnerability was shown to vary from 0.93 to 0.99, and the maximum value occurs with the inflow conditions of 1980 and 2009. The probable maximum loss occurs with the inflow condition of 2006, and risk is approximately equal to 0.7 billion CNY. After using the transferred water and reclaimed water, all of the values for consequence vulnerability and risk are reduced, but the situation regarding supply and demand remains at a disadvantage in 2020.

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References

  • Alessa L, Kliskey A, Lammers R, Arp C, White D, Hinzman L, Busey R (2008) The Arctic water resource vulnerability index: an integrated assessment tool for community resilience and vulnerability with respect to freshwater. Environ Manag 42(3):523–541. doi:10.1007/s00267-008-9152-0

    Article  Google Scholar 

  • Alexander D (2000) Confronting catastrophe. Terra, Hertfordshire

    Google Scholar 

  • Aubrecht C, Fuchs S, Neuhold C (2013) Spatio-temporal aspects and dimensions in integrated disaster risk management. Nat Hazards 68:1205–1216

    Article  Google Scholar 

  • Aven T (2007) A unified framework for risk and vulnerability analysis and management covering both safety and security. Reliab Eng Syst Saf 92:745–754

    Article  Google Scholar 

  • Aven T (2010) On how to define, understand and describe risk. Reliab Eng Syst Saf 95:623–631

    Article  Google Scholar 

  • Babel MS, Wahid SM (2009) Freshwater under threat. Southeast Asia. http://indiaenvironmentportal.org.in/files/SEA_Water_report.pdf. Accessed 18 April 2010

  • Beijing Municipal Development and Reform Commission and Beijing Municipal Bureau of Water Affairs (2009) Beijing City comprehensive planning of water resources. China Water Power Press, Beijing (in Chinese)

    Google Scholar 

  • Deng JL (2002) Grey prediction and grey decision making. Huazhong University of Science and Technology Press, Wuhan (in Chinese)

    Google Scholar 

  • Department of mathematics of Tongji University (2007) Higher mathematics. Higher Education Press, Beijing (in Chinese)

    Google Scholar 

  • Dilley M, Chen RS, Deichmann U, Lerner-Lam AL, Arnold M (2005) Natural disaster hotspots: a global risk analysis. World Bank, Washington, DC

    Book  Google Scholar 

  • Du D, Pang QH, Wu Y (2008) Modern comprehensive assessment methods and case selection. Tsinghua University Press, Beijing (in Chinese)

    Google Scholar 

  • Hahn H (2003) Indicators and other instruments for local risk management for communities and local governments. Document prepared as part of the documents related to the Project: local risk management for communities and local governments. The German Technical Cooperation Agency, GTZ, for IADB

  • Haimes YY (2006) On the definition of vulnerability in measuring risks to infrastructures. Risk Anal 26(2):293–296

    Article  Google Scholar 

  • Haimes YY (2009) On the complex definition of risk: a systems-based approach. Risk Anal 29(12):1647–1654

    Article  Google Scholar 

  • Han YP, Ruan BQ (2007) Economic loss assessment of shortage risk of water resources. J Hydraul Eng 38(10):1253–1257 (in Chinese)

    Google Scholar 

  • Hashimoto T, Stedinger JR, Loucks DP (1982) Reliability, resiliency and vulnerability criteria for water resources system performance evaluation. Water Resour Res 18(1):14–20

    Article  Google Scholar 

  • Hausken K (2002) Probabilistic risk analysis and game theory. Risk Anal 22(1):17–27

    Article  Google Scholar 

  • Huang CF (2011) Discussion on basic method for risk analysis. J Nat Disasters 20(5):1–9 (in Chinese)

    Google Scholar 

  • ISDR (2004) Living with risk. A global review of disaster reduction initiatives. http://www.unisdr.org, 23 Nov 2004

  • Jaiswal P, Westen CJV (2013) Use of quantitative landslide hazard and risk information for local disaster risk reduction along a transportation corridor: a case study from Nilgiri district, India. Nat Hazards 65:887–913

    Article  Google Scholar 

  • Jin JL, Zhang XL, Ding J (2002) Projection pursuit model for evaluating grade of flood disaster loss. Syst Eng Theory Pract 22(2):140–144 (in Chinese)

    Google Scholar 

  • Kaplan S, Garrick BJ (1981) On the quantitative definition of risk. Risk Anal 1(1):11–27

    Article  Google Scholar 

  • Lane ME, Kirshen PH, Vogel RM (1999) Indicators of impacts of global climate change on U.S. water resources. J Water Resour Plann Manag 125(4):194–204

    Article  Google Scholar 

  • Li BN (2007) Fuzzy mathematics and its application. Publication of Hefei University of Technology, Hefei (in Chinese)

    Google Scholar 

  • Marulanda MC, Carreoño ML, Cardona OD, Ordaz MG, Barbat AH (2013) Probabilitic earthquake risk assesment using CAPRA: application to the city of Barcelona, Spain. Nat Hazards. doi: 10.1007/s11069-013-0685-z

  • Mujumdar PP, Sasikumar K (2002) A fuzzy risk approach for seasonal water quality management of a river system. Water Resour Res 38(1):1–9

    Article  Google Scholar 

  • Plummer R, de Rob Loë, Armitage D (2012) A systematic review of water vulnerability assesment tools. Water Resour Manage 26:4327–4346

    Article  Google Scholar 

  • Poolman E, Chikoore H, Lucio F (2008) Public benefits of the Severe Weather Forecasting Demonstration Project in southeastern Africa. MeteoWorld, Dec 2008, www.wmo.int/pages/publications/meteoworld/archive/dec08/swfdp_en.html

  • Qian LX, Wang HR, Jiang GR, Yu S (2011) Model for risk analysis between supply and demand of water resources based on logistic regression and NFCA and its application. J Nat Resour 26(12):2039–2049 (in Chinese)

    Google Scholar 

  • Qian LX, Wang HR, Zhang KN (2014) Evaluation criteria and model for risk between water supply and water demand and its application in Beijing. Water Resour Manag. doi:10.1007/s11269-014-0624-0

    Google Scholar 

  • Rajagopalan B, Nowak K, Prairie J, Hoerling M, Harding B, Barsugli J, Ray A, Udall B (2009) Water supply risk on the Colorado River: Can management mitigate? Water Resour Res 45:W08201. doi:10.1029/2008WR007652

    Article  Google Scholar 

  • Ruan BQ, Han YP, Wang H, Jiang RF (2005) Fuzzy comprehensive assessment of water shortage risk. J Hydraul Eng 36(8):906–912 (in Chinese)

    Google Scholar 

  • Sandoval-Solis S, McKinney DC, Loucks DP (2011) Sustainability index for water resources planning and management. J Water Resour Plan Manag 137(5):381–390

    Article  Google Scholar 

  • Sheng ZX, Xie SQ, Pan CY (2008) Probability theroy and mathematical statistics. China Higher Education Press, Beijing (in Chinese)

    Google Scholar 

  • Smith K (1996) Environmental Hazards: Assessing Risk and Reducing Disaster. Routledge, London

    Google Scholar 

  • Statistical Bureau of Beijing City (2014) Statistical yearbook 2014 of Beijing City. China Statistics Press, Beijing (in Chinese)

    Google Scholar 

  • Sullivan CA (2010) Quantifying water vulnerability: a multi-dimensional approach. Stoch Environ Res Risk Assess 25(4):627–640

    Article  Google Scholar 

  • Tsakiris G (2014) Flood risk assessment: concepts, modeling, applications. Nat Hazards Earth Syst Sci Discuss 2:261–286

    Article  Google Scholar 

  • Verma M, Verter V (2007) Railroad transportation of dangerous goods: population exposure to airborne toxins. Comput Oper Res 34:1287–1303

    Article  Google Scholar 

  • Villagrán De León J (2006) Vulnerabilty. A conceptual and methodological review. SOURCE No. 4. UNU-EHS, Bonn

  • Wang HR, Qian LX, Xu XY, Wang Y (2009) Model for evaluating water shortage risk based on fuzzy probability and its application. J Hydraul Eng 40(7):813–821 (in Chinese)

    Google Scholar 

  • Willis HH (2007) Guiding resource allocations based on terrorism risk. Risk Anal 27(3):597–606

    Article  Google Scholar 

  • Wu WY, Yin SY, Liu HL, Chen HH (2014) Groundwater vulnerability assessment and feasibility mapping under reclaimed water irrigation by a modified DRASTIC model. Water Resour Manag. doi:10.1007/s11269-014-0536-z

    Google Scholar 

  • Xia J, Qiu B, Pan XY, Weng JB, Fu GB, OuYang RL (2012) Assessment of water resources vulnerability under climate change and human activities. Adv Earth Sci 27(4):443–481 (in Chinese)

    Google Scholar 

  • Zhang Q, Zhang JQ, Yan DH, Bao YL (2013) Dynamic risk prediction based on discriminant analysis for maize drought disaster analysis for maize drought disaster. Nat Hazards 65:1275–1284

    Article  Google Scholar 

Download references

Acknowledgments

The study is supported by the National Natural Science Foundation of China (Grant Nos. 51279006, 51479003, 41375002) and the National Natural Science Foundation of Jiangsu (No. BK2013123). The authors would like to thank the Associate Editor and all the anonymous reviewers for their valuable comments and constructive suggestions, which led to an improvement in the presentation of this paper.

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Correspondence to Ren Zhang.

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Qian, L., Zhang, R., Hong, M. et al. A new multiple integral model for water shortage risk assessment and its application in Beijing, China. Nat Hazards 80, 43–67 (2016). https://doi.org/10.1007/s11069-015-1955-8

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  • DOI: https://doi.org/10.1007/s11069-015-1955-8

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