Using a fuzzy approach to assess adaptive capacity for urban water resources
Adaptive capacity has become the focus of current research on climate change. A complete set of methods to assess the adaptive capacity for Beijing water resources was established in this study. Risk factors for water resources were identified by overlapping climate change, urbanization issues, and urban water resources, and a three-dimensional framework comprising 12 indicators specific to each risk factor was built to assess the adaptive capacity of the water resource systems. These three dimensions represent the three pillars of a sustainable water resource system: water supply, water demand, and water quality. An analytic hierarchy process was used to determine the weight for each indicator. Then a fuzzy version of the technique for order preference by similarity to an ideal solution was applied to calculate the ranking for the 11 districts in Beijing and quantify the adaptive capacity for water resources in these areas. The fuzzy approach results revealed that three indicators are key: comprehensive management capabilities for water supply, control capability for water demand, and management capabilities for water quality. Finally, adaptability proposals are proposed in accordance with the ranking results obtained.
KeywordsAdaptive capacity Analytic hierarchy process Climate change Technique for order preference by similarity to an ideal solution Triangular fuzzy number Water resources
This work was financially supported by the National Natural Science Foundation of China (51408022), Major Science and Technology Program for Water Pollution Control and Treatment (No. 2015ZX07406001), Beijing Municipal Excellent Talent Training Foundation (No. 2013D005017000009) and Beijing Municipal Natural Science Foundation (No. 8154044).
- Adger WN, Brooks N, Bentham G, Agnew, M, Eriksen S (2004) New Indicators of vulnerability and adaptive capacity. Tyndall Centre Climate Change ResGoogle Scholar
- Barclay P (2013) Climate change adaptation in great lakes Cities. The University Of Michigan, Ann ArborGoogle Scholar
- Hao X, Xia J, Wang R (2010) influence of climate change on surface water environment. J China Hydrol 30:67–72Google Scholar
- Li C, Li A (1999) The application of topsis method to comprehensive asse-ssment of environmental quality. J Geol Hazards Environ Preserv 10:10–14Google Scholar
- Li J, Zhang Y, Liu T (2013) Assessment method for cleaner production of vanadium extraction from stone coal. Environ Sci Technol 36:191–194Google Scholar
- Schipper L, Burton I (2009) Understanding adaptation: origins, concepts, practice and policy. In: Schipper L, Burton I (eds) Adaptation to climate change. Earthscan, London, pp 1–8Google Scholar
- Shi Y, Gao XJ, Wu J, Giorgi F (2010) Simulating future climate changes over North China with a high resolution regional climate model. J Appl Meteorol Sci 21(5):580–589Google Scholar
- Chen SJ, Hwang CL (1992) Fuzzy multiple attribute decision making methods. Fuzzy multiple attribute decision making: methods and applications. Berlin, HeidelbergGoogle Scholar
- Zhu L, Xu L (2011) analysis of effects of global change on terrestrial ecosystem. areal research and development. Areal Res Dev 30:161–165Google Scholar