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Evaluation of Adaptation Scenarios for Climate Change Impacts on Agricultural Water Allocation Using Fuzzy MCDM Methods

Abstract

Due to the impacts of climate change on agriculture and water allocation, an investigation of the farmers’ perceptions and stakeholders’ views on the adaptation strategies to climate change has a great of importance for sustainable development in the future. In this study, a fuzzy based decision support system has been developed to evaluate and rank the proposed adaptation scenarios to climate change in the Jarreh agricultural water resources system in southwest of Iran. Using output of ten coupled models inter comparison project phase 5 (CMIP5) under two representative concentration pathway scenarios (RCP 4.5, and RCP 8.5), the results indicated an increasing the annual mean temperature (1.64–1.84 °C under RCP 4.5, and 1.85–2.1 °C under RCP 8.5), reducing the amount of runoff into the reservoir (17.83–46.24% under RCP 4.5, and 21.54–50.91%under RCP 8.5), as well as increasing the amount of agricultural water requirement. Also, the results showed decreasing in reliability of system (12–53% under RCP 4.5, and 23–63% under RCP 8.5). Following, due to the main purpose of the system, six adaptation scenarios by using a questionnaire and stakeholders’ opinions are proposed to mitigate the effects of climate change. In the next step, by fuzzy mode of the technique for order of preference by similarity to ideal solution (TOPSIS) and fuzzy preference ranking organization method for enrichment of evaluations (PROMETHEE II), the proposed scenarios have been ranked according to the performance criteria. The final results of this study indicated the superiority of improving the irrigation efficiency and decreasing the area under cultivation among other proposed scenarios.

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References

  1. Ahmadianfar I, Adib A, Taghian M (2016) Optimization of fuzzified hedging rules for multipurpose and multireservoir systems. J Hydrol Eng 21(4):05016003

  2. Al-Faraj FAM, Tigkas D, Scholz M (2016) Irrigation efficiency improvement for sustainable agriculture in changing climate: a Transboundary watershed between Iraq and Iran. Environ Process 3:603–616. https://doi.org/10.1007/s40710-016-0148-0

  3. Ashofteh P-S, Bozorg-Haddad O, Loáiciga HA (2017) Development of adaptive strategies for irrigation water demand management under climate change. J Irrig Drain Eng 143:04016077. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001123

  4. Bhattarai MD, Secchi S, Schoof J (2017) Projecting corn and soybeans yields under climate change in a Corn Belt watershed. Agric Syst 152:90–99. https://doi.org/10.1016/j.agsy.2016.12.013

  5. Brier GW (1950) Verification of forecasts expressed in terms of probability. Mon Weather Rev 78:1–3. https://doi.org/10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2

  6. Chen S-J, Hwang C-L (1992) Fuzzy multiple attribute decision making methods. In: Fuzzy multiple attribute decision making. Springer, Berlin, Heidelberg. pp 289–486

  7. Chen Y-H, Wang T-C, Wu C-Y (2011) Strategic decisions using the fuzzy PROMETHEE for IS outsourcing. Expert Syst Appl 38:13216–13222. https://doi.org/10.1016/j.eswa.2011.04.137

  8. Dalezios NR, Dercas N, Spyropoulos NV, Psomiadis E (2019) Remotely sensed methodologies for crop water availability and requirements in precision farming of vulnerable agriculture. Water Resour Manag 33(4):1499–1519

  9. Donner LJ, Wyman BL, Hemler RS, Horowitz LW, Ming Y, Zhao M et al (2011) The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3. J Clim 24(13):3484–3519

  10. Dunne JP, John JG, Adcroft AJ, Griffies SM, Hallberg RW, Shevliakova E et al (2012) GFDL’s ESM2 global coupled climate-carbon earth system models. Part I: physical formulation and baseline simulation characteristics. J Clim 25(19):6646–6665

  11. Efron B (1979) Bootstrap methods: another look at the jackknife annals of statistics 7:1–26.

  12. Elleuch MA, Anane M, Euchi J, Frikha A (2019) Hybrid fuzzy multi-criteria decision making to solve the irrigation water allocation problem in the Tunisian case. Agric Syst 176:102644

  13. Franklin CN, Sun Z, Bi D, Dix M, Yan H, Bodas-Salcedo A (2013) Evaluation of clouds in ACCESS using the satellite simulator package COSP:global, seasonal, and regional cloud properties. J Geophys Res Atmos 118(2):732–748

  14. He S, Guo S, Yang G, Chen K, Liu D, Zhou Y (2019) Optimizing operation rules of Cascade reservoirs for adapting climate change. Water Resour Manag:1–20

  15. Hoekstra AY, Mekonnen MM (2012) The water footprint of humanity. Proc Natl Acad Sci 109:3232–3237. https://doi.org/10.1073/pnas.1109936109

  16. Hwang C-L, Yoon K (1981) Methods for multiple attribute decision making. In: Multiple attribute decision making. Springer, Berlin, Heidelberg. pp 58–191

  17. Iglesias A, Santillán D, Garrote L (2018) On the barriers to adaption to less water under climate change: policy choices in Mediterranean countries. Water Resour Manag 32(15):4819–4832

  18. Iversen T, Bentsen M, Bethke I, Debernard JB, Kirkevåg A, Seland Ø et al (2013) The Norwegian earth system model, NorESM1-M—part 2: climate response and scenario projections. Geosci Model Dev 6(2):389–415

  19. Kabir MJ, Alauddin M, Crimp S (2017) Farm-level adaptation to climate change in Western Bangladesh: an analysis of adaptation dynamics, profitability and risks. Land Use Policy 64:212–224. https://doi.org/10.1016/j.landusepol.2017.02.026

  20. Karamouz M, Mohammadpour P, Mahmoodzadeh D (2017) Assessment of sustainability in water supply-demand considering uncertainties. Water Resour Manag 31:3761–3778. https://doi.org/10.1007/s11269-017-1703-9

  21. Li S, Juhász-Horváth L, Harrison PA, Pintér L, Rounsevell MDA (2017) Relating farmer’s perceptions of climate change risk to adaptation behaviour in Hungary. J Environ Manag 185:21–30. https://doi.org/10.1016/j.jenvman.2016.10.051

  22. Luo Q, Bange M, Braunack M, Johnston D (2016) Effectiveness of agronomic practices in dealing with climate change impacts in the Australian cotton industry - a simulation study. Agric Syst 147:1–9. https://doi.org/10.1016/j.agsy.2016.05.006

  23. Mehdy Hashemy Shahdany S, Roozbahani A (2015) Selecting an appropriate operational method for main irrigation canals within multicriteria decision-making methods. J Irrig Drain Eng 142:4015064

  24. Mereu S, Sušnik J, Trabucco A et al (2016) Operational resilience of reservoirs to climate change, agricultural demand, and tourism: a case study from Sardinia. Sci Total Environ 543:1028–1038. https://doi.org/10.1016/j.scitotenv.2015.04.066

  25. Muller D. (2007). Adapting to climate variability and change: A guidance manual for development planning. Washngton, DC: US Agency for Int Dev

  26. Ndamani F, Watanabe T (2017) Developing indicators for adaptation decision-making under climate change in agriculture: a proposed evaluation model. Ecol Indic 76:366–375

  27. Perkins SE, Pitman AJ, Holbrook NJ, McAneney J (2007) Evaluation of the AR4 climate models’ simulated daily maximum temperature, minimum temperature, and precipitation over Australia using probability density functions. J Clim 20:4356–4376. https://doi.org/10.1175/JCLI4253.1

  28. Qin XS, Huang GH, Chakma A et al (2008) A MCDM-based expert system for climate-change impact assessment and adaptation planning - a case study for the Georgia Basin, Canada. Expert Syst Appl 34:2164–2179. https://doi.org/10.1016/j.eswa.2007.02.024

  29. Rotstayn LD, Jeffrey SJ, Collier MA, Dravitzki SM, Hirst AC, Syktus JI, Wong KK (2012) Aerosol-and greenhouse gas-induced changes in summer rainfall and circulation in the Australasian region: a study using single-forcing climate simulations. Atmos Chem Phys 12(14):6377–6404

  30. Shariat R, Roozbahani A, Ebrahimian A (2019) Risk analysis of urban stormwater infrastructure systems using fuzzy spatial multi-criteria decision making. Sci Total Environ 647:1468–1477

  31. Stevens B, Giorgetta M, Esch M, Mauritsen T, Crueger T, Rast S et al (2013) Atmospheric component of the MPI-M earth system model: ECHAM6. J Adv Model Earth Sy 5(2):146–172

  32. Storn R, Price K (1996) Minimizing the real functions of the ICEC’96 contest by differential evolution. In: Evolutionary computation, proceedings of IEEE international conference on (pp. 842–844). IEEE

  33. Sugeno M (1985) An introductory survey of fuzzy control. Inf Sci 36(1–2):59–83

  34. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106:7183–7192. https://doi.org/10.1029/2000JD900719

  35. Thrasher B, Nemani R (2012) NASA Earth exchange global daily downscaled projections (NEX-GDDP) 1. intent of this document and POC

  36. Tsai A-Y, Huang W-C (2011) Impact of climate change on water resources in Taiwan. Terr Atmos Ocean Sci 22(5):507–519

  37. Tukimat NNA, Harun S, Shahid S (2017) Modeling irrigation water demand in a tropical Paddy cultivated area in the context of climate change. J Water Resour Plan Manag 143:05017003. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000753

  38. Turner SWD, Ng JY, Galelli S (2017) Examining global electricity supply vulnerability to climate change using a high-fidelity hydropower dam model. Sci Total Environ 590–591:663–675. https://doi.org/10.1016/j.scitotenv.2017.03.022

  39. Varela-Ortega C, Blanco-Gutiérrez I, Esteve P, Bharwani S, Fronzek S, Downing TE (2016) How can irrigated agriculture adapt to climate change? Insights from the Guadiana Basin in Spain. Reg Environ Chang 16:59–70. https://doi.org/10.1007/s10113-014-0720-y

  40. Vieira J, Cunha MC, Luís R (2018) Integrated assessment of water reservoir systems performance with the implementation of ecological flows under varying climatic conditions. Water Resour Manag 32(15):5183–5205

  41. von Salzen K, Scinocca JF, McFarlane NA, Li J, Cole JNS, Plummer D et al (2013) The Canadian fourth generation atmospheric global climate model (CanAM4). Part I: representation of physical processes. Atmosphere-Ocean 51(1):104–125

  42. Yang C, Fraga H, Van Ieperen W, Santos JA (2017) Assessment of irrigated maize yield response to climate change scenarios in Portugal. Agric Water Manag 184:178–190. https://doi.org/10.1016/j.agwat.2017.02.004

  43. Yin Y, Huang YF, Huang GH (2002) An integrated approach for evaluating adaptation options to reduce climate change vulnerability in coastal region of the Georgia Basin. Geogr Inf Sci 8:86–96. https://doi.org/10.1080/10824000209480577

  44. Zadeh LA (1965) Fuzzy sets, inform. Control 8:338–353

  45. Zamani R, Berndtsson R (2019) Evaluation of CMIP5 models for west and Southwest Iran using TOPSIS-based method. Theor Appl Climatol 137:533–543. https://doi.org/10.1007/s00704-018-2616-0

  46. Zamani R, Akhond-Ali AM, Ahmadianfar I, Elagib NA (2017) Optimal reservoir operation under climate change based on a probabilistic approach. J Hydrol Eng 22:5017019. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001559

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Acknowledgements

The authors would like to gratefully acknowledge the Khuzestan Water and Power Authority (KWPA) for their sharing data. Also, we are grateful to the all anonymous reviewers and editorial boards for their valuable comments and effort to improve the manuscript.

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Correspondence to Reza Zamani.

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Zamani, R., Ali, A.M.A. & Roozbahani, A. Evaluation of Adaptation Scenarios for Climate Change Impacts on Agricultural Water Allocation Using Fuzzy MCDM Methods. Water Resour Manage 34, 1093–1110 (2020). https://doi.org/10.1007/s11269-020-02486-8

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Keywords

  • Adaptation scenarios
  • Climate change
  • Fuzzy MCDM
  • Agriculture
  • Reservoir operation