Skip to main content

Advertisement

SpringerLink
Log in

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

  • Published:
Water Resources Management Aims and scope Submit manuscript

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.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

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

    Article  Google Scholar 

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

    Chapter  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Water Resource Planning Center, Khuzestan Water and Power Authority (KWPA), Ahvaz, Iran

    Reza Zamani

  2. Department of Hydrology and Water Resources Engineering, Faculty of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Reza Zamani & Ali Mohammad Akhond Ali

  3. Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran, Tehran, Iran

    Abbas Roozbahani

Authors
  1. Reza Zamani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Mohammad Akhond Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abbas Roozbahani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Reza Zamani.

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

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-020-02486-8

Keywords

Search

Navigation