Skip to main content

Advertisement

SpringerLink
Log in

Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches

  • Published:
Environment, Development and Sustainability Aims and scope Submit manuscript

Abstract

One of the most crucial issues in water and food security is to assess the impacts of climate change on virtual water content (VWC) and crop yield of agricultural products. The objective of this study is to efficiently predict the VWC patterns and yields of different crops under various climate change conditions using three data mining approaches including artificial neural network (ANN), genetic programming (GP), and support vector machine (SVM). The study region included the eastern provinces of Iran containing North Khorasan, Khorasan Razavi, South Khorasan, Sistan-Baluchestan (in a range of latitude from 25 to 40°N). Specifically, VWC and crop yields were estimated for both baseline period (1985–2005) and several climate change conditions including four time horizons (2030, 2050, 2070, and 2090) under RCPs 2.6, 4.5, and 8.5 based on the second generation Canadian Earth System Model (CanESM2). The data mining models were evaluated with the RMSE and NSE goodness-of-fit criteria. The results showed that the SVM model achieved the highest NSE and lowest RMSE values. It was also found that under the climate change conditions, VWC increased from 6 to 42%, while crop yield decreased from 8 to 53% for all products in the southern regions. An opposite trend was observed in the northern regions for wheat and barley with an increase from 12 to 72% for VWC and from 4 to 27% for the yield.

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Akbari-Alashti, H., Bozorg-Haddad, O., Fallah-Mehdipour, E., & Mariño, M. A. (2014). Multi-reservoir real-time operation rules: A new genetic programming approach. Proceedings of the Institution of Civil Engineers: Water Management, 167(10), 561–576. https://doi.org/10.1680/wama.13.00021

    Article  Google Scholar 

  • Allan, J. A. (1997). “Virtual water: a long term solution for water short Middle Eastern economies?” London: School of Oriental and African Studies, University of London, (September), 24–29.

  • Allen, R. G., Pereira, L. S., Raes, D., and Smith, M. (1998). “Crop evapotranspiration-Guidelines for computing crop water requirements.” FAO Irrigation and drainage, paper 56.

  • Arefinia, A., Bozorg-Haddad, O., & Chang, H. (2021). “The Role of Data Mining in Water Resources Management.” In Essential Tools for Water Resources Analysis, Planning, and Management (pp. 85–99). Springer, Singapore.

  • Arefinia, A., Bozorg-Haddad, O., Oliazadeh, A., & Loáiciga, H. A. (2020). Reservoir water quality simulation with data mining models. Environmental Monitoring and Assessment, 192(7), 1–13.

    Article  Google Scholar 

  • Asefa, T., Kemblowski, M., McKee, M., & Khalil, A. (2006). Multi-time scale stream flow predictions: The support vector machines approach. Journal of Hydrology, 318(1–4), 7–16.

    Article  Google Scholar 

  • Ba, H., Guo, S., Wang, Y., Hong, X., Zhong, Y., & Lio, Z. (2017). Improving ANN model in runoff forecasting by adding soil moisture input and using data preprocessing techniques. Hydrology Research. https://doi.org/10.2166/nh.2017.048

    Article  Google Scholar 

  • Banzhaf, W., Keller, R. E., and Francone, F. (1998). “Genetic programming: An introduction on the automatic evolution of computer programs and its applications machine code genetic programming and parallelism view project drone Squadron optimization view project.”

  • Behzad, M., Asghari, K., Eazi, M., & Palhang, M. (2009). Generalization performance of support vector machines and neural networks in runoff modeling. Expert Systems with Applications, 36(4), 7624–7629.

    Article  Google Scholar 

  • Bozorg-Haddad, O., Zolghadr-Asli, B., Sarzaeim, P., Aboutalebi, M., Chu, X., and Loáiciga, H. A. (2020). “Evaluation of water shortage crisis in the Middle East and possible remedies.“ Journal of Water Supply: Research and Technology—AQUA, 69(1), 85–98.

  • Bozorg-Haddad, O., Afshar, A., & Mariño, M. A. (2009a). Optimization of non-convex water resource problems by honey-bee mating optimization (HBMO) algorithm. Engineering Computations (swansea, Wales), 26(3), 267–280. https://doi.org/10.1108/02644400910943617

    Article  Google Scholar 

  • Bozorg-Haddad, O., Moradi-Jalal, M., Mirmomeni, M., Kholghi, M. K. H., & Mariño, M. A. (2009b). Optimal cultivation rules in multi-crop irrigation areas. Irrigation and Drainage, 58(1), 38–49. https://doi.org/10.1002/ird.381

    Article  Google Scholar 

  • Bozorg-Haddad, O., Solgi, M., & Loáiciga, H. A. (2017). Meta-heuristic and evolutionary algorithms for engineering optimization. John Wiley & Sons.

    Book  Google Scholar 

  • Cortes, C. and Vapnik, V. (1995). “Support-vector networks.” Machine Learning, Springer Science and Business Media LLC, 20(3), 273–297.”

  • Das, S. K., Samui, P., & Sabat, A. K. (2012). Prediction of field hydraulic conductivity of clay liners using an artificial neural network and support vector machine. International Journal of Geomechanics, 12(5), 606–611.

    Article  Google Scholar 

  • De Angelis, E., Metulini, R., Bove, V., & Riccaboni, M. (2017). Virtual water trade and bilateral conflicts. Advances in Water Resources, 110, 549–561.

    Article  Google Scholar 

  • Dobesch, H., Pierre, D., & Izabela, D. (2013). Spatial interpolation for climate data: the use of GIS in climatology and meteorology. Hoboken: Wiley.

    Google Scholar 

  • IPCC, (2014). "Climate change 2014: Mitigation of climate change." Contribution of working Group III to the fifth assessment report of the intergovernmental panel on climate change, Cambridge University Press, Cambridge, United Kingdom and New York, NY.

  • Fallah-Mehdipour, E., Bozorg-Haddad, O., Beygi, S., & Mariño, M. A. (2011). Effect of utility function curvature of Young’s bargaining method on the design of WDNs. Water Resources Management, 25(9), 2197–2218. https://doi.org/10.1007/s11269-011-9802-5

    Article  Google Scholar 

  • Fang, S., Pei, H., Liu, Z., Beven, K., Wei, Z., Fang, S., Liu, Z., Wei, Z., Beven, K., & Pei, H. (2010). Water resources assessment and regional virtual water potential in the Turpan Basin, China. Water Resour Management, 24, 3321–3332.

    Article  Google Scholar 

  • Ghasemi, A., Heidarnejad, P., & Noorpoor, A. (2018). A novel solar-biomass based multi-generation energy system including water desalination and liquefaction of natural gas system: Thermodynamic and thermoeconomic optimization. Journal of Cleaner Production, 196, 424–437.

    Article  Google Scholar 

  • Habibifar, R., Khoshjahan, M., Saravi, V. S., & Kalantar, M. (2021). “Robust Energy Management of Residential Energy Hubs Integrated with Power-to-X Technology.” In 2021 IEEE Texas Power and Energy Conference (TPEC) (pp. 1–6). IEEE.

  • Han, C., Zhang, B., Chen, H., Wei, Z., & Liu, Y. (2019). Spatially distributed crop model based on remote sensing. Agricultural Water Management, 218, 165–173.

    Article  Google Scholar 

  • Hoekstra, A. (2003). ”Virtual water: An introduction .” virtual water trade 13.

  • Hoekstra, A. (2009). “Water security of nations: How international trade affects national water scarcity and dependency.” In “Threats to global water security” , pp. 27–36. Springer.

  • Hoekstra, A. Y., & Hung, P. Q. (2005). Globalisation of water resources: International virtual water flows in relation to crop trade. Global Environmental Change, 15(1), 45–56.

    Article  Google Scholar 

  • IPCC, (2014). "Climate change 2014: Impacts, adaptation and vulnerability. "Contribution of working Group II to the fifth assessment report of the intergovernmental panel on climate change, Cambridge University Press, Cambridge, United Kingdom and New York, NY.

  • IPCC. (2014b). Climate change 2014: Synthesis report. Cambridge University Press.

    Google Scholar 

  • Jahandideh-Tehrani, M., Jenkins, G., & Helfer, F. (2020). A comparison of particle swarm optimization and genetic algorithm for daily rainfall-runoff modelling: A case study for Southeast Queensland, Australia. Optimization and Engineering. https://doi.org/10.1007/s11081-020-09538-3

    Article  Google Scholar 

  • Karim, S. H. T., Tofiq, T. A., Shariati, M., Rad, H. N., & Ghasemi, A. (2021). 4E analyses and multi-objective optimization of a solar-based combined cooling, heating, and power system for residential applications. Energy Reports, 7, 1780–1797.

    Article  Google Scholar 

  • Khu, S. T., Liong, S. Y., Babovic, V., Madsen, H., & Muttil, N. (2001). Genetic programming and its application in real-time runoff forecasting. Journal of the American Water Resources Association, American Water Resources Association, 37(2), 439–451.

    Article  Google Scholar 

  • Koza, J. R. (1994). Genetic programming: On the programming of computers by means of natural selection. Biosystems, MIT Press, 33(1), 69–73.

    Google Scholar 

  • Levenberg, K. (1944). A method for the solution of certain non-linear problems in least squares. Quarterly of Applied Mathematics, 2(2), 164–168.

    Article  Google Scholar 

  • Liu, J., & Savenije, H. H. G. (2008). Food consumption patterns and their effect on water requirement in China. Hydrology and Earth System Sciences, 12(3), 887–898.

    Article  Google Scholar 

  • Mahjoobi, J., & Adeli Mosabbeb, E. (2009). Prediction of significant wave height using regressive support vector machines. Ocean Engineering, 36(5), 339–347.

    Article  Google Scholar 

  • Maity, R., Bhagwat, P. P., & Bhatnagar, A. (2010). Potential of support vector regression for prediction of monthly streamflow using endogenous property. Hydrological Processes, 24(7), 917–923.

    Article  Google Scholar 

  • Malik, A., and Bhagwat, A. (2021). “Modelling groundwater level fluctuations in urban areas using artificial neural network.” Groundwater for Sustainable Development, 12, 100484.

  • Marquardt, D. W. (1963). An algorithm for least-Squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, Society for Industrial & Applied Mathematics (SIAM), 11(2), 431–441.

    Article  Google Scholar 

  • Mohandes, M. A., Halawani, T. O., Rehman, S., & Hussain, A. A. (2004). Support vector machines for wind speed prediction. Renewable Energy, 29(6), 939–947.

    Article  Google Scholar 

  • Nabavi, M., Nazarpour, V., Alibak, A. H., Bagherzadeh, A., & Alizadeh, S. M. (2021b). “Smart tracking of the influence of alumina nanoparticles on the thermal coefficient of nanosuspensions: application of LS-SVM methodology.” Applied Nanoscience, 1–16.

  • Nabavi, M., Elveny, M., Danshina, S. D., Behroyan, I., & Babanezhad, M. (2021a). Velocity prediction of Cu/water nanofluid convective flow in a circular tube: Learning CFD data by differential evolution algorithm based fuzzy inference system (DEFIS). International Communications in Heat and Mass Transfer, 126, 105373.‏

  • Niu, W. J., and Feng, Z. K. (2021). “Evaluating the performances of several artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management.” Sustainable Cities and Society, 64, 102562.

  • Orouji, H., Bozorg-Haddad, O., Fallah-Mehdipour, E., & Mariño, M. A. (2014). Extraction of decision alternatives in project management: Application of hybrid PSO-SFLA. Journal of Management in Engineering, 30(1), 50–59. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000186

    Article  Google Scholar 

  • Pongratz, J., Lobell, D. B., Cao, L., & Caldeira, K. (2012). Crop yields in a geoengineered climate. Nature Climate Change, 2(2), 101–105.

    Article  CAS  Google Scholar 

  • Quan, Q., Gao, S., Shang, Y., & Wang, B. (2021). “Assessment of the sustainability of Gymnocypris eckloni habitat under river damming in the source region of the Yellow River”. The Science of the total environment, 778, 146312.

  • Sabbaghpour, S., Naghashzadehgan, M., Javaherdeh, K., & Bozorg-Haddad, O. (2012). HBMO algorithm for calibrating water distribution network of Langarud city. Water Science and Technology, 65(9), 1564–1569.

    Article  Google Scholar 

  • Salehi, A., Karbassi, A., Ghobadian, B., Ghasemi, A., & Doustgani, A. (2019). “Simulation process of biodiesel production plant.” Environmental Progress & Sustainable Energy, 38(6), e13264.

  • Salmoral, G., & Yan, X. (2018). Food-energy-water nexus: A life cycle analysis on virtual water and embodied energy in food consumption in the Tamar catchment, UK. Resources, Conservation and Recycling, 133, 320–330.

    Article  Google Scholar 

  • Sarzaeim, P. and Bozorg-Haddad, O. (2015). "Runoff prediction under climate change conditions: artificial neural network." Proceeding of the third international conference on advances in civil, structural and environmental engineering, Zurich, Switzerland, 10–11 October.

  • Sarzaeim, P., Muñoz-Arriola, F., and Jarquin, D. (2020). “Analytics for climate-uncertainty estimation and propagation in maize-phenotype predictions.” American Society of Agricultural and Biological Engineers. In 2020 ASABE Annual International Virtual Meeting (p. 1).

  • Sarzaeim, P., Bozorg-Haddad, O., Bozorgi, A., & Loáiciga, H. A. (2017). Runoff projection under climate change conditions with data-mining methods. Journal of Irrigation and Drainage Engineering, 143(8), 04017026.

    Article  Google Scholar 

  • Sarzaeim, P., Bozorg-Haddad, O., Zolghadr-Asli, B., Fallah-Mehdipour, E., & Loáiciga, H. A. (2018). Optimization of run-of-river hydropower plant design under climate change conditions. Water Resources Management, 32(12), 3919–3934.

    Article  Google Scholar 

  • Savic, D. A., Walters, G. A., & Davidson, J. W. (1999). A genetic programming approach to rainfall-runoff modelling. Water Resources Management, Kluwer Academic Publishers, 13(3), 219–231.

    Article  Google Scholar 

  • Secci, D., Patriche, C. V., Ursu, A., & Sfîcă, L. (2010). Spatial interpolation of mean annual precipitations in Sardinia. A comparative analysis of several methods. Geographia Technica, 9(1), 68–75.

    Google Scholar 

  • Sepahvand, R., Safavi, H. R., & Rezaei, F. (2019). Multi-objective planning for conjunctive use of surface and ground water resources using genetic programming. Water Resources Management, 33(6), 2123–2137.

    Article  Google Scholar 

  • Shayesteh, A. A., Koohshekan, O., Ghasemi, A., Nemati, M., & Mokhtari, H. (2019). Determination of the ORC-RO system optimum parameters based on 4E analysis; Water–Energy-Environment nexus. Energy Conversion and Management, 183, 772–790.

    Article  CAS  Google Scholar 

  • Sivapragasam, C., Vasudevan, G., & Vincent, P. (2007). Effect of inflow forecast accuracy and operating time horizon in optimizing irrigation releases. Water Resources Management, 21, 933–945.

    Article  Google Scholar 

  • Talebizadehsardari, P., Ehyaei, M. A., Ahmadi, A., Jamali, D. H., Shirmohammadi, R., Eyvazian, A., ... & Rosen, M. A. (2020). “Energy, exergy, economic, exergoeconomic, and exergoenvironmental (5E) analyses of a triple cycle with carbon capture.” Journal of CO2 Utilization, 41, 101258.

  • Vapnik, V. (1998). Statistical learning theory. John Wiley.

    Google Scholar 

  • Wichelns, D. (2005). The virtual water metaphor enhances policy discussions regarding scarce resources. Water International, 30, 428–437.

    Article  Google Scholar 

  • Yang, Y., Li, Y., Yao, J., Iglauer, S., Luquot, L., Zhang, K., Wang, Z. (2020). Dynamic Pore‐Scale Dissolution by CO2‐Saturated Brine in Carbonates: Impact of Homogeneous Versus Fractured Versus Vuggy Pore Structure. Water resources research, 56(4).

  • Yang, H., Wang, L., Abbaspour, K. C., & Zehnder, A. J. B. (2006). Virtual water trade: An assessment of water use efficiency in the international food trade. Hydrology and Earth System Sciences, 10, 443–454.

    Article  Google Scholar 

  • Zhang, B., Xu, D., Liu, Y., Li, F., Cai, J., & Du, L. (2016). Multi-scale evapotranspiration of summer maize and the controlling meteorological factors in north China. Agricultural and Forest Meteorology, 216, 1–12. https://doi.org/10.1016/j.agrformet.2015.09.015

    Article  Google Scholar 

  • Zhang, K., Chao, L., Wang, Q., Huang, Y., Liu, R., Hong, Y., & Ye, J. (2019a). Using multi-satellite microwave remote sensing observations for retrieval of daily surface soil moisture across China. Water Science and Engineering, 12(2), 85–97.

    Article  Google Scholar 

  • Zhang, K., Wang, Q., Chao, L., Ye, J., Li, Z., Yu, Z., & Ju, Q. (2019b). Ground observation-based analysis of soil moisture spatiotemporal variability across a humid to semi-humid transitional zone in China. Journal of Hydrology (amsterdam), 574, 903–914.

    Article  Google Scholar 

  • Zhao, Q., Liu, J., Khabarov, N., Obersteiner, M., and Westphal, M. (2014). “Ecological informatics impacts of climate change on virtual water content of crops in China.” Ecological Informatics, Elsevier B.V., 19, 26–34.

  • Zhou, H. (2020). “Artificial Neural Network.” In Learn Data Mining Through Excel (pp. 163–187). Apress, Berkeley, CA.

  • Zolghadr-Asli, B., Bozorg-Haddad, O., & Chu, X. (2019a). Effects of the uncertainties of climate change on the performance of hydropower systems. Journal of Water and Climate Change, 10(3), 591–609.

    Article  Google Scholar 

  • Zolghadr-Asli, B., Bozorg-Haddad, O., Sarzaeim, P., & Chu, X. (2019b). Investigating the variability of GCMs’ simulations using time series analysis. Journal of Water and Climate Change, 10(3), 449–463.

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank Iran’s National Science Foundation (INSF) for its financial support for this research.

Author information

Authors and Affiliations

  1. Department of Irrigation and Reclamation Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Tehran, Iran

    Ali Arefinia, Omid Bozorg-Haddad, Javad Bazrafshan & Babak Zolghadr-Asli

  2. Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resource, University of Tehran, Tehran, Iran

    Khaled Ahmadaali

  3. Department of Civil and Environmental Engineering, North Dakota State University, Dept 2470, Fargo, ND, 58108-6050, USA

    Xuefeng Chu

Authors
  1. Ali Arefinia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Omid Bozorg-Haddad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Khaled Ahmadaali
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Javad Bazrafshan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Babak Zolghadr-Asli
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xuefeng Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Omid Bozorg-Haddad.

Ethics declarations

Conflict of interest

The author declared that there is no conflict of interest.

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

Arefinia, A., Bozorg-Haddad, O., Ahmadaali, K. et al. Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches. Environ Dev Sustain 24, 8378–8396 (2022). https://doi.org/10.1007/s10668-021-01788-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10668-021-01788-0

Keywords

Search

Navigation