Predicting the impacts of the changes in crop pattern on water table level and electrical conductivity of an unconfined aquifer using a Bayesian decision network

Document Type : Research Paper

Authors

1 Graduate M.Sc. of Watershed Management, Faculty of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences & Natural Resources, Gorgan, Iran

2 Associate Professor, Department of Watershed Management, Faculty of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences & Natural Resources, Gorgan, Iran

3 Assistant Professor, Department of Arid Zone Management, Faculty of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences & Natural Resources, Gorgan, Iran

Abstract

The aim of this research is to predict the effects of cropping pattern changes on the water table level and electrical conductivity of an unconfined aquifer in the Ali-Abad region using a Bayesian Decision Network (BDN) as an integrated approach. A conceptual model framework illustrating the relationship among variables in the system was developed and three management scenarios of crop pattern (current condition, high water demand crops, and low water demand crops) were considered for the study area. Marginal probability, Conditional Probability Tables (CPTs) and total probabilities were characterized using observed data, results of CROPWAT model simulation, and expert knowledge. The Netica software was used to run the Bayesian model and to analyze the sensitivity of the model with two criteria namely reduction of variance and belief variance. The analysis shows that the probability of groundwater depletion occurring for low water requirement crops will be about five per cent less than that in the current condition. The probability of improvement in electrical conductivity for low water requirement crops will be about two per cent greater than that for the current crop pattern. The sensitivity analysis of the Bayesian model shows that the water requirement of crops plays an important role in unconfined aquifer's characteristics. The results demonstrate that the BDNs are capable to help planners for improved management of groundwater resources by predicting and displaying the effects of crop pattern changes on the quantitative and qualitative characteristics of unconfined aquifers in a probabilistic context.

Keywords

References

  1. Appelo CAJ, Postma D. Geochemistry, Groundwater and Pollution, 2nd Edn., 683 pp. London: Taylor & Francis, Leiden, The Netherlands; 2005. 683 p.
  2. Hillel Environmental soil physics: Fundamentals, applications, and environmental considerations. Elsevier; 1998.
  3. Emamgholizadeh Water quality assessment of the Kopal River (Iran). In: International Meeting on Soil Fertility Land Management and Agroclimatology. Turkey: Adnan Menderes Üniversitesi Ziraat Fakültesi Dergisi; 2008. p. 827–37.
  4. Riki Predicting the effects of changing cultivation patterns on groundwater characteristics using Bayesian decision networks [MSc Thesis]. [Gorgan]: Gorgan University of Agricultural Sciences and Natural Resource; 2006.
  5. Castelletti A, Soncini-Sessa R. Bayesian Networks and participatory modelling in water resource management. Environ Model Softw. 2007 Aug 1;22(8):1075–88.
  6. Molina J-L, Pulido-Velázquez D, García-Aróstegui JL, Pulido-Velázquez M. Dynamic Bayesian Networks as a Decision Support tool for assessing Climate Change impacts on highly stressed groundwater systems. Journal of Hydrology. 2013 Feb 4;479:113–29.
  7. Sadeghi Hesar A, Tabatabaee H, Jalali M. Monthly rainfall forecasting using bayesian belief networks. International Research Journal of Applied and Basic Sciences. 2012;3(11):2226–31.
  8. Carmona G, Varela-Ortega C, Bromley J. Stakeholder involvement in water management using Object-oriented Bayesian networks and economic models in Spain. 2009 Conference, August 16-22, 2009, Beijing, China. International Association of Agricultural Economists; 2009 [cited 2022 Jan 11]. (2009 Conference, August 16-22, 2009, Beijing, China). Report No.: 49897.
  9. Henriksen HJ, Barlebo HC. Reflections on the use of Bayesian belief networks for adaptive management. Journal of Environmental Management. 2008 Sep 1;88(4):1025–36.
  10. Li RA, McDonald JA, Sathasivan A, Khan SJ. A multivariate Bayesian network analysis of water quality factors influencing trihalomethanes formation in drinking water distribution systems. Water Research. 2021 Feb 15;190:116712.
  11. Haakonsson S, Rodríguez MA, Carballo C, Pérez M del C, Arocena R, Bonilla S. Predicting cyanobacterial biovolume from water temperature and conductivity using a Bayesian compound Poisson-Gamma model. Water Research. 2020 Jun 1;176:115710.
  12. Xie X, Liu Y, Luo Y, Du Q. Surface Water Quality Evaluation Based on Bayesian Network. Journal of Coastal Research. 2019 Sep 1;93(SI):54–60.
  13. Mohajerani H, Kholghi M, Mosaedi A, Farmani R, Sadoddin A, Casper M. Application of Bayesian Decision Networks for Groundwater Resources Management Under the Conditions of High Uncertainty and Data Scarcity. Water Resour Manage. 2017 Apr 1;31(6):1859–79.
  14. Sadoddin A, Shahabi M, Sheikh VB. A Bayesian decision model for drought management in rainfed wheat farms of North East Iran. International Journal of Plant Production. 2016;10(4):527–42.
  15. Troldborg M, Aalders I, Towers W, Hallett PD, McKenzie BM, Bengough AG, et al. Application of Bayesian Belief Networks to quantify and map areas at risk to soil threats: Using soil compaction as an example. Soil and Tillage Research. 2013 Aug 1;132:56–68.
  16. Behboudi Applying the decision model to predict the effects of biophysics and economics - the management of management actions is based on the fact that there is something in the water [MSc Thesis]. [Gorgan, Iran]: Gorgan University of Agricultural Sciences and Natural Resource; 2013.
  17. Molina JL, Bromley J, García-Aróstegui JL, Sullivan C, Benavente J. Integrated water resources management of overexploited hydrogeological systems using Object-Oriented Bayesian Networks. Environmental Modelling & Software. 2010 Apr 1;25(4):383–97.
  18. Ticehurst JL, Newham LTH, Rissik D, Letcher RA, Jakeman AJ. A Bayesian network approach for assessing the sustainability of coastal lakes in New South Wales, Australia. Environmental Modelling & Software. 2007 Aug 1;22(8):1129–39.
  19. Jensen FV, Kjarulff U. Bayesian networks and decision graphs: A 3-week course at Reykjavik University. Group of Machine Intelligence Department of Computer Science, Aalborg University; 2005.
  20. Davies Bayesian Decision Networks for Management of High Conservation Assets (National Water Initiative – Australian Government Water Fund. Report 6/6 Report to the Conservation of Freshwater Ecosystem Values Project, Water Resources Division, Department of Primary Industries and Water). Hobart: DPIWE, Hobart and Freshwater Systems; 2007 Oct.
  21. Pollino CA, Henderson C. Bayesian networks: A guide for their application in natural resource management and policy. Department of Environment, Water Heritage and the Arts, Australia Gov.: LANDSCAPE LOGIC; 2010 p. 48. Report No.: 14.
  22. Kuikka S, Varis O. Uncertainties of climatic change impacts in Finnish watersheds: a Bayesian network analysis of expert knowledge. Boreal Environment Research. 1997;2.
  23. Kragt A Beginners Guide to Bayesian Network Modelling for Integrated Catchment Management. Landscape Logic Technical Report No. 9. Australia: Department of Environment, Water Heritage and the Arts; 2009 p. 22. Report No.: 9.
  24. Salih AM, Sendil U. Evapotranspiration under extremely arid climates. Journal of irrigation and drainage engineering. 1984;110(3):289–303.
  25. Ghodrati Training in the application of ArcGIS in water engineering (hydrology and hydrogeology). Vol. 0. Tehran, Iran: Simai Danesh; 2012. 210 p.
  26. Civil Research Consulting Engineers. Mohammadi Mohammadi. Golestan Regional Water Company; 2010 p. 93.
  27. Ng Smy, Wai Who, Xu Zh, Li Ys, Jiang Yw. Application of GIS for retrieval and display of hydrodynamic and water quality data for the pearl river estuary. In: 4th International Conference on Environmental Informatics, ISEIS 2005. Xiamen, China: International Society for Environmental Information Sciences; 2005. p. 372–8.
  28. Marinoni Improving geological models using a combined ordinary–indicator kriging approach. Engineering Geology. 2003 Apr 1;69(1):37–45.
  29. Rezaei D, Tajdari K, Aboulpour B. Investigation The Spatial Variability Of Some Important Groundwater Quality Factors In Guilan, Iran. Journal of Water And Soil (Agricultural Sciences And Technology). 2010 Jan 1;24(5):932–41.
  30. Wu J, Zheng C, Chien CC. Cost-effective sampling network design for contaminant plume monitoring under general hydrogeological conditions. Journal of Contaminant Hydrology. 2005 Mar 1;77(1):41–65.
  31. Mohammadi Pedometry: Spatial Statistics. Vol. 2. Tehran, Iran: Pelk; 2007 [cited 2022 Jan 11]. 454 p.
  32. Norsys. Netica’s help system. Web Page. Norsys Software; 2011.
  33. Karimov AKh, Šimůnek J, Hanjra MA, Avliyakulov M, Forkutsa I. Effects of the shallow water table on water use of winter wheat and ecosystem health: Implications for unlocking the potential of groundwater in the Fergana Valley (Central Asia). Agricultural Water Management. 2014 Jan 1;131:57–69.
  34. Mende A, Astorga A, Neumann D. Strategy for groundwater management in developing countries: A case study in northern Costa Rica. Journal of Hydrology. 2007 Feb 20;334(1):109–24.
  35. Jeyaruba T, Thushyanthy M. The effect of agriculture on quality of groundwater: A case study. Middle-East Journal of Scientific Research. 2009;4(2):110–4.
  36. Lerner DN, Harris B. The relationship between land use and groundwater resources and quality. Land Use Policy. 2009 Dec 1;26:S265–73.

Barton DN, Saloranta T, Moe SJ, Eggestad HO, Kuikka S. Bayesian belief networks as a meta-modelling tool in integrated river basin management — Pros and cons in evaluating nutrient abatement decisions under uncertainty in a Norwegian river basin. Ecological Economics. 2008 May 15;66(1):91–104. 

Water Resources Engineering
Volume 14, Issue 51
January 2022
Pages 111-127

Files

History

  • Receive Date: 05 February 2021
  • Revise Date: 16 April 2021
  • Accept Date: 19 January 2022

Share

How to cite

Statistics