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A Framework for an Agent-Based Model to Manage Water Resources Conflicts

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Abstract

Competition for use of water is increasing and leads to many conflicts among competing interests with complex goals and water management systems. Technical system models are essential to create performance and other decision information, but models to simulate views of the competing parties are also needed to help resolve or mitigate conflicts. Agent-based models (ABMs) offer promise to fill this role, and in this study a new approach to agent-based modeling is introduced to simulate the behavior and interactions of the parties participating in a conflict scenario, which is modeled as a game. To develop this framework, we considered water issues of California’s Sacramento-San Joaquin Delta region as an example of a long-standing situation, with emphasis on the San Joaquin watershed. However, this approach can be used in other watersheds and more complex systems. The ABM explains the interactions among the parties and how they can be encouraged to cooperate in the game to work toward a solution. The model also enables decision-makers to test management scenarios and understand the consequences of their decisions on different stakeholders and their behaviors.

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References

  • Axtell RL (2000) Why agents? On the varied motivations for agent computing in the social sciences. In: Macal CM, Sallach D (eds) Proceedings of the workshop on agent simulation: applications, models, and tools. Argonne Natl. Lab, Argonne, pp 3–24

    Google Scholar 

  • Bandini S, Manzoni S, Vizzari G (2009) Agent-based modeling and simulation: an informatics perspective. J Artif Soc Social Simul 12(4):4, http://jasss.soc.surrey.ac.uk/12/4/4.html

    Google Scholar 

  • Bars ML, Attonaty JM, Pinson S (2002) An agent-based simulation for water sharing between different users. In: International proceedings of the first international joint conference on autonomous agents and multi agent systems, Bologna, Italy

  • Barthel R, Janisch S, Nickel D, Trifkovic A, Hörhan T (2010) Using the multiactor-approach in GLOWA-Danube to simulate decisions for thewater supply sector under conditions of global climate change. Water Resour Manag 24:239–275. doi:10.1007/s11269-009-9445-y

    Article  Google Scholar 

  • Berger T (2001) Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis. Agric Econ 25(2–3):245–260. doi:10.1111/j.1574-0862.2001.tb00205.x

    Article  Google Scholar 

  • Bonabeau E (2002) Agent-based modeling: methods and techniques for simulating human systems. Proc Natl Acad Sci 99(suppl 3):7280–7287

    Article  Google Scholar 

  • Bousquet F, Le Page C (2004) Multi-agent simulations and ecosystem management: a review. Ecol Modell 176:313–332. doi:10.1016/j.ecolmodel.2004.01.011

    Article  Google Scholar 

  • Brooks RA (1986) A robust layered control system for a mobile robot. IEEE J Robot Autom 2:14–23

    Article  Google Scholar 

  • CALFED (2000) Implementation plan. Final Programmatic EIS/EIR Technical Appendix

  • Chu J, Wang C, Chen J, Wang H (2009) Agent-based residentialwater use behavior simulation and policy implications: a case-study in Beijing City. Water Resour Manag 23:3267–3295. doi:10.1007/s11269-009-9433-2

    Article  Google Scholar 

  • Edmonds B, Bartheleny O, Moss S (2002) Domestic water demand and social influence: an agent-based modeling approach. CPM Report 02–103

  • Edwards M, Ferrand N, Goreaud F, Huet S (2005) The relevance of aggregating a water consumption model cannot be disconnected from the choice of information available on the resource. Simul Model Pract Theory 13(4):287–307, Available at: 10.1016/j.simpat.2004.11.008 [Accessed April 28, 2011]

    Article  Google Scholar 

  • Epstein JM (1999) Agent-based computational models and generative social science. Complexity 4:41–60. doi:10.1002/(SICI)1099-0526(199905/06)4:5<41::AID-CPLX9>3.0.CO;2-F

    Article  Google Scholar 

  • Galán JM, López-Paredes A, del Olmo R (2009) An agent-based model for domestic water management in Valladolid metropolitan area. Water Resour Res 45, W05401. doi:10.1029/2007WR006536

    Article  Google Scholar 

  • Gilbert N, Troitzsch KG (1999) Simulation for the social scientist. Open University Press, Buckingham

    Google Scholar 

  • Hanemann M, Dyckman C (2009) The San Francisco Bay-Delta: a failure of decision-making capacity. Environ Sci Policy 12(6):710–725

    Article  Google Scholar 

  • Hare MP (2000) Agent-base integrated assessment of policies for reducing groundwater pollution by nitrates from agricultural fertilizer, part I: pilot study model description and initial results. Working Report, Swiss Federal Institute of Environmental Science and Technology

  • Howitt RE, Kaplan J, Larson D, MacEwan D, Medellín-Azuara J, Horner G, Lee NS (2009) The economic impacts of Central Valley salinity. University of California Davis, Final Report to the State Water Resources Control Board Contract 05-417-150-0

  • Izquierdo LR, Gotts NM, Polhill JG (2003) FEARLUS - W: an agent-based model of river basin land use and water management. In: Dijst M, Schot P, de Jong K (eds) Framing land use dynamics. Reviewed abstracts International Conference, Faculty of Geographical Sciences, Utrecht University, Utrecht, The Netherlands, 16–18 April 2003. pp 163–165

  • Kennedy WG, Hailegiorgis AB, Rouleau M, Bassett JK, Coletti M, Balan GC, Gulden T (2010) An agent-based model of conflict in East Africa and the effect of watering holes, Proceedings of the 19th Conference on Behavior Representation in Modeling and Simulation, Charleston, SC, 21–24 March 2010

  • Kock BE (2008) Agent-based models of socio-hydrological systems for exploring the institutional dynamics of water resources conflict. MS Thesis, Massachusetts Institute of Technology

  • Little Hoover Commission (2009) Clearer structure, cleaner water: improving the performance and outcomes at the state water boards. State of California, Sacramento, CA

  • Lund JR, Palmer RN (1997) Water resource system modeling for conflict resolution. Water Resources Update, Issue No. 108, Summer, pp. 70–82

  • Lund JR, Hanak E, Fleenor W, Howitt R, Mount J, Moyle P (2007) Envisioning futures for the Sacramento-San Joaquin Delta. Public Policy Institute of California, San Francisco, CA, 300 pp. http://www.ppic.org/content/pubs/report/R_207JLR.pdf

  • Lund JR, Hanak E, Fleenor W, Bennett W, Howitt R, Mount J, Moyle P (2010) Comparing futures for the Sacramento-San Joaquin Delta. University of California Press, Berkeley

    Book  Google Scholar 

  • Macal CM, North MJ (2006a) Tutorial on agent-based modeling and simulation part 2: how to model with agents. Proc. of 2006 Winter Simulation Conference, pp.73–83

  • Macal CM, North MJ (2006b) Introduction to modeling and simulation (37 presentation slides). MCS LANS Informal Seminar, Argonne National Library, Available at: www.cas.anl.gov, Accessed March 29 2010

  • Macaulay S (2001) Resolution of water resources issues in the State of California. United States of America: The CALFED Bay-Delta Program, presented at III Encuentro de las Aguas, October 24–26, 2001, Santiago, Chile

  • Macy MW, Willer R (2002) From factors to actors: computational sociology and agent-based modeling. Annu Rev Sociol 28:143–166

    Article  Google Scholar 

  • Madani K, Lund JR (2012) California’s Sacramento-San Joaquin Delta conflict: from cooperation to chicken. Water Resour Plan Manag 138(2):90–99

    Article  Google Scholar 

  • Ng T, Eheart J, Cai X, Braden JB (2010) A watershed-scale agent-based model incorporating agent learning and interaction of farmers’ decisions subject to carbon and Miscanthus prices. American Geophysical Union, Fall Meeting 2010, abstract #H43C-1252

  • Nikolic VV, Simonovic SP, Milicevic DB (2013) Analytical support for integrated water resources management: a new method for addressing spatial and temporal variability. Water Resour Manag 27:401–417. doi:10.1007/s11269-012-0193-z

    Article  Google Scholar 

  • Parker DC, Manson SM, Janssen MA, Hoffmann MJ, Deadman P (2003) Multi-agent systems for the simulation of land-use and land-cover change: a review. Ann Assoc Am Geogr 93(2):314–337

    Article  Google Scholar 

  • Peterson DH, Cayan DR, Dettinger MD, Noble M, Riddle LG, Schemel LE, Smith RE, Uncles RJ, Walters R (1996) San Francisco Bay salinity: Observations. Numerical simulation, and statistical models. In: Hollibaugh JT (ed) San Francisco Bay–the ecosystem: San Francisco. American Association for the Advancement of Science, Pacific Division, pp 9–34

    Google Scholar 

  • Polhill JG, Gotts NM, Law ANR (2001) Imitative versus non-imitative strategies in a land use simulation. Cybern Syst 32:285–307

    Article  Google Scholar 

  • Schelling TC (1978) Micromotives and macrobehavior. Norton, New York

    Google Scholar 

  • Sheikh PA, Cody BA (2005) CALFED Bay-Delta Program: overview of institutional and water use issues. Congressional Research Service, Library of Congress, Washington, D.C., p 16

    Google Scholar 

  • Soman S, Misgna G, Kraft S, Lant C, Beaulieu J (2008) An agent-based model of multifunctional agricultural landscape using genetic algorithms. 2008 Annual Meeting, July 27–29, 2008, Orlando, Florida 6142, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association)

  • Terna P (1998) Simulation tools for social scientists: building agent based models with SWARM. JASSS 1(2). http://www.soc.surrey.ac.uk/JASSS/1/2/4.html

  • Tillman D, Larsen TA, Pahl WC, Gujer W (2001) Interaction analysis of stakeholders in water supply systems. Water Sci Technol 43(5):319–326

    Google Scholar 

  • United States Department of Agriculture (2009) Trade and agriculture: what’s at stake for California? Foreign Agriculture Service, Available at http://www.fas.usda.gov/info/factsheets/wto/states/ca.html

  • Wooldridge MJ, Jennings NR (1995) Intelligent agents: theory and practice. Knowl Eng Rev 10(2):115–152

    Article  Google Scholar 

  • Young HP (1999) Diffusion in social networks. Work. Pap. 2, Brookings Inst., Washington DC

  • Zechman E (2007) Agent-based modeling to simulate contamination events and to analyze threat management strategies in water distribution systems. In: Proceeding of the world environmental and water resources congress, Tampa, FL, May 2007

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Authors and Affiliations

  1. Center for Watershed Sciences, University of California Davis, Davis, CA, 95616, USA

    Masih Akhbari

  2. Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO, 80523-1372, USA

    Neil S. Grigg

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  1. Masih Akhbari
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  2. Neil S. Grigg
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Correspondence to Masih Akhbari.

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Akhbari, M., Grigg, N.S. A Framework for an Agent-Based Model to Manage Water Resources Conflicts. Water Resour Manage 27, 4039–4052 (2013). https://doi.org/10.1007/s11269-013-0394-0

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