Agent-Based Modelling of a Coupled Water Demand and Supply System at the Catchment Scale
Abstract
:1. Introduction
2. Platform Description
2.1. Agent-Based Modelling (ABM)
2.2. The Programming Software
2.3. The Resource Component
2.4. The Socioeconomic Component
2.5. Temporal and Spatial Resolution and Extent
2.6. Runtime Procedures
3. Modelling Methods
- Objective definition and stakeholder identification;
- Setting-up of temporal resolution and spatial extent;
- Agent identification and representation in the model;
- Specification of the agent behavior and interactions;
- Initialization and simulation;
- Verification, calibration, sensitivity analysis and validation.
3.1. Objective Definition and Stakeholder Identification
3.2. Setting-Up of Temporal Resolution and Spatial Extent
3.3. Agent Identification and Represenation in the Model
3.4. Specification of the Agent Behavior and Interactions
3.5. Initialization and Simulation
3.6. Verification, Calibration, Sensitivity Analysis and Validation
4. Implementation Example
4.1. The Model Setup
- The resource component. We intentionally use the same input for every simulation year—without any interannual variability or trends, e.g., due to climate change—to set the major focus on the various users and managers, with all their behavior and interactions.
- The socioeconomic component. Behavior rules and interactions of the managers and users in the villages 1 to 3 (Figure 1) are the following:
- Village 1. During years of sufficient water supply, tourism and therefore also the ‘demand’ of the hotels increases every year. Conversely, if there has been any severe scarcity situation for hotels or snowmaking-reservoirs in the previous year (i.e., the available water is satisfying less than 90% of their ‘demand’), the tourism is affected, therefore, leading to the decreasing ‘demand’ of hotels. Also, the ‘demand’ of the snowmaking reservoirs is directly coupled to the ‘demand’ changes of the hotels, but only being adapted every 5 years. Although these tourism trends are the result of an external (not simulated) process of tourists deciding their holiday destination, they can be coded with if/then rules as if they were the decisions of the hotels and snowmaking reservoirs.
- Village 2. A ‘starting year’ for the water consumption of the hydropower user can be defined, allowing simulations either without hydropower user at all (‘demand’ = 0) or a start during the simulation (before start: ‘demand’ = 0, after start: ‘demand’ as imported from data file). The hydropower user does not make decisions and does not change ‘demand’ during the simulation run.
- Village 3. The ‘demand’ of the farmers is calculated from auxiliary variables defining the irrigated area, the specific water demand per unit area and the traded water between the farmers. The farmers increase their irrigated area every 5 years. Moreover, the farmers trade water at the beginning of every year, i.e., every farmer with water scarcity in the previous year can ask every farmer with excess in the previous year for water units. The decision for or against the trade is made randomly in our example, with a fifty percent chance. In the case of agreement between two farmers, the ‘demands’ of both are adapted for the following year as the ‘demands’ only represent the amount of water they need to obtain from the irrigation manager, i.e., a farmer who receives water units from another farmer decreases his/her ‘demand’ and vice versa. The irrigation manager can adapt the amount of irrigation water (i.e., his or her ‘demand’) every 10 years. If any of the farmers had experienced a severe scarcity situation (i.e., the available water is satisfying less than 90% of their ‘demand’) within the last 10 years—and if the inhabitants did not experience any scarcity—the irrigation manager increases the irrigation amount. Conversely, in the case of scarcity of inhabitants within the last 10 years, he or she decreases the irrigation amount.
4.2. Scenario Simulation and Output
5. Discussion
5.1. Aqua.MORE as a Decision-Support Tool for Sustainable Water Management
5.2. Advantages and User Convenience of Aqua.MORE
5.3. Embedding of Aqua.MORE
6. Conclusions
- Given the modular structure and integrated functionalities and features, Aqua.MORE is adaptable to various case study sites and water management problems, supporting detailed analyses of coupled human–water systems.
- The analyses of the presented scenarios show that modelling the behavior and mutual interactions of individual water-related actors can provide unexpected insights into human–water system dynamics. This illustrates the potential of Aqua.MORE as a decision-support tool for watershed management.
- Ideally, Aqua.MORE complements an eco-hydrological model to allow the overall assessment and management of the resource water including all biotic and mutual human interactions and feedback loops with the water component.
- By recognizing the relevance of the mutual interplay between the human and the natural components of water supply and demand, we make a contribution towards understanding and managing sustainability challenges.
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Brauman, K.A.; Daily, G.C.; Duarte, T.K.; Mooney, H.A. The nature and value of ecosystem services: An overview highlighting hydrologic services. Annu. Rev. Environ. Resour. 2007, 32, 67–98. [Google Scholar] [CrossRef]
- Reynard, E.; Bonriposi, M.; Graefe, O.; Homewood, C.; Huss, M.; Kauzlaric, M.; Liniger, H.; Rey, E.; Rist, S.; Schädler, B.; et al. Interdisciplinary assessment of complex regional water systems and their future evolution: How socioeconomic drivers can matter more than climate. Wiley Interdiscip. Rev. Water 2014, 1, 413–426. [Google Scholar] [CrossRef]
- Wagener, T.; Sivapalan, M.; Troch, P.A.; McGlynn, B.L.; Harman, C.J.; Gupta, H.V.; Kumar, P.; Rao, P.S.C.; Basu, N.B.; Wilson, J.S. The Future of Hydrology: An Evolving Science for a Changing World. 2010. Available online: https://doi.org/10.1029/2009WR008906 (accessed on 24 September 2019).
- Montanari, A.; Young, G.; Savenije, H.H.G.; Hughes, D.; Wagener, T.; Ren, L.L.; Koutsoyiannis, D.; Cudennec, C.; Toth, E.; Grimaldi, S.; et al. “Panta Rhei—Everything Flows”: Change in hydrology and society—The IAHS Scientific Decade 2013–2022. Hydrol. Sci. J. 2013, 58, 1256–1275. [Google Scholar] [CrossRef]
- Sivapalan, M.; Savenije, H.H.G.; Blöschl, G. Socio-hydrology: A new science of people and water. Hydrol. Process. 2012, 26, 1270–1276. [Google Scholar] [CrossRef]
- Sivapalan, M.; Konar, M.; Srinivasan, V.; Chhatre, A.; Wutich, A.; Scott, C.A.; Wescoat, J.L.; Rodríguez-Iturbe, I. Socio-hydrology: Use-inspired water sustainability science for the Anthropocene. Earth’s Future 2014, 2, 225–230. [Google Scholar] [CrossRef]
- Di Baldassarre, G.; Sivapalan, M.; Rusca, M.; Cudennec, C.; Garcia, M.; Kreibich, H.; Konar, M.; Mondino, E.; Mård, J.; Pande, S.; et al. Sociohydrology: Scientific challenges in addressing the sustainable development goals. Water Resour. Res. 2019, 55, 6327–6355. [Google Scholar] [CrossRef]
- An, L. Modeling human decisions in coupled human and natural systems: Review of agent-based models. Ecol. Model. 2012, 229, 25–36. [Google Scholar] [CrossRef]
- Filatova, T.; Verburg, P.H.; Parker, D.C.; Stannard, C.A. Spatial agent-based models for socio-ecological systems: Challenges and prospects. Environ. Model. Softw. 2013, 45, 1–7. [Google Scholar] [CrossRef]
- Heckbert, S.; Baynes, T.; Reeson, A. Agent-based modeling in ecological economics. Ann. N. Y. Acad. Sci. 2010, 1185, 39–53. [Google Scholar] [CrossRef]
- Schlüter, M.; McAllister, R.R.J.; Arlinghaus, R.; Bunnefeld, N.; Eisenack, K.; Hölker, F.; Milner-Gulland, E.J.; Müller, B. New horizons for managing the environment: A review of coupled social-ecological systems modeling. Nat. Resour. Modeling 2012, 25, 219–272. [Google Scholar] [CrossRef]
- Akhbari, M.; Grigg, N.S. A framework for an agent-based model to manage water resources conflicts. Water Resour. Manag. 2013, 27, 4039–4052. [Google Scholar] [CrossRef]
- Schelling, T. Micromotives and Macrobehaviour; W.W Norton & Company: New York, NY, USA, 1978. [Google Scholar]
- Lansing, J.S.; Kremer, J.N. Emergent properties of balinese water temple networks: Coadaptation on a rugged fitness landscape. Am. Anthropol. 1993, 95, 97–114. [Google Scholar] [CrossRef]
- Tzima, F.; Anthanasiadis, I.; Mitkas, P. Agent-Based Modelling and Simulation in the Irrigation Management Sector: Applications and Potential. In Water Saving in Mediterranean Agriculture and Future Research Needs; Lamaddalena, N., Bogliotti, C., Todorovic, N., Scardigno, A., Eds.; CIHEAM: Bari, Italy, 2007; pp. 273–286. [Google Scholar]
- van Oel, P.R.; Krol, M.S.; Hoekstra, A.Y.; Taddei, R.R. Feedback mechanisms between water availability and water use in a semi-arid river basin: A spatially explicit multi-agent simulation approach. Environ. Model. Softw. 2010, 25, 433–443. [Google Scholar] [CrossRef]
- Belaqziz, S.; Fazziki, A.E.; Mangiarotti, S.; Le Page, M.; Khabba, S.; Raki, S.E.; Adnani, M.E.; Jarlan, L. An agent based modeling for the gravity irrigation management. Procedia Environ. Sci. 2013, 19, 804–813. [Google Scholar] [CrossRef]
- Koutiva, I.; Makropoulos, C. Modelling domestic water demand: An agent based approach. Environ. Model. Softw. 2016, 79, 35–54. [Google Scholar] [CrossRef]
- Darbandsari, P.; Kerachian, R.; Malakpour-Estalaki, S. An Agent-based behavioural simulation model for residential water demand management: The case-study of Tehran, Iran. Simul. Model. Pract. Theory 2017, 78, 51–72. [Google Scholar] [CrossRef]
- Yuan, X.-C.; Wei, Y.-M.; Pan, S.-Y.; Jin, J.-L. Urban household water demand in Beijing by 2020: An agent-based model. Water Resour. Manag. 2014, 28, 2967–2980. [Google Scholar] [CrossRef]
- Chu, J.; Wang, C.; Chen, J.; Wang, H. Agent-based residential water use behaviour simulation and policy implications: A case-study in beijing city. Water Resour. Manag. 2009, 23, 3267–3295. [Google Scholar] [CrossRef]
- Linkola, L.; Andrews, C.; Schuetze, T. An agent based model of household water use. Water 2013, 5, 1082–1100. [Google Scholar] [CrossRef]
- Khan, H.F.; Yang, Y.C.E.; Xie, H.; Ringler, C. A coupled modeling framework for sustainable watershed management in transboundary river basins. Hydrol. Earth Syst. Sci. 2017, 21, 6275–6288. [Google Scholar] [CrossRef] [Green Version]
- Barthel, R.; Janisch, S.; Schwarz, N.; Trifkovic, A.; Nickel, D.; Schulz, C.; Mauser, W. An integrated modelling framework for simulating regional-scale actor responses to global change in the water domain. Environ. Model. Softw. 2008, 23, 1095–1121. [Google Scholar] [CrossRef]
- Barthel, R.; Reichenau, T.G.; Krimly, T.; Dabbert, S.; Schneider, K.; Mauser, W. Integrated modeling of global change impacts on agriculture and groundwater resources. Water Resour. Manag. 2012, 26, 1929–1951. [Google Scholar] [CrossRef]
- Soboll, A.; Schmude, J. Simulating Tourism water consumption under climate change conditions using agent-based modeling: The example of ski areas. Ann. Assoc. Am. Geogr. 2011, 101, 1049–1066. [Google Scholar] [CrossRef]
- Soboll, A.; Elbers, M.; Barthel, R.; Schmude, J.; Ernst, A.; Ziller, R. Integrated regional modelling and scenario development to evaluate future water demand under global change conditions. Mitig. Adapt. Strateg. Glob. Chang. 2011, 16, 477–498. [Google Scholar] [CrossRef]
- Ernst, A.; Kuhn, S.; Barthel, R.; Janisch, S.; Krimly, T.; Sax, M.; Zimmer, M. DeepActor Models in DANUBIA. In Regional Assessment of Global Change Impacts; Springer: Cham, Switzerland, 2016; pp. 29–36. [Google Scholar]
- Viviroli, D.; Weingartner, R. The hydrological significance of mountains: From regional to global scale. Hydrol. Earth Syst. Sci. 2004, 8, 1016–1029. [Google Scholar] [CrossRef]
- Vanham, D. The Alps under climate change: Implications for water management in Europe. J. Water Clim. Chang. 2012, 3, 197–206. [Google Scholar] [CrossRef]
- Meisch, C.; Schirpke, U.; Huber, L.; Rüdisser, J.; Tappeiner, U. Assessing freshwater provision and consumption in the alpine space applying the ecosystem service concept. Sustainability 2019, 11, 1131. [Google Scholar] [CrossRef]
- de Jong, C. Challenges for mountain hydrology in the third millennium. Front. Environ. Sci. 2015, 3, 38. [Google Scholar] [CrossRef]
- Hohenwallner, D.; Saulnier, G.M.; Castaings, W.; Astengo, A.; Brenčič, M.; Bruno, C.; Carolli, M.; Chenut, J.; De Bona, A.; Doering, M.; et al. Water Management in a Changing Environment: Strategies against Water Scarcity in the Alps; Université de Savoie: Chambéry, France, 2011. [Google Scholar]
- Gobiet, A.; Kotlarski, S.; Beniston, M.; Heinrich, G.; Rajczak, J.; Stoffel, M. 21st century climate change in the European Alps—A review. Sci. Total Environ. 2014, 493, 1138–1151. [Google Scholar] [CrossRef]
- Wilensky, U. NetLogo: Center for Connected Learning and Computer-Based Modeling; Northwestern University: Evanston, IL, USA, 1999. [Google Scholar]
- Tisue, S.; Wilensky, U. NetLogo: A Simple environment for modeling complexity. In Proceedings of the International Conference on Complex Systems, Boston, MA, USA, 16–21 May 2004. [Google Scholar]
- Wilensky, U.; Shargel, B. BehaviourSpace, Center for Connected Learning and Computer Based Modeling; Northwestern University: Evanston, IL, USA, 2002. [Google Scholar]
- Stonedahl, F.; Wilensky, U. BehaviourSearch, Center for Connected Learning and Computer Based Modeling; Northwestern University: Evanston, IL, USA, 2013. [Google Scholar]
- Ng, T.L.; Eheart, J.W.; Cai, X.; Braden, J.B. An Agent-Based Model of Farmer Decision-Making and Water Quality Impacts at the Watershed Scale under Markets for Carbon Allowances and a Second-Generation Biofuel Crop. 2011. Available online: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011WR010399 (accessed on 8 February 2019).
- Giaconomi, M.H.; Kanta, L.; Zechman, E.M. Complec adaptive systems approach to simulate the sustainability of water resources and urbanization. J. Water Resour. Plan. Manag. 2013, 139, 554–564. [Google Scholar] [CrossRef]
- Lundqvist, J.; Falkenmark, M. Focus on the upstream-downstream conflicts of interests. Water Int. 2000, 25, 168–171. [Google Scholar] [CrossRef]
- Graversen, M.K. Regulating Water Extraction in a River Basin with Upstream-Downstream Communities; University of Copenhagen, Department of Food and Resource Economics: Copenhagen, Denmark, 2011. [Google Scholar]
- Macal, C.M.; North, M.J. Tutorial on Agent-Based Modeling and Simulation PART 2: How to model with agents. In Proceedings of the Conference on Winter Simulation, Monterey, CA, USA, 3–6 December 2006; Perrone, L.F., Wieland, F.P., Liu, J., Lawson, B.G., Nicol, D.M., Fujimoto, R.M., Eds.; 2006. [Google Scholar]
- Castellanos, M.A.L. Agent Based Simulation Approach to Assess Supply Chain Complexity and its Impact on Performance; Josef Eul Verlag: Lohmar, Germany, 2012; Volume 1. [Google Scholar]
- Schulze, J.; Müller, B.; Groeneveld, J.; Grimm, V. Agent-based modelling of social-ecological systems: Achievements, challenges, and a way forward. J. Artif. Soc. Soc. Simul. 2017, 20, 1–8. [Google Scholar] [CrossRef]
- Bert, F.E.; Rovere, S.L.; Macal, C.M.; North, M.J.; Podestá, G.P. Lessons from a comprehensive validation of an agent based-model: The experience of the pampas model of argentinean agricultural systems. Ecol. Model. 2014, 273, 284–298. [Google Scholar] [CrossRef]
- Damgaard, M.; Kjeldsen, C.; Sahrbacher, A.; Happe, K.; Dalgaard, T. Validation of an agent-based, spatio-temporal model for farming in the river gudenå landscape. Results from the mea-scope case study in Denmark. In Rural Landscapes and Agricultural Policies in Europe; Piorr, A., Müller, K., Eds.; Springer: Berlin, Germany, 2009; pp. 239–254. [Google Scholar]
- Grimm, V.; Berger, U.; DeAngelis, D.L.; Polhill, J.G.; Giske, J.; Railsback, S.F. The ODD protocol: A review and first update. Ecol. Model. 2010, 221, 2760–2768. [Google Scholar] [CrossRef] [Green Version]
- R Core Team. R: A Language and Environmental for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2019. [Google Scholar]
- Liu, J.; Dietz, T.; Carpenter, S.R.; Alberti, M.; Folke, C.; Moran, E.; Pell, A.N.; Deadman, P.; Kratz, T.; Lubchenco, J.; et al. Complexity of coupled human and natural systems. Science 2007, 317, 1513–1516. [Google Scholar] [CrossRef]
- Thiele, J.C.; Kurth, W.; Grimm, V. Facilitating parameter estimation and sensitivity analysis of agent-based models: A Cookbook using Netlogo and R. J. Artif. Soc. Soc. Simul. 2014, 17, 11. [Google Scholar] [CrossRef]
- Toset, H.P.W.; Gleditsch, N.P.; Hegre, H. Shared rivers and interstate conflict. Political Geogr. 2000, 19, 971–996. [Google Scholar] [CrossRef]
- Furlong, K.; Petter Gleditsch, N.; Hegre, H. Geographic opportunity and neomalthusian willingness: Boundaries, shared rivers, and conflict. Int. Interact. 2006, 32, 79–108. [Google Scholar] [CrossRef]
- Hamill, L. Agent-based modelling: The next 15 years. J. Artif. Soc. Soc. Simul. 2010, 13, 7. [Google Scholar] [CrossRef]
- Zalewsky, M.; Janauer, G.A.; Jolánkai, G. Ecohydrology: A New Paradigm for the Sustainable Use of Aquatic Resources; UNESCO: Paris, France, 1997. [Google Scholar]
Procedure Name | Operation |
---|---|
to create-waters | one water is generated at the upper border of the model environment, its variable ‘amount_water’ is set according to the ‘inflow’ list (imported from data file) |
to move-waters | all waters move one step forward |
to | the variable ‘demand’ of the managers and the users is updated according to the specific submodel |
to managers-extract | managers extract water (i.e., decrease the ‘amount_water’ of waters passing by according to their updated variable ‚demand‘, under consideration of ‘residualwater’, resulting in values of ‘scarcity’ or ‘excess’) and send new waters with the respective ‘amount_water’ to associated users |
to users-extract | users extract water (i.e., decrease the ‘amount_water’ of waters passing by, according to their updated variable ‘demand’, under consideration of ‘residualwater’, resulting in values of ‘scarcity’ or ‘excess’) |
to write-maxandmin | maximum, mean and/or minimum values of ‘scarcity’ and ‘excess’ of managers and users are recorded |
to measure-runoff | ‘amount_water’ of the waters at the lower end of the model environment is recorded |
to kill-waters | all waters that have reached the lower end of the model environment or that are exhausted (variable ‘amount_water’ = 0), are deleted |
Scenario | Hydropower User in Village 2 | Strategy of the Irrigation Manager |
---|---|---|
‘reference’ | no | reactive (increase for single amount) |
‘hydropower’ | yes, starting in year 21 (beginning of the 3rd decade) | reactive (increase for single amount) |
‘irrigation strategy’ | no | proactive (increase for doubled amount) |
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Huber, L.; Bahro, N.; Leitinger, G.; Tappeiner, U.; Strasser, U. Agent-Based Modelling of a Coupled Water Demand and Supply System at the Catchment Scale. Sustainability 2019, 11, 6178. https://doi.org/10.3390/su11216178
Huber L, Bahro N, Leitinger G, Tappeiner U, Strasser U. Agent-Based Modelling of a Coupled Water Demand and Supply System at the Catchment Scale. Sustainability. 2019; 11(21):6178. https://doi.org/10.3390/su11216178
Chicago/Turabian StyleHuber, Lisa, Nico Bahro, Georg Leitinger, Ulrike Tappeiner, and Ulrich Strasser. 2019. "Agent-Based Modelling of a Coupled Water Demand and Supply System at the Catchment Scale" Sustainability 11, no. 21: 6178. https://doi.org/10.3390/su11216178