Abstract
Determining the optimized policies in the exploitation of groundwater water resources is a complicated issue, especially when there are several different managers with conflicting goals. The current study presents a new multi-purpose method to reach a compromise among different stakeholders by determining optimal social policies and sustainable hydro-environmental management of underground water resources. This method simultaneously considers qualitative and quantitative simulation and optimization, stakeholders’ preferences, and uncertainty analysis. In this study, the recharge was determined and incorporated in MODFLOW groundwater current model and MT3DMS pollution transfer model by using the hydrological model SWAT. In addition, DREAM (zs) algorithm (derived from algorithms based on Markov chain Monte Carlo) was used to examine the uncertainty of MODFLOW model parameters. The optimal head and TDS rate were determined in the studied aquifer by linking the model with MOPSO. Then, the Pareto frontier derived from the previous step, was utilized to determine the allocation rate of groundwater resources among a set of non-dominated solutions using Social Choice Rules (SCR) including Condorcet, Median Voting Rule (MVR), and Fallback Bargaining (FB) including unanimity fallback bargaining and fallback bargaining with impasse. The results showed that almost all the selected methods of conflict resolution in this research behaved similarly, and their results were not significantly different from each other. However, the comparison of these methods indicated that the MVR with the minimum reduction in withdrawal discharge and the maximum elevation in response to optimal allocation policies had the best performance. The amount of water extracted from the study area is about 540 million m3/year, which reaches 395 million m3/year.
Similar content being viewed by others
Abbreviations
- MOPSO:
-
Multi-objective particle swarm optimization
- MODFLOW:
-
Finite difference groundwater flow modeling software
- TDS:
-
Total dissolved solids
- MCDM:
-
Multiple-criteria decision analysis
- FB:
-
Fallback bargaining
- NSGA-II:
-
Nondominated sorting genetic algorithm II
- MT3DMS:
-
Modular Three-Dimensional Multispecies Transport Model Dimensional Multispecies Transport Model
- RSBT:
-
Rubenstein sequential bargaining theory
- GAMS:
-
General Algebraic Modeling System
- SWAT:
-
Soil & Water Assessment Tool
- DREAM (zs):
-
Differential Evolution Adaptive Metropolis
- MVR:
- GMS:
-
Groundwater Modeling System
- DEM:
-
Digital elevation model
- SLS:
-
Standard least squares
- HRU:
-
Hydrological response unit
- SCEM-UA:
-
Shuffle Complex Evolution Metropolis
- CACO:
-
Continuous ant colony optimization
- GA:
-
Genetic Algorithm
- GAMS:
-
General Algebraic Modeling System
- θi :
-
initial population of parameters vector
- π (θi):
-
Density
- e:
-
Difference between the observed data and the simulated model data
- ith:
- e and ε:
-
Random phrases
- δ:
-
Number of paired chains
- νi :
-
Parameter series
- u:
-
Random number
- R:
-
Gelman and Rubin convergence
- HC:
-
Hydraulic Conductivity
- HA:
-
Horizontal Anisotropy
- SC:
-
Storage Coefficient
- RCH:
-
Recharge
- MCMC:
-
Markov chain Monte Carlo
- Ntp :
-
Total number of planning months
- Nj :
-
Total number of model cells
- Htj :
-
Head of water at the tth time step in the jth cell
- H1j :
-
Head of water at the first time step in the jth cell
- Ctj :
-
TDS of water at the tth time step in the jth cell
- C1j :
-
TDS of water at the first time step in the jth cell
- GWtp :
-
Total water pumped from the faming wells in the month of tp
- tp :
-
Month counter
- td :
-
Number of days in the month of tp
- Qk.tp :
-
Discharge of well of k in the month of tp
- k:
-
Well counter
- NW:
-
Total number of pumped wells available
- Ntp :
-
Total number of planned months
- Ckt,tp :
-
Concentration of TDS in the well of k in the month of tp (mg/lit)
- SWtp :
-
Amount of surfaced water consumed in the month of tp (m3)
- Dtp :
-
Water demanded by farmers in the month of tp
- SWmintp :
-
Minimum consumed surface water in the month of tp
- SWmaxtp :
-
Maximum surfaced water consumed in the month of tp
- Cmin :
-
Minimum TDS
- Cmax :
-
Maximum TDS
References
Alemayehu T, van Griensven A, Woldegiorgis BT, Bauwens W (2017) An improved SWAT vegetation growth module and its evaluation for four tropical ecosystems. Hydrol Earth Syst Sci 21:4449–4467
Alizadeh MR, Nikoo MR, Rakhshandehroo GR (2017a) Developing a multi-objective conflict-resolution model for optimal groundwater management based on fallback bargaining models and social choice rules: a case study. Water Resour Manag 31(5):1457–1472
Alizadeh MR, Nikoo MR, Rakhshandehroo GR (2017b) Hydro-environmental management of groundwater resources: a fuzzy-based multi-objective compromise approach. J Hydrol 551:540–554
Andik B, Niksokhan MH (2020) Waste load allocation under uncertainty using game theory approach and simulation-optimization process. J Hydroinf 22:815–841. https://doi.org/10.2166/hydro.2020.181
Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment part I: model development1
Arnold JG, Moriasi DN, Gassman PW, Abbaspour KC, White MJ, Srinivasan R, Kannan N (2012) SWAT: model use, calibration, and validation. Trans ASABE 55(4):1491–1508
Barberà S, Jackson M, Neme A (1997) Strategy-proof allotment rules. Games and Economic Behavior 18:1–21
Bazargan-Lari MR, Kerachian R, Mansoori A (2009) A conflict-resolution model for the conjunctive use of surface and groundwater resources that considers water-quality issues: a case study. Environ Manag 43(3):470–482
Brams SJ, Kilgour DM (2001) Fallback bargaining. Group Decis Negot 10(4):287–316
Degefu DM, HeW YL, Zhao JH (2016) Water allocation in transboundary river basins under water scarcity: a cooperative bargaining approach. Water Resour Manag 30(12):4451–4466
Easter KW, & Hearne R (1995) Water Markets and Decentralized Water Resources Management: International Problems and Opportunities. J Am Water Resour Assoc, 31(1) 9–20
Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory, paper presented at sixth international symposium on micromachine and human science, Inst of Electr and Electron Eng, Nagoya, Japan
Esteban E, Albiac J (2012) The problem of sustainable groundwater management: the case of La Mancha aquifers, Spain. Hydrogeol J 20(5):851–863
Esteban E, Dinar A (2013) Cooperative management of groundwater resources in the presence of environmental externalities. Environ Resour Econ 54(3):443–469
Farhadi S, Nikoo MR, Rakhshandehroo GR, Akhbari M, Alizadeh MR (2016) An agent-based-Nash modeling framework for sustainable groundwater management: a case study. Agric Water Manag 177:348–358
Gassman PW, Reyes MR, Green CH, Arnold JG (2007) The soil and water assessment tool: historical development, applications, and future research directions. Trans ASABE 50(4):1211–1250
Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7(4):457–472
Gleick PH (2000) A look at twenty-first century water resources development. Water Int 25(1):127–138
Howe C, Schurmeier D, Shaw W Jr (1986) Innovative approaches to water allocation: the potential for water markets. Water Resour Res 22:439–445
Huang Y, Janovsky P, Das S, Welch SM, DeLoach S (2016) Multi-agent system for groundwater depletion using game theory. arXiv preprint arXiv:1607.02376
Kamali A, Niksokhan MH (2017) Multi-objective optimization for sustainable groundwater management by developing of coupled quantity-quality simulation-optimization model. J Hydroinf 19(6):973–992
Kennedy J, Eberhart R (1995) Particle swarm optimization. Paper presented at the Proceedings of ICNN'95-International Conference on Neural Networks
Kerachian R, Fallahnia M, Bazargan-Lari MR, Mansoori A, Sedghi H (2010) A fuzzy game theoretic approach for groundwater resources management: application of Rubinstein bargaining theory. Resour Conserv Recycl 54(10):673–682
Ketabchi H, Ataie-Ashtiani B (2015) Evolutionary algorithms for the optimal management of coastal ground-water: a comparative study toward future challenges. J Hydrol 520:193–213
Lee TR, Jouravlev AS (1998) Los precios, la propiedad y los mercados en la asignación del agua. CEPAL (Naciones Unidas), Santiago de Chile
Loáiciga HA (2002) Reservoir design and operation with variable lake hydrology. J Water Resour Plan Manag 128(6):399–405
Loáiciga HA (2004) Analytic game—theoretic approach to ground-water extraction. J Hydrol 297(1–4):22–33
Madani K, Read L, Shalikarian L (2014) Voting under uncertainty: a stochastic framework for analyzing group decision making problems. Water Resour Manag 28(7):1839–1856
Madani K, Shalikarian L, Hamed A, Pierce T, Msowoya K, Rowney C (2015) Bargaining under uncertainty: a Monte-Carlo fallback bargaining method for predicting the likely outcomes of environmental conflicts conflict resolution in water resources and environmental management. springer, pp 201–212
Mahmoodzadeh D, Ketabchi H, Ataie-Ashtiani B, Simmons CT (2014) Conceptualization of a fresh groundwater lens influenced by climate change: a modeling study of an arid-region island in the Persian Gulf, Iran. J Hydrol 519:399–413
Martinez Y, Esteban E (2014) Social choice and groundwater management: application of the uniform rule. Ciencia e investigación agraria 41(2):153–162
McDonald MG, Harbaugh AW (1988) A modular three-dimensional finite-difference ground-water flow model: US geological survey
Metropolis N, Rosenbluth AW, RosenbluthMN TAH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21:1087–1092
Moridi A, Tabatabaie MRM, Esmaeelzade S (2018) Holistic approach to sustainable groundwater management in semi-arid regions. Int J Environ Res 12(3):347–355
Nafarzadegan AR, Vagharfard H, Nikoo MR, Nohegar A (2018) Socially-optimal and Nash Pareto-based alternatives for water allocation under uncertainty: an approach and application. Water Resour Manag 32(9):2985–3000
Nakas MD, Wichelns D, Montgomery L (2002) Game theory analysis of competition for groundwater involving El Paso, Texas and ciudad Juarez, Mexico. In: In moving with the speed of change the 2002 annual meeting of the American agricultural economics association. Beach, CA, Long
Nazari S, & Ahmadi A (2019) Non-cooperative stability assessments of groundwater resources management based on the tradeoff between the economy and the environment. J Hydrol 578:124075
Niksokhan MH, Kerachian R, Karamouz M (2009) A game theoretic approach for trading discharge permits in rivers. Water Sci Technol 60(3):793–804
Norouzi Khatiri K, Niksokhan MH, Sarang A (2020) Choosing various likelihood functions on uncertainty assessment in groundwater simulation-optimization model. Water Supply 20(2):737–750
Ostrom E (1990) Governing the commons: the evolution of institutions for collective action. Cambridge university press
Parsapour-Moghaddam P, Abed-Elmdoust A, Kerachian R (2015) A heuristic evolutionary game theoretic methodology for conjunctive use of surface and groundwater resources. Water Resour Manag 29(11):3905–3918
Peña-Haro S, Pulido-Velazquez M, Sahuquillo A (2009) A hydro-economic modelling framework for optimal management of groundwater nitrate pollution from agriculture. J Hydrol 373(1–2):193–203
Price KV, Storn RM, Lampinen JA (2005) Differential evolution. A practical approach to global optimization. Springer, Berlin, p 538
Raquel S, Ferenc S, Emery C Jr, Abraham R (2007) Application of game theory for a groundwater conflict in Mexico. J Environ Manag 84(4):560–571
Read L, Mokhtari S, Madani K, Maimoun M, Hanks C (2013) A multi-participant, multi-criteria analysis of energy supply sources for Fairbanks, Alaska. Paper presented at the world environmental and water resources congress 2013: showcasing the future
Roozbahani R, Schreider S, Abbasi B (2015) Optimal water allocation through a multi-objective compromise between environmental, social, and economic preferences. Environ Model Softw 64:18–30
Serrano R (2004) The theory of implementation of social choice rules. SIAM Rev 46(3):377–414
Sheikhmohammady M, Madani K (2008) Bargaining over the Caspian Sea—the largest lake on the earth. Paper presented at the world environmental and water resources congress 2008: Ahupua'A
Sheikhmohammady M, Kilgour DM, Hipel KW (2010) Modeling the Caspian Sea negotiations. Group Decis Negot 19(2):149–168
Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359
Ter Braak CJF (2006) A Markov chain Monte Carlo version of the genetic algorithm differential evolution: easy Bayesian computing for real parameter spaces. Stat Comput 16:239–249
van den Brink C, Zaadnoordijk WJ, van der Grift B, de Ruiter PC, Griffioen J (2008) Using a groundwater quality negotiation support system to change land-use management near a drinking-water abstraction in the Netherlands. J Hydrol 350(3–4):339–356
Vrugt JA, Gupta HV, Bouten W, Sorooshian S (2003) A shuffled complex evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resour Res 39(8)
Vrugt JA, ter Braak CJF, Clark MP, Hyman JM, Robinson BA (2008) Treatment of input uncertainty in hydrologic modeling: doing hydrology backward with Markov chain Monte Carlo simulation. Water Resour Res 44(12)
Vrugt JA, ter Braak CJF, Diks CDH, Robinson BA, Hyman JM, Higdon D (2009) Accelerating Markov chain Monte Carlo simulation using self-adaptative differential evolution with randomized subspace sampling. Intl J Nonlinear Sci Numer Simul 10:1–12
Walker WE, Loucks DP, Carr G (2015) Social responses to water management decisions. Environmental Processes 2(3):485–509
Zekri S, Karimi A, Madani K (2014) The value of cooperation in coastal aquifer management: lessons for Oman. Paper presented at the 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Zheng C, Wang PP (1999) MT3DMS: a modular three-dimensional multispecies transport model for simulation of advection, dispersion, and chemical reactions of contaminants in groundwater systems; documentation and user’s guide
Acknowledgments
The authors acknowledge the financial support from Iran National Science Foundation (INSF) under the contract No. 96005826.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
None.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Norouzi Khatiri, K., Niksokhan, M.H., Sarang, A. et al. Coupled Simulation-Optimization Model for the Management of Groundwater Resources by Considering Uncertainty and Conflict Resolution. Water Resour Manage 34, 3585–3608 (2020). https://doi.org/10.1007/s11269-020-02637-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-020-02637-x