Coupled Simulation-Optimization Model for the Management of Groundwater Resources by Considering Uncertainty and Conflict Resolution


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.

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Multi-objective particle swarm optimization


Finite difference groundwater flow modeling software


Total dissolved solids


Multiple-criteria decision analysis


Fallback bargaining


Nondominated sorting genetic algorithm II


Modular Three-Dimensional Multispecies Transport Model Dimensional Multispecies Transport Model


Rubenstein sequential bargaining theory


General Algebraic Modeling System


Soil & Water Assessment Tool

DREAM (zs):

Differential Evolution Adaptive Metropolis


Median Voting Rule


Groundwater Modeling System


Digital elevation model


Standard least squares


Hydrological response unit


Shuffle Complex Evolution Metropolis


Continuous ant colony optimization


Genetic Algorithm


General Algebraic Modeling System

θi :

initial population of parameters vector

π (θi):



Difference between the observed data and the simulated model data


Chain using differential evolution

e and ε:

Random phrases


Number of paired chains

νi :

Parameter series


Random number


Gelman and Rubin convergence


Hydraulic Conductivity


Horizontal Anisotropy


Storage Coefficient




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 :

Discharge of well of k in the month of tp


Well counter


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


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The authors acknowledge the financial support from Iran National Science Foundation (INSF) under the contract No. 96005826.

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Correspondence to Mohammad Hossein Niksokhan.

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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 (2020).

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  • Simulation-optimization
  • Groundwater
  • Uncertainty
  • Social choice
  • Conflict resolution