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Agent-Based Socio-Hydrological Hybrid Modeling for Water Resource Management


Hybrid socio-hydrological modeling has become indispensable for managing water resources in an increasingly unstable ecology caused by human activity. Most work on the subject has been focused on either qualitative socio-political recommendations with an unbounded list of vague factors or complex sociological and hydrological models with many assumptions and specialized usability. In this paper, we propose a simple agent-based socio-hydrological decision modeling framework for coupling dynamics associated with social behavior and groundwater contamination. The study shows that using social health risk, instead of contaminant concentration, as an optimization variable improves water management decisions aimed at maximizing social wellbeing. The social models and computational framework are designed with enough flexibility and simplicity to encourage extensions to more general socio-hydrological dynamics without compromising either computability or complexity for better data-/model-driven environmental management.

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JB and VV acknowledge the programmatic support from the LANL Environmental Management Directorate. DO acknowledges the support of a Los Alamos National Laboratory Director’s Postdoctoral Fellowship during the preparation of this manuscript. VV also acknowledges supported by the DiaMonD project (An Integrated Multifaceted Approach to Mathematics at the Interfaces of Data, Models, and Decisions, U.S. Department of Energy Office of Science, Grant #11145687).

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Correspondence to Joseph Bakarji.

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Appendix A: Social Dynamics

In this study, the results shown in Section 5 highlight the average dynamics of the entire society, expressed in terms of average social health risk and total water usage. However, it must be noted that even when the contaminant concentration or health risk reach quasi-constant value, individuals are always changing their usage habits and beliefs. This means that even though the regulators manage the average behavior and beliefs of a society, the beliefs and behavior of every single individual is uncontrollable and unpredictable. Figure 11 shows the social dynamics of a sample of the population (100 individuals) changing attributes (usage and beliefs) throughout 100 years of simulation. On the left, the population is sorted according to specialty. The first 20 individuals (shown in the bottom of the abscissa axis) are initially experts who turn into normal citizens when not employed by the regulators. The right plot shows the population sorted by attributes.

Fig. 11
figure 11

Dynamics of usage and belief over time for a representative sample of the population (100 individuals); the left subgraph shows the changes in the social attributes over time for each individual in the sample (the first 20 people, shown at the figure bottom, are initially experts who turn into normal citizens when not employed by the regulators); the right subgraph shows the population sorted by attributes

Appendix B: Analogy Between Water Resources Management and Proportional-Integral-Derivative (PID) Control

Social-ecological system management fits naturally in the framework of control theory. According to this analogy, shown in Fig. 12, the decision makers are the controllers, the plant is the social-environmental system and the sensors are managed by scientists and engineers. If a social-ecological system is unstable (i.e. threatens a deterioration of either the environment or social well-being) environmental management decisions aim at restoring the balance (i.e. reach a reference state) of the ecosystem (i.e. the social-environmental plant). In the context of control theory, this is done by minimizing the difference between a reference well-balanced state and a present unsustainable state. In this study, the control (management) variables of interest are health risk and contaminant concentration.

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figure 12

Analogy between water resources management and Proportional-Integral-Derivative (PID) control; comparison between concentration and risk control is illustrated

The proportional (K p), integral (K i) and derivative (K d) constants are tweaked to minimize the rise time to steady state and dampen the oscillations as much as possible. However, the values are not optimal. The choice is only meant to illustrate the advantage of using PID control in the context of environmental management.

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Bakarji, J., O’Malley, D. & Vesselinov, V.V. Agent-Based Socio-Hydrological Hybrid Modeling for Water Resource Management. Water Resour Manage 31, 3881–3898 (2017).

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  • Sociohydrology
  • Decision support systems
  • Computational social dynamics