Identifying Capabilities and Potentials of System Dynamics in Hydrology and Water Resources as a Promising Modeling Approach for Water Management
Abstract
:1. Introduction
2. System Dynamics (SD) Methodology
3. System Dynamics and Water Systems
3.1. SD Potential in Water Predictive Simulation Models
3.2. SD Potential in Water Descriptive Integrated Models
3.3. SD Potential in Water Participatory and Shared Vision Models
3.4. SD Potential in Water Combined Models
4. System Dynamics Software Packages used in Water and Hydrological Sectors
5. Challenges and Limitations of System Dynamics
5.1. Knowledge and Experience
5.2. Model Validation
5.3. Subsystems Integration
5.4. Combining Necessity
6. Concluding Remarks and Directions for Future Work
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Application | References | SD Modeling Objectives | Results with the SD Method |
---|---|---|---|
Groundwater and aquifer modeling | [52,53,54,55] | Using SD for groundwater flow, hydraulic redistribution, groundwater storage, and sustainable aquifer management. | Influences of climate change and water use on spatial and temporal differences in groundwater flow, with insights for management and policy. |
Hydroelectric power evaluation | [56,58] | Hydropower and hydraulically reservoir regime modeling. | Hydrologic influences and impacts on hydroelectricity production, with performance measures to quantify multi-reservoir regimes. |
Water quality assessment | [30,44,50,51,57,119] | Simulating water quality, total maximum daily load, salinity, treatment, vegetation cover, and eutrophication. | Water quality control, increased environmental functions, and urban planning to improve water quality and reduce salinity impacts. |
Application | References | SD Modeling Objectives | Results with the SD Method |
---|---|---|---|
Groundwater modeling | [7,11,61,75,121,122] | SD modeling for treating uncertainty with integrated hydroeconomics, considering the mutual relations among accessibility and growing uses of water, investigating groundwater dynamics under various economic conditions and studying hydrologic and socioeconomic relationships of groundwater balance. Developing a model for evaluation of groundwater polices. | The developed methodology effectively analyzed the effects of different management techniques, water stress, aquifer dynamics, economic behavior, area climatology and economic policies and rising groundwater withdrawal. The results showed the best strategies and scenarios for efficient groundwater management and decision-making related to this vital water resource. |
Surface water management modeling | [12,15,39,40,58,63,66,123,124] | Comprehensive SD approach for managing for the lake basin, evaluating the river system, and investigating reservoir balance considering hydrological and socioeconomic parameters. | The results showed the different positive aspects of SD in accurately understanding and expressing water demand, water supply, hydroelectricity generation, human–water systems, and climate change based on different scenarios and conditions. Assessments of population change effect, agricultural demand, and production and irrigation impacts for water, economic, energy, and food concerns using feedback processes and interactions were clearly demonstrated. |
Holistic water resources management | [10,14,60,68,69,71,72,73,76,79,95,97,119,125,126,127,128] | Presenting sociohydrological models based on SD methodology for integrated water management. Considering different parameters and subsystems such as population, demographic extension, water supply-and-demand, economic development, climatic conditions, water sustainability and water balance. | The developed model determined the socioeconomic, environmental, natural and ecological changes and impacts on water management. The results are used to identify and understand the potential for feedbacks between social and hydrological systems. This greatly helps in sustainable water management. |
Water scarcity and shortage simulation | [8,74,93] | Using SD methodology to study water scarcity and alleviating water shortages based on the dynamics of sociohydroeconomic processes. | The developed conceptual models were accurate in showing interactions and integrating several subsystems. The obtained findings were effective in assessing different water policies analytically and quantitatively. The urban expansion index and the feedbacks among human, ecological, and hydrological systems were analyzed, and their importance for understanding water scarcity shown. |
Water desalination and treatment | [62,80,92,129] | Introducing SD methodology for combining hydrological, economic, social, and technical scenarios and polices to analyze different dimensions of desalination and treatment processes. | They illustrated and demonstrated that desalination provides a feasible, practical, and safe water supply alternative. The results help to understand the relationship between water availability and use and will be a benefit in the processes of formulating water supply policies. |
Acequia (Irrigation canal) Modeling | [41,47,130] | Providing SD methodology for integrating hydrology, economic, social, environmental, and natural systems in acequia areas. The challenges in conventional irrigation communities have been described and analyzed in a dynamic way based on feedbacks. | The results indicate the ability of system dynamics to express the various interactions and feedbacks in acequias, as well as the important role of relations and links between natural and human systems and provide a solid basis for economic and political modeling of acequias in the future. |
Combined Method | References | Modeling Objectives | Results and Findings |
---|---|---|---|
Geographic Information System (GIS) | [13] | Introducing a new SD and GIS-based method for spatiotemporal simulation of flood damage assessment processes. | The new methodology can be employed to model an assortment of hydrological and environmental parameters, and consider space–time interaction, for example, in water resources processes and catastrophe management. |
Impact Analysis (IA) | [3] | Suggesting a process for combining SD method and IA to assess water strategy methodically and quantitatively and formulating a suitable approach to examine an equilibrium among alleviating water scarcities or shortages and capital expenditure. | The suggested model could be used to understand the interactive influence between the intensity of water shortage and financial spending for planning water demand in the future. In central Taiwan, the usefulness of the suggested approach is successfully checked by solving a water scarcity problem and total expense. |
Agent-Based Modeling and Simulation (ABMS) | [113] | Comparing and analyzing SD and ABMS techniques and determining the potential interfaces to build effective expert systems. | They concluded that the SD–ABMS approach is considerably and effectively suitable with modeling issues such as that in sociohydrological models. |
Artificial Intelligence (AI) and Agent Based Modeling (ABM) | [114] | Developing a novel sociotechnical modeling approach to investigate the relationships between socioeconomic environment and hydrologic unit using SD, AI, and ABM methods. | The developed methodology takes into account the socioeconomic parameters to be involved in the evaluation of awareness-raising campaigns for hydrological systems. This study also provides a detailed demonstration for the urban water resources cycle. |
Exploratory Modeling and Analysis (EMA) | [115] | Offering an approach for studying and analyzing the challenges to freshwater management using innovative SD and EMA techniques and presenting a methodology for a coastal area relies on groundwater to analyze a saline water intrusion problem. | In a decision tree, the results are discussed to help enforce the correct approaches and policies for the analysis and management groundwater in coastal areas. The research will help clarify the future path of population growth, economic activities, agricultural systems, and techniques for water purification for coastal aquifers management. |
[116] | Exploring and studying the limits on global use of freshwater. Identifying the best and worst water stress conditions and world population development using a combination of SD and EMA. | The authors demonstrated that under extreme water stress or restricted population growth the global water usage can be effectively studied and evaluated using the developed methodology. | |
Analytic Hierarchy Process (AHP) | [67] | Combining SD and AHP to develop a simulation model and an assessment index system for the local water environmental carrying capacity. | The findings of this investigation could be used for developing socioeconomic solutions and addressing hydroecological problems. |
[117] | Proposing new integrated decision support model based on the synergy of SD and AHP techniques for relieving the dangers of urban flooding. | They concluded that this innovative approach could be used for many other related issues around the world where decision makers and policymakers need to make strategic decisions that have significant long-term consequences for water and hydrological systems. | |
Fuzzy Logic (FL) | [109] | Presenting the SD technique coupled with FL for analyzing flood risk. | The novel model demonstrated to be a precise method for forecasting spatiotemporal difference of water elevation for flood risk management and evaluation. |
Bayesian Network (BN) | [118] | Introducing a methodology to support decision making based on the approaches of SD and BN for the management of groundwater–agricultural systems. | The key purpose of this study was to incorporate hydrological and socioeconomic relations and to address the problems and difficulties of reconciliation between sustainable aquifer management and productive agricultural yield. |
[111] | Developing an incorporated model dependent on a combination of SD with BN to simulate and analyze water quality. | The outcomes show the developed methodology could offer decision making tool for watershed management to avert water quality impairments. | |
Neural Network (NN) | [5] | Water scarcity analysis and modeling using SD and NN methods to explain how environmental, ecological, and social parameters influence water demand and supply. | The results give us an overview of how water supply and demand can be changed. Water scarcity was successfully estimated and predicted, and using the suggested approach, the most influential elements such as precipitation, population, and runoff were identified. |
Game Theory (GT) | [107] | Introducing modeling methodology based on an integration of SD with GT for water conflict resolution. | The developed model is able to help in addressing water conflict in the river systems and showed a high capability with complexity and uncertainty conditions in the hydrological systems. This approach is also fairly simple to use when assessing and managing essential water resources. This provides all the priorities and choices for stakeholders and decision makers on water resource systems management and planning. |
Software | Vensim | IThink/Stella | PowerSim |
---|---|---|---|
Issued year | 1990 | 1985 | Mid-1980s |
Developer | Ventana Systems | isee Systems | PowerSim Software AS |
Website | www.vensim.com | www.iseesystems.com | www.powersim.no |
Origin | United States | United States | Norway |
Main Features | Works on Windows and Macintosh PCs. Provides dynamic functions such as arrays, uncertainty analysis, statistics and graphical user interface. Supports data series, validation, optimization and Markov chain Monte Carlo (MCMC) methods. | Macintosh and Windows computers. Visualize and communicate complex systems. Supports hierarchical models, multidimensional data, sensitivity, and Monte Carlo simulation. | A Windows-based environment. Facilitates packaging as learning environments. Boosts multidimensional data, optimization, and Monte Carlo analyses. |
% of Usage | 52% | 39% | 9% |
Examples of Use | [55,63,95] | [39,81,84] | [41,43,48] |
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Share and Cite
Mashaly, A.F.; Fernald, A.G. Identifying Capabilities and Potentials of System Dynamics in Hydrology and Water Resources as a Promising Modeling Approach for Water Management. Water 2020, 12, 1432. https://doi.org/10.3390/w12051432
Mashaly AF, Fernald AG. Identifying Capabilities and Potentials of System Dynamics in Hydrology and Water Resources as a Promising Modeling Approach for Water Management. Water. 2020; 12(5):1432. https://doi.org/10.3390/w12051432
Chicago/Turabian StyleMashaly, Ahmed F., and Alexander G. Fernald. 2020. "Identifying Capabilities and Potentials of System Dynamics in Hydrology and Water Resources as a Promising Modeling Approach for Water Management" Water 12, no. 5: 1432. https://doi.org/10.3390/w12051432
APA StyleMashaly, A. F., & Fernald, A. G. (2020). Identifying Capabilities and Potentials of System Dynamics in Hydrology and Water Resources as a Promising Modeling Approach for Water Management. Water, 12(5), 1432. https://doi.org/10.3390/w12051432