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System Dynamics Modeling of Transboundary Systems: The Bear River Basin Model
Author(s) -
Sehlke Gerald,
Jacobson Jake
Publication year - 2005
Publication title -
groundwater
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 94
eISSN - 1745-6584
pISSN - 0017-467X
DOI - 10.1111/j.1745-6584.2005.00065.x
Subject(s) - structural basin , system dynamics , geology , computer science , geomorphology , artificial intelligence
System dynamics is a computer‐aided approach to evaluating the interrelationships of different components and activities within complex systems. Recently, system dynamics models have been developed in areas such as policy design, biological and medical modeling, energy and the environmental analysis, and in various other areas in the natural and social sciences. The Idaho National Engineering and Environmental Laboratory, a multipurpose national laboratory managed by the Department of Energy, has developed a system dynamics model in order to evaluate its utility for modeling large complex hydrological systems. We modeled the Bear River basin, a transboundary basin that includes portions of Idaho, Utah, and Wyoming. We found that system dynamics modeling is very useful for integrating surface water and ground water data and for simulating the interactions between these sources within a given basin. In addition, we also found that system dynamics modeling is useful for integrating complex hydrologic data with other information (e.g., policy, regulatory, and management criteria) to produce a decision support system. Such decision support systems can allow managers and stakeholders to better visualize the key hydrologic elements and management constraints in the basin, which enables them to better understand the system via the simulation of multiple “what‐if” scenarios. Although system dynamics models can be developed to conduct traditional hydraulic/hydrologic surface water or ground water modeling, we believe that their strength lies in their ability to quickly evaluate trends and cause‐effect relationships in large‐scale hydrological systems, for integrating disparate data, for incorporating output from traditional hydraulic/hydrologic models, and for integration of interdisciplinary data, information, and criteria to support better management decisions.

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