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Water resource systems often contain numerous components that are intertwined or even contradictory, such as power production, water delivery, recreation, and environmental needs. This complexity makes it difficult to holistically assess management alternatives. In addition, hydro climatic and ecological uncertainties complicate efforts to evaluate the impacts of management scenarios. We need new tools that are able to inform managers and researchers of the tradeoffs or consequences associated with flow alternatives, while also explicitly incorporating sources of uncertainty. My research addresses this limitation using two modeling approaches: stochastic system dynamics modeling and Bayesian network modeling. I developed a stochastic system dynamics model to evaluate the impacts of environmental flow alternatives on multiple water users in the Rio Chama basin, New Mexico. Specifically, my work examined the influence of flow alternatives on cottonwood recruitment, reservoir storage, hydropower production, and whitewater boating. In addition, I coupled two-dimensional hydrodynamic and Bayesian network models to assess the impacts of management scenarios on cottonwood recruitment on the Gila River, New Mexico. The Bayesian network approach explicitly incorporated spatial variability, as well as hydrologic and ecological uncertainties. These methods are useful for more thoroughly assessing the tradeoffs of management decisions, integrating system components within a holistic framework, and evaluating ecological consequences of management scenarios at fine spatial scales.



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