ArticleEnvironmental management2018
Assessing the Hydrogeomorphic Effects of Environmental Flows using Hydrodynamic Modeling.
Article in Environmental management, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
Water managers are increasingly using environmental flows (e-flows) as a tool to improve ecological conditions downstream from impoundments. Recent studies have called for e-flow approaches that explicitly consider impacts on hydrogeomorphic processes when developing management alternatives. Process-based approaches are particularly relevant in river systems that have been highly modified and where water supplies are over allocated. One-dimensional (1D) and two-dimensional (2D) hydrodynamic models can be used to resolve hydrogeomorphic processes at different spatial and temporal scales to support the development, testing, and refinement of e-flow hypotheses. Thus, the objective of this paper is to demonstrate the use of hydrodynamic models as a tool for assisting stakeholders in targeting and assessing environmental flows within a decision-making framework. We present a case study of e-flows on the Rio Chama in northern New Mexico, USA, where 1D and 2D hydrodynamic modeling was used within a collaborative process to implement an e-flow experiment. A specific goal of the e-flow process was to improve spawning habitat for brown trout by flushing fine sediments from gravel features. The results revealed that the 2D hydrodynamic model provided much greater insight with respect to hydrodynamic and sediment transport processes, which led to a reduction in the recommended e-flow discharge. The results suggest that 2D hydrodynamic models can be useful tools for improving process understanding, developing e-flow recommendations, and supporting adaptive management even when limited or no data are available for model calibration and validation.
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