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Revision as of 01:52, 21 August 2024 by Sky (talk | contribs) (Created page with "{ "USGS Publications Warehouse": { "@context": "https://schema.org", "@type": "Article", "additionalType": "Journal Article", "name": "The Importance of Uncertainty and Sensitivity Analysis in Process-based Models of Carbon and Nitrogen Cycling in Terrestrial Ecosystems with Particular Emphasis on Forest Ecosystems \u2014 Selected Papers from a Workshop Organized by the International Society for Ecological Modelling (ISEM) at the Third Biennal Meeting o...")
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   "name": "The Importance of Uncertainty and Sensitivity Analysis in Process-based Models of Carbon and Nitrogen Cycling in Terrestrial Ecosystems with Particular Emphasis on Forest Ecosystems \u2014 Selected Papers from a Workshop Organized by the International Society for Ecological Modelling (ISEM) at the Third Biennal Meeting of the International Environmental Modelling and Software Society (IEMSS) in Burlington, Vermont, USA, August 9-13, 2006",
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   "abstract": "Many process-based models of carbon (C) and nitrogen (N) cycles have been developed for terrestrial ecosystems, including forest ecosystems. They address many basic issues of ecosystems structure and functioning, such as the role of internal feedback in ecosystem dynamics. The critical factor in these phenomena is scale, as these processes operate at scales from the minute (e.g. particulate pollution impacts on trees and other organisms) to the global (e.g. climate change). Research efforts remain important to improve the capability of such models to better represent the dynamics of terrestrial ecosystems, including the C, nutrient, (e.g. N) and water cycles. Existing models are sufficiently well advanced to help decision makers develop sustainable management policies and planning of terrestrial ecosystems, as they make realistic predictions when used appropriately. However, decision makers must be aware of their limitations by having the opportunity to evaluate the uncertainty associated with process-based models (Smith and Heath, 2001\u00a0and\u00a0Allen et al., 2004). The variation in scale of issues currently being addressed by modelling efforts makes the evaluation of uncertainty a daunting task.",
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