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      Ensemble modeling informs hypoxia management in the northern Gulf of Mexico

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          Abstract

          <p id="d9487345e241">The number of coastal hypoxia areas is spreading worldwide, with severe environmental and societal impacts. The second-largest hypoxic zone occurs in the northern Gulf of Mexico, where anthropogenic nutrient load is a key driving factor, as in many coastal waters. We address policy-relevant questions raised by Gulf stakeholders and decision-makers using an ensemble approach that integrates results from multiple models. Through development of a rigorous framework to propagate intramodel and intermodel uncertainty into the ensemble, we provide policymakers with the response of hypoxic area to a range of different nitrogen load reduction scenarios, with corresponding probabilistic statements that allow for quantitative risk assessment of alternative policy strategies. </p><p class="first" id="d9487345e244">A large region of low-dissolved-oxygen bottom waters (hypoxia) forms nearly every summer in the northern Gulf of Mexico because of nutrient inputs from the Mississippi River Basin and water column stratification. Policymakers developed goals to reduce the area of hypoxic extent because of its ecological, economic, and commercial fisheries impacts. However, the goals remain elusive after 30 y of research and monitoring and 15 y of goal-setting and assessment because there has been little change in river nitrogen concentrations. An intergovernmental Task Force recently extended to 2035 the deadline for achieving the goal of a 5,000-km <sup>2</sup> 5-y average hypoxic zone and set an interim load target of a 20% reduction of the spring nitrogen loading from the Mississippi River by 2025 as part of their adaptive management process. The Task Force has asked modelers to reassess the loading reduction required to achieve the 2035 goal and to determine the effect of the 20% interim load reduction. Here, we address both questions using a probabilistic ensemble of four substantially different hypoxia models. Our results indicate that, under typical weather conditions, a 59% reduction in Mississippi River nitrogen load is required to reduce hypoxic area to 5,000 km <sup>2</sup>. The interim goal of a 20% load reduction is expected to produce an 18% reduction in hypoxic area over the long term. However, due to substantial interannual variability, a 25% load reduction is required before there is 95% certainty of observing any hypoxic area reduction between consecutive 5-y assessment periods. </p>

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          Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support

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            Ecological forecasts: an emerging imperative.

            Planning and decision-making can be improved by access to reliable forecasts of ecosystem state, ecosystem services, and natural capital. Availability of new data sets, together with progress in computation and statistics, will increase our ability to forecast ecosystem change. An agenda that would lead toward a capacity to produce, evaluate, and communicate forecasts of critical ecosystem services requires a process that engages scientists and decision-makers. Interdisciplinary linkages are necessary because of the climate and societal controls on ecosystems, the feedbacks involving social change, and the decision-making relevance of forecasts.
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              Dynamics and distribution of natural and human-caused hypoxia

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                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proc Natl Acad Sci USA
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                August 15 2017
                August 15 2017
                : 114
                : 33
                : 8823-8828
                Article
                10.1073/pnas.1705293114
                5565443
                28760996
                6c426083-f5ff-4cca-ad63-bf782790b35d
                © 2017

                http://www.pnas.org/site/misc/userlicense.xhtml

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