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A Decision Support System for Watershed-Scale Management of Ecosystem Services using Evolutionary Algorithms

机译:使用进化算法的生态系统服务流域管理的决策支持系统

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Increased production of ecosystem services (e.g., sediment and nutrient load reduction, flood peak reduction, wildlife habitat, and biodiversity) is likely to be achieved through strategic management of agricultural landscapes. This paper explores the role of landscapes in improving ecosystem service generation on a watershed scale. Four ecosystem service related objectives are considered; they involve reduction of flood peaks and sediment, nitrogen and phosphorus loads. The authors have developed a model to identify land uses and management practices that most cost-effectively generate these ecosystem services. Cost effectiveness is integrated by considering resulting impacts to landowner income, interpreted as a fifth objective. The spatial decision support model used to solve the underlying watershed management problem couples the U.S. Department of Agriculture’s Soil and Water Assessment Tool (SWAT) and a fairly new, multi-objective evolutionary algorithm known as SPEA2. Application of the model to the Big Creek watershed, a complex basin located in southern Illinois, demonstrates that it allows the assessment of tradeoffs between competing objectives, namely agricultural commodity production and generation of ecosystem services. Moreover, it confirms that this integrative modeling approach could enable stakeholders and policy makers in identifying a suitable set of land use and management practices at a watershed scale.
机译:通过农业景观的战略管理,可能会逐渐实现生态系统服务的增加(例如,沉积物和养分负荷减少,洪水峰值减少,野生动物栖息地和生物多样性。本文探讨了景观在改善流域规模上的生态系统服务发电方面的作用。考虑了四种生态系统服务相关目标;它们涉及减少洪水峰和沉积物,氮和磷荷载。作者开发了一种识别土地使用和管理实践的模型,最具成本有效地生成这些生态系统服务。通过考虑导致对土地所有者收入的影响,将成本效益融为一体,被解释为第五个目标。用于解决底层流域管理问题的空间决策支持模型对美国农业部的土壤和水分评估工具(SWAT)和一个相当新的多目标进化算法称为SPEA2。该模型在伊利诺伊州南部的复杂盆地将模型应用于大溪水域,表明它允许评估竞争目标之间的权衡,即农产品生产和生态系统服务的一代。此外,它证实,这种综合建模方法可以使利益相关者和决策者在分水岭规模上识别合适的土地利用和管理实践。

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