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Classification of watersheds into integrated social and biophysical indicators with clustering analysis

机译:利用聚类分析将流域分类为社会和生物物理综合指标

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摘要

In this work, we classify watersheds in the US portion of the Great Lakes basin according to a wide range of social and environmental characteristics. Classified watershed indicators serve to provide organizing principles for prescribing effective management strategies and for developing regional scale monitoring and modeling efforts. Classifications also provide a means for synthesizing seemingly disparate ecological attributes into powerful indicators. We use a robust watershed classification scheme based on cluster analysis that integrates a set of 12 social and environmental factors chosen to reflect the state of water resources in the Great Lakes basin. We found five statistically distinct classified watershed indicators: Urban Centers, Intensive Agriculture, Cultivated Rural, Northwoods, and Lakes Destinations. Within these classifications, we distinguished relationships between impacts on water resources and biophysical, demographic, land-use, and social characteristics of the landscape. We found that agricultural areas can be divided into those with high and low water impact, and that watersheds with considerable influence of seasonal homes are further distinguished into watersheds with inland lakes and relatively high socioeconomic status, contrasted with watersheds with wetlands and relatively low socioeconomic status.
机译:在这项工作中,我们根据广泛的社会和环境特征对美国大湖流域的分水岭进行了分类。分类的分水岭指标可为制定有效的管理策略以及开展区域规模的监测和建模工作提供组织原则。分类还提供了一种将看似完全不同的生态属性综合为有效指标的方法。我们使用基于聚类分析的稳健的分水岭分类方案,该方案综合了12种社会和环境因素,以反映大湖流域的水资源状况。我们发现了五个统计上不同的分类流域指标:城市中心,集约化农业,耕种农村,诺斯伍德和湖泊目的地。在这些分类中,我们区分了对水资源的影响与景观的生物物理,人口,土地利用和社会特征之间的关系。我们发现,农业区域可分为水影响高和低的区域,具有季节性家园影响的流域可进一步区分为内陆湖泊和社会经济地位相对较高的流域,与湿地和社会经济地位相对较低的流域形成对比。 。

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