Updating the US hydrologic classification: an approach to clustering and stratifying ecohydrologic data

作者: Ryan A. McManamay , Mark S. Bevelhimer , Shih-Chieh Kao

DOI: 10.1002/ECO.1410

关键词: Computer scienceData miningOutlierSample size determinationRandom forestEcohydrologyCurse of dimensionalityCluster analysisClass (biology)HydrologyStreamflow

摘要: Hydrologic classifications unveil the structure of relationships among groups streams with differing stream flow and provide a foundation for drawing inferences about principles that govern those relationships. classes template to describe ecological patterns, generalize hydrologic responses disturbance, stratify research management needs applicable ecohydrology. We developed two updated continental US using streamflow datasets varying reference standards. Using only reference-quality gages, we classified 1715 gages into 12 across US. By including more (n=2618) in separate classification, increased dimensionality (i.e. classes) distinctiveness within regions at expense decreasing natural standards quality). Greater numbers higher regional affiliation our compared previous classification (Poff, 1996) suggested level variation resolution was not completely represented smaller sample sizes. Part utility systems rests their ability classify new objects analyses. constructed random forests predict class membership based on indices or landscape variables. In addition, an approach assessingmore » potential outliers due alteration assignment. Departures from disturbance take account multiple simultaneously; thus, can be used determine if disturbed are functioning realm hydrology.« less

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