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A Gis‐Based Approach to Watershed Classification for Nebraska Reservoirs 1
Author(s) -
Bulley Henry N.N.,
Merchant James W.,
Marx David B.,
Holz John C.,
Holz Aris A.
Publication year - 2007
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.2007.00048.x
Subject(s) - watershed , water quality , geospatial analysis , environmental science , hydrology (agriculture) , water resources , hydrogeology , watershed management , decision tree , agency (philosophy) , water resource management , environmental resource management , computer science , data mining , geography , ecology , cartography , engineering , machine learning , geotechnical engineering , biology , philosophy , epistemology
The U.S. Environmental Protection Agency is charged with establishing standards and criteria for assessing lake water quality. It is, however, increasingly evident that a single set of national water quality standards that do not take into account regional hydrogeologic and ecological differences will not be viable as lakes clearly have different inherent capacities to meet such standards. We demonstrate a GIS‐based watershed classification strategy for identifying groups of Nebraska reservoirs that have similar potential capacity to attain a certain level of water quality standard. A preliminary cluster analysis of 78 reservoirs was performed to determine the potential number of Nebraska reservoir groups. Subsequently, a Classification Trees method was used to refine number of classes, describe the structure of reservoir watershed classes, and to develop a predictive model that relates watershed conditions to reservoir classes. Results suggest that Nebraska reservoirs can be represented by nine classes and that soil organic matter content in the watershed is the most important single variable for segregating the reservoirs. The cross‐validation prediction error rate of the Classification Tree model was 26.3%. Because all geospatial data used in this work are available nationally, the method could be adopted throughout the U.S. Hence, this GIS‐based watershed classification approach could provide water resources managers an effective decision‐support tool in managing reservoir water quality.