A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
SELECTING HYDROLOGIC INDICES FOR THE PRAIRIE PROVINCES
The large and accumulating body of evidence for both the controlling effect of the flow regime on river ecology and for the dependence of river health on the natural flow regime has led to the increasing use of hydrologic indices in instream flow studies. The myriad of collinear hydrologic indices present a daunting challenge to water managers trying to select a manageable number of indices for use in a hydrology‐based environmental flow framework.
In this study, a large number of hydrologic indices were calculated from gauging sites in the prairie provinces of Canada. Principal component analysis (PCA) and two rank‐based non‐parametric techniques are compared in their ability to select a small number of statistically informative indices. Despite the data being skewed and far from normal, PCA and the non‐parametric technique called BioEnv + stepwise (BEST) both led to similar interpretations and could identify a small number of indices that capture a majority of the statistical variability. BEST selected indices more evenly from among conceptual categories of flow than PCA. Copyright © 2011 John Wiley & Sons, Ltd.
SELECTING HYDROLOGIC INDICES FOR THE PRAIRIE PROVINCES
The large and accumulating body of evidence for both the controlling effect of the flow regime on river ecology and for the dependence of river health on the natural flow regime has led to the increasing use of hydrologic indices in instream flow studies. The myriad of collinear hydrologic indices present a daunting challenge to water managers trying to select a manageable number of indices for use in a hydrology‐based environmental flow framework.
In this study, a large number of hydrologic indices were calculated from gauging sites in the prairie provinces of Canada. Principal component analysis (PCA) and two rank‐based non‐parametric techniques are compared in their ability to select a small number of statistically informative indices. Despite the data being skewed and far from normal, PCA and the non‐parametric technique called BioEnv + stepwise (BEST) both led to similar interpretations and could identify a small number of indices that capture a majority of the statistical variability. BEST selected indices more evenly from among conceptual categories of flow than PCA. Copyright © 2011 John Wiley & Sons, Ltd.
SELECTING HYDROLOGIC INDICES FOR THE PRAIRIE PROVINCES
Beveridge, D. (author) / A. Curry, R. (author) / St‐Hilaire, A. (author)
River Research and Applications ; 28 ; 1595-1608
2012-12-01
14 pages
Article (Journal)
Electronic Resource
English
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