Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 263171
Loughborough University

Loughborough University Institutional Repository

Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/13079

Title: Selection of river flow indices for the assessment of hydroecological change
Authors: Monk, Wendy A.
Wood, Paul J.
Hannah, David M.
Wilson, Douglas A.
Keywords: River flow regimes
Principal components analysis
Lotic-invertebrate Index for Flow Evaluation (LIFE)
Data redundancy
Issue Date: 2007
Publisher: © John Wiley and Sons
Citation: MONK, W.A. ... et al, 2007. Selection of river flow indices for the assessment of hydroecological change. River Research and Applications, 23 (1), pp.113-122.
Abstract: A wide range of ‘ecologically relevant’ hydrological indices (variables) have been identified as potential drivers of riverine communities. Recently, concerns have been expressed regarding index redundancy (i.e. similar patterns of variance) across the host of hydrological descriptors on offer to researchers and water resource managers. Some guiding principles are required to aid selection of the most statistically defensible and meaningful river flow indices for hydroecological analysis. In this short communication, we investigate the utility of a principal components analysis (PCA)-based method that identifies 25 hydrological variables to characterise the major modes of statistical variation in 201 hydrological indices for 83 rivers across England and Wales. The emergent variables, and all 201 hydrological variables, are used to develop regression models [for the whole data set and three river flow regime shape (i.e. annual hydrograph form) classes] for an 11-year macroinvertebrate community dataset (i.e. LIFE scores). The same ‘best’ models are produced using the PCA-based method and all 201 hydrological variables for two of the three river flow regime groups. However, weaker models are yielded by the PCA-based method for the remaining (flashy) river flow regime class and the whole data set (all 83 rivers). Thus, it is important to exercise caution when employing data reduction/ index redundancy approaches, as they may reject variables of ecological significance due to the assumption that the statistically dominant sources of hydrological variability are the principal drivers of, perhaps more subtle (sensitive), hydroecological associations.
Description: This is the pre-peer reviewed version of the article, which has been published in final form at: http://dx.doi.org/10.1002/rra.964
Version: Submitted for publication
DOI: 10.1002/rra.964
URI: https://dspace.lboro.ac.uk/2134/13079
Publisher Link: http://dx.doi.org/10.1002/rra.964
ISSN: 1535-1459
Appears in Collections:Published Articles (Geography and Environment)

Files associated with this item:

File Description SizeFormat
Monk2007submitted.pdfSubmitted version538.5 kBAdobe PDFView/Open


SFX Query

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.