Abstract
AbstractHydrological classification systems seek to provide information about the dominant processes in the catchment to enable information to be transferred between catchments. Currently, there is no widely agreed‐upon system for classifying river catchments. This paper develops a novel approach to classifying catchments based on the temporal dependence structure of daily mean river flow time series, applied to 116 near‐natural ‘benchmark’ catchments in the UK. The classification system is validated using 49 independent catchments. Temporal dependence in river flow data is driven by the flow pathways, connectivity and storage within the catchment and can thus be used to assess the influence catchment characteristics have on moderating the precipitation‐to‐flow relationship. Semi‐variograms were computed for the 116 benchmark catchments to provide a robust and efficient way of characterising temporal dependence. Cluster analysis was performed on the semi‐variograms, resulting in four distinct clusters. The influence of a wide range of catchment characteristics on the semi‐variogram shape was investigated, including: elevation, land cover, physiographic characteristics, soil type and geology. Geology, depth to gleyed layer in soils, slope of the catchment and the percentage of arable land were significantly different between the clusters. These characteristics drive the temporal dependence structure by influencing the rate at which water moves through the catchment and/or the storage in the catchment. Quadratic discriminant analysis was used to show that a model with five catchment characteristics is able to predict the temporal dependence structure for un‐gauged catchments. This method could form the basis for future regionalisation strategies, as a way of transferring information on the precipitation‐to‐flow relationship between gauged and un‐gauged catchments. © 2014 The Authors. Hydrological Processes by published by John Wiley & Sons, Ltd.
Highlights
Hydrology has yet to achieve a widely agreed-upon system which classifies catchments based on the movement and storage of water within the catchment (Wagener et al, 2007; Ley et al, 2011)
The influence catchment characteristics have on the temporal dependence of each of these clusters is analysed in two ways: through box plots, to investigate the distribution of catchment characteristics for each cluster; and by using Quadratic Discriminant Analysis (QDA) to independently predict membership of the clusters using catchment characteristics rather than the semi-variogram
The analysis identifies whether the mean of the catchment characteristic differs between clusters
Summary
Hydrology has yet to achieve a widely agreed-upon system which classifies catchments based on the movement and storage of water within the catchment (Wagener et al, 2007; Ley et al, 2011). Even though internal complexity will remain within each class as every catchment is unique (Beven, 2000), a broad classification process should be possible. This is based on the general assumption that some level of organisation and predictability in catchment ‘function’ (i.e. the translation of catchment input into river flow) exists (Dooge, 1986; Bloschl et al, 2013). A broad classification process should cluster together similar catchments, limiting the variability within classes and maximising the variability between them. Classification is a means to identify the dominant processes and mechanisms operating in a given catchment type, as well as the most important controls on water fluxes and pathways (McDonnell and Woods, 2004). Being able to classify catchments has a range of benefits (Grigg, 1965, 1967): 1. To give names to things (enable grouping as seen in other disciplines)
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