Which catchment characteristics control the temporal dependence structure of daily river flows?. Issue 6 (15th July 2014)

• Record Type: Journal Article
• Authors: Chiverton, Andrew; Hannaford, Jamie; Holman, Ian; Corstanje, Ron; Prudhomme, Christel; Bloomfield, John; Hess, Tim M.
• Is Part Of: Hydrological processes. Volume 29:Issue 6(2015:Mar.)
• Page Start: 1353
• Page End: 1369
• Publication Date: 2014-07-15
• Subjects: Hydrology -- Periodicals; Hydrology -- Research -- Periodicals; Hydrologic models -- Periodicals; Hydrological forecasting -- Periodicals; 631.432
• DOI: 10.1002/hyp.10252
• Languages: English
• ISSNs: 0885-6087

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>Hydrological 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. <italic>Hydrological Processes</italic> by published by John Wiley & Sons, Ltd.</p> </abstract>