Abstract
Abstract. The formulation of objective procedures for the delineation of homogeneous groups of catchments is a fundamental issue in both operational and research hydrology. For assessing catchment similarity, a variety of hydrological information may be considered; in this paper, gauged sites are characterised by a set of streamflow signatures that include a representation, albeit simplified, of the properties of fine time-scale flow series and in particular of the dynamic components of the data, in order to keep into account the sequential order and the stochastic nature of the streamflow process. The streamflow signatures are provided in input to a clustering algorithm based on unsupervised SOM neural networks, obtaining groups of catchments with a clear hydrological distinctiveness, as highlighted by the identification of the main patterns of the input variables in the different classes and the interpretation of their interrelations. In addition, even if no geographical, morphological nor climatological information is provided in input to the SOM network, the clusters exhibit an overall consistency as far as location, altitude and precipitation regime are concerned. In order to assign ungauged sites to such groups, the catchments are represented through a parsimonious set of morphometric and pluviometric variables, including also indexes that attempt to synthesise the variability and correlation properties of the precipitation time series, thus providing information on the type of weather forcing that is specific to each basin. Following a principal components analysis, needed for synthesizing and better understanding the morpho-pluviometric catchment properties, a discriminant analysis finally assigns the ungauged catchments, through a leave-one-out cross validation, to one of the above identified hydrologic response classes. The approach delivers a quite satisfactory identification of the membership of ungauged catchments to the streamflow-based classes, since the comparison of the two cluster sets shows a misclassification rate of around 20%. Overall results indicate that the inclusion of information on the properties of the fine time-scale streamflow and rainfall time series may be a promising way for better representing the hydrologic and climatic character of the study catchments.
Highlights
The identification of groups of hydrologically similar catchments is a fundamental issue in both operational and research hydrology: it is essential to ensure the transferability of information when applying regionalisation methods, but can provide valuable indications to improve the understanding of the dominant physical phenomena in the different groups (McDonnell and Woods, 2004; Wagener et al, 2007; Sawicz et al, 2011)
Despite the above mentioned limitation, the SOM classifications based on streamflow signatures seem overall to indicate a good grouping ability, as highlighted by the consistent interpretation of how the main features of the input variables vary in the different classes and of their interrelations, as illustrated through the analysis of their topology maps
The methodology developed in this study first provides a means for identifying groups of similar catchments on the basis of streamflow indexes and successively classifies, in the same clusters, ungauged basins on the basis of climate and landscape characteristics
Summary
The identification of groups of hydrologically similar catchments is a fundamental issue in both operational and research hydrology: it is essential to ensure the transferability of information when applying regionalisation methods, but can provide valuable indications to improve the understanding of the dominant physical phenomena in the different groups (McDonnell and Woods, 2004; Wagener et al, 2007; Sawicz et al, 2011). On the other hand, such representations do not allow to take into account the sequential order and the stochastic nature of the streamflow process; these properties would, for example, be crucial if the regionalisation aimed, as often needed in the hydrological practice, at the parameterisation of a rainfall–runoff model at fine temporal scale and the catchment similarity should be guaranteed in terms of continuous streamflow generation It may be important representing and comparing, in addition to mean values or percentiles, the properties of the low time-scale streamflow series and in particular the dynamic components of the data.
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