Catchment classification based on characterisation of streamflow and precipitation time-series

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, providing an overall reasonable grouping of catchments on the basis of their hydrological response. 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 synthesize 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 classifies the ungauged catchments, through a leave-one-out cross-validation, to one of the above identified hydrologic response classes. The approach delivers quite satisfactory results for ungauged catchments, since the comparison of the two cluster sets shows an acceptable overlap. 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.