Catchment classification by runoff behaviour with self-organizing maps (SOM)

Abstract

Abstract. Catchments show a wide range of response behaviour, even if they are adjacent. For many purposes it is necessary to characterise and classify them, e.g. for regionalisation, prediction in ungauged catchments, model parameterisation.In this study, we investigate hydrological similarity of catchments with respect to their response behaviour. We analyse more than 8200 event runoff coefficients (ERCs) and flow duration curves of 53 gauged catchments in RhinelandPalatinate, Germany, for the period from 1993 to 2008, covering a huge variability of weather and runoff conditions. The spatio-temporal variability of event-runoff coefficients and flow duration curves are assumed to represent how different catchments "transform" rainfall into runoff. From the runoff coefficients and flow duration curves we derive 12 signature indices describing various aspects of catchment response behaviour to characterise each catchment.Hydrological similarity of catchments is defined by high similarities of their indices. We identify, analyse and describe hydrologically similar catchments by cluster analysis using Self-Organizing Maps (SOM). As a result of the cluster analysis we get five clusters of similarly behaving catchments where each cluster represents one differentiated class of catchments.As catchment response behaviour is supposed to be dependent on its physiographic and climatic characteristics, we compare groups of catchments clustered by response behaviour with clusters of catchments based on catchment properties. Results show an overlap of 67 % between these two pools of clustered catchments which can be improved using the topologic correctness of SOMs.

Identification of runoff generation processes using hydrometric and tracer methods in a meso-scale catchment in Rwanda

“…Moderate differences in runoff coefficient are observed between the studied climatic regions, with higher values in the Mediterranean region. Ley et al (2011) found that the annual mean runoff coefficients in nested catchments of Rhineland-Palatinate, Germany, may range from 2 % to 15 % in the summer period, while during winter time they range from 5 % to 56 %. The high runoff coefficients observed in Germany in winter are due to snow influence and can be the same as the case in the Rwanda areas but due to heavy rainfall observed during the events (see Fig.…”

Catchment classification based on characterisation of streamflow and precipitation time series

“…In the recent years, also non-supervised neural networks, and in particular of the SOM (self-organising mapping) type, were successfully applied (and sometimes compared with other methods such as K-means or Fuzzy C-means) for catchments classification purposes (Hall and Minns, 1999;Hall et al, 2002;Jingyi and Hall, 2004;Chang et al, 2008;Srinivas et al, 2008;Di Prinzio et al, 2011;Ley et al, 2011). SOM-type neural networks learn to cluster the input data by recognizing different patterns organising the data on the basis of their similarity, quantified by means of a distance measure (in the present case, like in the majority of applications, the Euclidean distance).…”

“…Nathan and McMahon, 1990;Laaha and Bloeschl, 2006;Vezza et al, 2010) or the entire flow duration curve (e.g. Singh et al, 2001;Ley et al, 2011;Patil and Stieglitz, 2011;Sauquet and Catalogne, 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 therefore be guaranteed in terms of continuous streamflow generation.…”

Analysis of projected hydrological behavior of catchments based on signature indices

“…In order to combine the strengths of both approaches, we define in this study nine hydrologically meaningful signature indices: five indices are sampled on the FDC (similar to Yilmaz et al, 2008), four indices are closely linked to the probability distribution of event runoff coefficients (Ley et al, 2011). We apply this signature index concept to the output from a hydrologic catchment model run for 3 subcatchments located in the Nahe basin (Western Germany) to detect differences in runoff behavior resulting from different meteorological data sets.…”

“…These metrics provide dynamic aspects of the watershed system or hydrological model (Yadav et al, 2007;Zhang et al, 2008) and may therefore allow for a quantitative evaluation of hydrological behavior . Examples for hydrologically-based metrics are signature indices derived from flow duration curves (Yilmaz et al, 2008) or from distributions of runoff event coefficients (Merz et al, 2006;Ley et al, 2011).…”