Flow signatures and catchment’s attributes for HCA clustering in a hydrologic similarity assessment (Tunisian case)

Abstract

Abstract Partitioning methods such as cluster analysis are advantageous in pooling catchments into hydrometric similar regions. However, these study cases are always infrequent in Sud Mediterranean zones and remain under-represented in international publications. This paper illustrates a Tunisian application case, which aims to pool catchments with hierarchical clustering method based on distances calculated in multidimensional physiographical and hydrometric space. Homogeneity of generated clusters is checked by Silhouette index. Current study considers nineteen Tunisian catchments, in a semi-arid climate observed since 1992. Areas and annual average rainfall respectively vary in [1–10 km2 ] and [280–500 mm] ranges. Twelve physiographical attributes and nine rainfall and streamflow signatures are considered in hierarchical partitioning procedure with two clusters. Correlation distance provides the most homogeneous clusters. Statistics demonstrate that: percentage of area affected by anti-erosive practices, percentage of forest cover and catchment’s area are the most discriminant attributes. However, hydrometrical signatures appear to be not relevant. This partitioning highlight two different hydrological behaviors which must be in support of forecasting. Results are promising as a Sud Mediterranean case, where the shortage of hydrometrical data is an occurring problem. They have the advantageous of enabling hydrologic forecasting without need of heavy information.