![]() ![]() Thus, Metric distances are commonly identified between catchments in multidimensional attribute space to assess their proximity 5, 6. Similarities in their hydrologic and/or physiographical and climatic attributes applied with clustering approaches. The transfer of information from one or several gauged catchments (donors) to another ungauged catchment (receiver) 4 requires the identification of similar gauged catchments, which can be selected through: They belong to two categories statistical or process based. Regionalisation techniques are PUB tools that are necessary for transferring information. PUB was designed to develop a better scientific basis for hydrology with greater consistency, increasing the prospects for scientific breakthroughs and reducing uncertainties 3. This is the whole raison d’être of the Prediction of Ungauged Basin (PUB) initiative 2. Therefore, runoff prediction at an ungauged river or catchment is carried out through some kind of extrapolation from a gauged site to an ungauged site, and this is not straightforward. Nevertheless, several catchments in many parts of the world are ungauged or poorly gauged, this lack of data often increases with decreasing catchment sizes that leads to great difficulties in their management 1, 2. Water resources management (ex: land use planning, irrigation, hydraulic structure design, flood forecasting) requires knowledge of water quantity at a target site or catchment. They have the advantage of enabling hydrologic forecasting without requiring heavy information. Results are promising in the Sud-Mediterranean case, where the shortage of hydrometrical data is an ongoing problem. These partitions highlight two different hydrological behaviors that must support forecasting. However, hydrometrical signatures appear to be irrelevant. Statistically the: percentage of area affected by anti-erosive practices, percentage of forest cover and catchment area are the most discriminating attributes. Correlation distance provides the most homogeneous clusters. Twelve physiographical attributes, nine rainfall and streamflow signatures are considered in the HCA with two clusters. ![]() Nineteen semi-arid Tunisian catchments monitored since 1992 are studied. Then the distances efficiencies are compared. The homogeneity of generated clusters is checked by the silhouette index. This paper illustrates a Tunisian application case, that aims to pool catchments with a hierarchical clustering algorithm (HCA) based on distances calculated in multidimensional physiographical and hydrometric space. Without accurate forecasts, it is difficult to assess and manage water resources efficiently this situation won't be of any assistance to hydrology decision-makers. They help overcome data shortage in ungauged catchments, which is a common problem in Sud Mediterranean zones. Partitioning methods such as cluster analysis are advantageous in pooling catchments into hydrometric similar regions. ![]()
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