Characterisation of rainfall events in northern Tunisia using self-organising maps

Study region: The study is carried out for northern Tunisia. Study focus: Precipitations are often analysed via intensity or accumulation for a specified time -scale (e.g., annual, seasonal, etc). We propose in this study to analyse regional rainfall variability by adopting a variable time step through the rain event concept. This event-based approach, ensures the integration of information related to rain intermittency, which is one of the fundamental properties of precipitations. This study focuses essentially on wet spells characteristics derived from the aggregation of daily winter dataset over a 50 years period (1960-2009). The multivariate analysis, based on the combination of two clustering approaches, i.e., self-organizing map and hierarchical clustering, allows the identification of different rainfall regimes. This study helps to understand rainfall variability patterns and to address rainfall regionalization and water use management issues. New hydrological insights for the region: The winter precipitations of northern Tunisia are classified into 4 typical situations: Extremely dry seasons with a few short and weak rainfall events, dry seasons, with high frequency of weak events, intermediate seasons with medium amount of rain and intermittent events and rainiest seasons with long and intense events. The regionalization yields two geographical regions: northern sector characterized by rainy seasons, whereas the stations of the southern sector are mostly dry. The temporal variability analysis shows that the dry season classes dominate extending over three consecutive decades from 1970 to 2000.

Automated summary

This study analyzes the variability of precipitation in northern Tunisia using a variable time step and the concept of rain events. Through clustering methods, different rainfall patterns are identified, and the characteristics of wet spells and regionalization results are obtained. The findings of this study are important for understanding the precipitation patterns and water resource management in the region.