Flood hazard assessment in the Leben basin (central Tunisia) using multi-criteria analysis, remote sensing and G.I.S.
Keywords:
Floods assessment, Tunisia, multi-criteria analysis, G.I.S., remote sensingAbstract
Abstract:
Floods are the most important natural hazard in the world. Approaches to assess their risk and dangerous areas are constantly increasing. Approaches that take into account the principal factors that cause flooding are often the most reliable. In this regard, the present study aims to use a multi-criteria analysis to estimate flood risk. It is composed of several factors to which weights are assigned according to their influences. Each factor is classified into several classes of values and each class is assigned a weight. This allowed the production of thematic maps with the influence of each factor. Overlaying all layers of data resulted in the flood risk index map. Results for each factor show the preponderance of elevations, slopes and land use over the others in the occurrence of flooding. While areas with high and very high elevation indices represent just under half of the land (46%), areas with slope indices are of the order of 86%, i.e. the majority of the land. Land use index gives 50% of the land favorable to flooding. Final flood risk index map shows that about 1/4 (26.67%) of the areas are at serious risk of flooding (high and very high). Areas at high risk of flooding are located around the downstream zones of the main watercourses and include the town of Meknassy and represent a dangerous area for construction, development and crops. Historical events collected show that this area has effectively suffered in recent decades from fewer disastrous floods, of both material and human damage. The 1969, 1973 and 1990 floods are encrusted in people' s memory of this region. Destruction of communication networks, damage caused by livestock and the burying of the city by sediment are also documented. The study of floods requires, first and foremost, an understanding of the factors that cause them, their influences on each other and their ultimate contribution in causing the phenomenon. This study also shows that a multi-criteria approach combined with GIS and remote sensing techniques can be useful and, above all, inexpensive for decision-makers.