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Floods are considered among the gravest natural disasters worldwide and have resulted in enormous human and material damage. The Manouba–Sijoumi basin (Northeast of Tunisia) is often flooded due to urban expansion, population growth and unplanned land use. This study aims to identify and to define the flood-prone areas of this basin for the 2003 and 2018 extreme events based on a Geographic Information System, a qualitative method (analytic network process-ANP) and a statistical model (frequency ratio-FR). The flood risk maps obtained by both models were validated using the receiver operating characteristic, the area under the curve (AUC) and inventory map. Areas of high and very high flood sensitivity are located mainly in urban settings, with an increase in risk between 2003 and 2018. The AUC values for both models were of the same significance (98%) for the year 2003 while those for the year 2018 were 94% and 98% for the ANP and FR models, respectively. This would imply that both models yielded reasonable results. However, the FR model showed an ability to reduce the uncertainty associated with expert judgements. The results indicate that the most influential factor on flooding in this area was land use/cover. Indeed, populations were largely settled in unsuitable sites for urbanization and in potentially flood-prone areas located mainly around the Sijoumi Sebkha, especially to the west and south of it. The findings of the study are of great value for policy makers and state authorities to achieve greater awareness and adopt strategies for environmental preparedness and management. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop an overall methodology for assessing soil erosion hazard. Two models are implemented in Haffouz watershed (central Tunisia), which is generally prone to erosion processes. The first is a multi-parametric approach based on expert's knowledge and Analytic Hierarchy Process (AHP), while the second is a statistical technique based on logistic regression (LR). Six parameters (slope gradient, annual precipitation, lithofacies, vegetation index, drainage density, and land use) are used for both methodologies. A thematic layer map is generated for each parameter using geographic information system (GIS) and remote sensing, while the weighting coefficients of each methodology are used for the compilation of AHP and LR final maps. Both models give similar results depicting that, in general, a moderate hazard condition of soil erosion was found in the study area. The proposed approaches also resulted in the identification of the highly eroded areas, requiring immediate action. [ABSTRACT FROM AUTHOR]

[Display omitted] • Soil erosion risk is assessed using qualitative, statistical and empirical models; • The prediction accuracy of used models was evaluated using ROC curves; • Different erosion conditioning factors were integrated and mapped using the GIS; • Proposed framework can be used as a decision tool to other areas with limited data. Erosion is a natural phenomenon, which may have destructive effects on environments and human societies around the world. This study aims to identify and map soil erosion hazard in the Mellah Watershed (North-eastern Algeria), based on an empirical model (RUSLE: Revised Universal Soil Loss Equation), a semi quantitative model (AHP: Analytic Hierarchy Process) and a statistical model (FR: Frequency Ratio). Several erosion-conditioning factors were used as input data, namely rainfall, elevation, slope, land cover, soil type, Normalized Difference Vegetation Index (NDVI), Stream Power Index (SPI), Topographic Wetness Index (TWI) and distances from roads and streams. The study resulted in generating vulnerability maps with five erosion risk classes, ranging from very low to very high risk. Areas of very high erosion risk were shown to occupy 5.06%, 10.91% and 12.57%, of the watershed area, using RUSLE, FR and AHP models respectively. Results obtained by the three adopted models were validated using the Receiver Operating Characteristic curves (ROCs), and the Areas Under the Curve (AUC). The AUC values were 97.9%, 95.7% and 94.2% for RUSLE, AHP and FR models respectively, implying reasonably good performances for the three models. [ABSTRACT FROM AUTHOR]