Aurelia Sole

Flood-prone areas assessment using linear binary classifiers based on flood maps obtained from 1D and 2D hydraulic models

The identification of flood-prone areas is a critical issue becoming everyday more pressing for our society. A preliminary delineation can be carried out by DEM-based procedures that rely on basin geomorphologic features. In the present paper, we investigated the dominant topographic controls for the flood exposure using techniques of pattern classification through linear binary classifiers based on DEM-derived morphologic features. Our findings may help the definition of new strategies for the delineation of flood-prone areas with DEM-based procedures. With this aim, local features—which are generally used to describe the hydrological characteristics of a basin—and composite morphological indices are taken into account in order to identify the most significant one. Analyses are carried out on two different datasets: one based on flood simulations obtained with a 1D hydraulic model, and the second one obtained with a 2D hydraulic model. The analyses highlight the potential of each morphological descriptor for the identification of the extent of flood-prone areas and, in particular, the ability of one geomorphologic index to represent flood-inundated areas at different scales of application.

DEM-Based Approaches for the Delineation of Flood-Prone Areas in an Ungauged Basin in Africa

AbstractIn the present work, the flood hazard exposure in an ungauged basin in Africa is assessed exploiting the basin morphological characteristics. Flood-prone areas are identified using linear binary classifiers based on several geomorphic descriptors extracted from digital elevation models (DEMs). The classifiers are calibrated individually and evaluated by comparing their outputs with a flood inundation map obtained by two-dimensional (2D) hydraulic simulations and using receiver operating characteristics (ROC) curves as performance measures. The best-performing descriptors for the subcatchment of the Bulbula River, near the city of Addis Ababa (Ethiopia), are the elevation difference, H between the location under exam and the nearest drainage network, and the composite index ln[hr/H], that compares an estimate of the water level in the nearest point of the river network to the difference in elevation between the point under exam and the river. These simple procedures allow extending the flood deline...

Collaborative Strategies for Sustainable EU Flood Risk Management: FOSS and Geospatial Tools—Challenges and Opportunities for Operative Risk Analysis

An analysis of global statistics shows a substantial increase in flood damage over the past few decades. Moreover, it is expected that flood risk will continue to rise due to the combined effect of increasing numbers of people and economic assets in risk-prone areas and the effects of climate change. In order to mitigate the impact of natural hazards on European economies and societies, improved risk assessment, and management needs to be pursued. With the recent transition to a more risk-based approach in European flood management policy, flood analysis models have become an important part of flood risk management (FRM). In this context, free and open-source (FOSS) geospatial models provide better and more complete information to stakeholders regarding their compliance with the Flood Directive (2007/60/EC) for effective and collaborative FRM. A geospatial model is an essential tool to address the European challenge for comprehensive and sustainable FRM because it allows for the use of integrated social and economic quantitative risk outcomes in a spatio-temporal domain. Moreover, a FOSS model can support governance processes using an interactive, transparent and collaborative approach, providing a meaningful experience that both promotes learning and generates knowledge through a process of guided discovery regarding flood risk management. This article aims to organize the available knowledge and characteristics of the methods available to give operational recommendations and principles that can support authorities, local entities, and the stakeholders involved in decision-making with regard to flood risk management in their compliance with the Floods Directive (2007/60/EC).

Denudation processes and landforms map of the Camastra River catchment (Basilicata – South Italy)

This paper performs denudation processes and landforms characterizing the Camastra River catchment (Basilicata – South Italy), on a 1:40,000 scale map. The map, which includes gravitational processes and landforms and water erosion processes and landforms, was obtained by combining field surveys with the analysis of topographic maps and of multi-temporal aerial photos, ranging from 1954 to 2010. These latter provided information on both spatial and temporal evolution of geomorphic processes. The integration and the elaboration of the data obtained in a GIS environment provided the inventory map of denudation processes and landforms. Landslides are widespread in the study area, and play an important role in the present-day landscape evolution. A total of 953 landslides were recognized, occupying a surface of 79 km2, about 22% of the whole study area. The recognized landslides were mapped on the basis of movement type, as follows: slides, flows, falls and complex landslides. With regard to water erosion processes, the most evident and spectacular landforms in the study area are represented by badlands (the so called calanchi), due to concentration of running water on steep clayey slopes, producing narrow and knife-edge ridges. Finally, sheet, rill and gully erosion are particularly active on areas devoid of vegetation cover, as well as on cultivated fields. This kind of map is an useful tool for land planning policy. Also, these types of studies are basic and complementary to applied methods for investigation and mapping of land susceptibility to denudation processes, as landslides and water erosion.

Flood-Prone Areas Assessment Using Linear Binary Classifiers based on Morphological Indices

The identification of flood-prone areas is a critical issue becoming everyday more pressing for our society. A preliminary delineation can be carried out by DEM-based procedures that relay on basin geomorphologic features. In the present paper, we investigated the dominant topographic controls for the flood exposure using techniques of pattern classification through linear binary classifiers based on DEMderived morphologic features. With this aim, local features - which are generally used to describe the hydrological characteristics of a basin - and composite morphological indices are taken into account in order to identify the most significant one. The analyses highlight the potential of each morphological descriptor for the identification of the extend of flood-prone areas. Our findings may help the definition of new strategies for the delineation of flood-prone areas with DEM-based procedures.

Landslide Susceptibility Mapping Using Artificial Neural Network in the Urban Area of Senise and San Costantino Albanese (Basilicata, Southern Italy)

Landslides are significant natural hazards in many areas of the world. Mapping the areas that are susceptible to landslides is essential for a wise territorial approach and should become a standard tool to support land-use management. A landslide susceptibility map indicates landslide-prone areas by considering the predisposing factors of slope failures in the past. In the presented work, we evaluate the landslide susceptibility of the urban area of Senise and San Costantino Albanese towns (Basilicata, southern Italy) using an Artificial Neural Network (ANN). In order, this method has required the definition of appropriate thematic layers, which parameterize the area under study. To evaluate and validate landslide susceptibility, the landslides have been randomly divided into two groups, each representing the 50% of the total area subject to instability. The results of this research show that most of the investigated area is characterized by a high landslide hazard.

Use of Remote Sensing Data for Landslide Change Detection: Montescaglioso Large Landslide (Basilicata, Southern Italy)

Remote sensing techniques have been widely used since the 1990s in landslide research, deploying for this purpose different spatial and spectral resolution imagery. This research includes photo-interpretation and inventory of large landslides, determinant factors analysis, stereo-plotting of movements, and automatic detection by textural analysis. The potential or intrinsic factors of landslides include geological and morphological factors, while the external or triggering factors include earthquakes, climate, and hydrological and human activities (deforestation, the expansion of urban areas, and the increase of agricultural activity). In this paper, the variations of land use are analyzed using a historical series of aerial-photographic and satellite data (1988 – 2006). Land use affects the stability of landslides. In this paper the proposed model has been applied in the Montescagliso municipality (Basilicata, Southern Italy).

FloodRisk: a collaborative, free and open-source software for flood risk analysis

ABSTRACT The European ‘Floods Directive’ 2007/60/EC focuses on the development of flood risk maps and management plans on the basis of the most appropriate and advanced tools. This pushed a paradigm shift for moving to sustainable development through processes of stakeholder engagement to improve the efficiency and transparency of decision processes. In this context, this research project developed a free and open-source GIS software, called FloodRisk, to operatively support stakeholders in their compliance with risk map delineation and the management of current and future flood risk based on their needs for multi-purpose applications. In this paper, a high-resolution impact assessment framework based on 2D inundation modelling with different return periods was used, as input, within the FloodRisk model to reconstruct the socio-economic damages based on a case study showing how structural and non-structural measures can significantly decrease the cost of floods for households. The sensitivity of the FloodRisk model was also examined and it was found to be highly dependent on the selection of damage functions and the economic values of the exposed assets.

A GIS tool for cost-effective delineation of flood-prone areas

Abstract Delineation of flood hazard and flood risk areas is a critical issue, but practical difficulties regularly make complete achievement of the task a challenge. In data-scarce environments (e.g. ungauged basins, large-scale analyses), useful information about flood hazard exposure can be obtained using geomorphic methods. In order to advance this field of research, we implemented in the QGIS environment an automated DEM-based procedure that exhibited high accuracy and reliability in identifying the flood-prone areas in several test sites located in Europe, the United States and Africa. This tool, named Geomorphic Flood Area tool (GFA tool), enables rapid and cost-effective flood mapping by performing a linear binary classification based on the recently proposed Geomorphic Flood Index (GFI). The GFA tool provides a user-friendly strategy to map flood exposure over large areas. A demonstrative application of the GFA tool is presented in which a detailed flood map was derived for Romania.

Assessment of Systemic Vulnerability in Flood Prone Areas

This paper deals with the conception, the development and the subsequent validation of an integrated numerical model for the assessment of systemic vulnerability in complex and urbanized areas subject to flood risk. The proposed model, which is based on the studies of Tamura et al. (Eu. J. Oper. Res., 2000) and Pascale et al. (Ad. Geo., 2007) considers vulnerability not as a characteristic of a particular element at risk, but as a peculiarity of a complex territorial system, in which different elements are reciprocally linked in a functional way. Therefore, it facilitates the identification, in selected areas, of the elements that are mainly responsible for functional loss and which thus make the whole system critical. This feature makes the proposed model effectively able to support correct territorial planning and suitable management of an emergency following natural disasters that trigger or remobilize mass movements.