Caterina Samela

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...

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.

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.

Exploiting the use of physical information for the calibration of a lumped hydrological model

1420 Copyright

Dataset of 100-year flood susceptibility maps for the continental U.S. derived with a geomorphic method

Efficient strategies for preparing communities to protect against, respond to, recover from, and mitigate flood hazard are often hampered by the lack of information about the position and extent of flood-prone areas. Hydrologic and hydraulic analyses allow to obtain detailed flood hazard maps, but are a computationally intensive exercise requiring a significant amount of input data, which are rarely available both in developing and developed countries. As a consequence, even in data-rich environments, official flood hazard graduations are often affected by extensive gaps. In the U.S., for instance, the detailed floodplain delineation produced by the Federal Emergency Management Agency (FEMA) is incomplete, with many counties having no floodplain mapping at all. In this article we present a mapping dataset containing 100-year flood susceptibility maps for the continental U.S. with a 90 m resolution. They have been obtained performing a linear binary classification based on the Geomorphic Flood Index (GFI). To the best knowledge of the authors, there are no available flood-prone areas maps for the entire continental U.S. with resolution lower that 30׳׳×30׳׳ (approximatively 1 km at the equator).

The Use of DEM-Based Approaches to Derive a Priori Information on Flood-Prone Areas

Knowing the location and the extent of areas exposed to floods is the most basic information needed for planning flood management strategies. Unfortunately, a complete identification of these areas is still lacking in many countries. Recent studies have highlighted that a significant amount of information regarding the inundation process is already contained in the structure and morphology of a river basin. Therefore, several geomorphic approaches have been proposed for the delineation of areas exposed to flood inundation using DEMs. Such DEM-based approaches represent a useful tool, characterized by low cost and simple data requirements, for a preliminary identification of the flood-prone areas or to extend flood hazard mapping over large areas. Moreover, geomorphic information may be used as external constraint in remote-sensing algorithms for the identification of inundated areas during or after a flood event.

Exploring the optimal experimental setup for surface flow velocity measurements using PTV

Advances in flow monitoring are crucial to increase our knowledge on basin hydrology and to understand the interactions between flow dynamics and infrastructures. In this context, image processing offers great potential for hydraulic monitoring, allowing acquisition of a wide range of measurements with high spatial resolution at relatively low costs. In particular, the particle tracking velocimetry (PTV) algorithm can be used to describe the dynamics of surface flow velocity in both space and time using fixed cameras or unmanned aerial systems (UASs). In this study, analyses allowed exploration of the optimal particle seeding density and frame rate in different configurations. Numerical results provided useful indications for two field experiments that have been carried out with a low-cost quadrocopter equipped with an optical camera to record RGB videos of floating tracers manually distributed over the water surface. Field measurements have been carried out using different natural tracers under diverse hydraulic and morphological conditions; PTV’s processed velocities have been subsequently benchmarked with current meter measurements. The numerical results allowed rapid identification of the experimental configuration (e.g., required particle seeding density, image resolution, particle size, and frame frequency) producing flow velocity fields with high resolution in time and space with good agreement with the benchmark velocity values measured with conventional instruments.