Tag: Flooding

A DEM-based method for a rapid estimation of flood inundation depth

In recent years, the acquisition of data from multiple sources, together with improvements in computational capabilities, has allowed to improve our understanding on natural hazard through new approaches based on machine learning and Big Data analytics. This has given new potential to flood risk mapping, allowing the automatic extraction of flood prone areas using digital elevation model (DEM) based geomorphic approaches. Most of the proposed geomorphic approaches are conceived mainly for the identification of flood extent. In this article, the DEM‐based method based on a geomorphic descriptor—the geomorphic flood index (GFI)—has been further exploited to predict inundation depth, which is useful for quantifying flood induced damages. The new procedure is applied on a case study located in southern Italy, obtaining satisfactory performances. In particular, the inundation depths are very similar to the ones obtained by hydraulic simulations, with a root‐mean‐square error (RMSE) = 0.335 m, in the domain where 2D dynamics prevail. The reduced computational effort and the general availability of the required data make the method suitable for applications over large and data‐sparse areas, opening new horizons for flood risk assessment at national/continental/global scale.

GFI Flood Depth

How to cite: Manfreda, S., C. Samela, A DEM-based method for a rapid estimation of flood inundation depth, Journal of Flood Risk Management, 12 (Suppl. 1):e12541, (doi: 10.1111/jfr3.12541) 2019. [pdf]

GFA tool – Geomorphic Flood Area tool

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GFA – tool is an open-source QGIS plug-in to realize a fast and cost-effective delineation of the floodplains in the contexts where the available data is scarce to carry out hydrological/hydraulic analyses.

The delineation of flood hazard and flood risk areas is a critical issue whose complete achievement regularly encounters several practical difficulties. 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, United States and Africa. This tool, named Geomorphic Flood Area tool (GFA tool), enables a fast 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. 

How to cite: Samela, C., R. Albano, A. Sole, S. Manfreda, An open source GIS software tool for cost effective delineation of flood prone areas, Computers, Environment and Urban Systems, 70, 43-52 (doi: 10.1016/j.compenvurbsys.2018.01.013), 2018.  [pdf]

Github repository

QGIS Python Plugins Repository

Advances in Large-Scale Flood Monitoring and Detection

The last decades have seen a massive advance in technologies for Earth Observation (EO) and environmental monitoring, which provided scientists and engineers with valuable spatial information for studying hydrologic processes. At the same time, the power of computers and newly developed algorithms have grown sharply. Such advances have extended the range of possibilities for hydrologists, who are trying to exploit these potentials the most, updating and re-inventing the way hydrologic and hydraulic analyses are carried out. A variety of research fields have progressed significantly, ranging from the evaluation of water features, to the classification of land-cover, the identification of river morphology, and the monitoring of extreme flood events. The description of flood processes may particularly benefit from the integrated use of recent algorithms and monitoring techniques. In fact, flood exposure and risk over large areas and in scarce data environments have always been challenging topics due to the limited information available on river basin hydrology, basin morphology, land cover, and the resulting model uncertainty. The ability of new tools to carry out intensive analyses over huge datasets allows us to produce flood studies over large extents and with a growing level of detail. The present Special Issue aims to describe the state-of-the-art on flood assessment, monitoring, and management using new algorithms, new measurement systems and EO data. More specifically, we collected a number of contributions dealing with: (1) the impact of climate change on floods; (2) real time flood forecasting systems; (3) applications of EO data for hazard, vulnerability, risk mapping, and post-disaster recovery phase; and (4) development of tools and platforms for assessment and validation of hazard/risk models.

How to cite: Manfreda S., C. Samela, A. Refice, V. Tramutoli, F. Nardi, Advances in Large Scale Flood Monitoring and Detection, Hydrology, 5, 49, (doi: 10.3390/hydrology5030049) 2018. [pdf]

IDROLOGIA DI LARGA SCALA

TEMA 2: IDROLOGIA DI LARGA SCALA – BIG DATA IN HYDROLOGY
Il tema del Big Data offre nuove opportunità per l’avanzamento delle conoscenze data la
crescente disponibilità di dati satellitari e osservazioni diffuse provenienti da varie fonti. La
ricerca che deve fare sintesi identificando pattern e strutture di correlazione, ma anche
rimuovere rumore ed errori di misura. In tale ambito, sono stati utilizzati algoritmi di Machine
Learning (Linear Binary Classifier, Random Forest) per la ricostruzione di mappe di
inondazione su grande scala (e.g., regionale, nazionale o continentale). Ad esempio, è stato
sviluppato un tool denominato Smartflood che fornisce la mappatura della pericolosità
idraulica a scala europea, sfruttando informazioni provenienti da varie fonti (Fig. 1).


Figura 1. WebGIS per il calcolo delle aree inondabili a scala europea.
Curriculum Vitae di Salvatore Manfreda Ultimo Aggiornamento 17/01/19


Sono stati inoltre sviluppati sistemi di gestione, controllo e ricostruzione delle misure idrologiche (database regionali) derivate da reti di monitoraggio e modelli metereologici a scala locale per la previsione di eventi di piena e di frana attraverso modellazione idrologica distribuita (Fig. 2). Sono stati implementati sistemi di assimilazione utilizzando Extended Kalman filter (EKF) e Ensamble Kalman filter (EnKF) a supporto della modellazione e del monitoraggio idraulico/idrologico.


Figura 2. Sistema di allertamento regionale della Protezione Civile della Basilicata dove vengono evidenziate le aree di criticità idrogeologica sulla base delle misure idrologiche.

A digital elevation model based method for a rapid estimation of flood inundation depth

In recent years, the acquisition of data from multiple sources, together with improvements in computational capabilities, has allowed to improve our understanding on natural hazard through new approaches based on machine learning and Big Data analytics. This has given new potential to flood risk mapping, allowing the automatic extraction of flood prone areas using digital elevation model (DEM) based geomorphic approaches. Most of the proposed geomorphic approaches are conceived mainly for the identification of flood extent. In this article, the DEM-based method based on a geomorphic descriptor — the geomorphic flood index (GFI) — has been further exploited to predict inundation depth, which is useful for quantifying flood induced damages. The new procedure is applied on a case study located in southern Italy, obtaining satisfactory performances. In particular, the inundation depths are very similar to the ones obtained by hydraulic simulations, with a root-mean-square error (RMSE) = 0.335 m, in the domain where 2D dynamics prevail. The reduced computational effort and the general availability of the required data make the method suitable for applications over large and data-sparse areas, opening new horizons for flood risk assessment at national/continental/global scale.

How to cite: Salvatore Manfreda, Caterina Samela, A digital elevation model based method for a rapid estimation of flood inundation depth, The Chartered Institution of Water and Environmental Management, Journal of Flood Risk Management, John Wiley & Sons Ltd 2019 (doi: 10.1111/jfr3.12541), 2019. [pdf]

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.

How to cite: Salvatore Manfreda, Caterina Samela and Tara J. Troy, The Use of DEM-Based Approaches to Derive a Priori Information on Flood-Prone Areas, Springer International Publishing AG 2018, Pages 61 – 79, (doi: https://doi.org/10.1007/978-3-319-63959-8_3), 2018. [pdf]

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

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.

How to cite: Caterina Samela, Raffaele Albano, Aurelia Sole, Salvatore Manfreda, A GIS tool for cost-effective delineation of flood-prone areas, Computers, Environment and Urban Systems (doi: https://doi.org/10.1016/j.compenvurbsys.2018.01.013), 2018. [pdf]

Dataset of 100-year geomorphic flood-prone areas for the continental U.S.

Efficient strategies for preparing communities to protect against,respond to, recover from, and mitigateflood hazard are oftenhampered by the lack of information about the position and extentofflood-prone areas. Hydrologic and hydraulic analyses allow toobtain detailedflood hazard maps, but are a computationallyintensive exercise requiring a significant amount of input data,which are rarely available both in developing and developedcountries. As a consequence, even in data-rich environments,officialflood hazard graduations are often affected by extensivegaps. In the U.S., for instance, the detailedfloodplain delineationproduced by the Federal Emergency Management Agency (FEMA)is incomplete, with many counties having nofloodplain mappingat all. In this article we present a mapping dataset containing 100-yearflood susceptibility maps for the continental U.S. with a 90 mresolution. They have been obtained performing a linear binaryclassification based on the Geomorphic Flood Index (GFI). To thebest knowledge of the authors, there are no availableflood-proneareas maps for the entire continental U.S. with resolution lowerthat 30”30” (approximatively 1 km at the equator).

How to cite: Samela C., S. Manfreda, T. J. Troy, 100-year geomorphic flood-prone areas for the continental U.S., Data in Brief, 12, 203-207, (doi: 10.1016/j.dib.2017.03.044), 2017. [pdf]

Geomorphic classifiers for flood-prone areas delineation for data-scarce environments

Knowing the location and the extent of the areas exposed to flood hazards is essential to any strategy for minimizing the risk. Unfortunately, in ungauged basins the use of traditional floodplain mapping techniques is prevented by the lack of the extensive data required. The present work aims to overcome this limitation by defining an alternative simplified procedure for a preliminary floodplain delineation based on the use of geomorphic classifiers. To validate the method in a data-rich environment, eleven flood-related morphological descriptors derived from remotely sensed elevation data have been used as linear binary classifiers over the Ohio River basin and its sub-catchments. Their performances have been measured at the change of the topography and the size of the calibration area, allowing to explore the transferability of the calibrated parameters, and to define the minimum extent of the calibration area. The best performing classifiers among those analysed have been applied and validated across the continental U.S. The results suggest that the classifier based on the Geomorphic Flood Index (GFI), is the most suitable to detect the flood-prone areas in data-scarce regions and for large-scale applications, providing good accuracies with low requirements in terms of data and computational costs. This index is defined as the logarithm of the ratio between the water depth in the element of the river network closest to the point under exam (estimated using a hydraulic scaling function based on contributing area) and the elevation difference between these two points.

How to cite: Caterina Samela, Tara J. Troy, Salvatore Manfreda, Geomorphic   classifiers   for   flood-prone   areas   delineation   for data-scarce   environments, Advances   in   Water   Resources (doi: 10.1016/ j.advwatres.2017.01.007) 2017. [pdf]

A Bayesian Network for Flood Detection Combining SAR Imagery and Ancillary Data

Accurate flood mapping is important for both planning activities during emergencies and as a support for the successive assessment of damaged areas. A valuable information source for such a procedure can be remote sensing synthetic aperture radar (SAR) imagery. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground. For this reason, a data fusion approach of remote sensing data with ancillary information can be particularly useful. In this paper, a Bayesian network is proposed to integrate remotely sensed data, such as multitemporal SAR intensity images and interferometric-SAR coherence data, with geomorphic and other ground information. The methodology is tested on a case study regarding a flood that occurred in the Basilicata region (Italy) on December 2013, monitored using a time series of COSMO-SkyMed data. It is shown that the synergetic use of different information layers can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which may affect algorithms based on data from a single source. The produced flood maps are compared to data obtained independently from the analysis of optical images; the comparison indicates that the proposed methodology is able to reliably follow the temporal evolution of the phenomenon, assigning high probability to areas most likely to be flooded, in spite of their heterogeneous temporal SAR/InSAR signatures, reaching
accuracies of up to 89%.

How to cite: ’Addabbo, A., A. Refice, G. Pasquariello, F. Lovergine, D. Capolongo and S. Manfreda, A Bayesian Network for Flood Detection Combining SAR Imagery and Ancillary Data, IEEE Transactions on Geoscience and Remote Sensing, 54(6), 3612 -3625, (doi: 10.1109/TGRS.2016.2520487), 2016.  [pdf]