Flooding Archivi - Prof. Salvatore Manfreda

Delineation of flood-prone areas in cliffed coastal regions through a procedure based on the geomorphic flood index

The geomorphic flood index (GFI) method provides a good representation of flood-prone areas. However, the method does not account for floodwater transfers in undefined interbasins (UIBs), which represent intercluded small basins along the coastline likely to be flooded by adjacent major rivers. The present work addresses this shortcoming by complementing the GFI approach with an iterative procedure that considers UIBs and water transfers between basins. The methodology was tested on a coastal basin in southern Italy and the outcome was compared with a flood map obtained by a two-dimensional hydraulic simulation. GFI performance as a morphological descriptor improved from 74% (standard method) to 94% with the addition of the iterative procedure. The proposed methodology, with the same parameterization, was applied on a second adjacent coastal basin obtaining improvements both in terms of true positive (from 56 to 79%) and false negative rates (from 44 to 21%). Finally, a sensitivity analysis to the flood return periods highlighted a strong influence on model parameterization for return periods below 20 years. This achievement represents a new development in the application of the GFI method, which can help stakeholders in a more time- and cost-effective flood risk management in hazard-prone areas.

How to cite: Albertini, C., D. Miglino, V. Iacobellis, F. De Paola, S. Manfreda, Flood-prone areas delineation in coastal regions using the Geomorphic Flood Index, Journal of Flood Risk Management, e12766,(https://doi.org/10.1111/jfr3.12766) 2021.

Advances in Large Scale Flood Monitoring and Detection

Climate change and landuse transformations have induced an increased flood risk worldwide. These phenomena are impacting dramatically on ordinary life and economy. Research and technology offer new strategy to quantify and predict such phenomena and also to mitigate the impact of flooding. In particular, the growing computational power is offering new strategies for a more detailed description of the flooding over large scales. This book offers an overview of the most recent outcomes of the research on this argument.

LINK: https://www.mdpi.com/books/pdfview/book/3066

Flood extents modelling using geomorphic and climatic‐hydrologic catchment characteristics

A topographic index (flood descriptor) that combines the scaling of bankfull depth with morphology was shown to describe the tendency of an area to be flooded. However, this approach depends on the quality and availability of flood maps and assumes that outcomes can be directly extrapolated and downscaled. This work attempts to relax these problems and answer two questions: 1) Can functional relationships be established between a flood descriptor and geomorphic and climatic‐hydrologic catchment characteristics? 2) If so, can they be used for low‐complexity predictive modelling of envelope flood extents? Linear stepwise and random forest regressions are developed based on classification outcomes of a flood descriptor, using high‐resolution flood modelling results as training benchmarks, and on catchment characteristics. Elementary catchments of four river basins in Europe (Thames, Weser, Rhine and Danube) serve as training dataset, while those of the Rhône river basin in Europe serve as testing dataset. Two return periods are considered, the 10‐ and 10,000‐year. Prediction of envelope flood extents and flood‐prone areas show that both models achieve high hit rates with respect to testing benchmarks. Average values were found to be above 60% and 80% for the 10‐ and the 10,000‐year return periods, respectively. In spite of a moderate to high false discovery rate, the critical success index value was also found to be moderate to high. It is shown that by relating classification outcomes to catchment characteristics the prediction of envelope flood extents may be achieved for a given region, including ungauged basins.

ow to cite: Tavares da Costa, R., S. Zanardo, S. Bagli, A. G. J. Hilberts, S. Manfreda, C. Samela, and A. Castellarin, Predictive modelling of envelope flood extents using geomorphic and climatic-hydrologic catchment characteristics, Water Resources Research, (doi: 10.1029/2019WR026453), 2020.

Large Scale Flood Risk Mapping in Data Scarce Environments

Large-scale flood risk assessment is essential in supporting national and global policies, emergency operations and land-use management. The present study proposes a cost-efficient method for the large-scale mapping of direct economic flood damage in data-scarce environments. The proposed framework consists of three main stages: (i) deriving a water depth map through a geomorphic method based on a supervised linear binary classification; (ii) generating an exposure land-use map developed from multi-spectral Landsat 8 satellite images using a machine-learning classification algorithm; and (iii) performing a flood damage assessment using a GIS tool, based on the vulnerability (depth–damage) curves method. The proposed integrated method was applied over the entire country of Romania (including minor order basins) for a 100-year return time at 30-m resolution. The results showed how the description of flood risk may especially benefit from the ability of the proposed cost-efficient model to carry out large-scale analyses in data-scarce environments. This approach may help in performing and updating risk assessments and management, taking into account the temporal and spatial changes in hazard, exposure, and vulnerability.

How to cite: Albano, R.; Samela, C.; Crăciun, I.; Manfreda, S.; Adamowski, J.; Sole, A.; Sivertun, Å.; Ozunu, A. Large Scale Flood Risk Mapping in Data Scarce Environments: An Application for Romania. Water 2020, 12, 1834.

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.

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

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.

References:

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]

Samela, C., T.J. Troy, S. Manfreda, Geomorphic classifiers for flood-prone areas delineation for data-scarce environments, Advances in Water Resources, 102, 13-28, (doi: 10.1016/j.advwatres.2017.01.007), 2017. [pdf]

Manfreda, S., C. Samela, A. Gioia, G. Consoli, V. Iacobellis, L. Giuzio, A. Cantisani, A. Sole, Flood-Prone Areas Assessment Using Linear Binary Classifiers based on flood maps obtained from 1D and 2D hydraulic models, Natural Hazards, 79 (2), 735-754, (doi: 10.1007/s11069-015-1869-5), 2015. [pdf]

Manfreda, S., F. Nardi, C. Samela, S. Grimaldi, A. C. Taramasso, G. Roth and A. Sole, Investigation on the Use of Geomorphic Approaches for the Delineation of Flood Prone Areas, Journal of Hydrology, 517, 863-876, (doi: 10.1016/j.jhydrol.2014.06.009), 2014.

Github repository

QGIS Python Plugins Repository

MATLAB CODE

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]