Large Scale Flood Risk Mapping in Data Scarce Environments: An Application for Romania

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 202012, 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.

GFI Flood Depth

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

A web application for hydrogeomorphic flood hazard mapping

A detailed delineation of flood-prone areas over large regions represents a challenge that cannot be easily solved with today’s resources. The main limitations lie in algorithms and hardware, but also costs, scarcity and sparsity of data and our incomplete knowledge of how inundation events occur in different river floodplains. We showcase the implementation of a data-driven web application for regional analyses and detailed (i.e., tens of meters) mapping of floodplains, based on (a) the synthesis of hydrogeomorphic features into a morphological descriptor and (b) its classification to delineate flood-prone areas. We analysed the skill of the descriptor and the performance of the mapping method for European rivers. The web application can be effectively used for delineating flood-prone areas, reproducing the reference flood maps with a classification skill of 88.59% for the 270 major river basins analysed across Europe and 84.23% for the 64 sub-catchments of the Po River.

How to cite: Tavares da Costa, R., S. Manfreda, V. Luzzi,  C. Samela, P. Mazzoli, A. Castellarin, S. Bagli, A web application for hydrogeomorphic flood hazard mappingEnvironmental Modelling and Software, Volume 118, August 2019, Pages 172-186 (doi: 10.1016/j.envsoft.2019.04.010) 2019.  [pdf]

GFA tool – Geomorphic Flood Area tool

Plugin icon

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 areasComputers, 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 environmentsAdvances 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 modelsNatural 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 AreasJournal of Hydrology, 517, 863-876, (doi: 10.1016/j.jhydrol.2014.06.009), 2014.

Github repository

QGIS Python Plugins Repository

MATLAB CODE

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]

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

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]

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 DataIEEE Transactions on Geoscience and Remote Sensing, 54(6), 3612 -3625, (doi: 10.1109/TGRS.2016.2520487), 2016.  [pdf]

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

In 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 delineation derived with the hydraulic model over the entire river basin. The study highlights the potential for the detection of flood-prone areas over ungauged basins and large areas.

How to cite: Caterina Samela, Salvatore Manfreda, Francesco De Paola, Maurizio Giugni, Aurelia Sole and Mauro Fiorentino, DEM-Based Approaches for the Delineation of Flood-Prone Areas in an Ungauged Basin in Africa, Journal of Hydraulic Engineering (doi: 10.1061/(ASCE)HE.1943-5584.0001272), 2015. [pdf]