Predictive modelling of envelope flood extents 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: 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

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 DetectionHydrology, 5, 49, (doi: 10.3390/hydrology5030049) 2018. [pdf]

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.

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]