scholarly journals Real-time coastal flood hazard assessment using DEM-based hydrogeomorphic classifiers

2021 ◽  
Author(s):  
Keighobad Jafarzadegan ◽  
David Muñoz ◽  
Hamed Moftakhari ◽  
Joseph Gutenson ◽  
Guarav Savant ◽  
...  

Abstract. Deltas, estuaries, and wetlands are prone to frequent coastal flooding throughout the world. In addition, a large number of people in the United States have settled in these low-lying regions. Therefore, the ecological merit of wetlands for maintaining sustainable ecosystems highlights the importance of flood risk and hazard management in these regions. Typically, hydrodynamic models are used for coastal flood hazard mapping. The huge computational resources required for hydrodynamic modeling and the long-running time of these models (order of hours or days) are two major drawbacks that limit the application of these models for prompt decision-making by emergency responders. In the last decade, DEM-based classifiers based on Height Above Nearest Drainage (HAND) have been widely used for rapid flood hazard assessment demonstrating satisfactory performance for inland floods. The main limitation is the high sensitivity of HAND to the topography which degrades the accuracy of these methods in flat coastal regions. In addition, these methods are mostly used for a given return period and generate static hazard maps for past flood events. To cope with these two limitations, here we modify HAND and propose a composite hydrogeomorphic index for rapid flood hazard assessment in coastal areas. We also propose the development of hydrogeomorphic threshold operative curves for real-time flood hazard mapping. We select the Savannah river delta as a testbed, calibrate the proposed hydrogeomorphic index on Hurricane Matthew and validate the performance of the developed operative curves for Hurricane Irma. Validation results demonstrate that the operative curves can rapidly generate flood hazard maps with satisfactory accuracy. This indicates the high efficiency of our proposed methodology for fast and accurate estimation of hazard areas for an upcoming coastal flood event which can be beneficial for emergency responders and flood risk managers.

2021 ◽  
Author(s):  
Andrea Magnini ◽  
Michele Lombardi ◽  
Simone Persiano ◽  
Antonio Tirri ◽  
Francesco Lo Conti ◽  
...  

<p><span xml:lang="EN-US" data-contrast="auto"><span>Every year flood events cause worldwide vast economic losses, as well as heavy social and environmental impacts, which have been steadily increasing for the last five decades due to the complex interaction between climate change and anthropogenic pressure (</span></span><span xml:lang="EN-US" data-contrast="auto"><span>i.e.</span></span><span xml:lang="EN-US" data-contrast="auto"><span> land-use and land-cover modifications). As a result, the body of literature on flood risk assessment is constantly and rapidly expanding, aiming at developing faster, computationally lighter and more efficient methods relative to the traditional and resource</span></span><span xml:lang="EN-US" data-contrast="auto"><span>-</span></span><span xml:lang="EN-US" data-contrast="auto"><span>intensive hydrodynamic numerical models. Recent and reliable fast-processing techniques for flood hazard assessment and mapping consider binary geomorphic classifiers retrieved from the analysis of Digital Elevation Models (DEMs). These procedures (termed herein “DEM-based methods”) produce binary maps distinguishing between floodable and non-floodable areas based on the comparison between the local value of the considered geomorphic classifier and a threshold, which in turn is calibrated against existing flood hazard maps. Previous studies have shown the reliability of DEM-based methods using a single binary classifier, they also highlighted that different classifiers are associated with different performance, depending on the geomorphological, climatic and hydrological characteristics of the study area. The present study maps flood-prone areas and predicts water depth associated with a given non-exceedance probability by combining several geomorphic classifiers and terrain features through regression trees and random forests. We focus on Northern Italy (c.a. 100000 km</span></span><sup><span xml:lang="EN-US" data-contrast="auto"><span>2</span></span></sup><span xml:lang="EN-US" data-contrast="auto"><span>, including Po, Adige, Brenta, Bacchiglione and Reno watersheds), and we consider the recently compiled MERIT (Multi-Error Removed Improved-Terrain) DEM, with 3sec-resolution (~90m at the Equator). We select the flood hazard maps provided by (</span></span><span xml:lang="EN-US" data-contrast="auto"><span>i</span></span><span xml:lang="EN-US" data-contrast="auto"><span>) the Italian Institute for Environmental Protection and Research (ISPRA), and (ii) the Joint Research Centre (JRC) of the European Commission as reference maps. Our findings (a) confirm the usefulness of machine learning techniques for improving univariate DEM-based flood hazard mapping, (b) enable a discussion on potential and limitations of the approach and (c) suggest promising pathways for further exploring DEM-based approaches for predicting a likely water depth distribution with flood-prone areas.</span></span><span> </span></p>


Author(s):  
Keighobad Jafarzadegan ◽  
David Muñoz ◽  
Hamed Moftakhari ◽  
Joseph Gutenson ◽  
Guarav Savant ◽  
...  

2018 ◽  
Vol 13 (1) ◽  
pp. 14-21 ◽  
Author(s):  
Win Win Zin ◽  
Akiyuki Kawasaki ◽  
Wataru Takeuchi ◽  
Zin Mar Lar Tin San ◽  
Kyaw Zaya Htun ◽  
...  

Flood hazard mapping is an effective non-structural measure for sustainable urban planning, protecting human properties, lives, and disaster risk reduction. In this study, flood hazard assessment for the Bago river basin was performed. The flood inundation map of the Bago river basin was developed by coupling a hydrological and hydraulic model with geographical information systems. Flood hazard maps with different return periods were developed. The flood hazard map can be utilized to enhance the effectiveness of disaster risk management activities.


Author(s):  
David Didier ◽  
Pascal Bernatchez ◽  
Guillaume Marie ◽  
Geneviève Boucher-Brossard

2020 ◽  
Vol 198 ◽  
pp. 106870
Author(s):  
José Betancourt ◽  
François Bachoc ◽  
Thierry Klein ◽  
Déborah Idier ◽  
Rodrigo Pedreros ◽  
...  

Author(s):  
M. A. Gusyev ◽  
Y. Kwak ◽  
M. I. Khairul ◽  
M. B. Arifuzzaman ◽  
J. Magome ◽  
...  

Abstract. This study introduces a flood hazard assessment part of the global flood risk assessment (Part 2) conducted with a distributed hydrological Block-wise TOP (BTOP) model and a GIS-based Flood Inundation Depth (FID) model. In this study, the 20 km grid BTOP model was developed with globally available data on and applied for the Ganges, Brahmaputra and Meghna (GBM) river basin. The BTOP model was calibrated with observed river discharges in Bangladesh and was applied for climate change impact assessment to produce flood discharges at each BTOP cell under present and future climates. For Bangladesh, the cumulative flood inundation maps were produced using the FID model with the BTOP simulated flood discharges and allowed us to consider levee effectiveness for reduction of flood inundation. For the climate change impacts, the flood hazard increased both in flood discharge and inundation area for the 50- and 100-year floods. From these preliminary results, the proposed methodology can partly overcome the limitation of the data unavailability and produces flood~maps that can be used for the nationwide flood risk assessment, which is presented in Part 2 of this study.


2021 ◽  
Vol 13 (18) ◽  
pp. 10232
Author(s):  
Efthimios Karymbalis ◽  
Maria Andreou ◽  
Dimitrios-Vasileios Batzakis ◽  
Konstantinos Tsanakas ◽  
Sotirios Karalis

This study deals with the flood-hazard assessment and mapping in the catchment of Megalo Rema (East Attica, Greece). Flood-hazard zones were identified utilizing Multi-Criteria Decision Analysis (MCDA) integrated with Geographic Information System (GIS). Five factors were considered as the most influential parameters for the water course when high storm-water runoff exceeds drainage system capacity and were taken into account. These factors include slope, elevation, distance from stream channels, geological formations in terms of their hydro-lithological behavior and land cover. To obtain the final weights for each factor, rules of the Analytic Hierarchy Process (AHP) were applied. The final flood-hazard assessment and mapping of the study area were produced through Weighted Linear Combination (WLC) procedures. The final map showed that approximately 26.3 km2, which corresponds to 22.7% of the total area of the catchment, belongs to the high flood risk zone, while approximately 25 km2, corresponding to ~15% of the catchment, is of very high flood risk. The highly and very highly prone to flooding areas are located mostly at the southern and western parts of the catchment. Furthermore, the areas on both sides of the channel along the lower reaches of the main stream are of high and very high risk. The highly and very highly prone to flooding areas are relatively low-lying, gently sloping and extensively urbanized, and host the densely populated settlements of Rafina-Pikermi, Penteli, Pallini, Peania, Spata, Glika Nera, Gerakas and Anthousa. The accuracy of the flood-hazard map was verified by correlating flood events of the last 30 years, the Hydrologic Engineering Center’s River Analysis System (HEC–RAS) simulation and quantitative geomorphological analysis with the flood-hazard level. The results of our approach provide decision makers with important information for land-use planning at a regional scale, determining safe and unsafe areas for urban development.


2021 ◽  
Vol 193 (4) ◽  
Author(s):  
Guido Borzi ◽  
Alejandro Roig ◽  
Carolina Tanjal ◽  
Lucía Santucci ◽  
Macarena Tejada Tejada ◽  
...  

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