scholarly journals Accounting for uncertainties in compound flood hazard assessment: The value of data assimilation

2021 ◽  
pp. 104057
Author(s):  
David F. Muñoz ◽  
Peyman Abbaszadeh ◽  
Hamed Moftakhari ◽  
Hamid Moradkhani
Keyword(s):  
Data Assimilation ◽  
Hazard Assessment ◽  
Flood Hazard ◽  
Flood Hazard Assessment

scholarly journals Flood hazard assessment in large plain basins with a scarce slope in the Pampean Plain, Argentina

2021 ◽  
Vol 193 (4) ◽  
Author(s):  
Guido Borzi ◽  
Alejandro Roig ◽  
Carolina Tanjal ◽  
Lucía Santucci ◽  
Macarena Tejada Tejada ◽  
...  
Keyword(s):  
Hazard Assessment ◽  
Flood Hazard ◽  
Pampean Plain ◽  
Flood Hazard Assessment

scholarly journals Risk Analysis and Flood Hazard Assessment in the Hornád River Basin

2021 ◽  
Vol 656 (1) ◽  
pp. 012010
Author(s):  
M Zeleňáková ◽  
M Šugareková ◽  
P Purcz ◽  
S Gałaś ◽  
M M Portela ◽  
...  
Keyword(s):  
Risk Analysis ◽  
River Basin ◽  
Hazard Assessment ◽  
Flood Hazard ◽  
Flood Hazard Assessment

scholarly journals Safer_RAIN: a Fast-Processing DEM-Based Algorithm for Pluvial Flood Hazard Assessment Across Large Urban Areas

2019 ◽  
Author(s):  
Attilio Castellarin ◽  
Caterina Samela ◽  
Simone Persiano ◽  
Stefano Bagli ◽  
Valerio Luzzi ◽  
...  
Keyword(s):  
Hazard Assessment ◽  
Urban Areas ◽  
Flood Hazard ◽  
Flood Hazard Assessment ◽  
Fast Processing

A structured approach to enhance flood hazard assessment in mountain streams

2011 ◽  
Vol 67 (3) ◽  
pp. 991-1009 ◽  
Author(s):  
B. Mazzorana ◽  
F. Comiti ◽  
S. Fuchs
Keyword(s):  
Hazard Assessment ◽  
Flood Hazard ◽  
Mountain Streams ◽  
Flood Hazard Assessment ◽  
Structured Approach

scholarly journals Compounding effects of sea level rise and fluvial flooding

2017 ◽  
Vol 114 (37) ◽  
pp. 9785-9790 ◽  
Author(s):  
Hamed R. Moftakhari ◽  
Gianfausto Salvadori ◽  
Amir AghaKouchak ◽  
Brett F. Sanders ◽  
Richard A. Matthew
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Hazard Assessment ◽  
Failure Probability ◽  
River Flow ◽  
Flood Hazard ◽  
High Tide ◽  
Probability Method ◽  
Flood Hazards ◽  
Flood Hazard Assessment

Sea level rise (SLR), a well-documented and urgent aspect of anthropogenic global warming, threatens population and assets located in low-lying coastal regions all around the world. Common flood hazard assessment practices typically account for one driver at a time (e.g., either fluvial flooding only or ocean flooding only), whereas coastal cities vulnerable to SLR are at risk for flooding from multiple drivers (e.g., extreme coastal high tide, storm surge, and river flow). Here, we propose a bivariate flood hazard assessment approach that accounts for compound flooding from river flow and coastal water level, and we show that a univariate approach may not appropriately characterize the flood hazard if there are compounding effects. Using copulas and bivariate dependence analysis, we also quantify the increases in failure probabilities for 2030 and 2050 caused by SLR under representative concentration pathways 4.5 and 8.5. Additionally, the increase in failure probability is shown to be strongly affected by compounding effects. The proposed failure probability method offers an innovative tool for assessing compounding flood hazards in a warming climate.

scholarly journals Multi-Mechanism Flood Hazard Assessment: Critical Review of Current Practice and Approaches

2020 ◽  
Author(s):  
Michelle Bensi ◽  
Somayeh Mohammadi ◽  
Shih-Chieh Kao ◽  
Scott T. DeNeale
Keyword(s):  
Hazard Assessment ◽  
Critical Review ◽  
Current Practice ◽  
Flood Hazard ◽  
Flood Hazard Assessment

scholarly journals Flood Hazard Assessment of Residential Areas Outside the Protected Area of the Red River Dike System in Hanoi, Vietnam

2021 ◽  
Vol 55 (2) ◽  
pp. 147-160
Author(s):  
Sai Hong ANH ◽  
Toshinori TABATA ◽  
Kazuaki HIRAMATSU ◽  
Masayoshi HARADA
Keyword(s):  
Hazard Assessment ◽  
Protected Area ◽  
Flood Hazard ◽  
Red River ◽  
Residential Areas ◽  
Flood Hazard Assessment ◽  
River Dike

Combination of geomorphic classifiers through Machine Learning-based techniques for flood hazard assessment 

2021 ◽  
Author(s):  
Andrea Magnini ◽  
Michele Lombardi ◽  
Simone Persiano ◽  
Antonio Tirri ◽  
Francesco Lo Conti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hazard Assessment ◽  
Water Depth ◽  
Flood Hazard ◽  
Research Centre ◽  
Hazard Mapping ◽  
Hazard Maps ◽  
Flood Hazard Assessment ◽  
Flood Prone Areas ◽  
Flood Hazard Maps

<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>

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