Flood hazard in mountain streams: the key role of geomorphic processes during high magnitude events

Author(s):  
Nicola Surian ◽  
Andrea Brenna ◽  
Marco Borga ◽  
Marco Cavalli ◽  
Francesco Comiti ◽  
...  

<p>Although channel dynamics (i.e. channel lateral mobility, intense sediment and wood transport) are commonly dominant processes in mountain streams during high-magnitude floods, hazard assessment still mostly focuses on water flooding only. Therefore, there is a need to include river geomorphological hazard to produce reliable flood hazard mapping and define effective mitigation measures. This work deals with the “Vaia” storm that occurred in the Eastern Alps (Italy) on 27-30 October 2018. Our aims are (i) to improve the understanding of geomorphic processes in response to large floods and (ii) to improve the prediction capability of the reaches more prone to undergo intense channel dynamics (e.g. channel widening, in-channel sedimentation) during such events.</p><p>An integrated approach was deployed to study the flood event in the Cordevole river catchment (876 km<sup>2</sup>). The approach includes (i) analysis of geomorphological processes, by comparing remote sensing data acquired before and after floods and field survey (e.g. recognition of different flow types); (ii) hydrological and hydraulic analysis (collection of rainfall and streamflow data, estimation of peak discharges at multiple sites in ungauged streams, and model-based consistency check of rainfall and discharge data); (iii) landslide mapping and analysis of sediment delivery to the channel network.</p><p>Intense sediment and wood transport took place. A wide range of transport processes (i.e. debris, hyperconcentrated and water flows) was recognized in the channel network and notable channel aggradation occurred at specific location (e.g. in channelized reaches). Channel widening was the most relevant geomorphic response along the fluvial network. Width ratio (i.e. channel width after / channel width before the flood) reached up to 2.1 and 4.4, respectively in the Cordevole and in its tributaries. Locally, the valley slopes were eroded (e.g. slope retreat up to 14 m). This means that the lateral channel dynamics affected not only large portions of the valley floor (e.g. forested floodplain) but also the valley slopes, especially if made of Quaternary deposits or soft bedrock.</p><p>These results have several implications in terms of flood hazard assessment in mountain streams. Since channel widening is a major process (streams may take up the whole floodplain and, locally, erode the valley slopes), so-called “river morphodynamic corridors” need to be defined and integrated into flood hazard maps. During high-magnitude floods the sediment mobilization may take place through mechanisms (e.g. hyperconcentrated flows) that can be different from those expected for ordinary water floods. Since major channel changes commonly occur during large floods, their prediction is needed and should accompany flood hydraulic modelling to obtain reliable flood event scenarios.</p>

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

2021 ◽  
Vol 656 (1) ◽  
pp. 012010
Author(s):  
M Zeleňáková ◽  
M Šugareková ◽  
P Purcz ◽  
S Gałaś ◽  
M M Portela ◽  
...  

2019 ◽  
Author(s):  
Attilio Castellarin ◽  
Caterina Samela ◽  
Simone Persiano ◽  
Stefano Bagli ◽  
Valerio Luzzi ◽  
...  

2017 ◽  
Vol 114 (37) ◽  
pp. 9785-9790 ◽  
Author(s):  
Hamed R. Moftakhari ◽  
Gianfausto Salvadori ◽  
Amir AghaKouchak ◽  
Brett F. Sanders ◽  
Richard A. Matthew

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.


2020 ◽  
Author(s):  
Michelle Bensi ◽  
Somayeh Mohammadi ◽  
Shih-Chieh Kao ◽  
Scott T. DeNeale

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>


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