2020
DOI: 10.3390/w12030893
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A New Framework to Model Hydraulic Bank Erosion Considering the Effects of Roots

Abstract: Floods and subsequent bank erosion are recurring hazards that pose threats to people and can cause damage to buildings and infrastructure. While numerous approaches exist on modeling bank erosion, very few consider the stabilizing effects of vegetation (i.e., roots) for hydraulic bank erosion at catchment scale. Taking root reinforcement into account enables the assessment of the efficiency of vegetation to decrease hydraulic bank erosion rates and thus improve risk management strategies along forested channel… Show more

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Cited by 17 publications
(15 citation statements)
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“…A few stochastic models have been developed, generally at the river reach scale (Bragg et al, 2000; Eaton et al, 2012; Gregory & Meleason, 2003) to account for uncertainties and lack of empirical observations. At the catchment scale, the proposed probabilistic approaches usually simulate single wood supply processes, such as landslides (see Cislaghi et al, 2018 or van Zadelhoff et al, 2022) or bank erosion (Gasser et al, 2018, 2020), neglecting the occurrence of multiple processes and the interaction between them. Most of the models used to study wood supply are based on existing geospatial information (e.g., hazard or susceptibility maps to recruitment processes) or expert‐based buffers (Mazzorana et al, 2009; Steeb et al, 2017), and they were not intended to model the wood supply processes.…”
Section: Instream Wood Supplymentioning
confidence: 99%
“…A few stochastic models have been developed, generally at the river reach scale (Bragg et al, 2000; Eaton et al, 2012; Gregory & Meleason, 2003) to account for uncertainties and lack of empirical observations. At the catchment scale, the proposed probabilistic approaches usually simulate single wood supply processes, such as landslides (see Cislaghi et al, 2018 or van Zadelhoff et al, 2022) or bank erosion (Gasser et al, 2018, 2020), neglecting the occurrence of multiple processes and the interaction between them. Most of the models used to study wood supply are based on existing geospatial information (e.g., hazard or susceptibility maps to recruitment processes) or expert‐based buffers (Mazzorana et al, 2009; Steeb et al, 2017), and they were not intended to model the wood supply processes.…”
Section: Instream Wood Supplymentioning
confidence: 99%
“…Similarly, Gasser et al (2018Gasser et al ( , 2020 proposed two frameworks to model shallow landslides, and geotechnical and hydraulic bank erosion applying two physically-based stochastic models together with a tree detection algorithm (Dorren, 2017) to estimate LW supply. Zischg et al…”
Section: Large Wood Supply Models: a Reviewmentioning
confidence: 99%
“…At the catchment scale, a probabilistic multi-dimensional approach has recently been proposed (Cislaghi et al, 2018) to study wood sources from hillslopes, modelling areas susceptible to landslides, but it neglects other processes such as bank erosion. The latter process has been considered in one of the most recent studies on LW (Gasser et al, 2020).…”
Section: Qualitative Comparison Of Ega and Fga With Other Similar App...mentioning
confidence: 99%
“…Bank erosion can be quantitatively estimated by measuring volume of material removed within specified spatial and temporal scales using techniques including erosion pins, repeat bank surveys on the ground, or sequential remote imagery (e.g., Lawler, 1993; Williams et al., 2020). Bank erosion can also be inferred using numerical models (e.g., Gasser et al., 2020; Rinaldi & Darby, 2007). Given the spatial and temporal heterogeneity of bank erosion (Janes et al., 2018; Konsoer et al., 2016), spatial and temporal scale and resolution become a primary consideration in applying any measurement technique or model.…”
Section: Floodplain Budgetsmentioning
confidence: 99%