2018
DOI: 10.1029/2018gl079405
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Comparing the Cohesive Effects of Mud and Vegetation on Delta Evolution

Abstract: Cohesive sediment exerts a significant influence on delta evolution, increasing shoreline rugosity and decreasing channel mobility. Vegetation has been assumed to play a similar role in delta evolution, but its cohesive effects have not been explicitly studied. We use the model DeltaRCM to directly explore two effects of vegetation: decreasing lateral transport of sediment and increasing flow resistance. We find that vegetation and cohesive sediment do alter delta morphology and channel dynamics in similar way… Show more

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Cited by 25 publications
(42 citation statements)
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“…In addition, the modeling conducted in this study does not account for the effects of vegetation, permafrost, or ice on deltaic evolution. We note that the DeltaRCM model has been modified to simulate these effects (Lauzon & Murray, 2018; Lauzon et al., 2019; Piliouras et al., 2021), however, these modifications were kept out of this study for the sake of simplicity.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…In addition, the modeling conducted in this study does not account for the effects of vegetation, permafrost, or ice on deltaic evolution. We note that the DeltaRCM model has been modified to simulate these effects (Lauzon & Murray, 2018; Lauzon et al., 2019; Piliouras et al., 2021), however, these modifications were kept out of this study for the sake of simplicity.…”
Section: Resultsmentioning
confidence: 99%
“…Simplified models have been developed to understand, for example, the lobate growth of delta landforms (Moodie et al., 2019; Seybold et al., 2007). Established models have been modified to explore the influence that multiple variables such as waves and SLR (Ratliff et al., 2018), mud and vegetation (Lauzon & Murray, 2018), and ice and permafrost (Lauzon et al., 2019) have on delta morphology and dynamics. In contrast to physical experiments, numerical models allow more experiments to be conducted and therefore additional conditions and forcings to be tested.…”
Section: Introductionmentioning
confidence: 99%
“…For example, Toby et al () recognized that the scale of autogenic volume fluctuations that defines transfer thresholds likely varies as a function of the mean and stochastic components of a system's forcing. Work to define the magnitude of autogenic fluctuations as functions of forcing conditions is starting to accelerate, with studies quantifying autogenic scales as functions of the ratio of sediment to water supply (Powell et al, ; Straub & Wang, ), sediment grain size (Caldwell & Edmonds, ) and cohesion (Edmonds & Slingerland, ; Hoyal & Sheets, ; Li et al, ), vegetation (Lauzon & Murray, ; Piliouras et al, ), flashiness of system hydrographs (Esposito et al, ; Ganti et al, ; Miller et al, ), basin water depth (Carlson et al, ), and wave (Ratliff et al, ) and tidal climate (Kleinhans et al, ; Lentsch et al, ).…”
Section: Future Directionsmentioning
confidence: 99%
“…Simulations used the DeltaRCM numerical delta model , implemented in Python as pyDeltaRCM (Moodie, Hariharan, Barefoot, & Passalacqua, in review). DeltaRCM has been robustly validated (Liang, Ge-leynse, Edmonds, & Passalacqua, 2015b; and used to examine delta morphology and evolution under various external forcings, including sea-level rise , and presence of vegetation (Lauzon & Murray, 2018), and ice and permafrost (Lauzon, Piliouras, & Rowland, 2019;Piliouras, Lauzon, & Rowland, 2021). The perturbation was modeled as instantaneous vertical land movement (i.e., displacement without hanging-wall rotation or translation), with channel evolution and geometry before and following displacement emerging based on model boundary conditions (Supporting Information).…”
Section: Simulating Faulting-induced Subsidence Across a Range Of Scalesmentioning
confidence: 99%