Wood Jam Dynamics Database and Assessment Model (WooDDAM): A framework to measure and understand wood jam characteristics and dynamics

Scott, Daniel N.; Wohl, Ellen; Yochum, Steven E.

doi:10.1002/rra.3481

Cited by 16 publications

(18 citation statements)

References 49 publications

(36 reference statements)

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“…This approach allows quantification of the physical role of LW jams in generating geomorphic and ecological diversity and stabilizing channel networks (Collins et al., 2012; Walter & Merritts, 2008). C A could be used to characterize physical properties of preexisting LW jams (Dixon, 2016; Wohl et al., 2010) and compare LW jams across river systems through collaborative LW data collection (Scott et al., 2019). C A may also be predicted from LW jam length, solid volume fraction, and log diameter, allowing representation of engineered logjam designs in a flood model or network analysis (Hankin et al., 2020; Leakey et al., 2020; Persi et al., 2019; Ruiz Villanueva et al., 2014) as a composite structure (Equations and ).…”

Section: Discussionmentioning

confidence: 99%

Momentum and Energy Predict the Backwater Rise Generated by a Large Wood Jam

Follett

Schalko

Nepf

2020

Geophysical Research Letters

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Wood reintroduction is now considered an important aspect of stream restoration, due to ecohydraulic benefits associated with wood presence. Channel‐spanning wood jams create an upstream backwater, increasing flow heterogeneity, sediment deposition, and ecological productivity, but also flood risk. Backwater rise prediction is necessary to evaluate flood hazards in hydraulic models, improve design of engineered logjam projects, and compare jam effects across river systems. We present experimental results demonstrating that a jam can be modeled as a porous obstruction generating momentum loss proportional to the number, size, and packing density of the logs and the jam length. Energy and momentumconstraints are combined to predict backwater rise from unit discharge and a dimensionless structural parameter. This novel approach allows description of preexisting jams with a common metric. The model was used to demonstrate how backwater length, pool size, and upstream sediment deposition depend on jam structure and channel slope.

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Section: Discussionmentioning

confidence: 99%

Momentum and Energy Predict the Backwater Rise Generated by a Large Wood Jam

Follett

Schalko

Nepf

2020

Geophysical Research Letters

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“…appendix 3, Compton, 1985, see Supporting Information), applies categorical descriptions linked to ranges of porosities (e.g. Scott et al, 2019; Ventres‐Pake et al, 2019), and is the sole surveyor of all jams in a single study.…”

Section: Review Of Methodsmentioning

confidence: 99%

“…A common approach for estimating wood volume involves measuring the jam volume and then estimating the jam porosity, or fraction of void space ( Φ j ) versus filled space (1 − Φ j ), within the jam volume to determine the wood volume. Accurate knowledge of Φ j is also useful for assessing the impacts of jams on geomorphic and ecological processes, as jam porosity may be one of the most important measurable variables driving geomorphic change around jams (Manners et al, 2007; Dixon, 2016; Scott et al, 2019). Porosity can determine the degree to which jams alter stream channel hydraulics, with lower porosity resulting in greater backwater effects, erosion potential, and habitat creation, while higher porosity has less overall influence on local hydraulics and habitat (Manners et al, 2007; Dixon, 2016; Ventres‐Pake et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Porosity problems: Comparing and reviewing methods for estimating porosity and volume of wood jams in the field

Livers

Lininger

Kramer

et al. 2020

Earth Surf Processes Landf

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Porosity, or void space, of large wood jams in stream systems has implications for estimating wood volumes and carbon storage, the impacts of jams on geomorphic and ecological processes, and instream habitat. Estimating porosity and jam dimensions (i.e. jam volume) in the field is a common method of measuring wood volume in jams. However, very few studies explicitly address the porosity values in jams, how porosity is calculated and assessed for accuracy, and the effect such estimates have on carbon and wood budgets in river corridors. We compare methods to estimate jam porosity and wood volume using field data from four different depositional environments in North America (jam types include small in-channel jams, large channel-margin jams, a large island apex jam, and a large coastal jam), and compare the results with previous studies. We find that visual estimates remain the most time-efficient method for porosity estimation in the field, although they appear to underpredict back-calculated porosity values; the accuracy of jam porosity, and thus wood volume, estimates are difficult to definitively measure. We also find that porosity appears to be scale invariant, dictated mostly by jam type, (which is influenced by depositional processes), rather than the size of the jam. Wood piece sorting and structural organization are likely the most influential properties on jam porosity, and these factors vary according to depositional environment. We provide a framework and conceptual model that uses these factors to demonstrate how modeled jam porosity values differ and give recommendations as a catalyst for future work on porosity of wood jams. We conclude that jam type and size and/or the study goals may dictate which porosity method is the most appropriate, and we call for greater transparency and reporting of porosity methods in future studies.

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“…In particular, research on the turbulent flow patterns around engineered wood structures is limited. Studies on large wood have emphasized the ecological (see Scott, Wohl, & Yochum, 2019) and geomorphic (Gurnell, Piégay, Swanson, & Gregory, 2002) functions, force balances (D'Aoust & Millar, 2000;Shields & Alonso, 2012), transport (Mazzorana, Hübl, Zischg, & Largiader, 2011;Ruiz-Villanueva et al, 2014), and management (Wohl et al, 2016) of large wood. Although there is a general belief that wood restoration is ecologically beneficial (Roni, Beechie, Pess, & Hanson, 2014), it is unclear how much wood, or in what arrangement, is necessary to produce measurable benefits to fish habitat (Kail, Hering, Muhar, Gerhard, & Preis, 2007).…”

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confidence: 99%

Effects of an engineered log jam on spatial variability of the flow field across submergence depths

L'Hommedieu¹,

Tullos

Jones

2020

River Research & Apps

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Engineered log jams (ELJs) are commonly implemented in rivers to diversify fish habitat, but few studies have verified the hydraulic benefits of ELJs by assessing their effects on the spatial variability and turbulence structure of the flow field. This field and modelling study assessed the effects of an ELJ on various characteristics of the flow field with increasing submergence. A 2‐D hydrodynamic model was applied to simulate flow conditions of the Calapooia River, Oregon, with an ELJ installed at the upstream end of a meander. Output was analyzed using coherent flow structure identification, maps of turbulence measures, and wavelet analysis along linear transects. At the highest discharge (62% ELJ submergence), more and smaller coherent flow structures were identified than in the no ELJ model run, but the magnitudes of turbulence metrics were unaffected. At low flow (1% jam submergence), the presence of the ELJ did not affect the number of coherent flow structures identified and produced only small and localized effects on turbulence measures. Taken together, results indicate that the ELJ primarily affected the flow field by increasing the number of flow structures at higher discharges and submergence depths. However, these changes were small and local to the ELJ. The limited effects of this wood jam over limited spatial scales raise questions about the conditions under which such structures increase hydraulic diversity and highlight the need for more detailed understanding on spatial variability around habitat structures with complex shapes.

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Wood Jam Dynamics Database and Assessment Model (WooDDAM): A framework to measure and understand wood jam characteristics and dynamics

Cited by 16 publications

References 49 publications

Momentum and Energy Predict the Backwater Rise Generated by a Large Wood Jam

Momentum and Energy Predict the Backwater Rise Generated by a Large Wood Jam

Porosity problems: Comparing and reviewing methods for estimating porosity and volume of wood jams in the field

Effects of an engineered log jam on spatial variability of the flow field across submergence depths

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