Integrating Large Wood Jams into Hydraulic Models: Evaluating a Porous Plate Modeling Method

Abstract

Research Impact Statement: Hydraulic models are error-prone where rivers interact with large wood jams. Our method for representing wood jams improves hydraulic model accuracy and ecohydraulic analysis.ABSTRACT: Large wood (LW) jams are key riverine habitat features that affect hydraulic processes and aquatic habitat. The hydraulic influence of LW jams is poorly understood due to the complexity of fluid dynamics around irregular, porous structures. Here we validated a method for two-dimensional hydraulic modeling of porous LW jams using the open-source modeling software Delft3D-FLOW. We sampled 19 LW jams at three reaches across the Columbia River Basin in the United States. We used computer-generated porous plates to represent LW jams in the modeling software and calibrated our modeling method by comparing model outputs to measured depths and velocities at validation points. We found that modeling outputs are error-prone when LW jams are not represented. By representing LW jams as porous plates we reduced average velocity root mean square error (RMSE) values (i.e., improved model accuracy) by 42.8% and reduced average depth RMSE values by 5.2%. These differences impacted habitat suitability index modeling. We found a 15.1% increase in weighted useable area for juvenile steelhead at one test site when LW jams were simulated vs. when they were ignored. We investigated patterns in average RMSE improvements with varying jam size, bankfull obstruction, porosity, and structure type, and river complexity. We also identified research gaps related to field estimation of LW jam porosity and porous structure modeling methods.(KEYWORDS: rivers/streams; fluvial processes; 2D simulations; field measurements; large wood jam; hydraulic modeling; fish habitat suitability; river restoration.) Paper No. JAWRA-18-0166-P of the Journal of the American Water Resources Association (JAWRA).

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

“…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.…”

“…unless otherwise indicated, but rarely do studies report α, the percentage of the jam that is solid, but not wood ( Figure 1A). Time consuming to determine WV; not possible for some field situations Manners et al, 2007;Sanhueza et al, 2019;Thevenet et al, 1998; Visual Visual estimate of proportion of void space (or proportion of filled space subtracted from unity) of jam Best guess, difficult to replicate; internal and/or submerged portion of jams nearly impossible to see and characterize Steeb et al, 2017;Ventres-Pake et al, 2019 Stratigraphic characterization Estimate porosity in measured vertical and/or horizonal layers (either visual estimation or back-calculation of plot or segment); porosity estimates weighted to get a porosity estimate for entire jam Direct measurement Measure all LW pieces through deconstruction or in situ and use equation for cylinder to convert to WV; or weigh all pieces and use wood density estimates to convert to WV Time consuming; often only focuses on LW and smaller material either visually estimated or ignored; assumption of cylindrical shape introduces inaccuracy Livers and Wohl, 2016;Manners et al, 2007;Ravazzolo et al, 2015;Sanhueza et al, 2019;Thevenet et al, 1998 Complete box JV completely encloses all wood, organic matter, and void space; 0.9 porosity used to convert air-wood space (JV) to WV with Equation 1 or Equation 2; Figure 1(B) A 0.9 porosity specific to Thevenet et al, 1998; calibration curves needed to account for variability in LW density and jam types across regions or depositional processes Boivin et al, 2015;Thevenet et al, 1998 Best-fit box JV defined by a box, prism, or other simple geometric shape that best fits the jam but may not totally enclose all jam materials; convert to WV; JV is subjective; pieces extending outside best-fit box must be accounted for appropriately to obtain total WV Dixon, 2016;…”

“…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). Pore spaces in jams provide habitat and shelter for aquatic insects and fish (Angermeier and Karr, 1984; Monzyk et al, 1997), making porosity values useful to land managers that aim to quantify aquatic habitats.…”

“…Porosity is important for designing infrastructure such as bridges, since it influences bridge backwater rise and structure stability (Schmocker and Hager, 2011; Gschnitzer et al, 2017; Schalko et al, 2018). Porosity or porosity proxies have been used in modeling flood inundation from jams (Dixon, 2013), hydraulic fields around jams (Ventres‐Pake et al, 2019), and two‐dimensional (2D) flood routing models (Yu and Lane, 2006). It is thus important to determine ways to better constrain porosity values.…”

Physical modelling of large wood (LW) processes relevant for river management: Perspectives from New Zealand and Switzerland

“…Spreitzer et al (2019cSpreitzer et al ( , 2020aSpreitzer et al ( , 2020b provided some approaches, which could help to improve point cloud and mesh reconstruction algorithms, to compute LW accumulation skeleton based on surface texture and recognizable LW elements with uniform, cylindrical geometries, similar to algorithms used in tree and plant reconstruction (Raumonen et al, 2013;Hackenberg et al, 2015). Such LW accumulation skeletons may then be of use in computational fluids dynamic (CFD) applications (Allen & Smith, 2012;Lai, 2016;Ventres-Pake et al, 2020).…”