Identification and mapping of riverbed sediment facies in the Columbia River through integration of field observations and numerical simulations

Hou, Zhangshuan; Scheibe, T. D.; Murray, Christopher J L; Perkins, William; Arntzen, Evan; Ren, Huiying; Mackley, Rob D.; Richmond, Marshall C.

doi:10.1002/hyp.13396

Cited by 14 publications

(16 citation statements)

References 73 publications

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“…Moreover, riverbed conductance, a coefficient associated with the permeability and thickness of alluvium layer at river bottom, was assumed to be constant across the entire reach, although it was shown to be the most sensitive parameter in calculating exchange flux across river‐aquifer interface (Bao et al, ; G. E. Hammond & Lichtner, ). Spatially distributed conductance values, if mapped in future characterization efforts, could likely change the spatiotemporal patterns of the HEFs (Hou et al, ). In addition, fine‐scale morphologic features with size smaller than a grid cell (100 m), such as in‐channel bedforms (i.e., bars, dunes, pool‐step, or pool‐riffle sequences), were not resolved in the current model, even though they are important in driving short flow paths across the riverbed (Magliozzi et al, ).…”

Section: Resultsmentioning

confidence: 99%

“…E. Hammond & Lichtner, 2010). Spatially distributed conductance values, if mapped in future characterization efforts, could likely change the spatiotemporal patterns of the HEFs (Hou et al, 2019). In addition, fine-scale morphologic features with size smaller than a grid cell (100 m), such as inchannel bedforms (i.e., bars, dunes, pool-step, or pool-riffle sequences), were not resolved in the current model, even though they are important in driving short flow paths across the riverbed (Magliozzi et al, 2018).…”

Section: 1029/2018wr024193mentioning

confidence: 99%

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Dam Operations and Subsurface Hydrogeology Control Dynamics of Hydrologic Exchange Flows in a Regulated River Reach

Shuai

Song

Hammond

et al. 2019

Water Resources Research

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Hydrologic exchange flows (HEFs) across the river‐aquifer interface have important implications for biogeochemical processes and contaminant plume migration in the river corridor, yet little is known about the hydrogeomorphic factors that control HEFs dynamics under dynamic flow conditions. Here, we developed a 3‐D numerical model for a large regulated river corridor along the Columbia River to study how HEFs are controlled by the interplays between dam‐regulated flow conditions and hydrogeomorphic features of such river corridor system. Our results revealed highly variable intra‐annual spatiotemporal patterns in HEFs along the 75‐km river reach, as well as strong interannual variability with larger exchange volumes in wet years than dry years. In general, the river was losing during late spring to early summer when the river stage was high, and river was gaining in fall and winter when river stage was low. The magnitude and timing of river stage fluctuations controlled the timing of high exchange rates. Both river channel geomorphology and the thickness of a highly permeable river bank geologic layer controlled the locations of exchange hot spots, while the latter played a dominant role. Dam‐induced, subdaily to daily river stage fluctuations drove high‐frequency variations in HEFs across the river‐aquifer interfaces, resulting in greater overall exchange volumes as compared to the case without high‐frequency flows. Our results demonstrated that upstream dam operations enhanced the exchange between river water and groundwater with strong potential influence on the associated biogeochemical processes and on the fate and transport of groundwater contaminant plumes in such river corridors.

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Section: Resultsmentioning

confidence: 99%

Section: 1029/2018wr024193mentioning

confidence: 99%

Dam Operations and Subsurface Hydrogeology Control Dynamics of Hydrologic Exchange Flows in a Regulated River Reach

Shuai

Song

Hammond

et al. 2019

Water Resources Research

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“…USFWS conducted survey operations along the reach to characterize grain size distributions in terms of dominant substrate sizes (Anglin et al, 2006;Hou et al, 2019) to study fish habitats. To determine the dominant substrate size, each one square meter area was evaluated by assigning a representative grainsize range equal to the median grain size D50.…”

Section: Dominant Substrate Sizementioning

confidence: 99%

“…There have been studies linking riverbed grain size statistics (e.g., D50) to hydraulic conductivity with empirical formulas (e.g., Shepherd, 1989;Lu et al, 2012). In a recently published paper (Hou et al, 2019), the effective hydraulic conductivity field for a 7-km reach of the Columbia River was estimated based on the integrated relationships amongst shear stress facies, substrate sizes, and point hydraulic conductivity measurements. For large-scale study sites, grain-size analysis is one of the least expensive and most straightforward practical approaches, and it is not dependent on the geometry (Chen, 2000;Landon et al, 2001;Kasenow, 2002;Odong, 2007).…”

Section: Introductionmentioning

confidence: 99%

Spatial Mapping of Riverbed Grain-Size Distribution Using Machine Learning

et al. 2020

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Recent alluvial sediments in riverbeds play a significant role in controlling hydrologic exchange flows (HEFs) in river systems. The alluvial layer is usually associated with strong heterogeneity in physical properties (e.g., permeability and hydraulic conductivity), which affects local HEFs and therefore biogeochemical processes. The spatial distribution of these physical properties needs to be determined to inform the numerical models used to reveal the realistic hydro-biogeochemical behaviors. Such information can be obtained based on the intrinsic link between sediment grain-size distribution and hydraulic properties where sediment texture information is available. However, grain-size measurements are usually spatially sparse and do not have adequate coverage and resolution, particularly for a relatively large domain such as the Hanford Reach of the Columbia River. In this paper, we adopted machine learning (ML) approaches for categorizing and mapping the spatial distributions of riverbed substrate grain size and filling in missing areas of substrate data using the ML models along the reach. Such ML models for substrate size mapping were trained at 13,372 locations using measured substrate sizes along with observed and simulated attributes, including bathymetric attributes (e.g., elevation, slope, and aspect ratio) from LIDAR and bathymetric surveys, and hydrodynamic properties (e.g., water depth, velocity, shear stress, and their statistical moments). An ensemble bagging-based ML technique, Random Forest, was adopted to identify the most influential factors as predictors to develop the predictive models with over-fitting issues addressed. The models were evaluated with respect to each individual substrate size class and the lumped group, and then used to generate the final substrate size maps covering all the grid cells in the numerical modeling domain.

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“…Finally, the river channel geomorphology and hydrogeology also have strong controls on the locations of exchange hot spots and potentially RTDs (Shuai et al, 2019), which were not fully accounted in this study with the 1.6km river reach model. The influence of larger scale (10~100km scale) channel geomorphology, hydrogeology and also riverbed sediment heterogeneity (Hou et al, 2019) on RTDs will be addressed in our future work.…”

Section: Limitations Of This Studymentioning

confidence: 99%

Controls of River Dynamics on Residence Time and Biogeochemical Reactions of Hydrological Exchange Flows in A Regulated River Reach

Song¹,

Zachara²,

Shuai³

et al. 2019

Preprint

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Residence Time Distributions (RTDs) exerts an important control on biogeochemical translations in watershed systems. RTDs tend to follow time-invariant exponential, lognormal, or heavy-tailed RTDs that have power-law behaviors for long tails in headwater or low-order streams. However, there is increasing recognition that RTDs can be more complicated and time-variable in response to dynamic hydrological forcing. In this study, we use particle tracking to estimate RTDs along the Hanford Reach of the Columbia River and to quantify the influences of river stage fluctuations on RTDs and biogeochemical reaction potentials. Particle tracking is conducted using the velocity fields from high-resolution three-dimensional groundwater flow simulations. The effects of dynamic hydrological forcing on the RTDs were evaluated by applying time-varying river flow boundary conditions and continuously releasing particles in different time windows. Our results revealed that dynamic stage fluctuations created rapidly changing losing-gaining conditions in the river, which led to highly transient RTDs and resulted in multiple modes of RTDs. Dam-induced high-frequency (sub-daily) flow variations increased the fraction of short (sub-daily) residence times of the RTDs. Deviation of the reactant consumption under the single-mode assumption compared to the multimodal RTDs was relatively small (~5%) and the maximum deviation appeared when the Damköhler number was close to one. Dam-induced high-frequency stage variations potentially increase the biogeochemical reactions by 27%. These findings suggest that current large-scale hydrobiogeochemical models (reach to basin scales) could be improved by accounting for dynamic hydrologic exchange flows and associated transient RTDs influenced by both short- and long- term river stage fluctuations.

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Identification and mapping of riverbed sediment facies in the Columbia River through integration of field observations and numerical simulations

Cited by 14 publications

References 73 publications

Dam Operations and Subsurface Hydrogeology Control Dynamics of Hydrologic Exchange Flows in a Regulated River Reach

Dam Operations and Subsurface Hydrogeology Control Dynamics of Hydrologic Exchange Flows in a Regulated River Reach

Spatial Mapping of Riverbed Grain-Size Distribution Using Machine Learning

Controls of River Dynamics on Residence Time and Biogeochemical Reactions of Hydrological Exchange Flows in A Regulated River Reach

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