Fine particles (0.1–100 microns) are ubiquitous within the water column. Observations on the interactions between suspended fine particles and sediment beds remain limited, reducing our ability to understand the interactions and feedbacks between fine particles, morphodynamics, and hyporheic flow. We performed laboratory experiments to explore changes in bedform morphodynamics and hyporheic flow following the progressive addition of kaolinite clay to the water column above a mobile sand bed. We characterized these interactions by taking high‐frequency time series measurements of bed topography and freestream clay concentration combined with solute injections and bed sediment cores to characterize subsurface properties. Deposition of initially suspended clay resulted in a decrease of bedform height, celerity, and sediment flux by 14%, 22%, and 29% when 1000 g was accumulated within the bed (equal to clay/sand mass ratio of 0.4% in the bed). The hyporheic exchange flux decreased by almost a factor of 2 for all clay additions, regardless of the amount of clay eventually deposited in the bed. Post experiment sediment cores showed clay accumulation within and below the mobile layer of the bedforms, with the peak concentration occurring at the most frequent bedform scour depth. These results demonstrate the tight coupling between bed sediment morphodynamics, fine particle (clay) deposition, and hyporheic exchange. Suspended and bed load transport rates are diminished by the transfer of suspended load to the sediment via hyporheic exchange. This coupling should be considered when estimating sediment transport rates.

In this study we extend the Spatial Markov model, which has been successfully used to upscale conservative transport across a diverse range of porous media flows, to test if it can accurately upscale reactive transport, defined by a spatially heterogeneous first order degradation rate. We test the model in a well known highly simplified geometry, commonly considered as an idealized pore or fracture structure, a periodic channel with wavy boundaries. The edges of the flow domain have a layer through which there is no flow, but in which diffusion of a solute still occurs. Reactions are confined to this region. We demonstrate that the Spatial Markov model, an upscaled random walk model that enforces correlation between successive jumps, can reproduce breakthrough curves measured from microscale simulations that explicitly resolve all pertinent processes. We also demonstrate that a similar random walk model that does not enforce successive correlations is unable to reproduce all features of the measured breakthrough curves.

Rivers are a vital part of global ecosystems due to their major role in sediment distribution and cycling of nutrients and carbon (Cole et al., 2007;Tiegs et al., 2019). This is accomplished largely through the interactions between the flow in the stream and the underlying sediment bed. Interactions such as bed motion and water exchange between the stream and the subsurface are influenced by numerous physical properties including stream flow, streambed slope, particle size distribution of the bed, and so on. Biogeochemical processes in streams particularly depend on delivery of nutrients and substrates to microbes that are mostly found in the streambed (

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The Spatial Markov Model (SMM) is an upscaled model that has been used successfully to predict effective mean transport across a broad range of hydrologic settings. Here we propose a novel variant of the SMM, applicable to spatially periodic systems. This SMM is built using particle trajectories, rather than travel times. By applying the proposed SMM to a simple benchmark problem we demonstrate that it can predict mean effective transport, when compared to data from fully resolved direct numerical simulations. Next we propose a methodology for using this SMM framework to predict measures of mixing and dilution, that do not just depend on mean concentrations, but are strongly impacted by pore-scale concentration fluctuations. We use information from trajectories of particles to downscale and reconstruct pore-scale approximate concentration fields from which mixing and dilution measures are then calculated. The comparison between measurements from fully resolved simulations and predictions with the SMM agree very favorably