This study deals with the riverbed of the Columbia river in the vicinity of the Hanford 300 Area study site in eastern Washington, where fluctuations in river stage take place both naturally (i.e. seasonally) and in conjunction with hydroelectric power dam operations. These fluctuations create conditions conducive to the influx and transport of fine-grained POM (a biological colloid originating from the river water and/or in situ periphyton production), within near-surface riverbed sediments. Although a great deal is known about dissolved organic matter (DOM) transport and metabolism in hyporheic zone sediments, there is a paucity of quantitative information on POM dynamics and its influence on hyporheic zone biogeochemistry (e.g. dissolved oxygen dynamics). We have developed a hydrobiogeochemical model capable of simulating the transport and metabolism of POM and its impact on dissolved oxygen (DO) distribution within the riverbed as influenced by periodic changes in river stage and fluid flow rate and direction. The model was employed as a tool to interpret the results of in situ measurements of POM intrusion into the riverbed made using “POM traps” emplaced within the upper 20 cm of the riverbed, as well as real-time in situ dissolved oxygen concentrations determined with a novel optical sensor buried directly in the riverbed at 20 cm depth. The simulations reproduced the accumulation of fresh POM within the upper few 5 cm of the riverbed observed in field POM trap deployments. Once sufficient surface POM accumulation takes place, an underlying zone of DO depletion develops as a consequence of variation in the rate of fluid exchange and POM/DOM degradation. The model predicted cyclic, hydrologically-driven variations in near-surface DO that are consistent with the results of the in situ DO probe deployments together with parallel measurements of fluid conductivity and hydrologic pressure. Our results suggest a complex interplay between fluid flow rate/direction and DO distribution that has important implication for riverbed biogeochemical dynamics at a variety of scales, as influenced by hydrological variability as well as the relative intensity of POM input and the availability of oxygen and other electron acceptors for microbial metabolism.

Quantification of heterogeneous multiscale permeability in geologic porous media is key for understanding and predicting flow and transport processes in the subsurface. Recent utilization of in situ imaging, specifically positron emission tomography (PET), enables the measurement of three-dimensional (3-D) time-lapse radiotracer solute transport in geologic media. However, accurate and computationally efficient characterization of the permeability distribution that controls the solute transport process remains challenging. Leveraging the relationship between local permeability variation and solute advection rates, an encoder-decoder based convolutional neural network (CNN) is implemented as a permeability inversion scheme using a single PET scan of a radiotracer pulse injection experiment as input. The CNN consists of Densely Connected Neural Networks that can accurately capture the 3-D spatial correlation between the permeability and the radiotracer solute arrival time difference maps in geologic cores. We first test the inversion accuracy using 500 synthetic test datasets. We then use a suite of experimental PET imaging datasets acquired on four different geologic cores. The network-inverted permeability maps from the geologic cores are used to parameterize forward numerical models that are directly compared with the experimental PET imaging datasets. The results indicate that a single trained network can generate robust, denoised 3-D permeability inversion maps in seconds. Numerical models parameterized with these permeability maps closely capture the experimental solute arrival time behavior. This approach presents an unprecedented improvement for efficiently characterizing multiscale permeability heterogeneity in complex geologic materials.