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Abstract Streambed biogeochemical processes strongly influence riverine water quality and gaseous emissions. These processes depend largely on flow paths through the hyporheic zone (HZ), the streambed volume saturated with stream water. Boulders and other macroroughness elements are known to induce hyporheic flows in gravel‐bed streams. However, data quantifying the impact of these elements on hyporheic chemistry are lacking. We demonstrate that, in gravel‐bed rivers, the amount of dissolved oxygen (DO) in the bed depends chiefly on changes in bed shape, or morphology, such as the formation of scour and depositional areas, caused by the boulders, among other factors. The study was conducted by comparing DO distributions across different bed states and hydraulic conditions. Our experimental facility replicates conditions observed in natural gravel‐bed streams. We instrumented a section in the bed with DO sensors. Results generally indicate that boulder placement on planar beds has some effects, which are significant at high base flows, on increasing hyporheic oxygen amount compared to the planar case without boulders. Conversely, boulder‐induced morphological changes noticeably and significantly increase the amount of oxygen in the HZ, with the increase depending on sediment inputs during flood flows able to mobilize the sediment. Therefore, streambeds of natural, plane‐bed streams may have deeper oxic zones than previously thought because the presence of boulders and the occurrence of flood flows with varying sediment inputs induce streambed variations among these elements. 

Decision-support systems for environmental management of coastal areas must account for brine and seawater dynamics. Physics-based models of these phenomena are computationally expensive, which limits their usefulness for decision-making under uncertainty. Data-driven modeling techniques, such as extended dynamic mode decomposition (xDMD), ameliorate these challenges. We demonstrate that xDMD, equipped with a novel domain decomposition component, effectively represents a validated, real-world, coupled nonlinear seawater inundation model. It serves as an efficient surrogate of process-based simulations, capable of accurate reproduction and reconstruction of missing pressure and salinity data in the interpolation regime. It accurately predicts low-rank pressure distributions (repeated dynamics) but struggles to forecast long-term salinity dynamics (cumulative evolution). The addition of domain decomposition improves the robustness and accuracy of xDMD, with the overlapping domain approach outperforming the nonoverlapping one in the projection accuracy. In our experiments, xDMD is 1700 times faster than the process-based model and requires 800 times less storage, while efficiently capturing pressure and salinity dynamics. 

Physical systems are characterized by inherent symmetries, one of which is encapsulated in theunits of their parameters and system states. These symmetries enable a lossless order-reduction, e.g.,via dimensional analysis based on the Buckingham theorem. Despite the latter's benefits, machinelearning (ML) strategies for the discovery of constitutive laws seldom subject experimental and/ornumerical data to dimensional analysis. We demonstrate the potential of dimensional analysis to significantlyenhance the interpretability and generalizability of ML-discovered secondary laws. Ournumerical experiments with creeping fluid flow past solid ellipsoids show how dimensional analysisenables both deep neural networks and sparse regression to reproduce old results, e.g., Stokes law fora sphere, and generate new ones, e.g., an expression for an ellipsoid misaligned with the flow direction.Our results suggest the need to incorporate other physics-based symmetries and invariancesinto ML-based techniques for equation discovery.