Anthropogenic activities, especially dam operations, often induce larger and more frequent stage fluctuations than those occurring in natural rivers. However, long‐term impacts of such flow variations on thermal and biogeochemical dynamics of the associated hyporheic zone (HZ) are poorly understood. In this study, we built a heterogeneous, two‐dimensional, thermo‐hydro‐biogeochemical model for an aerobic respiration dominated river system. Our results revealed important interactions between subdaily to weekly flow variations and mean flow conditions controlled by snowpack at the watershed. High‐frequency stage fluctuations had their strongest thermal and biogeochemical impacts when mean river stage was low during fall and winter. In an abnormal drought year (2015) with low river stages during summer and early fall, high‐frequency stage fluctuations caused the HZ to be warmer than average. Furthermore, high‐frequency stage fluctuation enhanced all biogeochemical reactions by increasing nutrient supply and creating more oxygenated conditions. Overall carbon consumption in the HZ increased due to high‐frequency flow variations. Thermal dynamics altered by high‐frequency stage fluctuation impacted biogeochemical reactions in the HZ less than effects imposed by enhanced nutrient and oxygen supply. In addition to these results, we demonstrated that the HZ's hydrogeologic properties control flow paths that influence residence times and nutrient supply and consequently control spatial distribution of biogeochemical reaction hot spots in the HZ. Here we provide scientific basis for assessing potential ecological consequences of high‐frequency flow variations in a regulated river, as well as guidance for maximizing potential benefits—or minimizing drawbacks—of dam regulation to riverine ecosystems.

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.

Sensitivity analysis is an important tool for development and improvement of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance‐based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study, we developed a new sensitivity analysis method that integrates the concept of variance‐based method with a hierarchical uncertainty quantification framework. Different uncertain inputs are grouped and organized into a multilayer framework based on their characteristics and dependency relationships to reduce the dimensionality of the sensitivity analysis. A set of new sensitivity indices are defined for the grouped inputs using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high‐dimensional spatially distributed input variables.

Sensitivity analysis is a vital tool in numerical modeling to identify important parameters and processes that contribute to the overall uncertainty in model outputs. We developed a new sensitivity analysis method to quantify the relative importance of uncertain model processes that contain multiple uncertain parameters. The method is based on the concepts of Bayesian networks (BNs) to account for complex hierarchical uncertainty structure of a model system. We derived a new set of sensitivity indices using the methodology of variance‐based global sensitivity analysis with the Bayesian inference. The framework is capable of representing the detailed uncertainty information of a complex model system using BNs and affords flexible grouping of different uncertain inputs given their characteristics and dependency structures. We have implemented the method on a real‐world biogeochemical model at the groundwater‐surface water interface within the Hanford Site's 300 Area. The uncertainty sources of the model were first grouped into forcing scenario and three different processes based on our understanding of the complex system. The sensitivity analysis results indicate that both the reactive transport and groundwater flow processes are important sources of uncertainty for carbon‐consumption predictions. Within the groundwater flow process, the structure of geological formations is more important than the permeability heterogeneity within a given geological formation. Our new sensitivity analysis framework based on BNs offers substantial flexibility for investigating the importance of combinations of interacting uncertainty sources in a hierarchical order, and it is expected to be applicable to a wide range of multiphysics models for complex systems.

The facies‐based approach has been widely adopted to delineate an aquifer into distinct geological units with unique distributions of hydraulic, physical, and/or chemical properties. The recent development in ensemble‐based data assimilation methods allows both the direct and indirect data to be used to improve facies delineation. A major difficulty in those applications is to honor the spatial continuity and avoid overfitting after data assimilation. We introduce a new facies delineation framework to integrate ensemble data assimilation with traditional transition probability‐based geostatistics. A level‐set concept is used to parametrize discrete facies indicators and for updating facies shape. During the iterative data assimilation process, we impose spatial continuity by conditioning facies field generation on points selected adaptively based on their sensitivity to observation data. This reconditioning step is a key step to maintain spatial continuity and overcome overfitting problems in inversion. We selected two examples to evaluate the performance of the new framework in estimating facies‐based permeability field. The first example is a two‐dimensional synthetic system with transient head data induced by pumping tests used for delineating two facies. The second example is a three‐dimensional case with three facies, conceptualized from a field tracer experiment within the Columbia River corridor in Washington State, USA. Both examples demonstrate that the new method can adequately capture the spatial pattern of hydrofacies with reconditioning, which leads to the improved prediction of system behaviors.

Transit time distributions (TTDs) exert important controls on biogeochemical processes in watershed systems. TTDs are often assumed to follow time‐invariant exponential, lognormal, or heavy‐tailed power law distributions in headwater or low‐order streams. However, under dynamic hydrological forcing, transit time could exhibit more complex distribution patterns with strong spatial and temporal variability. In this study, we used a numerical particle tracking approach to characterize TTDs along the Hanford Reach of the Columbia River under the influences of river stage fluctuations and evaluate the associated effects on biogeochemical reaction potentials within the river corridor. Particle tracking was conducted using velocity fields simulated by high‐resolution three‐dimensional groundwater flow models that capture both the river stage fluctuations and physical heterogeneity. Our results revealed that multifrequency flow variations led to multimodal TTDs that varied in time and space. Such characteristics can only be captured by multiyear numerical simulations supported by multiyear field monitoring. Dam‐induced high‐frequency (subweekly) flow variations increased additional hydrologic exchange flows with short (subweekly) transit times, which accounted for up to 44% of reactant consumption in the river corridor along the Hanford Reach. The dam‐induced river stage fluctuations have more significant impacts on faster biogeochemical reactions because they cause a larger fraction of shorter transit times. Numerical particle tracking provides an efficient alternative for characterizing TTDs for large complex systems where in situ field experiments are not feasible. Such a numerical approach is thus essential for improving large‐scale biogeochemical modeling from watersheds to basins.