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.

Global Sensitivity Analysis (GSA) is key to assisting appraisal of the behavior of hydrological systems through model diagnosis considering multiple sources of uncertainty. Uncertainty sources typically comprise incomplete knowledge in (a) conceptual and mathematical formulation of models and (b) parameters embedded in the models. In this context, there is the need for detailed investigations aimed at a robust quantification of the importance of model and parameter uncertainties in a rigorous multi‐model context. This study aims at evaluating and comparing two modern multi‐model GSA methodologies. These are the first GSA approaches embedding both model and parameter uncertainty sources and encompass the variance‐based framework based on Sobol indices (as derived by Dai & Ye, 2015, https://doi.org/10.1016/j.jhydrol.2015.06.034) and the moment‐based approach upon which the formulation of the multi‐model AMA indices (as derived by Dell'Oca et al., 2020, https://doi.org/10.1029/2019wr025754) is based. We provide an assessment of various aspects of sensitivity upon considering a joint analysis of these two approaches in a multi‐model context. Our work relies on well‐established scenarios that comprise (a) a synthetic setting related to reactive transport across a groundwater system and (b) an experimentally‐based study considering heavy metal sorption onto a soil. Our study documents that the joint use of these GSA approaches can provide different while complementary information to assess mutual consistency of approaches and to enrich the information content provided by GSA under model and parameter uncertainty. While being related to groundwater settings, our results can be considered as reference for future GSA studies coping with model and parameter uncertainty.

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.