Hydrologic exchange is a crucial component of the water cycle. The strength of the exchange directly affects the biogeochemical and ecological processes that occur in the hyporheic zone and aquifer from micro to reach scales. Hydrologic exchange fluxes (HEFs) can be quantified using many field measurement approaches, however, in a relatively large river (scale > 10 3 m), these approaches are limited by site accessibility, the difficulty of performing representative sampling, and the complexity of geomorphologic features and subsurface properties. In rivers regulated by hydroelectric dams, quantifying HEF rates becomes more challenging because of frequent hydropeaking events, featuring hourly to daily variations in flow and river stages created by dam operations. In this study, we developed and validated a new approach based on field measurements to estimate shallow water HEF rates across the river bed along the shoreline of the Columbia River, USA. Vertical thermal profiles measured by self-recording thermistors were combined with time series of hydraulic gradients derived from river stages and inland water levels to estimate the HEF rates. The results suggest that the HEF rates had high spatial and temporal heterogeneities over the riverbed, with predicted flux rates varied from +1 × 10 −6 m s −1 to −1.5 × 10 −6 m s −1 under different flow conditions.

Hydrologic exchange flux (HEF) is an important hydrologic component in river corridors that includes both bidirectional (hyporheic) and unidirectional (gaining/losing) surface water‐groundwater exchanges. Quantifying HEF rates in a large regulated river is difficult due to the large spatial domains, complexity of geomorphologic features and subsurface properties, and the great stage variations created by dam operations at multiple time scales. In this study, we developed a method that combined numerical modeling and field measurements for estimating HEF rates across the riverbed in a 7 km long reach of the highly regulated Columbia River. A high‐resolution computational fluid dynamics (CFD) modeling framework was developed and validated by field measurements and other modeling results to characterize the HEF dynamics across the riverbed. We found that about 85% of the time from 2008 to 2014 the river was losing water with an annual average net HEF rates across the riverbed (Qz) of −2.3 m3 s−1 (negative indicating downwelling). June was the only month that the river gained water, with monthly averaged Qz of 0.8 m3 s−1. We also found that the daily dam operations increased the hourly gross gaining and losing rate over an average year of 8% and 2%, respectively. By investigating the HEF feedbacks at various time scales, we suggest that the dam operations could reduce the HEF at seasonal time scale by decreasing the seasonal flow variations, while also enhance the HEF at subdaily time scale by generating high‐frequency discharge variations. These changes could generate significant impacts on biogeochemical processes in the hyporheic zone.

Hydrologic exchange is a critical mechanism that shapes hydrological and biogeochemical processes along a river corridor. Because of limitations in field accessibility, computational demand, and complexities of geomorphology and subsurface geology, full three‐dimensional modelling studies to quantify hydrologic exchange fluxes (HEFs) have been limited mostly to local‐scale applications. At reach scales, although surface flow conditions and subsurface physical properties are well‐known factors that modulate hydrologic exchanges, quantitative measures that can describe the effects of these factors on the strength and direction of such exchanges do not exist. To address this issue, we developed a one‐way coupled surface and subsurface water flow model using the commercial computational fluid dynamics (CFD) software STAR‐CCM+ and applied it to simulate HEFs in a 7‐km long reach along the main stem of the Columbia River in the United States. The model was validated against flow velocity measurements from an acoustic Doppler current profiler in the river, vertical HEFs estimated from a set of temperature profilers installed across the riverbed, and simulations from a reactive transport model. The validated model then was employed to systematically investigate how HEFs could be influenced by surface water fluid dynamics, subsurface structures, and hydrogeological properties. Our results suggest that reach‐scale HEFs are dominated primarily by the thickness of the riverbed alluvium layer, and then by the alluvium permeability, the depth of the underlying impermeable layer, and the pressure boundary condition. Our results also elucidate the scale dependence of HEFs on fluid dynamics that can be captured only by three‐dimensional CFD models. That is, while the net HEFs over the entire 7‐km domain are not significantly influenced by surface water dynamics pressure, the dynamic pressure induced by fluid dynamics can lead to more than 15% in net HEFs for a river section of a few hundred metres.

Recent alluvial sediments in riverbeds play a significant role in controlling hydrologic exchange flows (HEFs) in river systems. The alluvial layer is usually associated with strong heterogeneity in physical properties (e.g., permeability and hydraulic conductivity), which affects local HEFs and therefore biogeochemical processes. The spatial distribution of these physical properties needs to be determined to inform the numerical models used to reveal the realistic hydro-biogeochemical behaviors. Such information can be obtained based on the intrinsic link between sediment grain-size distribution and hydraulic properties where sediment texture information is available. However, grain-size measurements are usually spatially sparse and do not have adequate coverage and resolution, particularly for a relatively large domain such as the Hanford Reach of the Columbia River. In this paper, we adopted machine learning (ML) approaches for categorizing and mapping the spatial distributions of riverbed substrate grain size and filling in missing areas of substrate data using the ML models along the reach. Such ML models for substrate size mapping were trained at 13,372 locations using measured substrate sizes along with observed and simulated attributes, including bathymetric attributes (e.g., elevation, slope, and aspect ratio) from LIDAR and bathymetric surveys, and hydrodynamic properties (e.g., water depth, velocity, shear stress, and their statistical moments). An ensemble bagging-based ML technique, Random Forest, was adopted to identify the most influential factors as predictors to develop the predictive models with over-fitting issues addressed. The models were evaluated with respect to each individual substrate size class and the lumped group, and then used to generate the final substrate size maps covering all the grid cells in the numerical modeling domain.

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