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    Assessing the effect of different river water level interpolation schemes on modeled groundwater residence times
    Obtaining a quantitative understanding of river–groundwater interactions is of high practical relevance, for instance within the context of riverbank filtration and river restoration. Modeling interactions between river and groundwater requires knowledge of the river’s spatiotemporal water level distribution. The dynamic nature of riverbed morphology in restored river reaches might result in complex river water level distributions, including disconnected river branches, nonlinear longitudinal water level profiles and morphologically induced lateral water level gradients. Recently, two new methods were proposed to accurately and efficiently capture 2D water level distributions of dynamic rivers. In this study, we assessed the predictive capability of these methods with respect to simulated groundwater residence times. Both methods were used to generate surface water level distributions of a 1.2 km long partly restored river reach of the Thur River in northeastern Switzerland. We then assigned these water level distributions as boundary conditions to a 3D steady-state groundwater flow and transport model. When applying either of the new methods, the calibration-constrained groundwater flow field accurately predicted the spatial distribution of groundwater residence times; deviations were within a range of 30% when compared to residence times obtained using a reference method. We further tested the sensitivity of the simulated groundwater residence times to a simplified river water level distribution. The negligence of lateral river water level gradients of 20–30 cm on a length of 200 m caused errors of 40–80% in the calibration-constrained groundwater residence time distribution compared to results that included lateral water level gradients. The additional assumption of a linear water level distribution in longitudinal river direction led to deviations from the complete river water level distribution of up to 50 cm, which caused wide-spread errors in simulated groundwater residence times of 200–500%. For an accurate simulation of groundwater residence times, it is therefore imperative that the longitudinal water level distribution is correctly captured and described. Based on the confirmed predictive capability of the new methods to estimate 2D river water level distributions, we can recommend their application to future studies that model dynamic river–groundwater systems.