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000279774 1001_ $$0P:(DE-HGF)0$$aRahman, M.$$b0$$eCorresponding author
000279774 245__ $$aEvaluating the dual-boundary forcing concept in subsurface-land surface interactions of the hydrological cycle
000279774 260__ $$aNew York, NY$$bWiley$$c2016
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000279774 520__ $$aSubsurface and land surface processes (e.g. groundwater flow, evapotranspiration) of the hydrological cycle are connected via complex feedback mechanisms, which are difficult to analyze and quantify. In this study, the dual-boundary forcing concept that reveals space–time coherence between groundwater dynamics and land surface processes is evaluated. The underlying hypothesis is that a simplified representation of groundwater dynamics may alter the variability of land surface processes, which may eventually affect the prognostic capability of a numerical model. A coupled subsurface–land surface model ParFlow.CLM is applied over the Rur catchment, Germany, and the mass and energy fluxes of the coupled water and energy cycles are simulated over three consecutive years considering three different lower boundary conditions (dynamic, constant, and free-drainage) based on groundwater dynamics to substantiate the aforementioned hypothesis. Continuous wavelet transform technique is applied to analyze scale-dependent variability of the simulated mass and energy fluxes. The results show clear differences in temporal variability of latent heat flux simulated by the model configurations with different lower boundary conditions at monthly to multi-month time scales (~32–91 days) especially under soil moisture limited conditions. The results also suggest that temporal variability of latent heat flux is affected at even smaller time scales (~1–3 days) if a simple gravity drainage lower boundary condition is considered in the coupled model. This study demonstrates the importance of a physically consistent representation of groundwater dynamics in a numerical model, which may be important to consider in local weather prediction models and water resources assessments, e.g. drought prediction
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000279774 7001_ $$0P:(DE-HGF)0$$aSulis, M.$$b1
000279774 7001_ $$0P:(DE-Juel1)151405$$aKollet, Stefan$$b2
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