Quantifying water and heat fluxes at the surface water (SW)–groundwater (GW) interface is crucial for ensuring sustainable water management and quality. However, direct field-based quantification remains challenging due to the dynamic nature of SW-GW interactions, which are influenced by poorly constrained boundary conditions and spatial hydrofacies distributions. Traditionally, these parameters are inferred through model calibration using conventional data, such as hydraulic heads and river discharge. Many regional studies have treated rivers as curvilinear GW divides, with flow either converging toward or diverging from the river center—an assumption rooted in Tóth’s theory, which correlates surface and subsurface drainage boundaries. However, this oversimplification fails to account for geological heterogeneity, river morphology, variable hydraulic conditions, and anthropogenic influences like withdrawals. While regional-scale studies commonly examine SW-GW exchanges, their coarse resolution limits the ability to resolve localized hydraulic gradients. Understanding flow dynamics in heterogeneous environments, such as alluvial plains, requires a more detailed, integrated approach. Here, we present a multi-method framework to strengthen numerical simulations and improve hydrodynamic and thermal parameter calibration in both space and time. Applied to the Orgeval Critical Zone Observatory (France), our approach estimates SW-GW fluxes using a combination of long-term hydrological data (10 years), time-lapse seismic imaging, and numerical modeling. We demonstrate how high-resolution geophysical imaging, combined with geotechnical data, enables a detailed characterization of hydrofacies and provides valuable prior constraints on hydrodynamic properties. Time-lapse seismic acquisitions offer a high-resolution view of groundwater table (WT) dynamics, with each seismic snapshot carefully inverted to capture spatial WT variations. By integrating these geophysical insights with long-term hydrogeological observations (hydraulic head and temperature), we refine parameterization within the hydrogeological modeling domain, leading to improved estimates of transient stream-aquifer exchanges. Finally, we outline future steps toward achieving a fully coupled hydrogeophysical model to further enhance SW-GW interaction predictions.
Research into consumer attitudes toward food products is important to help people adopt healthier, more sustainable diets. A positive attitude regarding an object is a prerequisite for its adoption. This study compares French consumers’ implicit attitudes toward pulses and cereals. Many studies have measured attitudes by explicit methodologies (e.g., questionnaires). Such methods are often biased by social desirability, and consumers may not be consciously aware of their attitudes toward food. A Sorting Paired Feature Task measures the strength of automatic associations, pairing images of pulses or cereals and adjectives with positive or negative valence. Participants sorted 120 paired stimuli as fast as possible. Pairs composed of pulses and negative adjectives were sorted faster than pairs composed of cereals and negative adjectives. Cereals with positive adjectives were sorted more rapidly than pulses with positive adjectives. Mistaken associations were more frequent for pairs composed of cereals and negative adjectives than for pairs composed of pulses and negative adjectives. These results highlight more negative implicit attitudes toward pulses than cereals. This study provides the first potential evidence of negative implicit attitudes toward pulses, which could explain the low consumption of these products.
