Observations are the cornerstone of understanding hydrological processes, providing data for model development and validation. However, integrating monitoring strategies and measurement tools with data analysis and modelling remains a major challenge in vadose zone hydrology. This session brings together experimentalists, modelers, and data scientists working on innovative approaches for observing and simulating vadose zone processes, with emphasis on observation–model links across scales, from laboratory and plot experiments to field, catchment, regional, and continental applications. Advances in remote sensing, high-performance computing, artificial intelligence, digital twins, and the Internet of Things offer new opportunities to address these challenges. Integrating observations and models can improve quantification of water and solute fluxes, characterization of soil hydraulic properties, process identification, and assessment of responses to environmental change, including droughts, heatwaves, land-use change, soil degradation, salinization, erosion, contamination, and agricultural and water-management practices, while reducing predictive uncertainty.
We welcome contributions addressing:
• Innovative observations and monitoring technologies: laboratory and field methods, sensor networks, remote and proximal sensing, geophysics, environmental tracers, and measurements of soil water, energy, solute fluxes, and soil physical, hydraulic, and biogeochemical properties.
• Multi-scale data integration and analytics: data fusion and assimilation, uncertainty analysis, artificial intelligence and machine learning, and analysis of large, heterogeneous, high-frequency datasets.
• Vadose zone modelling: process-based, data-driven, and hybrid approaches; parameterization and validation; inverse modelling; model–data integration; upscaling and downscaling; uncertainty quantification.
• Applications under environmental change: vadose zone responses to droughts, heatwaves, climate variability and change, land-use change, irrigation and agricultural management, soil degradation, salinization, erosion, contamination, and other natural or anthropogenic disturbances.
• Emerging challenges and research frontiers: digital twins, interoperable monitoring infrastructures, high-performance computing, cross-scale and cross-environment transferability, and new strategies for linking observations with predictive models.