Abstract submission at EGU2026: SSS10.3 " Integrating observations and physics-informed modeling for understanding vadose zone processes"
Paolo Nasta
Dear colleagues,
We are writing to invite you to contribute to the session SSS10.3 at the EGU General Assembly 2026 in Vienna:
SSS10.3 " Integrating observations and physics-informed modeling for understanding vadose zone processes"
https://meetingorganizer.copernicus.org/EGU26/session/56886
The abstract submission is open until January 15, 2026, 13:00 CET.
Alert: The deadline to apply for the Roland Schlich Travel Support for on-site participants is December 1, 2025, at 13:00 CET
Session
description
Observations are the cornerstone of understanding hydrological processes and
advancements in technologies provide a great source of information. Yet,
integrating these multiple sources of measurement into data-driven and
physics-informed models remains a significant challenge in vadose zone
hydrology.
Recent advancements in deep learning have opened new avenues for modeling
complex earth system processes. This session will explore the cutting-edge
application of deep learning approaches to characterize soil hydrothermal
properties and to model and predict soil water, heat, and solute transport.
Therefore, soil processes can be simulated over different spatial scales,
enabling reliable predictions of climate change, contamination, salinization,
erosion, agricultural practices, and land-use impacts on soil and water
resources.
We invite contributions on the following topics:
• Innovative observation techniques and technologies: New methods for measuring
soil variables (e.g., soil moisture) and other vadose zone physical, chemical,
and hydraulic properties.
• Data mining and analysis: Advanced techniques for extracting meaningful
information from large and complex datasets.
• Data fusion and downscaling: Novel methods for bridging the gap between
coarse-resolution data and fine-scale applications by downscaling techniques
including machine learning.
• Model development and integration: Coupling of models with various
observation data sources and the application of novel approaches like deep and
machine learning (i.e., multiphysics-informed neural networks, closure term
modeling with machine learning).
• Applications and case studies: Demonstrations of how integrated observations
and models can address specific hydrological challenges and evaluate the impact
of natural and human disturbances on soil and water resources.
• Challenges and future directions: Discussions on the limitations and
opportunities for future research in vadose zone hydrology.
By fostering interdisciplinary collaboration, this session will significantly
advance our understanding and management of the vadose zone, a critical region
controlling the flow of water, nutrients, and pollutants, and linking between
atmospheric water, surface water, and groundwater.
We look forward to the possibility of having you join us at EGU 2026,
Best Regards,
Na, Sarem, Yonghong, Martine, Paolo