2025-04-08, 15:15–15:30 (UTC), HugoTECH
Compaction reduces the infiltration capacity of the soil surface and may result in water ponding during heavy rainfall events. The same ponding effect is expected for soils with low organic content, limiting the formation of structural pores with high drainage capacity. The resulting presence of ponding water generates run-off and anaerobic conditions in the topsoil, increasing erosion rate and reducing the biological activity and productivity. The longer the duration of a rainfall event, the lower the infiltration capacity of the soil and the higher the risk that the infiltration capacity becomes limiting, defining the ‘ponding time’ as the time of onset of water accumulation at the surface. The ponding time is a comprehensive soil physical property, integrating aspects of soil water retention, hydraulic conductivity, and initial water content. The lower the ponding time for a certain precipitation rate, the more frequent is the expected occurrence of water ponding at the surface. In this study, we apply an analytical expression for ponding time to compute it at the Pan-European scale. Using maps of basic soil information (soil texture, organic carbon, and bulk density), we can estimate values of ponding time and thus the frequency of ponding (and run-off) by infiltration excess. This map provides a reference for the quantification of soil health by reducing ponding time by compaction and loss of organic carbon (‘unhealthy’) and increasing ponding time by structure formation (‘healthy’). Applying pedotransfer functions linking soil hydraulic properties with basic soil information and land use, we provide a sensitivity map to estimate the change in ponding time statistics by modifying land management. The generated maps have a spatial resolution of 1 km and allow a comparison with national statistics of hydromorphic constraints, revealing the relationship between infiltration excess and saturation excess. To apply ponding maps in soil health assessment and land use management, they must be developed at higher resolution. We will show the potential of providing ponding maps at higher resolution (30 m, Soil Health Data Cube for Europe) and linking them to remote sensing observations of ponding using satellite data.
I am a Senior Scientist at ETH Zurich (Environmental Systems Science). I study soil physical processes and their controlling structures at various scales ranging from pores to global mapping. As a lecturer, I teach soil physics and experimental methods to quantify processes in the vadose zone. I'm part of the AI4SoilHealth Project and manage the Swiss pilot site.
Annett Wania holds a PhD in Geography and has 21 years of experience in using geospatial and Earth Observation data for the analysis of the impact of human activities. After obtaining her PhD in Geography from the University of Strasbourg, France, she has been working at the European Commission’s Joint Research Centre for 13 years on applications in the environmental and agricultural domain as well as applications on urban environments and disaster management. During the last six years at JRC she was working on satellite-based mapping for disaster management under the Copernicus Emergency Management Service. During her time at the JRC she has transitioned from conducting technical work to managing scientific and technical projects and teams. Since January 2021 she is working at Planet Labs in the Earth Observation Lab, where she is managing a team of six engineers and data scientists which is implementing a number of research and development projects aiming at testing and further developing Planet’s image products for applications in the environmental and agricultural domain (crop classification, phenology, environmental impact of mining activities). In addition to traditional remote sensing methods, the team’s focus is on experimenting with innovative machine learning techniques to extract information from Planet’s high-cadence imagery and multi-modal datasets and define solutions for customers, which help them build new applications based on Planet data.