2025-04-08, 18:40–18:44, W - Invite
Soil degradation poses critical challenges to sustainable food production and environmental stability. In this study, we integrate simulations from 18 global climate models under two combined SSP-RCP scenarios (SSP2-4.5 and SSP5-8.5) with land use fractions from the Land Use Harmonization (LUH2) dataset to assess future soil degradation risks across Europe. We adopt a machine learning framework to link a Soil Degradation Proxy (an index integrating multiple soil health indicators including erosion rate, pH, electrical conductivity, and soil organic carbon; SDP) to topography, soil characteristics, climatic factors, and land use practices, enabling projections of how these factors collectively influence future soil degradation trends.
Our projections indicate that under the higher-emission SSP5-8.5 scenario, approximately 54% of European soil observation sites could face increased vulnerability to degradation by the far future (2071–2100). This heightened degradation risk is especially evident in northern European regions, such as Estonia and Latvia, where SDP may rise by up to 16%, largely influenced by changing climate conditions. In contrast, southern regions of Europe (e.g., Spain and Italy) could experience a decrease in SDP, suggesting potential improvements in soil health tied to evolving land use practices.
By combining climate projections, land use practices, and soil type, this work provides new insights into future trends and patterns of soil degradation across Europe. These findings support the urgent need for developing targeted soil management strategies to mitigate the negative impacts of climate and land use change on soil health conditions.
David is a Principal research scientist at UKCEH specialising in soil science and monitoring. He is responsible for UKCEH soil observatories and elements of national soil monitoring in the UK. In addition, his research interests focus on soil physics including soil hydrology, structure and erosion. Moreover, he investigates soil physical function and its interplay with biology, within the wider context of soil monitoring and ecosystem service assessment in response to climate and land use change drivers.
I am a Research Associate at the Institute of Geo-Hydroinformatics, Hamburg University of Technology (TUHH) in Germany. My research focuses on combining statistical and machine learning approaches with process-based models to enhance water and food security. My academic journey began at Urmia University in Iran, where I obtained a Bachelor of Science in Water Engineering and a Master of Science in Water Resources Engineering. I then pursued a Doctor of Philosophy in Civil Engineering – Water Resources at the Middle East Technical University (METU) in Ankara, Turkey. Following my PhD, I held post-doctoral roles in the United Kingdom, first at The University of Manchester, where I integrated crop modeling, satellite data, and ground observations to strengthen agricultural insurance solutions. Later, at the John Innes Centre in Norwich, I focused on refining process-based crop models by incorporating biological and genetic insights. Subsequently, I served as an Assistant Professor at METU from January. Currently, as a Research Associate at TUHH, I am developing machine learning frameworks to assess Europe’s soil vulnerability to degradation under various land use and climate change scenarios, helping to identify sustainable land management practices and mitigation strategies that preserve long-term soil health.