Modelling Biomass Projections in Grasslands of Central Spain Under Climate Change Scenarios

Grasslands are vital ice-free ecosystems that provide essential ecosystem services, including carbon sequestration, biodiversity preservation, and pollination. However, these systems face significant threats from rising temperatures and reduced precipitation, necessitating a deeper understanding of their dynamics to inform sustainable management. This study investigates the potential changes in grassland biomass under future climate scenarios, offering insights into long-term trends and adaptive strategies.

The study focuses on grasslands in the Community of Madrid, central Spain, covering approximately 41% of the territory. To analyze biomass variations, we established a 5x5 km grid across the region, selecting research areas based on proximity to soil pits and a minimum grassland coverage of 40% per grid cell. Observational climate data (1975–2021) and future projections (2022–2100) were used, derived from SSP-2.6, SSP-4.5, SSP-7.0 and SSP-8.5 scenarios based on Shared Socioeconomic Pathways.

Biomass calculations are estimated using the SIMPAST model. This model, designed to predict biomass under varying climatic conditions, required inputs such as hydrological balance, solar radiation, and an initial seed count. Vegetation species and biomass measurements are being conducted from September to May 2024–2025 to refine and evaluate model accuracy and assess water use efficiency across the three study areas.

Preliminary results reveal significant spatiotemporal variations in grassland biomass, linked to projected changes in temperature and precipitation patterns. The findings underscore the importance of adaptive management strategies tailored to specific climate scenarios to maintain grassland ecosystem services.

Acknowledgements: The authors acknowledge the support of Project “Garantía Juvenil” scholarship from Comunidad de Madrid, as well as Universidad Politécnica project Clasificación de Pastizales Mediante Métodos Supervisados - SANTO (project number: RP220220C024).