PhenoFeedBacks – Biophysical impacts and feedbacks of plant phenological changes

• Applicants: Professor Dr. Matthias Forkel; Dr. Alexander J. Winkler, since 9/2021
• Subject Area: Ecology and Biodiversity of Plants and Ecosystems ; Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography ; Atmospheric Science
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 454149250

An earlier emergence of leafs in spring or a shift in the occurrence of senescence in autumn are indicators of climate change effects on plant phenology. Such shifts in plant phenology affect photosynthesis and the carbon uptake in ecosystems. However, the biophysical effects of phenological changes on albedo, surface energy balance, surface temperature, and leaf moisture content have been little studied. Although an earlier leaf development in spring can enhance photosynthetic carbon uptake and hence might dampen climate warming, the same changes also implies a higher absorption of shortwave radiation and increased temperature and might hence amplify climate warming. Furthermore, increased plant productivity in spring implies increased water uptake from the soil and can amplify drought conditions during summer and autumn. Such an acceleration of drought conditions might contribute to higher danger of fire occurrence and more vegetation burning. Hence, the overall effects on plant phenological changes on the climate system are unknown. The main objectives of PhenoFeedBacks are to understand and quantify the biophysical feedbacks of changing phenology on the climate system. Therefore, we will quantify impacts on land surface albedo, latent heat fluxes, land surface temperature, the surface energy balance, and vegetation moisture content by using data from global observation networks and from satellites. Time series of plant phenology from digital cameras, measurements of ecosystem-atmosphere exchange of carbon, water and energy, as well as phenological and biophysical parameters from satellite observations will be combined with novel machine learning methods to identify relations and partial sensitivities between changes in biophysical properties and plant phenology. Overall, the identified relationships and quantified sensitivities between plant phenological changes and induced changes in surface biophysics will advance the understanding and will provide the basis for future model development on the role of land ecosystems in the climate system.

• DFG Programme: Research Grants
• Ehemaliger Antragsteller: Mirco Migliavacca, Ph.D., until 8/2021