How do ecosystems respond to changing weather patterns, rising temperatures and increasing carbon dioxide concentrations? Is the effect of precipitation more important than that of temperature? Or are ecosystem dynamics more strongly affected by nutrient availability? What is the role of extreme events in shaping biogeochemical cycles? To find out the answers we need to understand the interactions among three complex systems: climate, vegetation, and soil. Thus, we combine experiments and in-situ long-term observation with Earth Observations gathered by aircraft and satellites across a range of spatial scales, and embrace data-driven machine learning and theory-driven mechanistic modelling. With our research, we try to understand how the terrestrial biosphere reacts to and exerts feedbacks on ongoing environmental change and variation in atmospheric conditions.
Latest publications
De, R., Bao, S., Koirala, S., Brenning, A., Reichstein, M., Tagesson, T., et al. (2025). Addressing challenges in simulating inter–annual variability of gross primary production. Journal of Advances in Modeling Earth Systems, 17(5), e2024MS004697. doi:10.1029/2024MS004697. // Figure 1: Graphical representation of the model–data–integration workflow adopted in this study. The blue box indicates the preparation of forcing and observation data at hourly and daily scales for each site, as well as defines the initial value of parameters and their range by surveying literature. Then five different model parameterization tasks were performed for the light use efficiency (LUE) model from Bao, Wutzler, et al. (2022; https://doi.org/10.1016/j.agrformet.2022.109185) at hourly scale (Baohr model) and at daily scale (Baodd model), P-model from Mengoli et al. (2022; https://doi.org/10.1029/2021MS002767) at hourly scale (Phr model), and Phr model with an explicit drought stress function (PhrW model) using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) (Hansen & Kern, 2004; https://doi.org/10.1007/978-3-540-30217-9_29), which is indicated by the red box. The cost function (f) is a function of observed (y) and simulated (x) gross primary production. The green box denotes that the whole workflow was applied for the 198 sites from the FLUXNET2015 data set (Pastorello et al., 2020; https://doi.org/10.1038/s41597-020-0534-3). The dotted orange box highlights the focus of this study. The parameter dynamics is explored in detail in a companion paper (De, Brenning, et al., 2025; https://doi.org/10.22541/essoar.174349993.30198378/v1). The figure was created in BioRender. R. De (2025; https://biorender.com/i01x768)
De, R., Bao, S., Koirala, S., Brenning, A., Reichstein, M., Tagesson, T., et al. (2025). Addressing challenges in simulating inter–annual variability of gross primary production. Journal of Advances in Modeling Earth Systems, 17(5), e2024MS004697. doi:10.1029/2024MS004697. // Figure 1: Graphical representation of the model–data–integration workflow adopted in this study. The blue box indicates the preparation of forcing and observation data at hourly and daily scales for each site, as well as defines the initial value of parameters and their range by surveying literature. Then five different model parameterization tasks were performed for the light use efficiency (LUE) model from Bao, Wutzler, et al. (2022; https://doi.org/10.1016/j.agrformet.2022.109185) at hourly scale (Baohr model) and at daily scale (Baodd model), P-model from Mengoli et al. (2022; https://doi.org/10.1029/2021MS002767) at hourly scale (Phr model), and Phr model with an explicit drought stress function (PhrW model) using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) (Hansen & Kern, 2004; https://doi.org/10.1007/978-3-540-30217-9_29), which is indicated by the red box. The cost function (f) is a function of observed (y) and simulated (x) gross primary production. The green box denotes that the whole workflow was applied for the 198 sites from the FLUXNET2015 data set (Pastorello et al., 2020; https://doi.org/10.1038/s41597-020-0534-3). The dotted orange box highlights the focus of this study. The parameter dynamics is explored in detail in a companion paper (De, Brenning, et al., 2025; https://doi.org/10.22541/essoar.174349993.30198378/v1). The figure was created in BioRender. R. De (2025; https://biorender.com/i01x768)
Khand, K.; Senay, G. B.; Friedrichs, M.; Yi, K.; Fisher, J. B.; Wang, L.; Suvočarev, K.; Ahmadi, A.; Chu, H.; Good, S.et al.; Mallick, K.; Missik, J.; Nelson, J. A.; Reed, D. E.; Wang, T.; Xiao, X.: A novel approach to increase accuracy in remotely sensed evapotranspiration through basin water balance and flux tower constraints. Journal of Hydrology 662/Part A, 133824 (2025)
Li, D.; Chen, J. M.; Duveiller, G.; Frankenberg, C.; Köhler, P.; Kang: A more precise retrieval of sun-induced chlorophyll fluorescence from satellite data using artificial neural networks. Remote Sensing of Environment 330, 114987 (2025)
Paulus, S.; Migliavacca, M.; Reichstein, M.; Orth, R.; Lee, S.-C.; Carrara, A.; Hildebrandt, A.; Nelson, J. A.: Insights into water vapor uptake by dry soils using a global eddy covariance observation network. Global Change Biology 31 (10), e70547 (2025)
Davidson, S. J.; Malhotra, A.; Jassey, V. E. J.; Strack, M.; Aitova, E.; Anderson, R.; Atkinson, L. J.; Barel, J. M.; Bird, M.; Brehier, C.et al.; Donaldson-Selby, G.; Duley, E.; Eklof, J.; de Eyto, E.; Granath, G.; Grant, A.; Hartmann, A.; Holland, A.; Huth, V.; Jones, C. P.; Lee, S.-C.; Lopatin, J.; Milner, A. M.; Peacock, M.; Peichl, M.; Perez-Quezada, J. F.; Perryman, C. R.; Pickard, A.; Rautakoski, H.; Silvester, E.; Virkkala, A.-M.; Wegener, E.: The PeatPic project: predicting plot-scale green leaf phenology across peatlands. Environmental Research Letters 20 (11), 114002 (2025)
Besnard, S.; Heinrich, V. H. A.; Carvalhais, N.; Ciais, P.; Herold, M.; Luijkx, I.; Peters, W.; Suarez, D. R.; Santoro, M.; Yang, H.: Global covariation of forest age transitions with the net carbon balance. Nature Ecology & Evolution 9, pp. 1848 - 1860 (2025)
Milis, A.; Hofmann, M.; Mäder, P.; Wäldchen, J.; de Haan, M.; Ballings, P.; Van der Beeten, I.; Goffinet, B.; Vanderpoorten, A.: Towards the automatized identification of moss species from their spore morphology. Annals of Botany, mcaf215 (2025)
