Wie reagieren die Ökosysteme auf sich ändernde Wetterverhältnisse, steigende Temperaturen und zunehmende Kohlendioxidkonzentrationen? Ist der Einfluss des Niederschlags wichtiger als der der Temperatur? Oder wird die Dynamik von Ökosystemen stärker durch die Verfügbarkeit von Nährstoffen beeinflusst? Welche Rolle spielen Extremereignisse bei der Entwicklung der biogeochemischen Kreisläufe? Um Antworten zu finden, müssen wir die Wechselwirkungen zwischen drei komplexen Systemen verstehen: Klima, Vegetation und Boden. Daher kombinieren wir Experimente und Langzeitbeobachtungen vor Ort mit Erdbeobachtungen, die von Flugzeugen und Satelliten in verschiedenen räumlichen Maßstäben gesammelt werden, und setzen datengestütztes maschinelles Lernen und theoriegestützte mechanistische Modellierung ein. Mit unserer Forschung versuchen wir zu verstehen, wie die terrestrische Biosphäre auf laufende Umweltveränderungen und Schwankungen der atmosphärischen Bedingungen reagiert und Rückkopplungen auf sie ausübt.
Neuste Publikationen
Die Publikationen sind nur in englischer Sprache verfügbar.
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).
Mahecha, M. D.; Kraemer, G.; Reinhardt, M.; Montero, D.; Gans, F.; Bastos, A.; Feilhauer, H.; Flika, I.; Jia, C.; Kattenborn, T.et al.; Migliavacca, M.; Mönks, M.; Quaas, J.; Sippel, S.; Walther, S.; Wieneke, S.; Wirth, C.; Camps-Valls, G.: Accelerated north–east shift of the global green wave trajectory. Proceedings of the National Academy of Sciences of the United States of America 123 (9), e2515835123 (2026)
Hanggara, B. B.; Stiegler, C.; Hata, Y.; Melling, L.; June, T.; Kumagai, T.; Hirano, T.; Knohl, A.: Trade-offs between carbon and water fluxes along a land use intensity gradient in southeast Asian forests and plantations. Global Change Biology 32 (2), e70753 (2026)
