Publikationen von Nuno Carvalhais

Poehls, J.; Alonso, L.; Koirala, S.; Carvalhais, N.; Reichstein, M.: Downscaling soil moisture to sub-km resolutions with simple machine learning ensembles. Journal of Hydrology 652, 132624 (2025)

Karasante, I.; Alonso, L.; Prapas, I.; Ahuja, A.; Carvalhais, N.; Papoutsis, I.: SeasFire cube - a multivariate dataset for global wildfire modeling. Scientific Data 12, 368 (2025)

Lee, H. T.; Jung, M.; Carvalhais, N.; Reichstein, M.; Forkel, M.; Bloom, A. A.; Pacheco-Labrador, J.; Koirala, S.: Spatial attribution of temporal variability in global land-atmosphere CO₂ exchange using a model-data integration framework. Journal of Advances in Modeling Earth Systems 17 (3), e2024MS004479v (2025)

Neigh, C. S. R.; Montesano, P. M.; Sexton, J. O.; Wooten, M.; Wagner, W.; Feng, M.; Carvalhais, N.; Calle, L.; Carroll, M. L.: Russian forests show strong potential for young forest growth. Communications Earth & Environment 6, 71 (2025)

Cohrs, K.-H.; Varando, G.; Camps-Valls, G.; Carvalhais, N.; Reichstein, M.: Causal hybrid modeling with double machine learning—applications in carbon flux modeling. Machine Learning: Science and Technology 5 (3), 035021 (2024)

Wang, S.; Yang, H.; Koirala, S.; Forkel, M.; Reichstein, M.; Carvalhais, N.: Understanding disturbance regimes from patterns in modeled forest biomass. Journal of Advances in Modeling Earth Systems 16 (6), e2023MS004099 (2024)

Yang, H.; Wang, S.; Son, R.; Lee, H. T.; Benson, V.; Zhang, W.; Zhang, Y.; Zhang, Y.; Kattge, J.; Boenisch, G. et al.; Schepaschenko, D.; Karaszewski, Z.; Stereńczak, K.; Moreno-Martínez, Á.; Nabais, C.; Birnbaum, P.; Vieilledent, G.; Weber, U.; Carvalhais, N.: Global patterns of tree wood density. Global Change Biology 30 (3), e17224 (2024)

Tao, F.; Houlton, B. Z.; Frey, S. D.; Lehmann, J.; Manzoni, S.; Huang, Y.; Jiang, L.; Mishra, U.; Hungate, B. A.; Schmidt, M. W. I. et al.; Reichstein, M.; Carvalhais, N.; Ciais, P.; Wang, Y.-P.; Ahrens, B.; Hugelius, G.; Hocking, T. D.; Lu, X.; Shi, Z.; Viatkin, K.; Vargas, R.; Yigini, Y.; Omuto, C.; Malik, A. A.; Peralta, G.; Cuevas-Corona, R.; Paolo, L. E. D.; Luotto, I.; Liao, C.; Liang, Y.-S.; Saynes, V. S.; Huang, X.; Luo, Y.: Reply to: Model uncertainty obscures major driver of soil carbon. Nature 627, S. E4 - E6 (2024)

Son, R.; Stacke, T.; Gayler, V.; Nabel, J. E. M. S.; Schnur, R.; Alonso, L.; Requena Mesa, C.; Winkler, A.; Hantson, S.; Zaehle, S. et al.; Weber, U.; Carvalhais, N.: Integration of a deep-learning-based fire model into a global land surface model. Journal of Advances in Modeling Earth Systems 16 (1), e2023MS003710 (2024)

Fan, N.; Santoro, M.; Besnard, S.; Cartus, O.; Koirala, S.; Carvalhais, N.: Implications of the steady-state assumption for the global vegetation carbon turnover. Environmental Research Letters 18 (10), 104036 (2023)

Bao, S.; Alonso, L.; Wang, S.; Gensheimer, J.; De, R.; Carvalhais, N.: Toward robust parameterizations in ecosystem‐level photosynthesis models. Journal of Advances in Modeling Earth Systems 15 (8), e2022MS003464 (2023)

Tao, Feng, F.; Huang, Y.; Hungate, B. A.; Manzoni, S.; Frey, S. D.; Schmidt, M. W. I.; Reichstein, M.; Carvalhais, N.; Ciais, P.; Jiang, L. et al.; Lehmann, J.; Wang, Y.-P.; Houlton, B. Z.; Ahrens, B.; Mishra, U.; Hugelius, G.; Hocking, T. D.; Lu, X.; Shi, Z.; Viatkin, K.; Vargas, R.; Yigini, Y.; Omuto, C.; Malik, A. A.; Peralta, G.; Cuevas-Corona, R.; Di Paolo, L. E.; Luotto, I.; Liao, C.; Liang, Y.-S.; Saynes, V. S.; Huang, X.; Luo, Y.: Microbial carbon use efficiency promotes global soil carbon storage. Nature 618, S. 981 - 985 (2023)

Pacheco-Labrador, J.; de Bello, F.; Migliavacca, M.; Ma, X.; Carvalhais, N.; Wirth, C.: A generalizable normalization for assessing plant functional diversity metrics across scales from remote sensing. Methods in Ecology and Evolution 14 (8), S. 2123 - 2136 (2023)

Lee, H. T.; Jung, M.; Carvalhais, N.; Trautmann, T.; Kraft, B.; Reichstein, M.; Forkel, M.; Koirala, S.: Diagnosing modeling errors in global terrestrial water storage interannual variability. Hydrology and Earth System Sciences 27 (7), S. 1531 - 1563 (2023)

Yang, H.; Munson, S. M.; Huntingford, C.; Carvalhais, N.; Knapp, A. K.; Li, X.; Peñuelas, J.; Zscheischler, J.; Chen, A.: The detection and attribution of extreme reductions in vegetation growth across the global land surface. Global Change Biology 29 (8), S. 2351 - 2362 (2023)

Zhang, W.; Jung, M.; Migliavacca, M.; Poyatos, R.; Miralles, D. G.; El-Madany, T. S.; Galvagno, M.; Carrara, A.; Arriga, N.; Ibrom, A. et al.; Mammarella, I.; Papale, D.; Cleverly, J. R.; Liddell, M.; Wohlfahrt, G.; Markwitz, C.; Mauder, M.; Paul-Limoges, E.; Schmidt, M.; Wolf, S.; Brümmer, C.; Arain, M. A.; Fares, S.; Kato, T.; Ardö, J.; Oechel, W.; Hanson, C.; Korkiakoski, M.; Biraud, S.; Steinbrecher, R.; Billesbach, D.; Montagnani, L.; Woodgate, W.; Shao, C.; Carvalhais, N.; Reichstein, M.; Nelson, J. A.: The effect of relative humidity on eddy covariance latent heat flux measurements and its implication for partitioning into transpiration and evaporation. Agricultural and Forest Meteorology 330, 109305 (2023)

Fan, N.; Reichstein, M.; Koirala, S.; Ahrens, B.; Mahecha , M. D.; Carvalhais, N.: Global apparent temperature sensitivity of terrestrial carbon turnover modulated by hydrometeorological factors. Nature Geoscience 15, S. 989 - 994 (2022)

Bao, S.; Ibrom, A.; Wohlfahrt, G.; Koirala, S.; Migliavacca, M.; Zhang, Q.; Carvalhais, N.: Narrow but robust advantages in two-big-leaf light use efficiency models over big-leaf light use efficiency models at ecosystem level. Agricultural and Forest Meteorology 326, 109185 (2022)

Pacheco-Labrador, J.; Migliavacca, M.; Ma, X.; Mahecha, M. D.; Carvalhais, N.; Weber, U.; Benavides, R.; Bouriaud, O.; Barnoaie, I.; Coomesl, D. A. et al.; Bohn, F. J.; Kraemer, G.; Heide, U.; Huth, A.; Wirth, C.: Challenging the link between functional and spectral diversity with radiative transfer modeling and data. Remote Sensing of Environment 280, 113170 (2022)

Küçük, Ç.; Koirala, S.; Carvalhais, N.; Miralles, D. G.; Reichstein, M.; Jung, M.: Observation-based assessment of secondary water effects on seasonal vegetation decay across Africa. Frontiers in Big Data 5, 967477 (2022)