Articles | Volume 7, issue 10
https://doi.org/10.5194/bg-7-3311-2010
https://doi.org/10.5194/bg-7-3311-2010
27 Oct 2010
 | 27 Oct 2010

The use of machine learning algorithms to design a generalized simplified denitrification model

F. Oehler, J. C. Rutherford, and G. Coco

Related subject area

Biogeochemistry: Air - Land Exchange
Tropical cyclones facilitate recovery of forest leaf area from dry spells in East Asia
Yi-Ying Chen and Sebastiaan Luyssaert
Biogeosciences, 20, 349–363, https://doi.org/10.5194/bg-20-349-2023,https://doi.org/10.5194/bg-20-349-2023, 2023
Short summary
Minor contributions of daytime monoterpenes are major contributors to atmospheric reactivity
Deborah F. McGlynn, Graham Frazier, Laura E. R. Barry, Manuel T. Lerdau, Sally E. Pusede, and Gabriel Isaacman-VanWertz
Biogeosciences, 20, 45–55, https://doi.org/10.5194/bg-20-45-2023,https://doi.org/10.5194/bg-20-45-2023, 2023
Short summary
Using atmospheric observations to quantify annual biogenic carbon dioxide fluxes on the Alaska North Slope
Luke D. Schiferl, Jennifer D. Watts, Erik J. L. Larson, Kyle A. Arndt, Sébastien C. Biraud, Eugénie S. Euskirchen, Jordan P. Goodrich, John M. Henderson, Aram Kalhori, Kathryn McKain, Marikate E. Mountain, J. William Munger, Walter C. Oechel, Colm Sweeney, Yonghong Yi, Donatella Zona, and Róisín Commane
Biogeosciences, 19, 5953–5972, https://doi.org/10.5194/bg-19-5953-2022,https://doi.org/10.5194/bg-19-5953-2022, 2022
Short summary
Forest–atmosphere exchange of reactive nitrogen in a remote region – Part II: Modeling annual budgets
Pascal Wintjen, Frederik Schrader, Martijn Schaap, Burkhard Beudert, Richard Kranenburg, and Christian Brümmer
Biogeosciences, 19, 5287–5311, https://doi.org/10.5194/bg-19-5287-2022,https://doi.org/10.5194/bg-19-5287-2022, 2022
Short summary
Growth and actual leaf temperature modulate CO2 responsiveness of monoterpene emissions from holm oak in opposite ways
Michael Staudt, Juliane Daussy, Joseph Ingabire, and Nafissa Dehimeche
Biogeosciences, 19, 4945–4963, https://doi.org/10.5194/bg-19-4945-2022,https://doi.org/10.5194/bg-19-4945-2022, 2022
Short summary

Cited articles

Alpaydin, E.: Introduction to Machine Learning (Adaptive Computation and Machine Learning), The MIT Press, 2004.
Arnold, J. G. and Fohrer, N.: SWAT2000: current capabilities and research opportunities in applied watershed modelling, Hydrol. Process., 19, 563–572, 2005.
Basset-Mens, C., Anibar, L., Durand, P., and van der Werf, H. M. G.: Spatialised fate factors for nitrate in catchments: Modelling approach and implication for LCA results, Sci. Total Environ., 367, 367–382, 2006.
Beaujouan, V., Durand, P., and Ruiz, L.: Modelling the effect of the spatial distribution of agricultural practices on nitrogen fluxes in rural catchments, Ecol. Model., 137, 93–105, 2001.
Beven, K.: Prophecy, Reality and Uncertainty in Distributed Hydrological Modeling, Adv. Water Resour., 16, 41–51, 1993.
Download
Altmetrics
Final-revised paper
Preprint