Articles | Volume 9, issue 1
https://doi.org/10.5194/bg-9-141-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-9-141-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
09 Jan 2012
Research article |
| 09 Jan 2012
A new concept for simulation of vegetated land surface dynamics – Part 1: The event driven phenology model
V. Kovalskyy
Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD 57007-3510 USA
G. M. Henebry
Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD 57007-3510 USA
Related subject area
Earth System Science/Response to Global Change: Models, Holocene/Anthropocene
Frost matters: incorporating late-spring frost into a dynamic vegetation model regulates regional productivity dynamics in European beech forests
Coupling numerical models of deltaic wetlands with AirSWOT, UAVSAR, and AVIRIS-NG remote sensing data
Meteorological history of low-forest-greenness events in Europe in 2002–2022
Modelling long-term alluvial-peatland dynamics in temperate river floodplains
Variable particle size distributions reduce the sensitivity of global export flux to climate change
Climate change will cause non-analog vegetation states in Africa and commit vegetation to long-term change
Uncertainties, sensitivities and robustness of simulated water erosion in an EPIC-based global gridded crop model
Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections
The capacity of northern peatlands for long-term carbon sequestration
Towards a more complete quantification of the global carbon cycle
Modeling seasonal and vertical habitats of planktonic foraminifera on a global scale
An enhanced forest classification scheme for modeling vegetation–climate interactions based on national forest inventory data
Sensitivity of woody carbon stocks to bark investment strategy in Neotropical savannas and forests
Modelling past, present and future peatland carbon accumulation across the pan-Arctic region
Biogenic sediments from coastal ecosystems to beach–dune systems: implications for the adaptation of mixed and carbonate beaches to future sea level rise
Modelling Holocene peatland dynamics with an individual-based dynamic vegetation model
Effects of climate change and land management on soil organic carbon dynamics and carbon leaching in northwestern Europe
Quantifying regional, time-varying effects of cropland and pasture on vegetation fire
HESFIRE: a global fire model to explore the role of anthropogenic and weather drivers
Impact of human population density on fire frequency at the global scale
Evaluation of biospheric components in Earth system models using modern and palaeo-observations: the state-of-the-art
A high-resolution and harmonized model approach for reconstructing and analysing historic land changes in Europe
Analyzing precipitationsheds to understand the vulnerability of rainfall dependent regions
Alternative methods to predict actual evapotranspiration illustrate the importance of accounting for phenology – Part 2: The event driven phenology model
The influence of land cover change in the Asian monsoon region on present-day and mid-Holocene climate
Sensitivity of Holocene atmospheric CO2 and the modern carbon budget to early human land use: analyses with a process-based model
Side effects and accounting aspects of hypothetical large-scale Southern Ocean iron fertilization
Combined biogeophysical and biogeochemical effects of large-scale forest cover changes in the MPI earth system model
Projected 21st century decrease in marine productivity: a multi-model analysis
Impact of atmospheric and terrestrial CO2 feedbacks on fertilization-induced marine carbon uptake
Benjamin F. Meyer, Allan Buras, Konstantin Gregor, Lucia S. Layritz, Adriana Principe, Jürgen Kreyling, Anja Rammig, and Christian S. Zang
Biogeosciences, 21, 1355–1370, https://doi.org/10.5194/bg-21-1355-2024, https://doi.org/10.5194/bg-21-1355-2024, 2024
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Late-spring frost (LSF), critically low temperatures when trees have already flushed their leaves, results in freezing damage leaving trees with reduced ability to perform photosynthesis. Forests with a high proportion of susceptible species like European beech are particularly vulnerable. However, this process is rarely included in dynamic vegetation models (DVMs). We show that the effect on simulated productivity and biomass is substantial, warranting more widespread inclusion of LSF in DVMs.
Luca Cortese, Carmine Donatelli, Xiaohe Zhang, Justin A. Nghiem, Marc Simard, Cathleen E. Jones, Michael Denbina, Cédric G. Fichot, Joshua P. Harringmeyer, and Sergio Fagherazzi
Biogeosciences, 21, 241–260, https://doi.org/10.5194/bg-21-241-2024, https://doi.org/10.5194/bg-21-241-2024, 2024
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This study shows that numerical models in coastal areas can greatly benefit from the spatial information provided by remote sensing. Three Delft3D numerical models in coastal Louisiana are calibrated using airborne SAR and hyperspectral remote sensing products from the recent NASA Delta-X mission. The comparison with the remote sensing allows areas where the models perform better to be spatially verified and yields more representative parameters for the entire area.
Mauro Hermann, Matthias Röthlisberger, Arthur Gessler, Andreas Rigling, Cornelius Senf, Thomas Wohlgemuth, and Heini Wernli
Biogeosciences, 20, 1155–1180, https://doi.org/10.5194/bg-20-1155-2023, https://doi.org/10.5194/bg-20-1155-2023, 2023
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This study examines the multi-annual meteorological history of low-forest-greenness events in Europe's temperate and Mediterranean biome in 2002–2022. We systematically identify anomalies in temperature, precipitation, and weather systems as event precursors, with noteworthy differences between the two biomes. We also quantify the impact of the most extensive event in 2022 (37 % coverage), underlining the importance of understanding the forest–meteorology interaction in a changing climate.
Ward Swinnen, Nils Broothaerts, and Gert Verstraeten
Biogeosciences, 18, 6181–6212, https://doi.org/10.5194/bg-18-6181-2021, https://doi.org/10.5194/bg-18-6181-2021, 2021
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Here we present a new modelling framework specifically designed to simulate alluvial peat growth, taking into account the river dynamics. The results indicate that alluvial peat growth is strongly determined by the number, spacing and movement of the river channels in the floodplain, rather than by environmental changes or peat properties. As such, the amount of peat that can develop in a floodplain is strongly determined by the characteristics and dynamics of the local river network.
Shirley W. Leung, Thomas Weber, Jacob A. Cram, and Curtis Deutsch
Biogeosciences, 18, 229–250, https://doi.org/10.5194/bg-18-229-2021, https://doi.org/10.5194/bg-18-229-2021, 2021
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A global model is constrained with empirical relationships to quantify how shifts in sinking-particle sizes modulate particulate organic carbon export production changes in a warming ocean. Including the effect of dynamic particle sizes on remineralization reduces the magnitude of predicted 100-year changes in export production by ~14 %. Projections of future export could thus be improved by considering dynamic phytoplankton and particle-size-dependent remineralization depths.
Mirjam Pfeiffer, Dushyant Kumar, Carola Martens, and Simon Scheiter
Biogeosciences, 17, 5829–5847, https://doi.org/10.5194/bg-17-5829-2020, https://doi.org/10.5194/bg-17-5829-2020, 2020
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Lags caused by delayed vegetation response to changing environmental conditions can lead to disequilibrium vegetation states. Awareness of this issue is relevant for ecosystem conservation. We used the aDGVM vegetation model to quantify the difference between transient and equilibrium vegetation states in Africa during the 21st century for two potential climate trajectories. Lag times increased over time and vegetation was non-analog to any equilibrium state due to multi-lag composite states.
Tony W. Carr, Juraj Balkovič, Paul E. Dodds, Christian Folberth, Emil Fulajtar, and Rastislav Skalsky
Biogeosciences, 17, 5263–5283, https://doi.org/10.5194/bg-17-5263-2020, https://doi.org/10.5194/bg-17-5263-2020, 2020
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We generate 30-year mean water erosion estimates in global maize and wheat fields based on daily simulation outputs from an EPIC-based global gridded crop model. Evaluation against field data confirmed the robustness of the outputs for the majority of global cropland and overestimations at locations with steep slopes and strong rainfall. Additionally, we address sensitivities and uncertainties of model inputs to improve water erosion estimates in global agricultural impact studies.
Lester Kwiatkowski, Olivier Torres, Laurent Bopp, Olivier Aumont, Matthew Chamberlain, James R. Christian, John P. Dunne, Marion Gehlen, Tatiana Ilyina, Jasmin G. John, Andrew Lenton, Hongmei Li, Nicole S. Lovenduski, James C. Orr, Julien Palmieri, Yeray Santana-Falcón, Jörg Schwinger, Roland Séférian, Charles A. Stock, Alessandro Tagliabue, Yohei Takano, Jerry Tjiputra, Katsuya Toyama, Hiroyuki Tsujino, Michio Watanabe, Akitomo Yamamoto, Andrew Yool, and Tilo Ziehn
Biogeosciences, 17, 3439–3470, https://doi.org/10.5194/bg-17-3439-2020, https://doi.org/10.5194/bg-17-3439-2020, 2020
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We assess 21st century projections of marine biogeochemistry in the CMIP6 Earth system models. These models represent the most up-to-date understanding of climate change. The models generally project greater surface ocean warming, acidification, subsurface deoxygenation, and euphotic nitrate reductions but lesser primary production declines than the previous generation of models. This has major implications for the impact of anthropogenic climate change on marine ecosystems.
Georgii A. Alexandrov, Victor A. Brovkin, Thomas Kleinen, and Zicheng Yu
Biogeosciences, 17, 47–54, https://doi.org/10.5194/bg-17-47-2020, https://doi.org/10.5194/bg-17-47-2020, 2020
Miko U. F. Kirschbaum, Guang Zeng, Fabiano Ximenes, Donna L. Giltrap, and John R. Zeldis
Biogeosciences, 16, 831–846, https://doi.org/10.5194/bg-16-831-2019, https://doi.org/10.5194/bg-16-831-2019, 2019
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Globally, C is added to the atmosphere from fossil fuels and deforestation, balanced by ocean uptake and atmospheric increase. The difference (residual sink) is equated to plant uptake. But this omits cement carbonation; transport to oceans by dust; riverine organic C and volatile organics; and increased C in plastic, bitumen, wood, landfills, and lakes. Their inclusion reduces the residual sink from 3.6 to 2.1 GtC yr-1 and thus the inferred ability of the biosphere to alter human C emissions.
Kerstin Kretschmer, Lukas Jonkers, Michal Kucera, and Michael Schulz
Biogeosciences, 15, 4405–4429, https://doi.org/10.5194/bg-15-4405-2018, https://doi.org/10.5194/bg-15-4405-2018, 2018
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The fossil shells of planktonic foraminifera are widely used to reconstruct past climate conditions. To do so, information about their seasonal and vertical habitat is needed. Here we present an updated version of a planktonic foraminifera model to better understand species-specific habitat dynamics under climate change. This model produces spatially and temporally coherent distribution patterns, which agree well with available observations, and can thus aid the interpretation of proxy records.
Titta Majasalmi, Stephanie Eisner, Rasmus Astrup, Jonas Fridman, and Ryan M. Bright
Biogeosciences, 15, 399–412, https://doi.org/10.5194/bg-15-399-2018, https://doi.org/10.5194/bg-15-399-2018, 2018
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Forest management shapes forest structure and in turn surface–atmosphere interactions. We used Fennoscandian forest maps and inventory data to develop a classification system for forest structure. The classification was integrated with the ESA Climate Change Initiative land cover map to achieve complete surface representation. The result is an improved product for modeling surface–atmosphere exchanges in regions with intensively managed forests.
Anna T. Trugman, David Medvigy, William A. Hoffmann, and Adam F. A. Pellegrini
Biogeosciences, 15, 233–243, https://doi.org/10.5194/bg-15-233-2018, https://doi.org/10.5194/bg-15-233-2018, 2018
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Tree fire tolerance strategies may significantly impact woody carbon stability and the existence of tropical savannas under global climate change. We used a numerical ecosystem model to test the impacts of fire survival strategy under differing fire and rainfall regimes. We found that the high survival rate of large fire-tolerant trees reduced carbon losses with increasing fire frequency, and reduced the range of conditions leading to either complete tree loss or complete grass loss.
Nitin Chaudhary, Paul A. Miller, and Benjamin Smith
Biogeosciences, 14, 4023–4044, https://doi.org/10.5194/bg-14-4023-2017, https://doi.org/10.5194/bg-14-4023-2017, 2017
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We employed an individual- and patch-based dynamic global ecosystem model to quantify long-term C accumulation rates and to assess the effects of historical and projected climate change on peatland C balances across the pan-Arctic. We found that peatlands in Scandinavia, Europe, Russia and central and eastern Canada will become C sources, while Siberia, far eastern Russia, Alaska and western and northern Canada will increase their sink capacity by the end of the 21st century.
Giovanni De Falco, Emanuela Molinaroli, Alessandro Conforti, Simone Simeone, and Renato Tonielli
Biogeosciences, 14, 3191–3205, https://doi.org/10.5194/bg-14-3191-2017, https://doi.org/10.5194/bg-14-3191-2017, 2017
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This study quantifies the contribution of carbonate sediments, produced in seagrass meadows and in photophilic algal communities, to the sediment budget of a beach–dune system. The contribution to the beach sediment budget represents a further ecosystem service provided by seagrass. The dependence of the beach sediment budget on carbonate production associated with coastal ecosystems has implications for the adaptation of carbonate beaches to the seagrass decline and sea level rise.
Nitin Chaudhary, Paul A. Miller, and Benjamin Smith
Biogeosciences, 14, 2571–2596, https://doi.org/10.5194/bg-14-2571-2017, https://doi.org/10.5194/bg-14-2571-2017, 2017
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We incorporated peatland dynamics into
Arcticversion of dynamic vegetation model LPJ-GUESS to understand the long-term evolution of northern peatlands and effects of climate change on peatland carbon balance. We found that the Stordalen mire may be expected to sequester more carbon before 2050 due to milder and wetter climate conditions, a longer growing season and CO2 fertilization effect, turning into a C source after 2050 because of higher decomposition rates in response to warming soils.
Maria Stergiadi, Marcel van der Perk, Ton C. M. de Nijs, and Marc F. P. Bierkens
Biogeosciences, 13, 1519–1536, https://doi.org/10.5194/bg-13-1519-2016, https://doi.org/10.5194/bg-13-1519-2016, 2016
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We modelled the effects of changes in climate and land management on soil organic carbon (SOC) and dissolved organic carbon (DOC) levels in sandy and loamy soils under forest, grassland, and arable land. Climate change causes a decrease in both SOC and DOC for the agricultural systems, whereas for the forest systems, SOC slightly increases. A reduction in fertilizer application leads to a decrease in SOC and DOC levels under arable land but has a negligible effect under grassland.
S. S. Rabin, B. I. Magi, E. Shevliakova, and S. W. Pacala
Biogeosciences, 12, 6591–6604, https://doi.org/10.5194/bg-12-6591-2015, https://doi.org/10.5194/bg-12-6591-2015, 2015
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People worldwide use fire to manage agriculture, but often also suppress fire in the landscape surrounding their fields. Here, we estimate the net result of these effects of cropland and pasture on fire at a regional, monthly level. Pasture is shown, for the first time, to contribute strongly to global patterns of burning. Our results could be used to improve representations of burning in global vegetation and climate models, improving our understanding of how people affect the Earth system.
Y. Le Page, D. Morton, B. Bond-Lamberty, J. M. C. Pereira, and G. Hurtt
Biogeosciences, 12, 887–903, https://doi.org/10.5194/bg-12-887-2015, https://doi.org/10.5194/bg-12-887-2015, 2015
W. Knorr, T. Kaminski, A. Arneth, and U. Weber
Biogeosciences, 11, 1085–1102, https://doi.org/10.5194/bg-11-1085-2014, https://doi.org/10.5194/bg-11-1085-2014, 2014
A. M. Foley, D. Dalmonech, A. D. Friend, F. Aires, A. T. Archibald, P. Bartlein, L. Bopp, J. Chappellaz, P. Cox, N. R. Edwards, G. Feulner, P. Friedlingstein, S. P. Harrison, P. O. Hopcroft, C. D. Jones, J. Kolassa, J. G. Levine, I. C. Prentice, J. Pyle, N. Vázquez Riveiros, E. W. Wolff, and S. Zaehle
Biogeosciences, 10, 8305–8328, https://doi.org/10.5194/bg-10-8305-2013, https://doi.org/10.5194/bg-10-8305-2013, 2013
R. Fuchs, M. Herold, P. H. Verburg, and J. G. P. W. Clevers
Biogeosciences, 10, 1543–1559, https://doi.org/10.5194/bg-10-1543-2013, https://doi.org/10.5194/bg-10-1543-2013, 2013
P. W. Keys, R. J. van der Ent, L. J. Gordon, H. Hoff, R. Nikoli, and H. H. G. Savenije
Biogeosciences, 9, 733–746, https://doi.org/10.5194/bg-9-733-2012, https://doi.org/10.5194/bg-9-733-2012, 2012
V. Kovalskyy and G. M. Henebry
Biogeosciences, 9, 161–177, https://doi.org/10.5194/bg-9-161-2012, https://doi.org/10.5194/bg-9-161-2012, 2012
A. Dallmeyer and M. Claussen
Biogeosciences, 8, 1499–1519, https://doi.org/10.5194/bg-8-1499-2011, https://doi.org/10.5194/bg-8-1499-2011, 2011
B. D. Stocker, K. Strassmann, and F. Joos
Biogeosciences, 8, 69–88, https://doi.org/10.5194/bg-8-69-2011, https://doi.org/10.5194/bg-8-69-2011, 2011
A. Oschlies, W. Koeve, W. Rickels, and K. Rehdanz
Biogeosciences, 7, 4017–4035, https://doi.org/10.5194/bg-7-4017-2010, https://doi.org/10.5194/bg-7-4017-2010, 2010
S. Bathiany, M. Claussen, V. Brovkin, T. Raddatz, and V. Gayler
Biogeosciences, 7, 1383–1399, https://doi.org/10.5194/bg-7-1383-2010, https://doi.org/10.5194/bg-7-1383-2010, 2010
M. Steinacher, F. Joos, T. L. Frölicher, L. Bopp, P. Cadule, V. Cocco, S. C. Doney, M. Gehlen, K. Lindsay, J. K. Moore, B. Schneider, and J. Segschneider
Biogeosciences, 7, 979–1005, https://doi.org/10.5194/bg-7-979-2010, https://doi.org/10.5194/bg-7-979-2010, 2010
A. Oschlies
Biogeosciences, 6, 1603–1613, https://doi.org/10.5194/bg-6-1603-2009, https://doi.org/10.5194/bg-6-1603-2009, 2009
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