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Journal articles on the topic 'Global vegetation modelling'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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1

Claussen, Martin, Victor Brovkin, Andrey Ganopolski, Claudia Kubatzki, and Vladimir Petoukhov. "Modelling global terrestrial vegetation–climate interaction." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 353, no. 1365 (1998): 53–63. http://dx.doi.org/10.1098/rstb.1998.0190.

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By coupling an atmospheric general circulation model asynchronously with an equilibrium vegetation model, manifold equilibrium solutions of the atmosphere–biosphere system have been explored. It is found that under present–day conditions of the Earth's orbital parameters and sea–surface temperatures, two stable equilibria of vegetation patterns are possible: one corresponding to present–day sparse vegetation in the Sahel, the second solution yielding savannah which extends far into the south–western part of the Sahara. A similar picture is obtained for conditions during the last glacial maximu

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2

Peng, Changhui. "From static biogeographical model to dynamic global vegetation model: a global perspective on modelling vegetation dynamics." Ecological Modelling 135, no. 1 (2000): 33–54. http://dx.doi.org/10.1016/s0304-3800(00)00348-3.

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3

Hughes, John K., Paul J. Valdes, and Richard Betts. "Dynamics of a global-scale vegetation model." Ecological Modelling 198, no. 3-4 (2006): 452–62. http://dx.doi.org/10.1016/j.ecolmodel.2006.05.020.

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4

Zuidema, Pieter A., Benjamin Poulter, and David C. Frank. "A Wood Biology Agenda to Support Global Vegetation Modelling." Trends in Plant Science 23, no. 11 (2018): 1006–15. http://dx.doi.org/10.1016/j.tplants.2018.08.003.

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5

Levavasseur, G., M. Vrac, D. M. Roche, and D. Paillard. "Statistical modelling of a new global potential vegetation distribution." Environmental Research Letters 7, no. 4 (2012): 044019. http://dx.doi.org/10.1088/1748-9326/7/4/044019.

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6

Lüdeke, Matthias, Alex Janecek, and Gundolf H. Kohlmaier. "Modelling the seasonal CO2 uptake by land vegetation using the global vegetation index." Tellus B: Chemical and Physical Meteorology 43, no. 2 (1991): 188–96. http://dx.doi.org/10.3402/tellusb.v43i2.15263.

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7

LUDEKE, MATTHIAS, ALEX JANECEK, and GUNDOLF H. KOHLMAIER. "Modelling the seasonal CO2 uptake by land vegetation using the global vegetation index." Tellus B 43, no. 2 (1991): 188–96. http://dx.doi.org/10.1034/j.1600-0889.1991.00012.x.

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8

Berrittella, C., and J. Van Huissteden. "Uncertainties modelling CH<sub>4</sub> emissions from northern wetlands in glacial climates: the role of vegetation." Climate of the Past Discussions 6, no. 6 (2010): 2651–85. http://dx.doi.org/10.5194/cpd-6-2651-2010.

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Abstract. The PEATLAND-VU methane (CH4) model has been used to simulate emissions from continental Europe under different climatic conditions during the last glacial (LG) and modern climates. Such emissions are reactive to hydrology and the results presented by this paper demonstrate high sensitivity to vegetation parameters. Vegetation influences natural CH4 emissions and thus affects its modelling. In wetlands ecosystems various interactions between plants and CH4 do take place and each type of plant is able to affect fluxes in its own way. However, effects of vegetation factors are rarely p

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Svirezhev, Yuri, and Nicolai Zavalishin. "“Forest–grass” global vegetation model with forest age structure." Ecological Modelling 160, no. 1-2 (2003): 1–12. http://dx.doi.org/10.1016/s0304-3800(02)00212-0.

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10

Hantson, Stijn, Almut Arneth, Sandy P. Harrison, et al. "The status and challenge of global fire modelling." Biogeosciences 13, no. 11 (2016): 3359–75. http://dx.doi.org/10.5194/bg-13-3359-2016.

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Abstract. Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, using either well-founded empirical relationships or process-based models with good predictive skill. While a large variety of models exist today, it is still unclear which type of model or degree of complexity

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11

Talebiesfandarani, Somayeh, Tianjie Zhao, Jiancheng Shi, et al. "Microwave Vegetation Index from Multi-Angular Observations and Its Application in Vegetation Properties Retrieval: Theoretical Modelling." Remote Sensing 11, no. 6 (2019): 730. http://dx.doi.org/10.3390/rs11060730.

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Monitoring global vegetation dynamics is of great importance for many environmental applications. The vegetation optical depth (VOD), derived from passive microwave observation, is sensitive to the water content in all aboveground vegetation and could serve as complementary information to optical observations for global vegetation monitoring. The microwave vegetation index (MVI), which is originally derived from the zero-order model, is a potential approach to derive VOD and vegetation water content (VWC), however, it has limited application at dense vegetation in the global scale. In this stu

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12

Forkel, Matthias, Wouter Dorigo, Gitta Lasslop, Irene Teubner, Emilio Chuvieco, and Kirsten Thonicke. "A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)." Geoscientific Model Development 10, no. 12 (2017): 4443–76. http://dx.doi.org/10.5194/gmd-10-4443-2017.

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Abstract. Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easi

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13

Leishman, MR, L. Hughes, K. French, D. Armstrong, and M. Westoby. "Seed and Seedling Biology in Relation to Modelling Vegetation Dynamics Under Global Climate Change." Australian Journal of Botany 40, no. 5 (1992): 599. http://dx.doi.org/10.1071/bt9920599.

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The distribution of many plant species will change with global climate change, depending on their ability to disperse into, and establish in, new communities. Past migrations of species under climate change have been an order of magnitude slower than the rate of predicted climate change for the next century. The limited evidence available suggests that chance long distance dispersal events will be critically important in determining migration rates. The JABOWA-derived gap replacement models and vital attributes/FATE models were examined, and the dispersal and establishment processes necessary

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14

Berrittella, C., and J. van Huissteden. "Uncertainties in modelling CH<sub>4</sub> emissions from northern wetlands in glacial climates: the role of vegetation parameters." Climate of the Past 7, no. 4 (2011): 1075–87. http://dx.doi.org/10.5194/cp-7-1075-2011.

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Abstract. Marine Isotope Stage 3 (MIS 3) interstadials are marked by a sharp increase in the atmospheric methane (CH4) concentration, as recorded in ice cores. Wetlands are assumed to be the major source of this CH4, although several other hypotheses have been advanced. Modelling of CH4 emissions is crucial to quantify CH4 sources for past climates. Vegetation effects are generally highly generalized in modelling past and present-day CH4 fluxes, but should not be neglected. Plants strongly affect the soil-atmosphere exchange of CH4 and the net primary production of the vegetation supplies orga

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Loptson, C. A., D. J. Lunt, and J. E. Francis. "Investigating vegetation-climate feedbacks during the early Eocene." Climate of the Past Discussions 9, no. 4 (2013): 4705–44. http://dx.doi.org/10.5194/cpd-9-4705-2013.

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Abstract. Evidence suggests that the early Eocene was a time of extreme global warmth, extending to the high latitudes. However, there are discrepancies between the results of many previous modelling studies and the proxy data at high latitudes, with models struggling to simulate the shallow temperature gradients of this time period to the same extent as the proxies indicate. Vegetation-climate feedbacks play an important role in the present day, but are often neglected in paleoclimate modelling studies and this may be a contributing factor to resolving the model-data discrepancy. Here we inve

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Loptson, C. A., D. J. Lunt, and J. E. Francis. "Investigating vegetation–climate feedbacks during the early Eocene." Climate of the Past 10, no. 2 (2014): 419–36. http://dx.doi.org/10.5194/cp-10-419-2014.

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Abstract. Evidence suggests that the early Eocene was a time of extreme global warmth. However, there are discrepancies between the results of many previous modelling studies and the proxy data at high latitudes, with models struggling to simulate the shallow temperature gradients of this time period to the same extent as the proxies indicate. Vegetation–climate feedbacks play an important role in the present day, but are often neglected in these palaeoclimate modelling studies, and this may be a contributing factor to resolving the model–data discrepancy. Here we investigate these vegetation–

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17

Svirezhev, Yuri M. "Ecosystem as a text: information analysis of the global vegetation pattern." Ecological Modelling 174, no. 1-2 (2004): 19–24. http://dx.doi.org/10.1016/j.ecolmodel.2003.12.041.

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18

Volařík, D. "Application of digital elevation model for mapping vegetation tiers." Journal of Forest Science 56, No. 3 (2010): 112–20. http://dx.doi.org/10.17221/74/2009-jfs.

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The aim of this paper is to explore possibilities of application of digital elevation model for mapping vegetation tiers (altitudinal vegetation zones). Linear models were used to investigate the relationship between vegetation tiers and variables derived from a digital elevation model – elevation and potential global radiation. The model was based on a sample of 138 plots located from the 2<SUP>nd</SUP> to the 5<SUP>th</SUP> vegetation tier. Potential global radiation was computed in r.sun module in geographic information system GRASS. The final model explained 84% of

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19

Woodward, F. I., M. R. Lomas, and R. A. Betts. "Vegetation-climate feedbacks in a greenhouse world." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 353, no. 1365 (1998): 29–39. http://dx.doi.org/10.1098/rstb.1998.0188.

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The potential for feedbacks between terrestrial vegetation, climate, and the atmospheric CO 2 partial pressure have been addressed by modelling. Previous research has established that under global warming and CO 2 enrichment, the stomatal conductance of vegetation tends to decrease, causing a warming effect on top of the driving change in greenhouse warming. At the global scale, this positive feedback is ultimately changed to a negative feedback through changes in vegetation structure. In spatial terms this structural feedback has a variable geographical pattern in terms of magnitude and sign.

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20

Zhang, Dou, Xiaolei Geng, Wanxu Chen, et al. "Inconsistency of Global Vegetation Dynamics Driven by Climate Change: Evidences from Spatial Regression." Remote Sensing 13, no. 17 (2021): 3442. http://dx.doi.org/10.3390/rs13173442.

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Global greening over the past 30 years since 1980s has been confirmed by numerous studies. However, a single-dimensional indicator and non-spatial modelling approaches might exacerbate uncertainties in our understanding of global change. Thus, comprehensive monitoring for vegetation’s various properties and spatially explicit models are required. In this study, we used the newest enhanced vegetation index (EVI) products of Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 to detect the inconsistency trend of annual peak and average global vegetation growth using the Mann–Kenda

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21

Svirezhev, Yuri M., and Werner von Bloh. "A zero-dimensional climate-vegetation model containing global carbon and hydrological cycle." Ecological Modelling 106, no. 2-3 (1998): 119–27. http://dx.doi.org/10.1016/s0304-3800(97)00187-7.

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22

Pavlick, R., D. T. Drewry, K. Bohn, B. Reu, and A. Kleidon. "The Jena Diversity-Dynamic Global Vegetation Model (JeDi-DGVM): a diverse approach to representing terrestrial biogeography and biogeochemistry based on plant functional trade-offs." Biogeosciences 10, no. 6 (2013): 4137–77. http://dx.doi.org/10.5194/bg-10-4137-2013.

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Abstract. Terrestrial biosphere models typically abstract the immense diversity of vegetation forms and functioning into a relatively small set of predefined semi-empirical plant functional types (PFTs). There is growing evidence, however, from the field ecology community as well as from modelling studies that current PFT schemes may not adequately represent the observed variations in plant functional traits and their effect on ecosystem functioning. In this paper, we introduce the Jena Diversity-Dynamic Global Vegetation Model (JeDi-DGVM) as a new approach to terrestrial biosphere modelling w

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23

Braakhekke, Maarten C., Karin T. Rebel, Stefan C. Dekker, Benjamin Smith, Arthur H. W. Beusen, and Martin J. Wassen. "Nitrogen leaching from natural ecosystems under global change: a modelling study." Earth System Dynamics 8, no. 4 (2017): 1121–39. http://dx.doi.org/10.5194/esd-8-1121-2017.

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Abstract. To study global nitrogen (N) leaching from natural ecosystems under changing N deposition, climate, and atmospheric CO2, we performed a factorial model experiment for the period 1901–2006 with the N-enabled global terrestrial ecosystem model LPJ-GUESS (Lund–Potsdam–Jena General Ecosystem Simulator). In eight global simulations, we used either the true transient time series of N deposition, climate, and atmospheric CO2 as input or kept combinations of these drivers constant at initial values. The results show that N deposition is globally the strongest driver of simulated N leaching,

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24

Perkins, William A., Zhuoran Duan, Ning Sun, et al. "Parallel Distributed Hydrology Soil Vegetation Model (DHSVM) using global arrays." Environmental Modelling & Software 122 (December 2019): 104533. http://dx.doi.org/10.1016/j.envsoft.2019.104533.

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25

Prentice, I. Colin, Martin T. Sykes, Michael Lautenschlager, Sandy P. Harrison, Olga Denissenko, and Patrick J. Bartlein. "Modelling Global Vegetation Patterns and Terrestrial Carbon Storage at the Last Glacial Maximum." Global Ecology and Biogeography Letters 3, no. 3 (1993): 67. http://dx.doi.org/10.2307/2997548.

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26

Xia, Li. "Modelling the Response of Vegetation in North-East China Transect to Global Change." Journal of Biogeography 22, no. 2/3 (1995): 515. http://dx.doi.org/10.2307/2845949.

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27

Forrest, M., J. T. Eronen, T. Utescher, et al. "Climate-vegetation modelling and fossil plant data suggest low atmospheric CO<sub>2</sub> in the late Miocene." Climate of the Past 11, no. 12 (2015): 1701–32. http://dx.doi.org/10.5194/cp-11-1701-2015.

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Abstract. There is an increasing need to understand the pre-Quaternary warm climates, how climate–vegetation interactions functioned in the past, and how we can use this information to understand the present. Here we report vegetation modelling results for the Late Miocene (11–7 Ma) to study the mechanisms of vegetation dynamics and the role of different forcing factors that influence the spatial patterns of vegetation coverage. One of the key uncertainties is the atmospheric concentration of CO2 during past climates. Estimates for the last 20 million years range from 280 to 500 ppm. We simula

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Forrest, M., J. T. Eronen, T. Utescher, et al. "Climate–vegetation modelling and fossil plant data suggest low atmospheric CO<sub>2</sub> in the late Miocene." Climate of the Past Discussions 11, no. 3 (2015): 2239–79. http://dx.doi.org/10.5194/cpd-11-2239-2015.

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Abstract. There is increasing need to understand the pre-Quaternary warm climates, how climate–vegetation interactions functioned in the past, and how we can use this information for understanding the present. Here we report vegetation modelling results for the Late Miocene (11–7 Ma) to study the mechanisms of vegetation dynamics and the role of different forcing factors that influence the spatial patterns of vegetation coverage. One of the key uncertainties is the atmospheric concentration of CO2 during past climates. Estimates for the last 20 million years range from 280 to 500 ppm. We simul

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29

Dupont, Sylvain, Yves Brunet, and Nathalie Jarosz. "Eulerian modelling of pollen dispersal over heterogeneous vegetation canopies." Agricultural and Forest Meteorology 141, no. 2-4 (2006): 82–104. http://dx.doi.org/10.1016/j.agrformet.2006.09.004.

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30

Quillet, Anne, Changhui Peng, and Michelle Garneau. "Toward dynamic global vegetation models for simulating vegetation–climate interactions and feedbacks: recent developments, limitations, and future challenges." Environmental Reviews 18, NA (2010): 333–53. http://dx.doi.org/10.1139/a10-016.

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There is a lack in representation of biosphere–atmosphere interactions in current climate models. To fill this gap, one may introduce vegetation dynamics in surface transfer schemes or couple global climate models (GCMs) with vegetation dynamics models. As these vegetation dynamics models were not designed to be included in GCMs, how are the latest generation dynamic global vegetation models (DGVMs) suitable for use in global climate studies? This paper reviews the latest developments in DGVM modelling as well as the development of DGVM–GCM coupling in the framework of global climate studies.

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31

Upchurch, Garland R., Bette L. Otto-Bliesner, and Christopher Scotese. "Vegetation–atmosphere interactions and their role in global warming during the latest Cretaceous." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 353, no. 1365 (1998): 97–112. http://dx.doi.org/10.1098/rstb.1998.0194.

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Forest vegetation has the ability to warm Recent climate by its effects on albedo and atmospheric water vapour, but the role of vegetation in warming climates of the geologic past is poorly understood. This study evaluates the role of forest vegetation in maintaining warm climates of the Late Cretaceous by (1) reconstructing global palaeovegetation for the latest Cretaceous (Maastrichtian); (2) modelling latest Cretaceous climate under unvegetated conditions and different distributions of palaeovegetation; and (3) comparing model output with a global database of palaeoclimatic indicators. Simu

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von Bloh, W., S. Rost, D. Gerten, and W. Lucht. "Efficient parallelization of a dynamic global vegetation model with river routing." Environmental Modelling & Software 25, no. 6 (2010): 685–90. http://dx.doi.org/10.1016/j.envsoft.2009.11.012.

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33

Butler, Adam, Ruth M. Doherty, and Glenn Marion. "Model averaging to combine simulations of future global vegetation carbon stocks." Environmetrics 20, no. 7 (2009): 791–811. http://dx.doi.org/10.1002/env.953.

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34

Murray, S. J., P. N. Foster, and I. C. Prentice. "Evaluation of global continental hydrology as simulated by the Land-surface Processes and eXchanges Dynamic Global Vegetation Model." Hydrology and Earth System Sciences Discussions 7, no. 4 (2010): 4219–51. http://dx.doi.org/10.5194/hessd-7-4219-2010.

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Abstract. Global freshwater resources are sensitive to changes in climate, land cover and population density and distribution. The Land-surface Processes and eXchanges Dynamic Global Vegetation Model (LPX-DGVM) is a development of the Lund-Potsdam-Jena model with improved representation of fire-vegetation interactions. It allows simultaneous consideration of the effects of changes in climate, CO2 concentration, natural vegetation and fire regime shifts on the continental hydrological cycle. Here the model is assessed for its ability to simulate large-scale spatial and temporal runoff patterns,

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35

Beerling, D. J., F. I. Woodward, M. R. Lomas, M. A. Wills, W. P. Quick, and P. J. Valdes. "The influence of Carboniferous palaeoatmospheres on plant function: an experimental and modelling assessment." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 353, no. 1365 (1998): 131–40. http://dx.doi.org/10.1098/rstb.1998.0196.

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Geochemical models of atmospheric evolution predict that during the late Carboniferous, ca . 300 Ma, atmospheric oxygen and carbon dioxide concentrations were 35% and 0.03%, respectively. Both gases compete with each other for ribulose–1,5–bisphosphate carboxylase/oxygenase–the primary C–fixing enzyme in C 3 land plants: and the absolute concentrations and the ratio of the two in the atmosphere have the potential to strongly influence land–plant function. The Carboniferous therefore represents an era of potentially strong feedback between atmospheric composition and plant function. We assessed

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36

Jørgensen, Sven Erik. "The role of terrestrial vegetation in the global carbon cycle: Measurement by remote sensing." Ecological Modelling 34, no. 1-2 (1986): 133–34. http://dx.doi.org/10.1016/0304-3800(86)90083-9.

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37

Beerling, D. J. "Long‐term responses of boreal vegetation to global change: an experimental and modelling investigation." Global Change Biology 5, no. 1 (1999): 55–74. http://dx.doi.org/10.1046/j.1365-2486.1998.00209.x.

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38

Willeit, M., A. Ganopolski, and G. Feulner. "On the effect of orbital forcing on mid-Pliocene climate, vegetation and ice sheets." Climate of the Past 9, no. 4 (2013): 1749–59. http://dx.doi.org/10.5194/cp-9-1749-2013.

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Abstract. We present results from modelling of the mid-Pliocene warm period (3.3–3 million years ago) using the Earth system model of intermediate complexity CLIMBER-2 analysing the effect of changes in boundary conditions as well as of orbital forcing on climate. First we performed equilibrium experiments following the PlioMIP (Pliocene Model Intercomparison Project) protocol with a CO2 concentration of 405 ppm, reconstructed mid-Pliocene orography and vegetation and a present-day orbital configuration. Simulated global Pliocene warming is about 2.5 °C, fully consistent with results of atmosp

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Murray, S. J., P. N. Foster, and I. C. Prentice. "Evaluation of global continental hydrology as simulated by the Land-surface Processes and eXchanges Dynamic Global Vegetation Model." Hydrology and Earth System Sciences 15, no. 1 (2011): 91–105. http://dx.doi.org/10.5194/hess-15-91-2011.

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Abstract. Global freshwater resources are sensitive to changes in climate, land cover and population density and distribution. The Land-surface Processes and eXchanges Dynamic Global Vegetation Model is a recent development of the Lund-Potsdam-Jena model with improved representation of fire-vegetation interactions. It allows simultaneous consideration of the effects of changes in climate, CO2 concentration, natural vegetation and fire regime shifts on the continental hydrological cycle. Here the model is assessed for its ability to simulate large-scale spatial and temporal runoff patterns, in

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40

Wang, H., I. C. Prentice, and J. Ni. "Data-based modelling and environmental sensitivity of vegetation in China." Biogeosciences 10, no. 9 (2013): 5817–30. http://dx.doi.org/10.5194/bg-10-5817-2013.

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Abstract. A process-oriented niche specification (PONS) model was constructed to quantify climatic controls on the distribution of ecosystems, based on the vegetation map of China. PONS uses general hypotheses about bioclimatic controls to provide a "bridge" between statistical niche models and more complex process-based models. Canonical correspondence analysis provided an overview of relationships between the abundances of 55 plant communities in 0.1° grid cells and associated mean values of 20 predictor variables. Of these, GDD0 (accumulated degree days above 0 °C), Cramer–Prentice α (an es

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Wang, H., I. C. Prentice, and J. Ni. "Data-based modelling and environmental sensitivity of vegetation in China." Biogeosciences Discussions 10, no. 1 (2013): 49–81. http://dx.doi.org/10.5194/bgd-10-49-2013.

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Abstract. A process-oriented niche specification (PONS) model was constructed to quantify climatic controls on the distribution of ecosystems, based on the vegetation map of China. PONS uses general hypotheses about bioclimatic controls to provide a "bridge" between statistical niche models and more complex process-based models. Canonical correspondence analysis provided an overview of relationships between the abundances of 55 plant communities in 0.1° grid cells and associated mean values of 20 predictor variables. Of these, GDD (accumulated degree days above 0 °C) Cramer–Prentice α (an esti

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42

Burton, Chantelle, Richard Betts, Manoel Cardoso, et al. "Representation of fire, land-use change and vegetation dynamics in the Joint UK Land Environment Simulator vn4.9 (JULES)." Geoscientific Model Development 12, no. 1 (2019): 179–93. http://dx.doi.org/10.5194/gmd-12-179-2019.

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Abstract. Disturbance of vegetation is a critical component of land cover, but is generally poorly constrained in land surface and carbon cycle models. In particular, land-use change and fire can be treated as large-scale disturbances without full representation of their underlying complexities and interactions. Here we describe developments to the land surface model JULES (Joint UK Land Environment Simulator) to represent land-use change and fire as distinct processes which interact with simulated vegetation dynamics. We couple the fire model INFERNO (INteractive Fire and Emission algoRithm f

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Lehsten, V., P. Harmand, I. Palumbo, and A. Arneth. "Modelling burned area in Africa." Biogeosciences Discussions 7, no. 3 (2010): 4385–424. http://dx.doi.org/10.5194/bgd-7-4385-2010.

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Abstract. The simulation of current and projected wildfires is crucial for predicting vegetation as well as pyrogenic emissions in the African continent. This study uses a data-driven approach to parameterize burned area models applicable to dynamic vegetation models (DVMs) and global circulation models (GCMs). Therefore we restricted our analysis to variables for which either projections based on climate scenarios are available, or which are calculated by DVMs and the spatial scale to one degree spatial resolution, a common scale for DVMs as well as GCMs. We used 9 years of data (2000–2008) f

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44

Woillez, M. N., M. Kageyama, G. Krinner, N. de Noblet-Ducoudré, N. Viovy, and M. Mancip. "Impact of CO<sub>2</sub> and climate on the Last Glacial Maximum vegetation." Climate of the Past Discussions 7, no. 1 (2011): 1–46. http://dx.doi.org/10.5194/cpd-7-1-2011.

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Abstract. Vegetation reconstructions from pollen data for the Last Glacial Maximum (LGM), 21 kyr ago, reveal lanscapes radically different from the modern ones, with, in particular, a massive regression of forested areas in both hemispheres. Two main factors have to be taken into account to explain these changes in comparison to today's potential vegetation: a generally cooler and drier climate and a lower level of atmospheric CO2. In order to assess the relative impact of climate and atmospheric CO2 changes on the global vegetation, we simulate the potential modern vegetation and the glacial

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Woillez, M. N., M. Kageyama, G. Krinner, N. de Noblet-Ducoudré, N. Viovy, and M. Mancip. "Impact of CO<sub>2</sub> and climate on the Last Glacial Maximum vegetation: results from the ORCHIDEE/IPSL models." Climate of the Past 7, no. 2 (2011): 557–77. http://dx.doi.org/10.5194/cp-7-557-2011.

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Abstract. Vegetation reconstructions from pollen data for the Last Glacial Maximum (LGM), 21 ky ago, reveal lanscapes radically different from the modern ones, with, in particular, a massive regression of forested areas in both hemispheres. Two main factors have to be taken into account to explain these changes in comparison to today's potential vegetation: a generally cooler and drier climate and a lower level of atmospheric CO2. In order to assess the relative impact of climate and atmospheric CO2 changes on the global vegetation, we simulate the potential modern vegetation and the glacial v

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Wang-Erlandsson, Lan, Wim G. M. Bastiaanssen, Hongkai Gao, et al. "Global root zone storage capacity from satellite-based evaporation." Hydrology and Earth System Sciences 20, no. 4 (2016): 1459–81. http://dx.doi.org/10.5194/hess-20-1459-2016.

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Abstract. This study presents an "Earth observation-based" method for estimating root zone storage capacity – a critical, yet uncertain parameter in hydrological and land surface modelling. By assuming that vegetation optimises its root zone storage capacity to bridge critical dry periods, we were able to use state-of-the-art satellite-based evaporation data computed with independent energy balance equations to derive gridded root zone storage capacity at global scale. This approach does not require soil or vegetation information, is model independent, and is in principle scale independent. In

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Kucharik, Christopher J., Carol C. Barford, Mustapha El Maayar, Steven C. Wofsy, Russell K. Monson, and Dennis D. Baldocchi. "A multiyear evaluation of a Dynamic Global Vegetation Model at three AmeriFlux forest sites: Vegetation structure, phenology, soil temperature, and CO2 and H2O vapor exchange." Ecological Modelling 196, no. 1-2 (2006): 1–31. http://dx.doi.org/10.1016/j.ecolmodel.2005.11.031.

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Landry, Jean-Sébastien, Lael Parrott, David T. Price, Navin Ramankutty, and H. Damon Matthews. "Modelling long-term impacts of mountain pine beetle outbreaks on merchantable biomass, ecosystem carbon, albedo, and radiative forcing." Biogeosciences 13, no. 18 (2016): 5277–95. http://dx.doi.org/10.5194/bg-13-5277-2016.

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Abstract. The ongoing major outbreak of mountain pine beetle (MPB) in forests of western North America has led to considerable research efforts. However, many questions remain unaddressed regarding its long-term impacts, especially when accounting for the range of possible responses from the non-target vegetation (i.e., deciduous trees and lower-canopy shrubs and grasses). We used the Integrated BIosphere Simulator (IBIS) process-based ecosystem model along with the recently incorporated Marauding Insect Module (MIM) to quantify, over 240 years, the impacts of various MPB outbreak regimes on l

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Chen, Qiuwen, Fei Ye, and Weifeng Li. "Cellular-automata-based ecological and ecohydraulics modelling." Journal of Hydroinformatics 11, no. 3-4 (2009): 252–65. http://dx.doi.org/10.2166/hydro.2009.026.

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Spatially lumped models may fail to take into account the effects of spatial heterogeneity and local interactions. These properties sometimes are crucial to the dynamics and evolutions of ecosystems. This paper started from the fundamental aspects of CA and focused on the development and application of the approach to ecological and ecohydraulics modelling. Application cases include modelling of prey–predator dynamics by stochastic CA and simulation of riparian vegetation successions in a regulated river by rule-based CA. The results indicated that spatially explicit paradigms such as cellular

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Kittel, T. G. F., W. L. Steffen, and F. S. Chapin. "Global and regional modelling of Arctic-boreal vegetation distribution and its sensitivity to altered forcing." Global Change Biology 6, S1 (2000): 1–18. http://dx.doi.org/10.1046/j.1365-2486.2000.06011.x.

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