Articles | Volume 14, issue 4
https://doi.org/10.5194/essd-14-1735-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-1735-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
13 Apr 2022
Data description paper |
| 13 Apr 2022
| 13 Apr 2022
High-resolution land use and land cover dataset for regional climate modelling: a plant functional type map for Europe 2015
Vanessa Reinhart
CORRESPONDING AUTHOR
Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Fischertwiete 1, 20095 Hamburg, Germany
Institute of Geography, Section Physical Geography, Center for Earth System Research and Sustainability (CEN), Cluster of Excellence “Climate, Climatic Change, and Society” (CLICCS), Universität Hamburg, Bundesstraße 55, 20146 Hamburg, Germany
Peter Hoffmann
Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Fischertwiete 1, 20095 Hamburg, Germany
Institute of Geography, Section Physical Geography, Center for Earth System Research and Sustainability (CEN), Cluster of Excellence “Climate, Climatic Change, and Society” (CLICCS), Universität Hamburg, Bundesstraße 55, 20146 Hamburg, Germany
Diana Rechid
Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Fischertwiete 1, 20095 Hamburg, Germany
Jürgen Böhner
Institute of Geography, Section Physical Geography, Center for Earth System Research and Sustainability (CEN), Cluster of Excellence “Climate, Climatic Change, and Society” (CLICCS), Universität Hamburg, Bundesstraße 55, 20146 Hamburg, Germany
Benjamin Bechtel
Department of Geography, Ruhr-Universität Bochum, Universitätsstraße 150/Gebäude IA, 44801 Bochum, Germany
Related authors
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data, 15, 3819–3852, https://doi.org/10.5194/essd-15-3819-2023, https://doi.org/10.5194/essd-15-3819-2023, 2023
Short summary
Short summary
This paper introduces the new high-resolution land use and land cover change dataset LUCAS LUC for Europe (version 1.1), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-252, https://doi.org/10.5194/essd-2021-252, 2021
Manuscript not accepted for further review
Short summary
Short summary
This paper introduces the new high-resolution land-use land-cover change dataset LUCAS LUC historical and future land use and land cover change dataset (Version 1.0), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Joni-Pekka Pietikäinen, Kevin Sieck, Lars Buntemeyer, Thomas Frisius, Christine Nam, Peter Hoffmann, Christina Pop, Diana Rechid, and Daniela Jacob
EGUsphere, https://doi.org/10.5194/egusphere-2025-1586, https://doi.org/10.5194/egusphere-2025-1586, 2025
Short summary
Short summary
This paper introduces REMO2020, a modernized version of the well-known and widely used REMO regional climate model. We demonstrate why REMO2020 will be our new model version for future dynamical downscaling activities. It outperforms our previous model version in many analyzed areas and is the biggest update to REMO so far. It also supports climate service needs based developments through new more modular structure.
Florian Knutzen, Paul Averbeck, Caterina Barrasso, Laurens M. Bouwer, Barry Gardiner, José M. Grünzweig, Sabine Hänel, Karsten Haustein, Marius Rohde Johannessen, Stefan Kollet, Mortimer M. Müller, Joni-Pekka Pietikäinen, Karolina Pietras-Couffignal, Joaquim G. Pinto, Diana Rechid, Efi Rousi, Ana Russo, Laura Suarez-Gutierrez, Sarah Veit, Julian Wendler, Elena Xoplaki, and Daniel Gliksman
Nat. Hazards Earth Syst. Sci., 25, 77–117, https://doi.org/10.5194/nhess-25-77-2025, https://doi.org/10.5194/nhess-25-77-2025, 2025
Short summary
Short summary
Our research, involving 22 European scientists, investigated drought and heat impacts on forests in 2018–2022. Findings reveal that climate extremes are intensifying, with central Europe being most severely impacted. The southern region showed resilience due to historical drought exposure, while northern and Alpine areas experienced emerging or minimal impacts. The study highlights the need for region-specific strategies, improved data collection, and sustainable practices to safeguard forests.
Jan Wohland, Peter Hoffmann, Daniela C. A. Lima, Marcus Breil, Olivier Asselin, and Diana Rechid
Earth Syst. Dynam., 15, 1385–1400, https://doi.org/10.5194/esd-15-1385-2024, https://doi.org/10.5194/esd-15-1385-2024, 2024
Short summary
Short summary
We evaluate how winds change when humans grow or cut down forests. Our analysis draws from climate model simulations with extreme scenarios where Europe is either fully forested or covered with grass. We find that the effect of land use change on wind energy is very important: wind energy potentials are twice as high above grass as compared to forest in some locations. Our results imply that wind profile changes should be better incorporated in climate change assessments for wind energy.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, https://doi.org/10.5194/gmd-16-7311-2023, 2023
Short summary
Short summary
Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data, 15, 3819–3852, https://doi.org/10.5194/essd-15-3819-2023, https://doi.org/10.5194/essd-15-3819-2023, 2023
Short summary
Short summary
This paper introduces the new high-resolution land use and land cover change dataset LUCAS LUC for Europe (version 1.1), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Lennart Marien, Mahyar Valizadeh, Wolfgang zu Castell, Christine Nam, Diana Rechid, Alexandra Schneider, Christine Meisinger, Jakob Linseisen, Kathrin Wolf, and Laurens M. Bouwer
Nat. Hazards Earth Syst. Sci., 22, 3015–3039, https://doi.org/10.5194/nhess-22-3015-2022, https://doi.org/10.5194/nhess-22-3015-2022, 2022
Short summary
Short summary
Myocardial infarctions (MIs; heart attacks) are influenced by temperature extremes, air pollution, lack of green spaces and ageing population. Here, we apply machine learning (ML) models in order to estimate the influence of various environmental and demographic risk factors. The resulting ML models can accurately reproduce observed annual variability in MI and inter-annual trends. The models allow quantification of the importance of individual factors and can be used to project future risk.
Matthias Demuzere, Jonas Kittner, Alberto Martilli, Gerald Mills, Christian Moede, Iain D. Stewart, Jasper van Vliet, and Benjamin Bechtel
Earth Syst. Sci. Data, 14, 3835–3873, https://doi.org/10.5194/essd-14-3835-2022, https://doi.org/10.5194/essd-14-3835-2022, 2022
Short summary
Short summary
Because urban areas are key contributors to climate change but are also susceptible to multiple hazards, one needs spatially detailed information on urban landscapes to support environmental services. This global local climate zone map describes this much-needed intra-urban heterogeneity across the whole surface of the earth in a universal language and can serve as a basic infrastructure to study e.g. environmental hazards, energy demand, and climate adaptation and mitigation solutions.
Swantje Preuschmann, Tanja Blome, Knut Görl, Fiona Köhnke, Bettina Steuri, Juliane El Zohbi, Diana Rechid, Martin Schultz, Jianing Sun, and Daniela Jacob
Adv. Sci. Res., 19, 51–71, https://doi.org/10.5194/asr-19-51-2022, https://doi.org/10.5194/asr-19-51-2022, 2022
Short summary
Short summary
The main aspect of the paper is to obtain transferable principles for the development of digital knowledge transfer products. As such products are still unstandardised, the authors explored challenges and approaches for product developments. The authors report what they see as useful principles for developing digital knowledge transfer products, by describing the experience of developing the Net-Zero-2050 Web-Atlas and the "Bodenkohlenstoff-App".
Priscilla A. Mooney, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Natalie de Noblet-Ducoudré, Marcus Breil, Rita M. Cardoso, Anne Sophie Daloz, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Susanna Strada, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 1383–1397, https://doi.org/10.5194/tc-16-1383-2022, https://doi.org/10.5194/tc-16-1383-2022, 2022
Short summary
Short summary
We use multiple regional climate models to show that afforestation in sub-polar and alpine regions reduces the radiative impact of snow albedo on the atmosphere, reduces snow cover, and delays the start of the snowmelt season. This is important for local communities that are highly reliant on snowpack for water resources and winter tourism. However, models disagree on the amount of change particularly when snow is melting. This shows that more research is needed on snow–vegetation interactions.
Giannis Sofiadis, Eleni Katragkou, Edouard L. Davin, Diana Rechid, Nathalie de Noblet-Ducoudre, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Lisa Jach, Ronny Meier, Priscilla A. Mooney, Pedro M. M. Soares, Susanna Strada, Merja H. Tölle, and Kirsten Warrach Sagi
Geosci. Model Dev., 15, 595–616, https://doi.org/10.5194/gmd-15-595-2022, https://doi.org/10.5194/gmd-15-595-2022, 2022
Short summary
Short summary
Afforestation is currently promoted as a greenhouse gas mitigation strategy. In our study, we examine the differences in soil temperature and moisture between grounds covered either by forests or grass. The main conclusion emerged is that forest-covered grounds are cooler but drier than open lands in summer. Therefore, afforestation disrupts the seasonal cycle of soil temperature, which in turn could trigger changes in crucial chemical processes such as soil carbon sequestration.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-252, https://doi.org/10.5194/essd-2021-252, 2021
Manuscript not accepted for further review
Short summary
Short summary
This paper introduces the new high-resolution land-use land-cover change dataset LUCAS LUC historical and future land use and land cover change dataset (Version 1.0), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Kevin Sieck, Christine Nam, Laurens M. Bouwer, Diana Rechid, and Daniela Jacob
Earth Syst. Dynam., 12, 457–468, https://doi.org/10.5194/esd-12-457-2021, https://doi.org/10.5194/esd-12-457-2021, 2021
Short summary
Short summary
This paper presents new estimates of future extreme weather in Europe, including extreme heat, extreme rainfall and meteorological drought. These new estimates were achieved by repeating model calculations many times, thereby reducing uncertainties of these rare events at low levels of global warming at 1.5 and 2 °C above
pre-industrial temperature levels. These results are important, as they help to assess which weather extremes could increase at moderate warming levels and where.
Marcus Breil, Edouard L. Davin, and Diana Rechid
Biogeosciences, 18, 1499–1510, https://doi.org/10.5194/bg-18-1499-2021, https://doi.org/10.5194/bg-18-1499-2021, 2021
Short summary
Short summary
The physical processes behind varying evapotranspiration rates in forests and grasslands in Europe are investigated in a regional model study with idealized afforestation scenarios. The results show that the evapotranspiration response to afforestation depends on the interplay of two counteracting factors: the transpiration facilitating characteristics of a forest and the reduced saturation deficits of forests caused by an increased surface roughness and associated lower surface temperatures.
Cited articles
Alkama, R. and Cescatti, A.: Biophysical climate impacts of recent changes in
global forest cover, Science, 351, 600–604, 2016. a
Anderegg, L. D. L., Griffith, D. M., Cavender-Bares, J., Riley, W. J., Berry,
J. A., Dawson, T. E., and Still, C. J.: Representing plant diversity in land
models: An evolutionary approach to make “Functional Types” more
functional, Glob. Change Biol., 28, 2541–2554,
https://doi.org/10.1111/gcb.16040, 2021. a
Bégué, A., Arvor, D., Bellon, B., Betbeder, J., De Abelleyra, D.,
Ferraz, R. P. D., Lebourgeois, V., Lelong, C., Simões, M., and Verón,
S. R.: Remote sensing and cropping practices: A review, Remote Sensing, 10, 99, https://doi.org/10.3390/rs10010099,
2018. a
Belda, M., Halenka, T., Huszar, P., Karlicky, J., and Nováková, T.: Do
we need urban parameterization in high resolution regional climate
simulations?, in: AGU Fall Meeting Abstracts, 2018AGUFM.A21L2878B,
2018. a
Bojinski, S., Verstraete, M., Peterson, T. C., Richter, C., Simmons, A., and
Zemp, M.: The concept of essential climate variables in support of climate
research, applications, and policy, B. Am. Meteorol.
Soc., 95, 1431–1443, 2014. a
Bonan, G. B.: Forests and climate change: forcings, feedbacks, and the climate
benefits of forests, Science, 320, 1444–1449, 2008. a
Bonan, G. B., Levis, S., Kergoat, L., and Oleson, K. W.: Landscapes as patches
of plant functional types: An integrating concept for climate and ecosystem
models, Global Biogeochem. Cy., 16, 5-1–5-23, https://doi.org/10.1029/2000GB001360, 2002. a, b
Bontemps S., Defourny P., Radoux J., Van Bogaert E., Lamarche C., Achard F., Mayaux P., Boettcher M., Brockmann C., Kirches G., Zülkhe M., Kalogirou V., Seifert F.M., and Arino O.: Consistent
global land cover maps for climate modelling communities: current
achievements of the ESA’s land cover CCI, in: Proceedings of the ESA living
planet symposium, Edinburgh, 9–13 September 2013, pp. 9–13, 2013. a
Box, E. O.: Plant functional types and climate at the global scale, J.
Veg. Sci., 7, 309–320, 1996. a
Bright, R. M., Zhao, K., Jackson, R. B., and Cherubini, F.: Quantifying surface
albedo and other direct biogeophysical climate forcings of forestry
activities, Glob. Change Biol., 21, 3246–3266, 2015. a
Burke, M. and Emerick, K.: Adaptation to climate change: Evidence from US
agriculture, Am. Econ. J.-Econ. Pol., 8, 106–40, 2016. a
Chapin III, F. S., McGuire, A. D., Randerson, J., Pielke Sr., R., Baldocchi, D., Hobbie,
S. E., Roulet, N., Eugster, W., Kasischke, E., Rastetter, E. B., Zimov, S. A., and Running, S. W.: Arctic
and boreal ecosystems of western North America as components of the climate
system, Glob. Change Biol., 6, 211–223, 2000. a
Chen, X., Zhang, X.-S., and Li, B.-L.: The possible response of life zones in
China under global climate change, Global Planet. Change, 38, 327–337,
2003. a
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Böhner, J.: System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015, 2015. a
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J., and Jones, P. D.:
An ensemble version of the E-OBS temperature and precipitation data sets,
J. Geophys. Res.-Atmos., 123, 9391–9409, 2018. a
Daly, C., Helmer, E. H., and Quiñones, M.: Mapping the climate of puerto
rico, vieques and culebra, Int. J. Climatol., 23, 1359–1381, 2003. a
Daniel, M., Lemonsu, A., Déqué, M., Somot, S., Alias, A., and Masson,
V.: Benefits of explicit urban parameterization in regional climate modeling
to study climate and city interactions, Clim. Dynam., 52, 2745–2764,
2019. a
Davin, E. L., Rechid, D., Breil, M., Cardoso, R. M., Coppola, E., Hoffmann, P., Jach, L. L., Katragkou, E., de Noblet-Ducoudré, N., Radtke, K., Raffa, M., Soares, P. M. M., Sofiadis, G., Strada, S., Strandberg, G., Tölle, M. H., Warrach-Sagi, K., and Wulfmeyer, V.: Biogeophysical impacts of forestation in Europe: first results from the LUCAS (Land Use and Climate Across Scales) regional climate model intercomparison, Earth Syst. Dynam., 11, 183–200, https://doi.org/10.5194/esd-11-183-2020, 2020. a, b
Di Gregorio, A.: Land cover classification system: classification concepts and
user manual: LCCS, vol. 2, Food and Agriculture Organization of the United Nations, Rome, ISBN 92-5-105327-8, 2005. a
Dierckx, W., Sterckx, S., Benhadj, I., Livens, S., Duhoux, G., Van Achteren,
T., Francois, M., Mellab, K., and Saint, G.: PROBA-V mission for global
vegetation monitoring: standard products and image quality, Int.
J. Remote Sens., 35, 2589–2614, 2014. a
Donlon, C., Berruti, B., Buongiorno, A., Ferreira, M.-H., Féménias, P.,
Frerick, J., Goryl, P., Klein, U., Laur, H., Mavrocordatos, C., Nieke, J., Rebhan, H., Seitz, B., Stroede, J., and Sciarrac, R.: The
global monitoring for environment and security (GMES) sentinel-3 mission,
Remote Sens. Environ., 120, 37–57, 2012. a
d’Andrimont, R., Yordanov, M., Martinez-Sanchez, L., Eiselt, B., Palmieri,
A., Dominici, P., Gallego, J., Reuter, H. I., Joebges, C., Lemoine, G., and
van der Velde, M.: Harmonised LUCAS in-situ land cover and use database for field
surveys from 2006 to 2018 in the European Union, Scientific Data, 7, 352, https://doi.org/10.1038/s41597-020-00675-z,
2020. a
ESA: MERIS Product Handbook, Issue: 2.1, https://earth.esa.int/eogateway/documents/20142/37627/MERIS-product-handbook.pdf, (last access: 4 April 2022), 2006. a
ESA: Land Cover CCI Product User Guide Version 2, Tech. Rep., http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (last access: 4 April 2022), 2017b. a
Foody, G. M.: Status of land cover classification accuracy assessment, Remote
Sens. Environ., 80, 185–201, 2002. a
Ge, J., Qi, J., Lofgren, B. M., Moore, N., Torbick, N., and Olson, J. M.:
Impacts of land use/cover classification accuracy on regional climate
simulations, J. Geophys. Res.-Atmos., 112, D05107, https://doi.org/10.1029/2006JD007404, 2007. a
Harris, I., Jones, P., Osborn, T., and Lister, D.: Updated high-resolution
grids of monthly climatic observations – the CRU TS3.10 Dataset,
Int. J. Climatol., 34, 623–642, https://doi.org/10.1002/joc.3711,
2014. a
Hartley, A., MacBean, N., Georgievski, G., and Bontemps, S.: Uncertainty in
plant functional type distributions and its impact on land surface models,
Remote Sens. Environ., 203, 71–89, 2017. a
Hastings, D. A. and Emery, W. J.: The advanced very high resolution radiometer
(AVHRR) – A brief reference guide, Photogramm. Eng. Rem.
S., 58, 1183–1188, 1992. a
Hoffmann, P., Katzfey, J., McGregor, J., and Thatcher, M.: Bias and variance
correction of sea surface temperatures used for dynamical downscaling,
J. Geophys. Res.-Atmos., 121, 12-877–12-890, 2016. a
Hua, T., Zhao, W., Liu, Y., Wang, S., and Yang, S.: Spatial consistency
assessments for global land-cover datasets: A comparison among GLC2000, CCI
LC, MCD12, GLOBCOVER and GLCNMO, Remote Sensing, 10, 1846, https://doi.org/10.3390/rs10111846, 2018. a, b
Hurtt, G. C., Chini, L. P., Frolking, S., Betts, R. A., Feddema, J., Fischer, G.,
Fisk, J. P., Hibbard, K., Houghton, R. A., Jones, C. D., Kindermann, G., Kinoshita, T., Klein Goldewijk, K., Riahi, K., Shevliakova, E., Smith, S., Stehfest, E., Thomson, A., Thornton, P., van Vuuren, D. P., and Wang, Y. P.:
Harmonization of
land-use scenarios for the period 1500–2100: 600 years of global gridded
annual land-use transitions, wood harvest, and resulting secondary lands,
Climatic Change, 109, 117–161, https://doi.org/10.1007/s10584-011-0153-2, 2011. a
Jung, M., Henkel, K., Herold, M., and Churkina, G.: Exploiting synergies of
global land cover products for carbon cycle modeling, Remote Sens.
Environ., 101, 534–553, 2006. a
Karthikeyan, L., Chawla, I., and Mishra, A. K.: A review of remote sensing
applications in agriculture for food security: Crop growth and yield,
irrigation, and crop losses, J. Hydrology, 586, 124905, https://doi.org/10.1016/j.jhydrol.2020.124905, 2020. a
Khatun, K., Imbach, P., and Zamora, J.: An assessment of climate change impacts
on the tropical forests of Central America using the Holdridge Life Zone
(HLZ) land classification system, iForest-Biogeosciences and Forestry, 6,
183–189, https://doi.org/10.3832/ifor0743-006, 2013. a
Kueppers, L. M., Snyder, M. A., and Sloan, L. C.: Irrigation cooling effect:
Regional climate forcing by land-use change, Geophys. Res. Lett.,
34, L03703, https://doi.org/10.1029/2006GL028679, 2007. a
Lattanzi, F. A.: C3/C4 grasslands and climate change, in: Proceedings of the 23rd General Meeting of the European Grassland Federation, Kiel, Germany, 29 August–2 September 2010, Mecke Druck und Verlag, 3–13, ISBN 978-3-86944-021-7, 2010. a
Lavorel, S., Díaz, S., Cornelissen, J. H. C., Garnier, E., Harrison,
S. P., McIntyre, S., Pausas, J. G., Pérez-Harguindeguy, N., Roumet, C.,
and Urcelay, C.: Plant functional types: are we getting any closer to the
Holy Grail?, in: Terrestrial ecosystems in a changing world, 1st edn., edited by: Canadell, J. G., Pataki, D. E., and Pitelka, L. F., pp. 149–164,
Springer, ISBN 978-3-540-32730-1, https://doi.org/10.1007/978-3-540-32730-1_13, 2007. a
Lawrence, D. and Vandecar, K.: Effects of tropical deforestation on climate and
agriculture, Nat. Clim. Change, 5, 27–36, 2015. a
Li, L., Yang, Z.-L., Matheny, A. M., Zheng, H., Swenson, S. C., Lawrence,
D. M., Barlage, M., Yan, B., McDowell, N. G., and Leung, L. R.:
Representation of Plant Hydraulics in the Noah-MP Land Surface Model: Model
Development and Multiscale Evaluation, J. Adv. Model. Earth
Sy., 13, e2020MS002214, https://doi.org/10.1029/2020MS002214, 2021. a
Li, W., MacBean, N., Ciais, P., Defourny, P., Lamarche, C., Bontemps, S., Houghton, R. A., and Peng, S.: Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015), Earth Syst. Sci. Data, 10, 219–234, https://doi.org/10.5194/essd-10-219-2018, 2018. a, b, c, d, e
Lobell, D., Bala, G., and Duffy, P.: Biogeophysical impacts of cropland
management changes on climate, Geophys. Res. Lett., 33, L06708, https://doi.org/10.1029/2005GL025492, 2006. a
Lu, Y. and Kueppers, L. M.: Surface energy partitioning over four dominant
vegetation types across the United States in a coupled regional climate model
(Weather Research and Forecasting Model 3–Community Land Model 3.5), J. Geophys. Res.-Atmos., 117, D06111, https://doi.org/10.1029/2011JD016991, 2012. a
Lugo, A. E., Brown, S. L., Dodson, R., Smith, T. S., and Shugart, H. H.: The
Holdridge life zones of the conterminous United States in relation to
ecosystem mapping, J. Biogeogr., 26, 1025–1038, 1999. a
Mahmood, R., Pielke Sr., R. A., Hubbard, K. G., Niyogi, D., Dirmeyer, P. A.,
McAlpine, C., Carleton, A. M., Hale, R., Gameda, S., Beltrán-Przekurat,
A., Baker, B., McNider, R., Legates, D. R., Shepherd, M., Du, J., Blanken, P. D., Frauenfeld, O. W., Nair U. S., and Fallt, S.: Land cover changes and their biogeophysical effects on climate,
Int. J. Climatol., 34, 929–953, 2014. a
Maisongrande, P., Duchemin, B., and Dedieu, G.: VEGETATION/SPOT: an operational
mission for the Earth monitoring; presentation of new standard products,
Int. J. Remote Sens., 25, 9–14, 2004. a
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and
Wulder, M. A.: Good practices for estimating area and assessing accuracy of
land change, Remote Sens. Environ., 148, 42–57, 2014. a
Ottlé, C., Lescure, J., Maignan, F., Poulter, B., Wang, T., and Delbart, N.: Use of various remote sensing land cover products for plant functional type mapping over Siberia, Earth Syst. Sci. Data, 5, 331–348, https://doi.org/10.5194/essd-5-331-2013, 2013. a
Ottosen, T.-B., Lommen, S. T., and Skjøth, C. A.: Remote sensing of cropping
practice in Northern Italy using time-series from Sentinel-2, Comput.
Electron. Agr., 157, 232–238, 2019. a
Pau, S., Edwards, E. J., and Still, C. J.: Improving our understanding of
environmental controls on the distribution of C3 and C4 grasses, Global
Change Biol., 19, 184–196, 2013. a
Perugini, L., Caporaso, L., Marconi, S., Cescatti, A., Quesada, B.,
de Noblet-Ducoudre, N., House, J. I., and Arneth, A.: Biophysical effects on
temperature and precipitation due to land cover change, Environ.
Res. Lett., 12, 053002, https://doi.org/10.1088/1748-9326/aa6b3f, 2017. a
Poulter, B., Ciais, P., Hodson, E., Lischke, H., Maignan, F., Plummer, S., and Zimmermann, N. E.: Plant functional type mapping for earth system models, Geosci. Model Dev., 4, 993–1010, https://doi.org/10.5194/gmd-4-993-2011, 2011. a, b
Poulter, B., MacBean, N., Hartley, A., Khlystova, I., Arino, O., Betts, R., Bontemps, S., Boettcher, M., Brockmann, C., Defourny, P., Hagemann, S., Herold, M., Kirches, G., Lamarche, C., Lederer, D., Ottlé, C., Peters, M., and Peylin, P.: Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative, Geosci. Model Dev., 8, 2315–2328, https://doi.org/10.5194/gmd-8-2315-2015, 2015. a, b, c
Rechid, D., Davin, E., de Noblet-Ducoudré, N., and Katragkou, E.: CORDEX Flagship Pilot Study LUCAS – Land Use & Climate Across Scales – a new initiative on coordinated regional land use change and climate experiments for Europe, in: 19th EGU General Assembly, EGU2017, Vienna, Austria, 23–28 April 2017, 19, p. 13172, 2017. a
Reinhart, V., Fonte, C. C., Hoffmann, P., Bechtel, B., Rechid, D., and
Böhner, J.: Comparison of ESA climate change initiative land cover to
CORINE land cover over Eastern Europe and the Baltic States from a regional
climate modeling perspective, Int. J. Appl. Earth
Obs., 94, 102221, https://doi.org/10.1016/j.jag.2020.102221, 2021a. a, b, c
Richardson, A. D., Keenan, T. F., Migliavacca, M., Ryu, Y., Sonnentag, O., and
Toomey, M.: Climate change, phenology, and phenological control of vegetation
feedbacks to the climate system, Agr. Forest Meteorol., 169,
156–173, 2013. a
Rufin, P., Frantz, D., Ernst, S., Rabe, A., Griffiths, P., Özdoğan,
M., and Hostert, P.: Mapping cropping practices on a national scale using
intra-annual landsat time series binning, Remote Sensing, 11, 232, https://doi.org/10.3390/rs11030232, 2019. a
Saad, R., Koellner, T., and Margni, M.: Land use impacts on freshwater
regulation, erosion regulation, and water purification: a spatial approach
for a global scale level, Int. J. Life Cycle Ass.,
18, 1253–1264, 2013. a
Santos-Alamillos, F., Pozo-Vázquez, D., Ruiz-Arias, J., and Tovar-Pescador,
J.: Influence of land-use misrepresentation on the accuracy of WRF wind
estimates: Evaluation of GLCC and CORINE land-use maps in southern Spain,
Atmos. Res., 157, 17–28, 2015. a
Sertel, E., Robock, A., and Ormeci, C.: Impacts of land cover data quality on
regional climate simulations, Int. J. Climatol., 30,
1942–1953, 2010. a
Siebert, S., Döll, P., Hoogeveen, J., Faures, J.-M., Frenken, K., and Feick, S.: Development and validation of the global map of irrigation areas, Hydrol. Earth Syst. Sci., 9, 535–547, https://doi.org/10.5194/hess-9-535-2005, 2005. a
Skov, F. and Svenning, J.-C.: Potential impact of climatic change on the
distribution of forest herbs in Europe, Ecography, 27, 366–380, 2004. a
Stehman, S. V.: Sampling designs for accuracy assessment of land cover,
Int. J. Remote Sens., 30, 5243–5272, 2009. a
Still, C. J., Berry, J. A., Collatz, G. J., and DeFries, R. S.: Global
distribution of C3 and C4 vegetation: carbon cycle implications, Global
Biogeochem. Cy., 17, 6–14, https://doi.org/10.1029/2001GB001807, 2003. a
Szelepcsényi, Z., Breuer, H., and Sümegi, P.: The climate of Carpathian
Region in the 20th century based on the original and modified Holdridge life
zone system, Centr. Eur. J. Geosci., 6, 293–307, 2014. a
Szelepcsényi, Z., Breuer, H., Kis, A., Pongrácz, R., and Sümegi,
P.: Assessment of projected climate change in the Carpathian Region using the
Holdridge life zone system, Theor. Appl. Climatol., 131,
593–610, 2018. a
Tatli, H. and Dalfes, H. N.: Defining Holdridge's life zones over Turkey,
Int. J. Climatol., 36, 3864–3872, 2016. a
Tatli, H. and Dalfes, H. N.: Analysis of temporal diversity of precipitation
along with biodiversity of Holdridge life zones, Theor. Appl.
Climatol., 144, 391–400, 2021. a
Thompson, C., Beringer, J., Chapin III, F. S., and McGuire, A. D.: Structural
complexity and land-surface energy exchange along a gradient from arctic
tundra to boreal forest, J. Veg. Sci., 15, 397–406, 2004. a
van Bodegom, P. M., Douma, J. C., and Verheijen, L. M.: A fully traits-based
approach to modeling global vegetation distribution, P.
Natl. Acad. Sci. USA, 111, 13733–13738,
https://doi.org/10.1073/pnas.1304551110, 2014. a
Wei, Y., Liu, S., Huntzinger, D., Michalak, A., Viovy, N., Post, W., Schwalm,
C., Schaefer, K., Jacobson, A., LU, C., Tian, H., Ricciuto, D., Cook, R.,
Mao, J., and Shi, X.: NACP MsTMIP: Global and North American Driver Data for
Multi-Model Intercomparison, NASA [data set], https://doi.org/10.3334/ORNLDAAC/1220, 2014. a, b, c
Winter, J. M., Pal, J. S., and Eltahir, E. A.: Coupling of integrated biosphere
simulator to regional climate model version 3, J. Climate, 22,
2743–2757, 2009. a
Wullschleger, S. D., Epstein, H. E., Box, E. O., Euskirchen, E. S., Goswami,
S., Iversen, C. M., Kattge, J., Norby, R. J., van Bodegom, P. M., and Xu, X.:
Plant functional types in Earth system models: past experiences and future
directions for application of dynamic vegetation models in high-latitude
ecosystems, Annals of Botany, 114, 1–16, https://doi.org/10.1093/aob/mcu077, 2014. a
Yang, Y., Zhu, Q., Peng, C., Wang, H., and Chen, H.: From plant functional
types to plant functional traits: A new paradigm in modelling global
vegetation dynamics, Prog. Phys. Geog.,
39, 514–535, https://doi.org/10.1177/0309133315582018, 2015. a
Yang, Y., Xiao, P., Feng, X., and Li, H.: Accuracy assessment of seven global
land cover datasets over China, ISPRS J. Photogramm., 125, 156–173, 2017. a
Yue, T., Liu, J., Jørgensen, S. E., Gao, Z., Zhang, S., and Deng, X.:
Changes of Holdridge life zone diversity in all of China over half a century,
Ecol. Model., 144, 153–162, 2001. a
Yue, T. X., Fan, Z. M., Liu, J. Y., and Wei, B. X.: Scenarios of major
terrestrial ecosystems in China, Ecol. Model., 199, 363–376, 2006. a
Short summary
The LANDMATE plant functional type (PFT) land cover dataset for Europe 2015 (Version 1.0) is a gridded, high-resolution dataset for use in regional climate models. LANDMATE PFT is prepared using the expertise of regional climate modellers all over Europe and is easily adjustable to fit into different climate model families. We provide comprehensive spatial quality information for LANDMATE PFT, which can be used to reduce uncertainty in regional climate model simulations.
The LANDMATE plant functional type (PFT) land cover dataset for Europe 2015 (Version 1.0) is a...
Altmetrics
Final-revised paper
Preprint