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Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data on European forests to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple data sources at different hierarchical and temporal scales, together with environmental driving data as well as the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate, CO2, nitrogen deposition, tree and forest stand level, and remote sensing data for nine contrasting forest stands distributed across Europe. Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat conduction and soil water are also available. The climate and nitrogen deposition data contain several datasets for the historic period and a wide range of future climate change scenarios following the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND DB is available freely as a “SQLite” relational database or “ASCII” flat file version (at https://doi.org/10.5880/PIK.2020.006/; Reyer et al., 2020). The data policies of the individual contributing datasets are provided in the metadata of each data file. The PROFOUND DB can also be accessed via the ProfoundData R package (https://CRAN.R-project.org/package=ProfoundData; Silveyra Gonzalez et al., 2020), which provides basic functions to explore, plot and extract the data for model set-up, calibration and evaluation.
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Earth Syst. Sci. Data, 12, 1295–1320, 2020
https://doi.org/10.5194/essd-12-1295-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
The PROFOUND Database for evaluating vegetation
models and simulating climate impacts on European
forests
Christopher P. O. Reyer1, Ramiro Silveyra Gonzalez1, Klara Dolos2, Florian Hartig3, Ylva Hauf1,
Matthias Noack4, Petra Lasch-Born1, Thomas Rötzer5, Hans Pretzsch5, Henning Meesenburg6,
Stefan Fleck6, Markus Wagner6, Andreas Bolte7, Tanja G. M. Sanders7, Pasi Kolari8, Annikki Mäkelä8,
Timo Vesala8, Ivan Mammarella8, Jukka Pumpanen9, Alessio Collalti10,11, Carlo Trotta11,
Giorgio Matteucci12, Ettore D’Andrea12, Lenka Foltýnová13, Jan Krejza13, Andreas Ibrom14,
Kim Pilegaard14, Denis Loustau15, Jean-Marc Bonnefond15, Paul Berbigier15, Delphine Picart15,
Sébastien Lafont15, Michael Dietze16, David Cameron17, Massimo Vieno18, Hanqin Tian19,
Alicia Palacios-Orueta20, Victor Cicuendez20, Laura Recuero20, Klaus Wiese20, Matthias Büchner1,
Stefan Lange1, Jan Volkholz1, Hyungjun Kim21, Joanna A. Horemans22, Friedrich Bohn23,
Jörg Steinkamp24, Alexander Chikalanov25, Graham P. Weedon26, Justin Sheffield27, Flurin Babst28,29,
Iliusi Vega del Valle1, Felicitas Suckow1, Simon Martel16, Mats Mahnken1, Martin Gutsch1, and
Katja Frieler1
1Potsdam Institute for Climate Impact Research, Member of the Leibniz Association,
P.O. Box 601203, 14412 Potsdam, Germany
2Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
3Faculty of Biology and Pre-Clinical Medicine, University of Regensburg, Regensburg, Germany
4Fachagentur Nachwachsende Rohstoffe e.V. (FNR), Gülzow-Prüzen, Germany
5Department of Ecology and Ecosystem Management, Technical University of Munich, Munich, Germany
6Northwest German Forest Research Institute, Göttingen, Germany
7Thünen Institute of Forest Ecosystems, 16225 Eberswalde, Germany
8Department of Forest Sciences, University of Helsinki, Helsinki, Finland
9Biogeochemistry Research Group, University of Eastern Finland, Kuopio, Finland
10National Research Council of Italy, Institute for Agriculture and Forestry Systems
in the Mediterranean, Perugia (PG), Italy
11Department of Innovation in Biological, Agro-food and Forest System, University of Tuscia,
01100 Viterbo, Italy
12National Research Council of Italy, Institute for Agriculture and Forestry System
in the Mediterranean, Ercolano (NA), Italy
13Global Change Research Institute, Brno, Czech Republic
14Department of Environmental Engineering, Technical University of Denmark, Lyngby, Denmark
15French National Institute for Agricultural Research, Bordeaux, France
16Department of Earth & Environment, Boston University, Boston, USA
17Centre for Ecology and Hydrology, Edinburgh, UK
18Centre for Ecology and Hydrology, Lancaster, UK
19School of Forestry and Wildlife Sciences, Auburn University, Auburn, USA
20Departamento de Silvopascicultura, Technical University of Madrid, Madrid, Spain
21Department of Human and Social Systems, University of Tokyo, Tokyo, Japan
22Centre of Excellence PLECO, University of Antwerp, Antwerp, Belgium
23Helmholtz Centre for Environmental Research, Leipzig, Germany
24Senckenberg Biodiversity and Climate Research Centre, Senckenberg, Germany
25University of Library Study and Information Technology, Sofia, Bulgaria
Published by Copernicus Publications.
1296 C. P. O. Reyer et al.: PROFOUND Database
26Met Office, Wallingford, UK
27Dept. Civil & Environ. Eng., Princeton University, Princeton, NJ 08544, USA
28W. Szafer Institute of Botany, Department of Ecology, Polish Academy of Sciences, Cracow, Poland
29Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
Correspondence: Christopher P. O. Reyer (reyer@pik-potsdam.de)
Received: 5 November 2019 – Discussion started: 29 November 2019
Revised: 6 May 2020 – Accepted: 8 May 2020 – Published: 12 June 2020
Abstract. Process-based vegetation models are widely used to predict local and global ecosystem dynamics
and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to
ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a
wide range of empirical data on European forests to calibrate and evaluate vegetation models that simulate cli-
mate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple
data sources at different hierarchical and temporal scales, together with environmental driving data as well as
the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate,
CO2, nitrogen deposition, tree and forest stand level, and remote sensing data for nine contrasting forest stands
distributed across Europe. Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat
conduction and soil water are also available. The climate and nitrogen deposition data contain several datasets
for the historic period and a wide range of future climate change scenarios following the Representative Concen-
tration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We also provide pre-industrial climate simulations that
allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity
changes. The PROFOUND DB is available freely as a “SQLite” relational database or “ASCII” flat file version
(at https://doi.org/10.5880/PIK.2020.006/; Reyer et al., 2020). The data policies of the individual contributing
datasets are provided in the metadata of each data file. The PROFOUND DB can also be accessed via the
ProfoundData R package (https://CRAN.R-project.org/package=ProfoundData; Silveyra Gonzalez et al., 2020),
which provides basic functions to explore, plot and extract the data for model set-up, calibration and evaluation.
1 Introduction
Process-based models are key tools for understanding sys-
tems and forecasting climate change impacts in ecology and
Earth system science (Schellnhuber, 1999). Vegetation is a
crucial component of the Earth system, and forests are par-
ticularly relevant through their influence on hydrological and
biogeochemical cycles, biodiversity and ecosystem services.
Process-based vegetation models are used as diagnostic tools
to disentangle the influence of different environmental and
human drivers on biogeochemical cycling as well as vegeta-
tion structure from local to plot-level (Eastaugh et al., 2011;
Fontes et al., 2010; Pretzsch et al., 2015; Tiktak and van
Grinsven, 1995) to global scales (Chang et al., 2017; Ito et
al., 2017). At the same time these models are also the main
tools to project climate change impacts on vegetation under
changing environmental conditions, again from local (Reyer,
2015; Rötzer et al., 2013) to global levels (Zhu et al., 2016).
With increasing model complexity, the inclusion of more
and more processes and models being increasingly used as
tools for making quantitative projections for policy and man-
agement, there is a strong need to install some quality con-
trol on their performance. A basic requirement would be that
models are actually able to match observed data. Moreover,
while informal methods for calibration and model compar-
isons were often used in the past, the community has shifted
in recent years towards more formal statistical methods for
such tasks (Dietze et al., 2013; Hartig et al., 2012), which cre-
ates a need for systematic benchmarking data. For all these
tasks, the availability of a wide range of data types crossing
different spatial–temporal scales is generally viewed as ben-
eficial (Grimm and Railsback, 2012).
The process of formal calibration, comparison and evalu-
ation of complex vegetation models is often hindered by the
availability and the harmonization of suitable data. The data
necessary to drive a vegetation model are often complex and
need to be compiled from different data sources (e.g. Bag-
nara et al., 2019). In particular for model comparisons, be-
sides data for the evaluation of individual models, common
input and driving data for process-based vegetation models
are needed to ensure fair comparisons between the partic-
ipating models. Although model comparisons have a long
tradition in vegetation modelling (Cramer et al., 1999, 2001;
Bugmann et al., 1996; Morales et al., 2005), they have often
been limited by overall data availability and comparability.
Common databases that are ready to use for thorough model
evaluation would allow the community to gain a better appre-
ciation of model differences, explore structural uncertainties
Earth Syst. Sci. Data, 12, 1295–1320, 2020 https://doi.org/10.5194/essd-12-1295-2020
C. P. O. Reyer et al.: PROFOUND Database 1297
Figure 1. Location of forest sites and main tree species. Background shows the European forest cover after Brus et al. (2012).
and provide a basis for more systematic ensemble projections
of climate impacts.
Recently, several initiatives have started compiling model
evaluation, input or driving data for a wide range of ap-
plications of process-based vegetation models (Huntzinger
et al., 2013; Kelley et al., 2013; Warszawski et al., 2014;
Sitch et al., 2015; Collier et al., 2018). Although these ini-
tiatives have leveraged important scientific progress, many
of them have focussed on the global scale, mostly provid-
ing evaluation, input and driving data from global products.
Such global products generally lack the breadth and depth of
process-level detail required to rigorously assess model per-
formance at smaller scales as for example they lack long-
term and detailed measurements of forest stand structure.
The database for the project “Towards robust projections
of European forests under climate change” (hereafter PRO-
FOUND DB) described here aims to bring together data from
a wide range of data sources to evaluate vegetation models
and simulate climate impacts at the forest stand scale. It has
been designed to fulfil two objectives:
to allow for a thorough evaluation of complex, process-
based vegetation models using multiple data streams
covering a range of processes at different temporal
scales and
to allow for climate impact assessments by providing
the latest climate scenario data.
The PROFOUND DB only provides data for individual
forest stands but contains a number of elements that are de-
signed to foster comparison of both global/regional models
and local models. The climate data, for example, are pro-
vided locally (or bias-corrected using local data) in the same
way that stand-scale vegetation models would need them and
also extracted from global gridded datasets that global veg-
etation models would use. The PROFOUND DB is also de-
signed to allow for disentangling of uncertainties that affect
quantitative model predictions in ecology (see Lindner et al.,
2014, and Dietze, 2017, for an explanation of different uncer-
tainty types), for example by facilitating standardized evalu-
ations of structural or process uncertainties via model com-
parisons. Model input and driver uncertainty are addressed
through a wide range of climate data from different sources,
covering the full range of Representative Concentration Path-
ways (RCPs). Collalti et al. (2018, 2019), for example, have
used the PROFOUND DB to study the effects of thinning on
carbon use efficiency across a combination of all four RCPs
and five global climate models. Finally, parametric uncer-
tainty can be assessed through the wide range of data that can
be used for inverse calibration. In the following we describe
the main components of the PROFOUND DB (Reyer et al.,
2020) and an R Package (Silveyra Gonzalez et al., 2020) de-
veloped to explore the database and allow rapid and easy ac-
cess for modellers.
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1298 C. P. O. Reyer et al.: PROFOUND Database
Table 1. Overview of the data available in the PROFOUND DB. The years indicate the first and the last years for which data are available
except for one-off measurements. The superscript letters indicate the temporal resolution of the data. O: one-off measurement(s); M=30 min
measurements; D: daily measurements; C: 8 d or 16 d composite; A: annual measurements.
Bily Kriz Collelongo Hyytiälä KROOF Le Bray Peitz Solling Sorø
Soil 2011O1995/2008O1995/1996O2003/2004O1995/2003/
2004/2005O2011O2010O1997/2004/
2006O
Local climate 2000–2008D1996–2014D1996–2014D1998–2010D1996–2008D1901–2010D1960–2013D1996–2012D
Reanalysis
climate
1901–2012D1901–2012D1901–2012D1901–2012D1901–2012D1901–2012D1901–2012D1901–2012D
Climate scenar-
ios (ISIMIP2b)
1661–2299D1661–2299D1661–2299D1661–2299D1661–2299D1661–2299D1661–2299D1661–2299D
Climate scenar-
ios (ISIMIPFT)
1950–2099D1950–2099D1950–2099D1950–2099D1950–2099D1950–2099D1950–2099D1950–2099D
Atmospheric
CO2
1765–2500A1765–2500A1765–2500A1765–2500A1765–2500A1765–2500A1765–2500A1765–2500A
Nitrogen
deposition
(ISIMIP2b)
1861–2100A1861–2100A1861–2100A1861–2100A1861–2100A1861–2100A1861–2100A1861–2100A
Nitrogen depo-
sition (EMEP)
1980–2014A1980–2014A1980–2014A1980–2014A1980–2014A1980–2014A1980–2014A1980–2014A
Forest tree data 1997–2015A1992–2012A2001–2008A1997–2010A– 1948–2011A1967–2014A1994–2017A
Forest stand
data
1997–2015A1992–2012A1995–2011A1997–2010A1986–2009A1937–2011A1967–2014A1994–2017A
MODIS 2000–2014C2000–2014C2000–2014C2000–2014C2000–2014C2000–2014C2000–2014C2000–2014C
Flux 2000–2008M1996–2014M1996–2014M– 1996–2008M– – 1996–2012M
Meteorological 2000–2008M1996–2014M1996–2014M– 1996–2008M– – 1996–2012M
Atmospheric
heat conduction
2000–2008M1996–2014M1996–2014M– 1996–2008M– – 1996–2012M
Soil flux series 2000–2008M1996–2014M1996–2014M– 1996–2008M– – 1996–2012M
2 The PROFOUND Database
2.1 Forest site selection and concept
The forest sites featured in the PROFOUND DB were se-
lected to provide a wide array of data sources across a Eu-
ropean gradient. We focussed in particular on providing long
time series of tree- and stand-level growth and yield as well
as carbon cycle data available from eddy-flux measurements
because these variables are most commonly in calibrating
and evaluating process-based vegetation models. The se-
lected sites spread along a wide climatic gradient across Eu-
rope (Fig. 1, Table 3) and cover some of the most common
European forest types, as well as the main central European
forest management history of favouring monospecific, even-
aged forests or mixtures of two tree species.
We compiled the data from existing data sources and col-
lected the definitions of variables, their units and information
about the main measurement methods from the site principal
investigators (PIs) and from official descriptions of the data
to harmonize the variables as much as possible. The over-
all guiding principle for the compilation of the data was to
provide data that can be easily used by modellers for setting
up and evaluating their models. In order to allow for data
uncertainty to be reflected in model calibration studies, we
also included uncertainty estimates for the measured data,
such as those available for carbon flux measurements (see
Sect. 3.2.9), wherever possible.
2.2 Data sources
The PROFOUND DB provides information on the site, soil
and forest stand as well as data for climate, atmospheric
CO2concentration, nitrogen deposition, carbon fluxes, at-
mospheric heat conduction and remote sensing at a range
of different temporal resolutions (i.e. from 30 min to decadal
measurements). Table 1 provides an overview of the different
data types and their temporal resolution available in the PRO-
FOUND DB. All variables available are listed in Tables S1–
S13 in the Supplement. In the following we describe how the
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C. P. O. Reyer et al.: PROFOUND Database 1299
Table 2. Overview of the main site characteristics provided for each forest site in the PROFOUND DB.
Aspect Elevation Slope FAO soil Main tree
Name Lat Long Country () (m.a.s.l.) (%) typeaspecies
Bily Kriz 49.30 18.32 CZ 180 875 12.5 Haplic Podzol Picea abies
Collelongo 41.85 13.59 IT 252 1560 10 Dystric Luvisol Fagus sylvatica
Hyytiälä 61.85 24.29 FI 180 185 2 Haplic Podzol Pinus sylvestris, Picea abies
KROOF 48.25 11.40 DE 1.8 502 2.1 Luvisol Picea abies, Fagus sylvatica
Le Bray 44.72 0.77 FR 61 0 Arenosol Pinus pinaster
Peitz 51.92 14.35 DE 50 0 Dystric Cambisol Pinus sylvestris
Solling (beech) 51.77 9.57 DE 225 504 1 Haplic Cambisol Fagus sylvatica
Solling (spruce) 51.76 9.58 DE 90 508 1 Haplic Cambisol Picea abies
(dystric, densic)
Sorø 55.49 11.64 DK 40 0 Alfisols–MollisolsbFagus sylvatica
aAccording to ISSS-ISRIC-FAO (1998). bDepending on base saturation under or over 50% with a 10–40 cm deep organic layer (see Pilegaard et al., 2003).
individual sub-datasets of the PROFOUND DB have been
brought together and describe the key variables and charac-
teristics of each dataset.
2.2.1 Site information
For each forest site, the PROFOUND DB contains informa-
tion on general site characteristics such as coordinates, ele-
vation and forest type (Table 2). There is also information on
the potential natural vegetation and main tree species belong-
ing to the regional flora (not shown).
2.2.2 Soil data
The description of the soil profiles contains information
about physical and chemical properties of each soil horizon
including the organic layer. Unfortunately the soil data are
very heterogeneous for the sites, and considerable amounts
of data are missing. In order not to lose the data that are avail-
able for only a subset of sites, we did not harmonize the indi-
vidual variables, but for each site we provide the soil data in
a consistent format. Despite these limitations, for most sites
important soil data such as the depth of horizons, soil texture,
bulk density, field capacity, wilting point, carbon and nitro-
gen content, and pH of the soil solution are available (see
Table S2).
2.2.3 Local climate
For every site we compiled the locally observed daily me-
teorological data, either from measurement towers or from
nearby meteorological stations. These time series cover the
main climatic variables required by vegetation models and
different time periods for each site (Table 3). They represent
the best possible climate information for each site and are
most suitable for model simulations comparing simulation
output to observations.
2.2.4 Reanalysis products
In order to cover longer historical time periods and to assess
uncertainties due to the choice of different climate inputs, the
PROFOUND DB also provides long historical daily climate
time series for each of the sites extracted from four different
global reanalysis/observational products:
Princeton’s Global Meteorological Forcing Dataset
(PGMFD v.2, hereafter Princeton) from 1901 to 2012
by Sheffield et al. (2006);
Global Soil Wetness Project Phase 3 (GSWP3) from
1901 to 2010 by Hjungjun Kim (personal communica-
tion, 2018, http://hydro.iis.u-tokyo.ac.jp/GSWP3/, last
access: 5 June 2020);
Water and Global Change programme (WATCH) from
1901 to 2001 by Weedon et al. (2011);
WATCH-Forcing-Data-ERA-Interim (WFDEI) from
1901 to 2010 by Weedon et al. (2014).
Climate variables for the forest stands were extracted from
the 0.5×0.5grid cell of the global reanalysis/observational
product in which the forest stand is located. The data are then
kept at the original 0.5×0.5resolution to allow for com-
paring the effects of choosing climate inputs for a vegetation
model from a global reanalysis product as opposed to the lo-
cal data presented in Sect. 3.2.3. The difference between the
local data and the reanalysis data is most obvious for those
sites located in complex, hilly terrain such as Collelongo or
KROOF (Table 2). In these hilly locations the grid box av-
erage heights of the reanalysis products differ substantially
from the heights of the site measurements.
2.2.5 Climate scenarios
The PROFOUND DB provides climate scenar-
ios based on simulations performed for CMIP5
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1300 C. P. O. Reyer et al.: PROFOUND Database
Table 3. Averages of the daily maximum temperature (Tmax), daily minimum temperature (Tmin), daily mean temperature (Tmean), annual
precipitation sum (P), daily mean relative humidity (RH), daily mean air pressure (AP), annual sum of global radiation (R, direct +diffuse
shortwave radiation) and daily mean wind speed (W) for each of the sites in the PROFOUND DB from five different sources: a locally
observed climate and four different global reanalysis/observational products (GSWP3, Princeton, WATCH, WFDEI). The column “Year”
indicates the years for which the mean climates have been calculated for the different sources. Please note that the two Solling sites have the
same climate. NA: not available.
Tmax Tmean Tmin PRH AP R W
Site Source Years (C) (C) (C) (mm) (%) (hPa) (J cm2) (m s1)
Bily Kriz Local 2000–2008 11.50 7.36 3.80 1434.56 81.99 913.19 378 774.86 2.19
GSWP3 2000–2008 12.65 7.66 3.03 1034.22 76.77 957.64 395 464.73 3.71
Princeton 2000–2008 12.47 7.67 2.85 914.89 78.77 960.22 402 658.93 3.12
WATCH 2000–2001 12.72 8.25 3.43 1124.52 75.08 948.34 322 865.69 2.05
WFDEI 2000–2008 12.43 7.66 2.81 1034.40 76.22 950.08 438 978.13 3.25
Collelongo Local 1996–2014 11.46 7.24 3.46 1178.62 74.03 849.59 541 888.38 1.73
GSWP3 1996–2010 20.64 15.12 10.46 977.40 68.42 903.78 530 247.74 3.83
Princeton 1996–2012 20.28 15.17 10.09 757.99 73.76 944.66 539 045.09 4.55
WATCH 1996–2001 20.57 15.21 9.99 962.33 69.66 897.07 465 115.41 2.11
WFDEI 1996–2010 20.40 15.12 10.22 972.10 75.02 903.20 549 826.57 2.40
Hyytiälä Local 1996–2014 7.40 4.36 1.13 604.01 77.95 991.08 309 628.86 3.42
GSWP3 1996–2010 8.03 4.00 0.20 689.08 83.96 998.01 350511.52 3.42
Princeton 1996–2012 7.88 4.06 0.37 574.87 83.41 1007.97 330 041.85 3.52
WATCH 1996–2001 7.93 3.88 0.17 690.02 81.29 993.85 280 668.38 2.44
WFDEI 1996–2010 7.97 4.00 0.26 668.75 79.23 993.60 328551.11 2.12
KROOF Local 1998–2010 12.99 8.15 3.91 849.46 80.73 NA 391563.62 1.08
GSWP3 1998–2010 14.43 9.65 5.23 1014.37 80.55 954.55 423 260.65 3.04
Princeton 1998–2010 14.15 9.66 4.95 772.08 82.05 935.11 433 277.37 3.18
WATCH 1998–2001 14.48 9.83 5.39 1061.27 76.35 959.58 337 605.56 2.78
WFDEI 1998–2010 14.41 9.65 5.22 976.78 76.67 954.13 431 629.74 2.58
Le Bray Local 1996–2008 17.76 13.37 9.39 920.18 76.11 1005.81 472 940.36 3.02
GSWP3 1996–2008 19.06 14.23 9.63 918.76 73.90 1014.64 490 253.28 4.90
Princeton 1996–2008 18.62 14.24 9.19 951.01 80.41 989.70 484 739.73 4.01
WATCH 1996–2001 18.60 13.98 9.34 1095.65 74.66 1021.76 398 738.50 4.28
WFDEI 1996–2008 19.20 14.23 9.78 988.57 74.37 1011.63 512 514.20 2.77
Peitz Local 1901–2010 13.50 9.02 4.93 533.10 76.37 1008.29 369 794.74 2.35
GSWP3 1901–2010 13.48 9.22 5.34 654.19 75.73 1007.39 365 709.48 3.74
Princeton 1901–2010 13.20 9.23 5.07 557.89 85.43 999.16 374 370.83 3.51
WATCH 1901–2001 13.36 9.06 5.20 601.44 76.93 1007.07 309 797.89 2.79
WFDEI 1901–2010 13.47 9.18 5.23 607.58 76.54 1006.45 335 821.69 3.02
Solling Local 1960–2013 10.54 6.75 3.39 1113.06 85.56 NA 285026.90 1.01
GSWP3 1960–2010 11.99 8.15 4.67 933.37 79.82 988.95 355 905.60 3.95
Princeton 1960–2012 11.76 8.20 4.42 734.76 85.55 995.05 364 950.89 3.75
WATCH 1960–2001 11.65 7.79 4.38 962.00 79.38 985.97 300 414.77 2.74
WFDEI 1960–2010 11.89 8.14 4.58 963.98 79.21 985.95 353 096.37 3.36
Sorø Local 1996–2012 10.66 8.26 5.91 760.52 82.95 1007.71 360 687.83 5.13
GSWP3 1996–2010 11.56 9.00 6.58 773.57 78.73 1012.59 376 613.02 5.86
Princeton 1996–2012 11.45 9.03 6.44 584.58 81.19 1005.25 363 852.90 4.98
WATCH 1996–2001 11.08 8.46 6.26 560.00 82.54 1009.39 343 133.71 5.66
WFDEI 1996–2010 11.52 9.01 6.55 640.02 83.06 1009.50 408 098.02 4.81
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C. P. O. Reyer et al.: PROFOUND Database 1301
Figure 2. Change in mean annual temperature (Tmean), annual precipitation sum (P) and annual sum of global radiation (R) over the
time period 1950–2099 relative to the 1980–2005 average for the ISIMIPFT scenarios. Please note that the two Solling sites have the same
climate.
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1302 C. P. O. Reyer et al.: PROFOUND Database
(https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip5;
last accessed: 5 June 2020) that were bias-corrected and
interpolated to a common grid resolution of 0.5×0.5
according to Hempel et al. (2013). The climate variables for
each site available were extracted from the grid cell of the
downscaled climate forcing dataset in which the forest plot
is located. The data can be used in very different ways by
the vegetation modelling community.
The “ISIMIP Fast Track” scenarios (ISIMIPFT) con-
sist of daily climate data available from five different
global climate models (GCMs) (HadGEM2-ES, IPSL-
CM5A-LR, MIROC-ESM-CHEM, GFDL-ESM2M and
NorESM1-M.) for all four RCPs (Warszawski et al.,
2014). The historical period lasts from 1950 to 2005 and
then splits up into the four RCPs from 2006 to 2099 for
each model. The RCPs cover future warming ranges of
about 0–9 C in the late 21st century compared to the
1980–2005 average (Fig. 2). These ISIMIPFT data are
best suited for scenario studies that require a large en-
semble of GCMs and RCPs.
The “ISIMIP2b” scenarios (ISIMIP2b) consist of daily
climate data available from four different GCMs (IPSL-
CM5A-LR, GFDL-ESM2M, MIROC5, HadGEM2-ES)
for RCP2.6 and RCP6.0 (Frieler et al., 2017; Lange,
2018) as well as RCP4.5 and RCP8.5. The historical
period lasts from 1861 to 2005 and then splits up into
the four RCPs for each GCM from 2006 to 2099. The
RCPs cover future warming ranges of about 1–9C in
the late 21st century compared to the 1980–2005 aver-
age (Fig. S1). For RCP2.6, RCP4.5 and RCP8.5 from
IPSL-CM5A-LR, HadGEM2-ES and MIROC5, addi-
tional data are also available for the period 2100–2299.
These long-term climatic pathways stabilize at around
1–2 C in the end of the 23rd century compared to
1980–2005 for RCP2.6, around 3–5 C for RCP4.5 and
up to 16 C for RCP8.5. For all four GCMs, there are
also time series of pre-industrial climatic conditions
available from 1661 to 2299 (or 1661–2099 for GFDL-
ESM2M), the so-called pre-industrial control run. The
pre-industrial climates from each GCM for the time pe-
riod 1661–1860 can be combined with the historical
climates from 1861 to 2005 and any future time pe-
riods from the corresponding GCM to create a long-
term time series of climate data from 1661 to 2299 (or
2099 depending on the GCM–RCP combinations) with-
out almost any resampling (Frieler et al., 2017). The
ISIMIP2b data are best suited to test the implications of
long-term stabilization pathways and different degrees
of warming relative to pre-industrial conditions in veg-
etation models.
The “ISIMIP2b locally bias-corrected” scenarios
(ISIMIP2bLBC) have the same structure as the
ISIMIP2b data but have been bias-corrected using an
improvement of the method of Hempel et al. (2013)
as described in Frieler et al. (2017) and Lange (2017)
and the local observed climatologies presented in
Sect. 3.2.3. The ISIMIP2bLBC data are hence best
suited for scenario studies that require climatic data to
be as consistent as possible with the observational data
(Fig. 3).
2.2.6 Atmospheric CO2concentrations
Time series of atmospheric CO2concentrations are provided
as annual, global data, hence as one time series for all sites
of the PROFOUND DB assuming a well-mixed atmosphere.
The historical time series of atmospheric CO2are based on
global atmospheric CO2concentrations from Meinshausen
et al. (2011) from 1765 to 2005 and have been extended
for the period 2006–2015 with data from Dlugokencky and
Tan (2014). The future annual atmospheric CO2concentra-
tions follow the four different Representative Concentration
Pathways (RCPs, RCP2.6, RCP4.5, RCP6.0 and RCP8.5)
from 2016 to 2500 from Meinshausen et al. (2011). Figure 4
shows the historical increase in CO2concentrations since
1765 and the projected future emissions according to the dif-
ferent RCPs. From RCP2.6 till RCP8.5 the total level of CO2
increases strongly, and also the date of stabilizing emissions
is reached much later in RCP8.5. RCP2.6 is the only RCP
that projects declining CO2levels in the long run.
2.2.7 Nitrogen deposition
The nitrogen deposition data, reported as total deposition of
reduced and oxidized wet and dry nitrogen deposition, re-
spectively, have been extracted for each site of the PRO-
FOUND DB from two different datasets which serve differ-
ent purposes.
EMEP data. For detailed model evaluation studies that
require the best possible estimates of local nitrogen de-
position, we extracted data from the “Co-operative pro-
gramme for monitoring and evaluation of long-range
transmission of air pollutants in Europe” (EMEP) for
the time period 1980–2014 (EMEP/CEIP, 2014a, b).
Sea-salt-corrected data are available from 1980 to 1995
in 5-year steps and from 1986 to 2014 at annual time
step and are derived by atmospheric transport modelling
(Simpson et al., 2012).
ISIMIP data. For model simulation studies, we also
provide nitrogen deposition estimates based on atmo-
spheric chemistry modelling for a historical time pe-
riod (1861–2005) and four future scenarios, where ni-
trogen deposition follows the four RCPs. The data are
further described in Lamarque et al. (2013a, b), sea-salt-
corrected and consistent with the global nitrogen depo-
sition data provided within ISIMIP (Frieler et al., 2017).
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Figure 3. Change in mean annual temperature (Tmean), annual precipitation sum (P) and annual sum of global radiation (R) over the time
period 1661–2299 relative to the 1980–2005 average for the ISIMIP2b locally bias-corrected (ISIMIP2bLBC) scenarios. Please note that the
two Solling sites have the same climate.
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Figure 4. Global atmospheric CO2concentrations provided for all sites in the PROFOUND DB. The historical time period extends from
1765 to 2015 and the scenarios from 2005 to 2500 for each RCP.
The data are taken from the global dataset without fur-
ther corrections and hence are not intended to represent
realistic, local forecasts but rather rough estimates of fu-
ture nitrogen projections.
For the 1980–2014 time period, the ISIMIP data are typ-
ically lower and less dynamic than the EMEP estimates
(Fig. 5). However, while they do not seem suitable for his-
torical model evaluations, they cover a much longer time pe-
riod and are clearly interesting for scenario studies because
they feature different nitrogen deposition pathways consis-
tent with RCP climates and CO2pathways. It is also im-
portant to note that measured throughfall of NO3and NH4
is on average lower than modelled total deposition, due to
canopy uptake (Marchetto et al., 2020). Moreover, for the
two Solling sites the data presented here are identical while
in reality total N deposition rates in the spruce stand should
be higher because of higher dry depositions. Actually, the
ratio between Solling spruce and Solling beech is 1.4 for
NH4throughfall fluxes, 1.6 for NO3throughfall fluxes, 1.4
for NH4total deposition and 1.4 for NO3total deposition,
both using a canopy budget model (Ulrich, 1994) for the pe-
riod 1980–2014. However, these ratios are not constant and
show an increasing trend over time.
2.2.8 Forest inventory data
For each site, the PROFOUND DB provides information
about the forest stand at tree and stand level (Table 4). The
data are available for different time periods and have differ-
ent measurement intervals, but generally they cover mostly
the second half of the 20th century and the first decade of
the 21st century (Table 1). The data also cover a wide ar-
ray of height–age and diameter at breast height (DBH)–age
relationships (Figs. 6–7). For seven out of nine sites individ-
ual tree DBH and height measurements are available. The
time series length ranges between 15 and 65 years within
the time period 1948–2015. For the Sorø site, the DBH and
heights have been reconstructed from tree-ring data (Babst et
al., 2014), and the full stand reconstruction is available from
1996 to 2010 at annual resolution (see Sect. S1 in the Sup-
plement). Individual tree data allow analysis and comparison
of model simulations with data on single-tree growth. From
the tree data, we calculated a range of widely used stand vari-
ables (see Table S8). Additional stand-level data are available
for some of the sites, such as leaf litter production or leaf area
index, and have been included (see Table S8).
2.2.9 Flux data
The carbon fluxes, i.e. net ecosystem exchange (NEE),
ecosystem respiration (RECO) and gross primary production
(GPP) are taken from the Tier One FLUXNET2015 dataset
(http://fluxnet.fluxdata.org/, last access: 5 June 2020). We
provide estimates of fluxes calculated using different esti-
mates for gap-filled and partitioned fluxes to give a rough
estimate of the uncertainty added to the long-term bud-
gets in the process. NEE data are filtered using two dif-
ferent methods to calculate uStar thresholds (Barr et al.,
2013, and a modified version of Papale et al., 2006; see also
FLUXNET2015, 2017). Daytime (i.e. Lasslop et al., 2012)
and night-time (i.e. Reichstein et al., 2005) refer to whether
ecosystem respiration parameters were estimated from only
night-time fluxes or also using daytime data (zero intercept of
GPP light response curve). In many cases the number of ac-
cepted night-time fluxes is low and the temperature range is
narrow, which leads to high uncertainty in the estimated res-
piration. This can be improved by also using daytime fluxes.
On the other hand in the daytime method the uncertainties of
photosynthetic light, temperature and possible vapour pres-
sure deficit (VPD) responses may be attributed to respira-
tion parameters. Further information about the daytime and
night-time methods is available in Lasslop et al. (2010) and
Reichstein et al. (2005) and also FLUXNET2015 (2017). We
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Figure 5. Total deposition of reduced (NHx) and oxidized (NOy) nitrogen (N) at each of the sites of the PROFOUND DB. The historical
period for the EMEP data extends from 1980 to 2014 and for the historical ISIMIP data from 1861–2005. The future scenarios are available
from 2006 to 2100 and follow the RCP2.6 and RCP6.0 scenarios. Please note that the two Solling sites have the same N depositions (see text
for further explanations).
also extracted different uncertainty estimates for each vari-
able. Additionally, we provide time series of the sensible and
latent heat flux, soil (soil water and soil temperature) and
meteorological variables at a 30 min time resolution from
the FLUXNET2015 database including measurement uncer-
tainty estimates. Table 5 provide an overview of the main
carbon fluxes at each of the sites featured in the PROFOUND
DB. Tables S9 and S11–S13 provides the full list of available
variables.
2.2.10 Remote sensing data
The PROFOUND DB includes remote sensing information
at different spatial scales and temporal frequencies, specific
for each product. We included five MODIS products (ORNL
DAAC, 2008a–e) and several vegetation indices calculated
from the surface reflectance data for each of the forest sites.
The original MODIS scenes are available at the NASA Land
Processes Distributed Archive Center (LP DAAC) (https:
//lpdaac.usgs.gov/, last access: 5 June 2020). The specific
time series included in the PROFOUND DB were down-
loaded from the Land Product Subset Web Service of the
Oak Ridge National Laboratory Distributed Active Archive
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Table 4. Summary of the main stand variables for the forest stands in the PROFOUND DB. The first number in each cell indicates the value
at the first measurement and the second number at the last measurement. The basal area-weighted mean height and DBH are shown for
all stands with the exception of Le Bray for which the arithmetic mean height and DBH are shown (marked by asterisks). The numbers in
brackets indicate different data availability for height than for the other variables.
No of. DBH Height BA Age Stem density
Name Main species obs. Year (cm) (m) (m2ha1) (year) (ha1)
Bily Kriz Picea abies 19 1997–2015 8.16–20.47 6.26–15.26 10.33–36.96 16–34 2408–1252
Collelongo Fagus sylvatica 5 1992–2012 27.95–33.65 22.03–24.08 32.25–43.76 100–120 905–740
Hyytiälä Picea abies 17 1995–2011 13.74–19.32 11.24–16.7 2.96–3.8 34–50 965–770
Hyytiälä Pinus sylvestris 17 1995–2011 15.89–20.58 12.61–18.62 12.64–18.33 34–50 870–684
KROOF Picea abies 8 1997–2010 30.96–37.49 (25.73) 30.26–39.66 47–60 512–434
(1) (1997)
KROOF Fagus sylvatica 8 1997–2010 26.5–31.64 (24.07) 12.44–13.2 54–67 324–220
(1) (1997)
Le BrayPinus pinaster 24 1986–2009 18.76–35.01 (14.61–22.44) 23.3–19.19 16–39 819–195
(18) (1991–2009)
Peitz Pinus sylvestris 13 1948–2011 8.96–23.54 6.75–17.86 20.66–36.36 48–111 4150–886
Solling (beech) Fagus sylvatica 16 1967–2014 40.19–53.4 25.45–30.78 26.99–25.52 120–168 245–130
Solling (spruce) Picea abies 17 1967–2014 32.25–48.74 24.51–33.36 44–49.46 85–133 595–290
Sorø Fagus sylvatica 24 1994–2017 28.99–48.25 24.23–31.15 18.50–29.76 62–87 407–199
Figure 6. Time series of tree diameter at breast height (DBH) versus age of the forest stands in the PROFOUND DB. The basal area-weighted
mean DBH is shown for all stands with the exception of Le Bray for which the arithmetic mean DBH is shown (marked by asterisks). For
Sorø, the DBHs have been reconstructed (see text in Sects. 4.9 and S1).
Center (ORNL DAAC) (https://daac.ornl.gov/MODIS/, last
access: 5 June 2020). The ORNL DAAC MODIS subsetting
web service is implemented to allow users access to mas-
sive amounts of remote sensing data (Santhana-Vannan et al.,
2011). In addition, a second set of vegetation indexes was
calculated from the reflectance values. A summary of this in-
formation is shown in Table 6. The full list of variables and
how they were aggregated is provided in Table S10.
The main difference among the forest sites is the data qual-
ity, which is highly dependent on the presence of clouds.
When possible, low-quality observations have been substi-
tuted by interpolated values, otherwise the cell was left blank.
In any case the alteration of the original data was minimal.
It is also important to note that the size of the pixel is large
compared to the plot size of the forest stands, which means
the pixel data also contain other vegetation types than the
ones present at the sites.
Three general types of data are included: (1) geophysi-
cal variables as measured from the MODIS sensor, i.e. re-
flectance and temperature; (2) spectral indexes derived di-
rectly from reflectance values at different wavelengths; and
(3) vegetation properties (i.e. FPAR, LAI, GPP and net pho-
tosynthesis) as estimated from physical variables through a
range of models.
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Figure 7. Time series of tree height versus age of the forest stands in the PROFOUND DB. The basal area-weighted mean height is shown
for all stands with the exception of Le Bray for which the arithmetic mean height is shown (marked by ). For Sorø, the heights have been
reconstructed (see text in Sects. 4.9 and S1).
Table 5. Summary of the observed carbon fluxes at the sites in the PROFOUND DB. Shown is the range (min and max) and the average (in
brackets) of the annual sums in the observational period. All data are estimates based on the CUTRef method with daytime data included for
RECO and GPP. GPP is expressed with negative values because it is considered a downward flux from the atmosphere. Likewise, negative
NEE values indicate a carbon sink and positive values a carbon source.
Name Years NEE (t C ha1) RECO (t C ha1) GPP (t C ha1)
Bily Kriz 2000–2008 9.117 to 3.277 (6.52) 5.478 to 10.295 (7.918) 20.477 to 11.071 (16.577)
Collelongo 1996–2014 25.129 to 3.36 (8.152) 4.495 to 15.936 (8.079) 26.675 to 5.259 (16.546)
Hyytiälä 1996–2014 8.167 to 1.22 (2.49) 1.668 to 11.511 (8.943) 14.984 to 10.0 (11.709)
Le Bray 1996–2008 7.396 to 0.104 (3.915) 8.236 to 21.609 (14.569) 23.651 to 12.648 (19.455)
Sorø 1996–2012 8.245 to 0.892 (1.92) 15.147 to 22.345 (17.335) 23.832 to 15.873 (19.163)
The year 2007 is without data for Hyytiälä and the year 2002 for Le Bray.
Although the MODIS sensor acquires daily information,
the PROFOUND DB includes only composite data; that is,
for each pixel the best value during a period of time (8 or
16 d) is selected as being representative of that specific pe-
riod. Spatial resolution is also specific for each product and
is dependent on the physical and technical limitations in the
acquisition process of the variables involved in the product
computation.
The NDVI and EVI at 250m spatial resolution com-
ing from the MOD13Q1 product were calculated from the
visible and near-infrared spectral regions. A temporal fre-
quency (16 d composite) was chosen to minimize the effect
of clouds. The EVI index was developed to correct for at-
mospheric and background effects so that it shows a larger
dynamic range in areas with high vegetation density (Didan
et al., 2015).
The spectral profiles in the whole optical domain (i.e. 459–
2155 nm) for each 8d composite are represented by the sur-
face spectral reflectance at seven wavelengths coming from
the MOD09A1 product at 500 m spatial resolution. The cri-
teria for the compositing process are low cloudiness, cloud
shadows and low solar zenith angle; when several of these
criteria are fulfilled the selection is based on the minimum
value in the blue band (Vermote et al., 2015).
The second set of spectral indexes was computed from
the MOD09A1 product. The indices based on the spectral
shape have the advantage of combining information on three
bands instead of two, and when the bands used are located
in the SWIR region relevant information related to water is
captured (Palacios-Orueta et al., 2005; Khanna et al., 2007;
Palacios-Orueta et al., 2012).
LAI is defined as the one-sided green leaf area per unit
ground area in broadleaf canopies and as one-half the to-
tal needle surface area per unit ground area in conifer-
ous canopies. The FPAR is the fraction of photosyntheti-
cally active radiation (400–700nm) that is absorbed by the
canopy (Myneni, 2015). Gross primary productivity and net
photosynthesis estimations are based on the light use ef-
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Table 6. Summary of the remote sensing data included in the PROFOUND DB. VIS, NIR and SWIR are the visible, near-infrared and
shortwave infrared regions of the electromagnetic spectrum. NDVI: normalized difference vegetation index; EVI: enhanced vegetation in-
dex; FPAR: fraction of photosynthetically absorbed radiation; LAI: leaf area index; GPP: gross primary productivity; NDWI: normalized
difference water index; AR: angle at red; ANIR: angle at NIR; AS1: angle at shortwave infrared 1; AS2: angle at shortwave infrared 2; SANI:
shortwave angle slope index; SASI: shortwave angle slope index.
MODIS Spatial Temporal
Variable source resolution (km) frequency (d) Time period
Reflectance (%) at seven spectral bands
in the optical domain VIS–NIR–SWIR
MOD09A1 0.5 8 2000–2015
Land surface temperature (night & day,
kelvin)
MOD11A2 1 8 2000–2015
NDVI, EVI MOD13Q1 0.25 16 2000–2015
FPAR, LAI
(dimensionless 1,1)
MOD15A2 1 8 2000–2015
GPP & net photosynthesis
(g C m2d1)
MOD17A2 1 8 2000–2014
EVI, NDVI, NDWI
(dimensionless 1,1)
Ratio indexes calculated from
MOD09A1
0.5 8 2000–2015
AR, ANIR, AS1, AS2
(radians, 0–3.14)
Angular indexes calculated
from MOD09A1
0.5 8 2000–2015
SANI (3.14–3.14)
SASI (314–314)
Angular normalized indexes
calculated from MOD09A1
0.5 8 2000–2015
Table 7. Generic future management scenarios for the main tree species featured in the PROFOUND DB.
Thinning Intensity (% of Interval Stand age for
Species regime basal area) (yr) final harvest References
Pinus sylvestris below 20 15 140 Pukkala et al. (1998), Fürstenau et al. (2007),
González et al. (2005), Lasch et al. (2005)
Picea abies below 30 15 120 Pape (1999), Pukkala et al. (1998), Hanewinkel
and Pretzsch (2000), Sterba (1987), Lähde et al.
(2010)
Fagus sylvatica above 30 15 140 Schütz (2006), Mund (2004), Hein and Dhôte
(2006), Cescatti and Piutti (1998)
Quercus robur/petraea above 15 15 200 Hein and Dhôte (2006), Fürstenau et al. (2007),
Štefanˇ
cík (2012), Kerr (1996), Gutsch et al.
(2011)
Pinus pinaster below 20 10 45 Loustau et al. (2005), De Lary (2015), Banos et
al. (2016)
ficiency (LUE) concept (Monteith, 1972) using satellite-
derived FPAR (from MOD15) and independent estimates
of PAR, besides other types of ancillary data. These are
highly aggregated variables that have gone through several
modelling steps already. Detailed information on the model
and information sources used can be found in Running and
Zhao (2015).
3 Description of the forest sites
The most northern site is Hyytiälä in Finland with a boreal
climate, while the most southern sites are Le Bray in France
and Collelongo in Italy with an oceanic and Mediterranean
montane climate, respectively. All other sites represent tem-
perate climatic conditions ranging, however, from oceanic
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Table 8. Planting information for the sites included in the PROFOUND DB. The numbers in brackets indicate plausible ranges (NA: not
available).
Density Age Height Age when DBH is
Name (ha1) (years) (m) reached (years) Remarks
Bily Kriz 4500 4 0.5 9 Historical planting density was
5000 ha1but current practices are
4500 ha1only.
Collelongo 10 000 4 1.3 4 Only a rough approximation; usually
natural regeneration is the regeneration
method. DBH =0.1 cm at height 1.3 m.
Hyytiälä 2250 (2000–2500) 2 0.25 (0.2–0.3) 6 (5–7) Regenerate as pure pine stand.
KROOF (beech) 6000 (5000–7000) 2 0.6 (0.5–0.7) 5 The planting density is for single-
species stands; hence when regenerat-
ing the two-species-stand KROOF, the
planting density of each species should
be halved.
KROOF (spruce) 2250 (2000–2500) 2 0.35 (0.3–0.4) 7 The planting density is for single-
species stands; hence when regenerat-
ing the two-species-stand KROOF, the
planting density of each species should
be halved.
Le Bray 1250 (1000–14 000) 1 0.2 (0.1–0.25) 3 (2–5) These are the current practices (De
Lary, 2015) and should be used for fu-
ture regeneration. Historically, the site
was seeded with 3000–5000 seedlings
per hectare and then cleared once or
twice to reach a density of 1250 ha1
at 7 years old when seedlings reach the
size for DBH recruitment.
Peitz 9000 (8000–10 000) 2 0.175 (0.1–0.25) 5 The “age when DBH is reached =5” is
an estimate.
Solling (beech) 6000 (5000–7000) 2 0.6 (0.5–0.7) 5 The actual stand was established in
1847 from natural regeneration. Until
the beginning of measurements in 1966,
the stand was regularly thinned. All fig-
ures in the table are estimates. Natu-
ral regeneration is the recommended re-
generation method of stand establish-
ment; stem count in 2014: 130.
Solling (spruce) 2250 (2000–2500) 2 0.35 (0.3–0.4) 7 The actual stand was planted in 1891 on
a former meadow. Until the beginning
of measurements in 1966, the stand was
regularly thinned. All figures in the ta-
ble are estimates; stem count in 2014:
290.
Sorø 6000 4 0.82 6 Planted in 1921, with stem count of
288 ha1in 2010 (Wu et al., 2013).
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(Belgium, Denmark) to temperate (France, Germany) to sub-
continental (Czech Republic). Unfortunately, sites represent-
ing more continental and (east) Mediterranean forests from
southern and southeastern Europe are missing.
3.1 Bily Kriz (CZ)
The Bily Kriz site belongs to the ICP Forests Level II net-
work and is a FLUXNET site located in the Moravian–
Silesian Beskydy Mountains, Czech Republic, at an altitude
of 875 ma.s.l. The climate is temperate with an annual mean
temperature of 7.4 C and an annual precipitation sum of
1434 mm over the 2000–2008 period. The soil is classified as
a Haplic Podzol. The site is typical for mountain regions of
temperate Europe such as the Black Forest, Bohemian Forest
Šumava and forested Carpathians (Hercynian (spruce–)fir
beech forests) but also the higher mountain belts in the (sub-
)Mediterranean. Stand-forming tree species for such sites are
Fagus sylvatica,Abies alba and Picea abies. Currently, a
large part of mixed mountain forests are strongly managed
for timber production. The main tree species occurring in
Bily Kriz are Picea abies, rarely with a small proportion
of Fagus sylvatica. The stand data represent an (even-aged)
Picea abies monoculture with a mean DBH of 19 cm (year
2015). The potential vegetation belongs to the geobiocoene-
type groups: Abieti-fageta (5AB3) – Abies alba Mill. +Fa-
gus sylvatica L. with understory: Calamagrostis arundinacea
(L.) Roth, Oxalis acetosella L., Vaccinium myrtillus L., De-
schampsia flexuosa (L.) Trin. More information about the site
can be found in Kratochvilova et al. (1989) and Meteorolog-
ical yearbook (2012).
3.2 Collelongo (IT)
The experimental site of Collelongo is located in Selva Pi-
ana, a pure Fagus sylvatica forest in Collelongo (AQ, central
Italy) at 1560 ma.s.l. Located 100 km from Rome, it is one of
the first Italian sites of the ICP network and also part of the
ILTER international network. The climate is Mediterranean
montane, with a mean annual temperature of 7.2 C and a
mean annual precipitation of 1179 mm in the period 1996–
2014. Bedrock consists of Cretaceous limestone. Soil depth
exhibits high spatial variability ranging from 40 to 100cm
and is classified as a Humic Alisol (Chiti et al., 2010) or Dys-
tric Luvisol according to the FAO classification. The stand is
a typical Apennine beech forest dominated by Fagus sylvat-
ica with sporadic trees of Taxus baccata. The phytosociologi-
cal association is Polysticho – Fagetum (Feoli and Lagonegro
1982). Currently, Collelongo constitutes a managed Fagus
sylvatica stand with mean DBH of 25 cm in 2012. In the area
around the eddy-flux tower there are only Fagus sylvatica
trees. Moreover the footprint of the tower is totally included
in the Fagus sylvatica forest. More information about the site
can be found in Chiti et al. (2010), Collalti et al. (2016) and
D’Andrea et al. (2019).
3.3 Hyytiälä (FI)
The most northern site included in the PROFOUND DB is
the ICP Forests Level II site Hyytiälä, Finland. It is also a
FLUXNET site and the coldest site with an annual temper-
ature of 4.4 C and 604 mm annual precipitation during the
1996–2014 period and lies at 185 ma.s.l. The soil is classi-
fied as a Haplic Podzol. Picea abies is the naturally dominant
tree species building Fennoscandian moss-rich spruce forests
with Pinus sylvestris. A Pinus sylvestris stand was sown
in 1962, today with admixtures of Picea abies and hard-
wood species (Betula pendula,Betula pubescens and Pop-
ulus tremula). Mean DBHs were 17cm for P. sylvestris, 5 cm
for P. abies and 7 cm for hardwood species in the year 2008.
More information about the site can be found in Haataja and
Vesala (1997), Rannik et al. (2004), Vesala et al. (2005), Il-
vesniemi et al. (2009), Mammarella et al. (2009), and Ilves-
niemi et al. (2010).
3.4 KROOF (DE)
The KROOF forest belongs to the “Kranzberg Forest Roof
Experiment” of the Technical University of Munich (TUM)
and the Helmholtz Zentrum München. The site is located
close to Freising, Germany, in the Kranzberger Forst in
502 ma.s.l. (wc-alt.). Mean annual temperature is around
8.2 C, and annual rainfall is around 849 mm during the pe-
riod 1998–2010. The soil type, Luvisol, is typical for the re-
gion. The potential natural vegetation is (sessile oak–) beech
forest (Fagus sylvatica, Quercus petraea, Quercus robur).
The establishment of the research plot dates back to 1992.
The mixed stand comprises large groups of Fagus sylvat-
ica surrounded by Picea abies with mean DBHs of 26 and
33 cm in 2010, respectively. Other occurring species are Acer
platanoides (20 cm), Pinus sylvestris (31 cm), Larix decidua
(26 cm) and Quercus robur (29 cm). More information about
the site can be found in Pretzsch et al. (1998, 2014) and
Matyssek et al. (2014).
3.5 Le Bray (FR)
The ICP Forests site Le Bray is located 20 km southwest
of Bordeaux, France, at an altitude of 61 ma.s.l. Mean
annual temperature is about 13.4 C, and precipitation is
920 mm during the 1996–2008 period, constituting a moder-
ate oceanic climate. The soil type is Arenosol (sandy and hy-
dromorphic podzol), which is one of the most common soils
in the region. The natural vegetation is formed by deciduous
broadleaf forests such as pedunculate oak forests (Quercus
robur), partly with Quercus pyrenaica,Quercus suber and
Pinus pinaster. The first measurements were made in 1986
in the monospecific planted Pinus pinaster stand. The site
experienced a storm in 1999 and lost a large number of trees.
In 2009, the mean DBH was 35 cm. The final clear cut of
the site occurred at the beginning of 2009. More information
Earth Syst. Sci. Data, 12, 1295–1320, 2020 https://doi.org/10.5194/essd-12-1295-2020
C. P. O. Reyer et al.: PROFOUND Database 1311
about the site can be found in Porté and Loustau (1998), Bosc
et al. (2003) and Berbigier et al. (2001).
3.6 Peitz (DE)
Peitz is a long-term research plot in eastern Brandenburg,
Germany. The site lies at about 50 ma.s.l. The annual rain-
fall amounts to more than 608 mm, and annual mean temper-
ature is around 9.2 C during the 1901–2010 period. The soil
type is a Dystric Cambisol. The potential natural vegetation
is a south Scandinavian, east central European dwarf shrub
and lichen-rich pine forest mix (Pinus sylvestris), partly
with Quercus robur in the understorey, with Vaccinium vitis-
idaea,Calluna vulgaris,Cladina spp. and Dicranum poly-
setum on sandy soils and siliceous rocks. The forest is a pine
forest (Pinus sylvestris) with a mean DBH of around 23 cm
and a stand height of 17 m in 2011. The understorey consists
partly of Quercus robur. Measurements were started in 1948.
More information about this site can be found in Riek and
Stähr (2004) and Noack (2011, 2012) and about the climate
data in Gerstengarbe et al. (2015).
3.7 Solling beech (DE)
Solling 304 is a long-term intensive forest monitoring plot
(Level II) of the ICP Forests network in central Germany.
The plot is also part of the LTER (site LTER_EU_DE_009)
and of the permanent soil monitoring programme of the state
of Lower Saxony. The site is situated in the centre of the
Solling plateau at an elevation of about 500m a.s.l. The mean
temperature was around 6.8 C and the mean annual rain-
fall amounted to 1113 mm during the period 1960–2013. The
bedrock consist of Triassic sandstone covered with a 60 to
80 cm deep solifluction layer of loess material from which
the soil, classified as an Haplic Cambisol, has developed. The
humus type is a typical Moder. The tree layer consists only of
European beech (Fagus sylvatica L.). Oxalis acetosella and
Luzula luzuloides are the major species of the sparse ground
vegetation. Actual vegetation was assigned to the Luzulo-
Fagetum typicum and is close to the potential natural veg-
etation. The forest is a 168-year-old stand with a mean DBH
of 50 cm and a mean height of 30.7m in 2016. More infor-
mation about the site can be found in Meiwes et al. (2009),
Meesenburg et al. (2009, 2016), Panferov et al. (2009), Le
Mellec et al. (2010), and Fleck et al. (2016).
3.8 Solling spruce (DE)
Solling 305 is also a long-term intensive forest monitoring
plot of the ICP Forests Level II network in central Ger-
many. As the Solling beech site it belongs to the LTER
(site LTER_EU_DE_009) and is a permanent soil monitor-
ing plot of the state of Lower Saxony. It is situated close to
the Solling beech site at an elevation of about 508m a.s.l.
and has similar site conditions as the Solling beech stand.
Potential natural vegetation is a Luzulo luzuloido Fagetum.
Dominant species of the actual ground vegetation are Vac-
cinium myrtillus,Polytrichum formosum and Deschampsia
flexuosa (Bolte et al., 2004). The forest is a 133-year-old Nor-
way spruce (Picea abies) stand with a mean DBH of 46.6 cm
and a mean height of 33.1 m in 2016. More information about
the site can be found in Le Mellec et al. (2010), Bonten et
al. (2011), Meesenburg et al. (2016), Fleck et al. (2016) and
Wegehenkel et al. (2017).
3.9 Sorø (DK)
The ICOS site Sorø (DK-Sor in the FLUXNET and ICOS
databases) is located in Denmark at an elevation of 40m a.s.l.
The climate is warm temperate and fully humid with a mean
annual temperature of 9 C and annual precipitation sum of
774 mm during the period 1996–2010. The soil has been
classified as Alfisols and Mollisols. Potential natural vege-
tation is deciduous broadleaved forest dominated by Fagus
sylvatica. Other species occurring in the area are Fraxinus
excelsior,Larix decidua,Picea abies,Quercus spp. and Acer
spp. However, the region is mostly used as cropland. Data
on tree DBH are reconstructed from tree-ring measurement
(Babst et al., 2014) and historical management information
for the time period from 1994 to 2017. Stand data are derived
from these data for the time period from 1994 to 2017 (see
Sect. S1). The mean DBH of this Fagus sylvatica stand was
41 cm in the year 2017. More information about the site can
be found in Ladekarl (2001), Pilegaard et al. (2003, 2011)
and Wu et al. (2013). More information about the site can be
found in Ladekarl (2001), Pilegaard et al. (2003, 2011) and
Wu et al. (2013).
4 Forest management of the sites
The sites available in the PROFOUND DB are managed
forests, and the historic management can be derived from the
tree and stand-level data (in terms of reduction of stem num-
bers). However, for future scenario studies, generic, simple
management and planting guidelines are available (Tables 7–
8). This future management corresponds best to “intensive
even-aged forestry” as defined by Duncker et al. (2012).
5 The PROFOUND R package (ProfoundData)
The ProfoundData R package provides functions to access
the PROFOUND DB (Figs. S2 and S3). The ProfoundData
package plus a detailed vignette explaining the functional-
ities are available on CRAN (https://CRAN.R-project.org/
package=ProfoundData, last access: 5 June 2020). The Pro-
foundData package serves as an interface for users that want
to access the PROFOUND DB as a relational database via
the R statistical software (R Core Team, 2016). The follow-
ing main functions are included to achieve this goal:
https://doi.org/10.5194/essd-12-1295-2020 Earth Syst. Sci. Data, 12, 1295–1320, 2020
1312 C. P. O. Reyer et al.: PROFOUND Database
“getData” to download data (data can be downloaded
for one forest site and one underlying dataset at a time);
“browseData” to check the available forest sites,
datasets, variables for a dataset, datasets for a forest
site and the database version, metadata, data policy and
original data source;
“plotData” to quickly inspect any variable of the
datasets visually;
“summarizeData” to summarize data from the database;
“queryDB” to pass self-defined queries;
“writeSim2netCDF” to write netCDF files and can be
used to convert data (and other files such as model sim-
ulation output) into netCDF files.
While the ProfoundData R package is meant to provide easy
access to the PROFOUND DB, the database is also fully
functional without the R package.
6 Data availability
The PROFOUND Database
(https://doi.org/10.5880/PIK.2020.006/, Reyer et al.,
2020) is available under the Creative Commons Attribution-
NonCommercial 4.0 International license (CC BY-NC
4.0). The PROFOUND R Package (ProfoundData,
https://CRAN.R-project.org/package=ProfoundData, last
access: 5 June 2020, Silveyra Gonzalez et al., 2020) is
available via a GLP3 license. An earlier version of the
database, including an outdated reconstruction of the Sorø
tree data has been published as Reyer et al. (2019).
7 Conclusions
A wide range of data are needed to properly evaluate com-
plex process-based vegetation models. The PROFOUND
database compiles data from soil, climate, stand and flux
measurements with data from remote sensing, atmospheric
nitrogen modelling and climate modelling. Moreover, by
providing data at 0.5×0.5grid level plus locally bias-
corrected climate data, the datasets can be used to compare
local forest models to global vegetation models. The PRO-
FOUND database thus facilitates model evaluation, calibra-
tion, uncertainty analysis and model intercomparisons, high-
lighting the immense value of long-term environmental mon-
itoring data for robust inferences about causal processes and
future dynamics of forests.
Earth Syst. Sci. Data, 12, 1295–1320, 2020 https://doi.org/10.5194/essd-12-1295-2020
C. P. O. Reyer et al.: PROFOUND Database 1313
Appendix A: List of FLUXNET sites
Table A1. List of FLUXNET sites used in PROFOUND DB.
Flux sites FLUXNET ID Data years Publication Funding
Bily Kriz CZ-BK1 2000–2008 Kratochvílová et al. (1989), Meteoro-
logical Yearbook (2012)
Ministry of Education, Youth and
Sports of CR within the CzeCOS pro-
gramme, grant number LM2015061
Collelongo IT-Col 1996–2014 Chiti et al. (2010) EUROFLUX, CARBOEUROFLUX,
CarboEurope, CARBO AGE, Car-
boExtreme
Hyytiälä FI-Hyy 1996–2014 Haataja and Vesala (1997), Rannik et
al. (2004), Vesala et al. (2005), Ilves-
niemi et al. (2009), Mammarella et
al. (2009), and Ilvesniemi et al. (2010)
ICOS, EUROFLUX, CARBOEU-
ROFLUX, CarboEurope, CarboEx-
treme and by the Academy of Finland
Centre of Excellence programme,
projects 118615, 141135 and 272041
Le Bray FR-LBr 1996–2008 Porté and Loustau (1998), Bosc et
al. (2003), and Berbigier et al. (2001)
INRA, EUROFLUX, CARBOEU-
ROFLUX, CarboEurope, CARBO
AGE, CarboExtreme
Sorø DK-Sor 1996–2012 Ladekarl (2001), Pilegaard et al. (2003,
2011) and Wu et al. (2013)
EUROFLUX, CarboEurope,
CarboEurope-IP, NITRO-EUROPE,
CarboExtreme and Risø-National Lab-
oratory (DK) and technical University
of Denmark (DTU)
https://doi.org/10.5194/essd-12-1295-2020 Earth Syst. Sci. Data, 12, 1295–1320, 2020
1314 C. P. O. Reyer et al.: PROFOUND Database
Supplement. The supplement related to this article is available
online at: https://doi.org/10.5194/essd-12-1295-2020-supplement.
Author contributions. CPOR and RSG contributed equally to the
paper. CPOR and FH initiated the research. CPOR RSG, KD and
FH designed the PROFOUND database. CPOR, RSG, YH and KD
harmonized and prepared data for the PROFOUND database. RSG
programmed the PROFOUND database and R package together
with FH, FB and JS. LK and JK provided data for Bily Kriz. AC,
GM, CT and EA provided data for Collelongo. PK, AM, TV, IM
and JP provided data for Hyyitälä. TR and HP provided data for
KROOF. DL, LMB, PB, DP and SL provided data for Le Bray. MN
and PLB provided data for Peitz. HM, SF and MW provided data
for the Solling sites. AI, KP and FB provided data for Sorø. DC and
MV prepared the EMEP nitrogen data. HT and MB prepared the
ISIMIP nitrogen data. AP, VC and RSG prepared the MODIS data.
MB, JV, SL and HK prepared the climate data. SL bias-corrected
the climate data. MM and MG checked the data and R Package. All
other authors provided expertise on individual datasets and how to
prepare them. CPOR wrote the manuscript with the support of all
authors.
Competing interests. The authors declare that they have no con-
flict of interest.
Acknowledgements. We are grateful for the support of all con-
tributing data entities. The climate scenarios have been provided
by ISIMIP (BMBF, grant no. 01L1201A1). The initial plot selec-
tion was supported with data from the International Co-operative
Programme on Assessment and Monitoring of Air Pollution Ef-
fects on Forests (ICP Forests) operating under the UNECE Conven-
tion on Long-range Transboundary Air Pollution (CLRTAP). The
data collection in Bily Kriz was supported by the Ministry of Ed-
ucation, Youth and Sports of CR within the CzeCOS programme,
grant number LM2015061. Data collection at the Collelongo site
was supported by the projects EUROFLUX, CANIF, CARBOEU-
ROFLUX, FORCAST, CarboEurope and PRIN-MIUR. Activity
and data analysis at the site are currently funded by resources avail-
able from the Ministry of University and Research (FOE-2019),
under projects CNR DTA.AD003.474 and CNR DBA.AD003.139.
The Hyytiälä data collection was supported by the projects EU-
ROFLUX, CARBOEUROFLUX, CarboEurope and CarboExtreme
and by the Academy of Finland Centre of Excellence programme,
projects 118615, 141135 and 272041. The KROOF data were pro-
vided by TU Munich funded through the DFG – Sonderforschungs-
bereich SFB 607 and the DFG – KROOF project “Interactions be-
tween Norway spruce and European beech under drought” (PR
292/12-1, MA 1763/7-1, MU 831/23-1) as well as by the Bavar-
ian State Ministry for Nutrition, Agriculture and Forestry and the
Bavarian State Ministry for Environment and Health and BaySF
(Bavarian State Forest Enterprise). The data for Le Bray were kindly
provided by INRA funded through the projects EUROFLUX, CAR-
BOEUROFLUX, CarboEurope, CARBO AGE and CarboExtreme.
The Peitz data were kindly provided by Eberswalde Forestry Com-
petence Centre. We are grateful to the Northwest German For-
est Research Institute, Göttingen, for providing the Solling Data.
Solling Data from January 2009 to June 2011 were co-funded
by LIFE+and the Regulation (EC) no. 614/2007 of the Euro-
pean Parliament and of the Council, project FutMon (Further De-
velopment and Implementation of an EU-level Forest Monitoring
System). The Sorø data collection has been funded through the
EU projects EUROFLUX, CarboEurope, CarboEurope-IP, NitroEu-
rope, CarboExtreme and Risø-National Laboratory (DK) and the
Technical University of Denmark (DTU). This work used eddy
covariance data acquired and shared by the FLUXNET commu-
nity, including these networks: CarboEurope-IP, CARBOITALY
and ICOS. The FLUXNET eddy covariance data processing and
harmonization were carried out by the ICOS Ecosystem Thematic
Center, AmeriFlux Management Project and Fluxdata project of
FLUXNET, with the support of CDIAC, and the OzFlux, Chi-
naFLUX and AsiaFLUX offices. Graham Weedon was supported
by the Joint DECC and Defra Integrated Climate Programme –
DECC/Defra (GA01101). CPOR and RSG acknowledge support
from the German Federal Office for Agriculture and Food (BLE,
grant no. 2816ERA06S). Friedrich Bohn acknowledges funding
from the project “Inside out” (no. POIR.04.04.00-00-5F85/18-00)
funded by the HOMING programme of the Foundation for Polish
Science co-financed by the European Union under the European Re-
gional Development Fund. Hyungjun Kim acknowledges the Grant-
in-Aid for Specially promoted Research 16H06291 and Scientific
Research (18KK0117) from the Japan Society for the Promotion
of Science. We are also grateful to Kirsten Elger, Robert Gieseke,
Katja Henning-Hofmann and Michael Flechsig for their support to
make the database open access. We are grateful to the many unmen-
tioned technicians and students for their substantial help to maintain
the continuous long-term field observations.
Financial support. The PROFOUND Database has been devel-
oped based on work from COST Action FP1304 PROFOUND
(Towards Robust Projections of European Forests under Climate
Change), supported by COST (European Cooperation in Science
and Technology, https://www.cost.eu/, last access: 5 June 2020),
the Inter-Sectoral Impact Model Intercomparison project (ISIMIP,
grant no. 01L1201A1).) and the I-Maestro project (“Innovative for-
est MAnagEment STRategies for a resilient bioecOnomy under cli-
mate change and disturbances”, grant nos. 773324 and 22035418)
funded by the ERA-NET Cofund Forest-Value and benefited from
discussions in the IUFRO Task Force on Climate Change and Forest
Health.
Review statement. This paper was edited by David Carlson and
reviewed by two anonymous referees.
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Forest management practices might act as nature-based methods to remove CO2 from the atmosphere and slow anthropogenic climate change and thus support an EU forest-based climate change mitigation strategy. However, the extent to which diversified management actions could lead to quantitatively important changes in carbon sequestration and stocking capacity at the tree level remains to be thoroughly assessed. To that end, we used a state-of-the-science bio-geochemically based forest growth model to simulate effects of multiple forest management scenarios on net primary productivity (NPP) and potential carbon woody stocks (pCWS) under twenty scenarios of climate change in a suite of observed and virtual forest stands in temperate and boreal European forests. Previous modelling experiments indicated that the capacity of forests to assimilate and store atmospheric CO2 in woody biomass is already being attained under business-as-usual forest management practices across a range of climate change scenarios. Nevertheless, we find that on the long-term, with increasing atmospheric CO2 concentration and warming, managed forests show both higher productivity capacity and a larger potential pool size of stored carbon than unmanaged forests as long as thinning and tree harvesting are of moderate intensity.
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