Journal of Geophysical Research - Biogeosciences
Supporting Information for
Effect of spatial sampling from European flux-towers for estimating carbon and
water fluxes with artificial neural networks
Dario Papale1,2, T. Andrew Black3, Nuno Carvalhais4,5, Alessandro Cescatti6, Jiquan
Chen7, Martin Jung4, Gerard Kiely8, Gitta Lasslop9, Miguel D. Mahecha4, Hank
Margolis10, Lutz Merbold11, Leonardo Montagnani12,13, Eddy Moors14,15, Jørgen E.
Olesen16, Markus Reichstein4, Gianluca Tramontana1, Eva van Gorsel17, Georg
Wohlfahrt18,19, Botond Ráduly20
1-Department for innovation in biological, agro-food and forest systems, University of Tuscia, 01100 Viterbo,
Italy
2-CzechGlobe, Global Change Research Centre AS CR, 60300 Brno, Czech Republic
3- Faculty of Land and Food Systems, University of British Columbia, Vancouver, BC, V6T 1Z4 Canada
4-Department Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, 07745 Jena, Germany
5-Departamento de Ciências e Engenharia do Ambiente, DCEA, Faculdade de Ciências e Tecnologia, FCT,
Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
6-Institute for Environment and Sustainability, Joint Research Centre, European Commission, 21027 Ispra
(VA), Italy
7-CGCEO/Department of Geography, Michigan State University, East Lansing, MI 48823, USA
8- Civil and Environmental Engineering Dept., and Environmental Research Institute, University College
Cork, Ireland.
9- Max Planck Institute for Meteorology, Fire in the Earth System, Land in the Earth System, Hamburg,
Germany
10-Centre d'étude de la forêt, Université Laval, Quebec, Quebec, G1V 0A6, Canada
11- Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zurich, 8092
Zurich, Switzerland
12- Forest Services, Autonomous Province of Bolzano, Via Brennero 6, 39100 Bolzano, Italy
13-Faculty of Sciences and Technology, Free University of Bolzano, Piazza Università 5, 39100
Bolzano,Italy.
14-Alterra Wageningen UR, PO Box 47, 6700 AA Wageningen, Netherlands
15-VU University Amsterdam, Boelelaan1085, 1105 HV Amsterdam, Netherlands
16- Department of Agroecology, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
17-CSIRO Oceans and Atmosphere Flagship, Yarralumla, Australia
18- Institute for Ecology, University of Innsbruck, 6020 Innsbruck, Austria
19- European Academy Bolzano, 39010 Bolzano, Italy
20-Department of Bioengineering, Sapientia Hungarian University of Transylvania, 530104 Miercurea Ciuc,
Romania
1
Contents of this file
Text S1
Figures S1 to S4
Additional Supporting Information (Files uploaded separately)
Caption for Table S1 (including references) that is uploaded as csv file
Introduction
The supporting information includes the result of the 10-fold validation performed to
evaluate the ANN error in the extrapolation (Figure S1) and additional result of the
simulations for the extrapolation in time (Figure S2) and in space (Figures S3, S4 and S5).
A supporting table (Table S1, uploaded as csv file) reports the list of the sites used and in
which of the different exercises have been included (validation, extrapolation in time
and space, GPP and LE). In this document, the table caption and the references list
related to the table are included.
Text S1.
The validation of the ANN’s capacity to extrapolate flux estimates was done using a 10fold approach where 10 groups of sites were created by excluding 10% of the sites every
time in a way that all the sites are excluded once. The comparison between site level
(directly measured in case of NEE and LE, derived using partitioning methods for GPP)
and modeled data was done comparing the monthly values and the monthly mean
annual values (average of all the months available for the site) to assess the inter-site
variability simulation capacity. The procedure followed was the same described in Jung
et al. [2011]. Figure S1 shows the result of this validation activity.
2
Figure S1. Comparison of measured (direct measurements for LE and NEE, flux
partitioning results based on NEE data for GPP) and predicted monthly values (10-fold
validation) for GPP, NEE and LE fluxes. The inter-site plots report the monthly mean
3
annual value for each site in gC m-2 day-1 for GPP and NEE and in MJ m-2 day-1 for LE. In
blue the 1:1 line.
Figure S2. Mean absolute error (MAE) and correlation coefficient (R) obtained in the
extrapolation in time in estimating GPP and LE. Each boxplot represents median, interquartile range (IQR), full range and outliers (indicated with “+” and defined as points
higher or lower respectively 75th percentile + [1.5*IQR] or 25th percentile – [1.5*IQR])
of the results obtained simulating each site. Data for ANN training have been selected
according to two different splitting procedures based on time: a) all site time series split
in two equally long periods; b) all European sites time series split in two equally long
periods. “t->” indicates the use of the oldest part of the time series to predict the
newest, “t<-“ indicates the opposite.
4
Figure S3. The annual mean flux (gC m-2 day-1 for GPP, MJ m-2 day-1 for LE) of Europe in
2005, estimated by ANNs trained with subsample of the available sites and the same
number of data points in each extraction class. The boxplots show the distribution of
GPP and LE results obtained with 50 random extractions of the subset of sites. Red
crosses indicate outliers. The number of data points extracted was always 300 and the
number of sites ranged between 10 and 30 (15 from Europe and 15 from North
America), i.e. 10 months from 30 sites, 12 months from 25 sites, 15 months from 20
sites, 20 months from 15 sites and 30 months from 10 sites. These results can not be
directly compared with the one presented in Figure 3 of the paper because the number
5
of datapoints is sensibly minor and the sites are from Europe and North America,
however the similar trend with convergence to a similar value when the number of sites
increase (but with less data points from each site).
Figure S4. Uncertainty component in the estimation of annual GPP due to the size of the
training dataset used to parameterize an ANN, expressed as percentage in respect to
6
the reference value (median of the 50 values obtained using all the 67 sites). For each
map, the values reported are the median of the absolute difference between each of
the 50 extractions and the reference value estimated using all the sites. The lower right
map (All sites) shows the uncertainty in model parameterization when all sites are used.
Note that the color scale is not linear.
7
Figure S5. Uncertainty component in the estimation of annual LE due to the size of the
training dataset used to parameterize an ANN, expressed as percentage in respect to
the reference value (median of the 50 values obtained using all the 67 sites). For each
map, the values reported are the median of the absolute difference between each of
the 50 extractions and the reference value estimated using all the sites. The lower right
map (All sites) shows the uncertainty in model parameterization when all sites are used.
Note that the color scale is not linear
Table S1. List of sites used in the different activities described in the manuscript: “PFT” =
Plant Functional Type according to the IGBP classification, “Area” = site continent (EU =
Europe, NA=North America, Other= Africa, Australia, South America, Asia), “Space Extr”
= sites used in the analysis of the uncertainty in extrapolation in space, “Time Extr” =
sites used to assess the uncertainty in extrapolation in time, “Fig.S1 GPP/NEE” and
“Fig.S1 LE” = sites used in the cross-validation presented in Fig. S1 for GPP and NEE and
for LE, respectively (same sites used in Jung et al. [2011]).
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