Semi-empirical modeling of abiotic and biotic factors controlling ecosystem respiration across eddy

65 Pages·2010·3 MB·English

Global Change Biology

Semi-empirical modeling of abiotic and biotic factors
controlling ecosystem respiration across eddy covariance
sites

F

o
Journal: Global Change Biology
r
Manuscript ID: GCB-10-0015
Wiley - Manuscript type: Primary Research Articles
R
Date Submitted by the
07-Jane-2010
Author:
Complete List of Authors: Migliavaccva, Mirco; University of Milano-Bicocca, Remote Sensing of
Environmentail Dynamics; Max Planck Institute for Biogeochemistry,
Model Data Integration Group
Reichstein, Marekus; Max Planck Institute for Biogeochemistry,
Model Data Integration Group
Richardson, Andrew; Harvard University, Department of Organismic
w
and Evolutionary Biology

Colombo, Roberto; University of Milano-Bicocca, Remote Sensing of
Environmental Dynamics
Sutton, Mark A.; Centre for Ecology and Hydrology, Edinburgh
O
Research Station
Lasslop, Gitta; Max Planck Institute for Biogeochemistry, Model
n
Data Integration Group
Wohlfahrt, Georg; University of Innslbruck, Institute of Ecology
Tomelleri, Enrico; Max Planck Institute for Biogeochemistry, Model
Data Integration Group y
Carvalhais, Nuno; Universidade Nova de Lisboa, Faculdade de
Ciências e Tecnologia; Max Planck Institute for Biogeochemistry,
Model Data Integration Group
Cescatti, Alessandro; European Commission, DG-JRC, Institute for
Environment and Sustainability
Mahecha, Miguel; Max Planck Institute for Biogeochemistry, Model
Data Integration Group; Swiss Federal Institute of Technology-,
Department of Environmental Sciences
Montagnani, Leonardo; Provincia Autonoma di Bolzano, Agenzia per
l'Ambiente, Servizi Forestali
Papale, Dario; University of Tuscia, DISAFRI
Zaehle, Sönke; Max Planck Institute for Biogeochemistry,
Department for Biogeochemical System
Arain, M Altaf; McMaster University, School of Geography & Earth
Sciences
Arneth, Almut; Lund University, 13- Department of Physical
Geography and Ecosystems Analysis
Page 1 of 62 Global Change Biology
1
2
3
4 Black, T Andrew; University of British Columbia, Faculty of Land
and Food Systems
5
Dore, Sabina; Northern Arizona University, School of Forestry
6
Gianelle, Damiano; Fondazione Edmund Mach, Centro di Ecologia
7
Alpina
8 Helfter, Carole; Centre for Ecology and Hydrology, Edinburgh
9 Research Station
10 Hollinger, David; USDA Forest Service, NE Research Station
11 Kutsch, Werner; Johann Heinrich von Thünen Institut, Institut für
12 Agrarrelevante Klimaforschung
Law, Beverly; Oregon State University, College of Forestry
13
Lafleur, Peter M; Trent University, 20- Department of Geography
14
Nouvellon, Yann; CIRAD, Persyst
15
Rebmann, Corinna; Max-Planck Institute for Biogeochemistry,
16 Biogeochemical Processes; University of Bayreuth, Department of
17 Micrometeorology
18 da Rocha, Humberto; Universidade de São Paulo, Dept. of
19 FAtmospheric Sciences
20 Rodeghiero, Mirco; Fondazione Edmund Mach, Centro di Ecologia
oAlpina
21
Olivier, Roupsard; CIRAD, Persyst; Centro Agronómico Tropical de
22 r
Investigación y Enseñanza, CATIE
23
Sebastià, Maria-Teresa; University of Lleida, Agronomical
24 Engineering School; Forest Technology Centre of Catalonia,
25 LRaboratory of Plant Ecology and Botany
26 Seufert, Guenther; Institute for Environment and Sustainability,
27 Europeean Commission, DG-JRC
Soussana, Jean-Francoise; Institut National de la Recherche
28
Agronomivque
29
van der Moleni, Michiel K; University de Boeleaan, Department of
30
Hydrology and Geo-Environmental Sciences
31
e
32 Ecosystem Respiration, Productivity, FLUXNET, Eddy Covariance,
Keywords:
33 Leaf Area Index, Inverse Modeling
34 w
In this study we examine d ecosystem respiration (RECO) data from
35
104 sites belonging to FLUXNET, the global network of eddy
36
covariance flux measurements. The main goal was to identify the
37 main factors involved in thOe variability of RECO: temporally and
38 between sites as affected by climate, vegetation structure and plant
39 functional type (PFT) (evergreenn needleleaf, grasslands, etc.).
40 We demonstrated that a model usingl only climate drivers as
41 predictors of RECO failed to describe part of the temporal variability
42 in the data and that the dependency oyn gross primary production
(GPP) needed to be included as an additional driver of RECO. The
43
maximum seasonal leaf area index (LAIMAX) had an additional
44
effect that explained the spatial variability of reference respiration
45 (the respiration at reference temperature Tref=15°C, without
Abstract:
46 stimulation introduced by photosynthetic activity and without water
47 limitations), with a statistically significant linear relationship
48 (r2=0.52 p<0.001, n=104) even within each PFT. Besides LAIMAX,
49 we found that the reference respiration may be explained partially
by total soil carbon content. For undisturbed temperate and boreal
50
forest a negative control of the total nitrogen deposition on the
51
reference respiration was also identified.
52
We developed a new semi-empirical model incorporating abiotic
53 factors (climate), recent productivity (daily GPP), general site
54 productivity and canopy structure (LAIMAX) which performed well
55 in predicting the spatio-temporal variability of RECO, explaining
56 >70% of the variance for most vegetation types. Exceptions include
tropical and Mediterranean broadleaf forests and deciduous
57
58
59
60
Global Change Biology Page 2 of 62
1
2
3
4 broadleaf forests. Part of the variability in respiration that could not
be described by our model could be attributed to a range of factors,
5
including phenology in deciduous broadleaf forests and
6
management practices in grasslands and croplands.
7
8

9

10
11
12
13
14

15
16
17
18
F
19
20
o
21
22 r
23
24
25 R
26
27 e
28
v
29
i
30
31
e
32
33
34 w

35
36
37 O
38
39 n
40 l
41
42 y
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
Page 3 of 62 Global Change Biology
1
2
3
1 Semi-empirical modeling of abiotic and biotic factors controlling ecosystem
4
5 2 respiration across eddy covariance sites
6 3
7 4 Mirco Migliavacca1,2, Markus Reichstein2, Andrew D. Richardson3, Roberto Colombo1, Mark A.
8
5 Sutton4, Gitta Lasslop2, Georg Wohlfahrt5, Enrico Tomelleri2, Nuno Carvalhais6,2, Alessandro
9
6 Cescatti7, Miguel D. Mahecha2,8, Leonardo Montagnani9, Dario Papale10 , Sönke Zaehle 11, Altaf
10
11 7 Arain12, Almut Arneth13, T. Andrew Black14, Sabina Dore15, Damiano Gianelle16, Carole Helfter4,
12 8 David Hollinger17, Werner L. Kutsch18, Beverly E. Law19, Peter M. Lafleur20, Yann Nouvellon21,
13 9 Corinna Rebmann22,23, Humberto Ribeiro da Rocha24, Mirco Rodeghiero16, Olivier Roupsard21,25,
14
10 Maria-Teresa Sebastià26,27, Guenther Seufert7, Jean-Francoise Soussana28, Michiel K. van der
15
11 Molen29
16
17 12
18 13 1- Remote Sensing of Environmental Dynamics Laboratory, DISAT, University of Milano-
19 14 Bicocca, Milano, Italy.
20 15 2- Model Data InteFgration Group, Max Planck Institute for Biogeochemistry, Jena, Germany.
21
16 3- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge MA,
22
23 17 USA. o
24 18 4- Centre for Ecology andr Hydrology, Edinburgh Research Station, Bush Estate, Penicuik,
25 19 Midlothian, Scotland, EH 26 0QB, UK.
26 20 5- Institut für Ökologie, Universität Innsbruck, Innsbruck, Austria.
27 21 6- Faculdade de Ciências e TeRcnologia, FCT, Universidade Nova de Lisboa, 2829-516,
28
22 Caparica, Portugal.
29 e
30 23 7- European Commission, DG-JRC, Institute for Environment and Sustainability, Climate
31 24 Change Unit, Via Enrico Fermi 27v49, T.P. 050, 21027 Ispra (VA), Italy.
32 25 8- Department of Environmental Scienceis, Swiss Federal Institute of Technology-ETH Zurich,
33
26 8092 Zurich, Switzerland.
e
34
27 9- Agenzia Provinciale per l'Ambiente, Via Amba-Alagi 5, 39100 Bolzano, Italy.
35
36 28 10- DISAFRI, University of Tuscia, via C. de Lwellis, 01100 Viterbo Italy.
37 29 11- Department for Biogeochemical System, Max P lanck Institute for Biogeochemistry, Jena,
38 30 Germany.
39 31 12- McMaster University, School of Geography & Earth Sciences, 1280 Main Street West,
O
40
32 Hamilton, ON, L8S 4K1, Canada.
41
42 33 13- Department of Physical Geography and Ecosystems Annalysis, Lund University, Sölvegatan
43 34 12, SE-223 62 Lund, Sweden. l
44 35 14- Faculty of Land and Food Systems, University of British Columbia, Vancouver, BC,
y
45 36 Canada.
46 37 15- Department of Biological Sciences and Merriam-Powell Center for Environmental
47
38 Research, Northern Arizona University, Flagstaff, Arizona, USA.
48
49 39 16- IASMA Research and Innovation Centre, Fondazione E. Mach, Environment and Natural
50 40 Resources Area, San Michele all’Adige, I-38040 Trento, Italy
51 41 17- USDA Forest Service, NE Research Station, Durham, NH, USA
52 42 18- Johann Heinrich von Thünen Institut (vTI), Institut für Agrarrelevante Klimaforschung,
53
43 Braunschweig, Germany
54
44 19- College of Forestry, Oregon State University, 97331-5752 Corvallis, OR, USA
55
56 45 20- Department of Geography, Trent University, Peterborough, ON K 9J 7B8, Canada.
57 46 21- CIRAD, Persyst, UPR80, TA10/D, 34398 Montpellier Cedex 5, France.
58 47 22- University of Bayreuth, Department of Micrometeorology, Bayreuth, Germany.
59 48 23- Max Planck Institute for Biogeochemistry, Jena, Germany.
60
49 24- Departamento de Ciências Atmosféricas/IAG/Universidade de São Paulo, Rua do Matão,
50 1226 - Cidade Universitária - São Paulo, SP - Brasil.
1
Global Change Biology Page 4 of 62
1
2
3 51 25- CATIE, Centro Agronómico Tropical de Investigación y Enseñanza, Turrialba Costa Rica.
4 52 26- Laboratory of Plant Ecology and Botany. Forest Technology Centre of Catalonia, Solsona,
5
53 Spain.
6
7 54 27- Agronomical Engineering School, University of Lleida, E-25198 Lleida, Spain.
8 55 28- INRA, Institut National de la Recherche Agronomique, Paris, France.
9 56 29- Department of Hydrology and Geo-Environmental Sciences, VU-University, de Boeleaan
10
57 1085, 1081 HV Amsterdam, The Netherlands.
11
58
12
13 59 Corresponding author:
14 60 Mirco Migliavacca
15 61 Remote Sensing of Environmental Dynamics
16
62 Laboratory, DISAT, University of Milano-Bicocca, P.zza della Scienza 1, 20126
17
63 Milan, Italy. Tel.: [phone]; fax: [phone].
18
19 64 E-mail address: [email protected]
20 65
F
21
22
o
23
24 r
25
26
27 R
28
29 e
30
31 v
32 i
33
e
34
35
36 w
37
38
39
O
40
41
42 n
43 l
44
y
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
2
Page 5 of 62 Global Change Biology
1
2
3
66
4
5 67 Abstract
6
68
7
8
69 In this study we examined ecosystem respiration (R ) data from 104 sites belonging to
9 ECO
10 70 FLUXNET, the global network of eddy covariance flux measurements. The main goal was to
11
12 71 identify the main factors involved in the variability of R : temporally and between sites as
ECO
13
14 72 affected by climate, vegetation structure and plant functional type (PFT) (evergreen needleleaf,
15
73 grasslands, etc.).
16
17
74 We demonstrated that a model using only climate drivers as predictors of R failed to
ECO
18
19 75 describe part of the temporal variability in the data and that the dependency on gross primary
20
F
21 76 production (GPP) needed to be included as an additional driver of R . The maximum seasonal
ECO
22
77 leaf area index (LAI o) had an additional effect that explained the spatial variability of
23 MAX
24 r
78 reference respiration (the respiration at reference temperature Tref=15°C, without stimulation
25
26 79 introduced by photosynthetic activity and without water limitations), with a statistically
27 R
28 80 significant linear relationship (r2=0.52 p<0.001, n=104) even within each PFT. Besides LAI ,
MAX
29 e
30 81 we found that the reference respiration may be explained partially by total soil carbon content.
31 v
82 For undisturbed temperate and boreal forest a negative control of the total nitrogen deposition on
32 i
33
83 the reference respiration was also identified.
e
34
35 84 We developed a new semi-empirical model incorporating abiotic factors (climate), recent
36 w
37 85 productivity (daily GPP), general site productiv ity and canopy structure (LAIMAX) which
38
86 performed well in predicting the spatio-temporal variability of R , explaining >70% of the
39 ECO
O
40
87 variance for most vegetation types. Exceptions include tropical and Mediterranean broadleaf
41
42 88 forests and deciduous broadleaf forests. Part of the varianbility in respiration that could not be
43 l
44 89 described by our model could be attributed to a range of factors, including phenology in
y
45
90 deciduous broadleaf forests and management practices in grasslands and croplands.
46
47
91
48
49
92 Keywords: Ecosystem Respiration, Productivity, FLUXNET, Eddy Covariance, Leaf Area
50
51 93 Index, Inverse Modeling
52
53 94
54
55 95 Introduction
56
96
57
58
97 Respiration of terrestrial ecosystems (R ) is one of the major fluxes in the global carbon cycle
59 ECO
60 98 and its responses to environmental change is important for understanding climate-carbon cycle
99 interactions (e.g. Cox et al., 2000, Houghton et al., 1998). It has been hypothesized that relatively
3
Global Change Biology Page 6 of 62
1
2
3 100 small climatic changes may impact respiration with the effect of rivalling the annual fossil fuel
4
5 101 loading of atmospheric CO (Jenkinson et al., 1991, Raich & Schlesinger, 1992).
2
6
7 102 Recently, efforts have been made to mechanistically understand how temperature and other
8
103 environmental factors affect ecosystem and soil respiration, and various modeling approaches have
9
10
104 been proposed (e.g. Davidson et al., 2006a, Lloyd & Taylor, 1994, Reichstein & Beer, 2008,
11
12105 Reichstein et al., 2003a). Nevertheless, the description of the conceptual processes and the complex
13
14106 interactions controlling R are still under intense research and this uncertainty is still hampering
ECO
15
107 bottom-up scaling to larger spatial scales (e.g. regional and continental) which is one of the major
16
17
108 challenges for biogeochemists and climatologists.
18
19109 Heterotrophic and autotrophic respiration in both data-oriented and process-based
20
F
21110 biogeochemical models are usually described as a function of air or soil temperature and
22
23111 occasionally soil water conotent (e.g. Lloyd & Taylor, 1994, Reichstein et al., 2005, Thornton et al.,
24 r
112 2002), although the functional form of these relationships varies from model to model. These
25
26
113 functions represent the dominant role of reaction kinetics, possibly modulated or confounded by
27 R
28114 other environmental factors such as soil water content or precipitation, which some model
29 e
30115 formulations include as a secondary effect (e.g. Carlyle & Ba Than, 1988, Reichstein et al., 2003a,
31 v
32116 Richardson et al., 2006). i
33
117 A large number of statistical, climate-driven models of ecosystem and soil respiration have been
e
34
35118 tested and compared using data from individual sites (Del Grosso et al., 2005, Janssens &
36 w
37119 Pilegaard, 2003, Richardson & Hollinger, 2005, Sava ge et al., 2009), multiple sites (Falge et al.,
38
120 2001, Rodeghiero & Cescatti, 2005), and from a wide range of models compared across different
39
O
40
121 ecosystem types and measurement techniques (Richardson et al., 2006).
41
42 n
122 Over the course of the last decades, the scientific community has debated the role of productivity
43 l
44123 in determining ecosystem and soil respiration. Several authors (Bahn et al., 2008, Curiel Yuste et
y
45
46124 al., 2004, Davidson et al., 2006a, Janssens et al., 2001, Reichstein et al., 2003a, Valentini et al.,
47
125 2000) have discussed and clarified the role of photosynthetic activity, vegetation productivity and
48
49
126 their relationship with respiration.
50
51127 Linking photosynthesis and respiration might be of particular relevance when modelling R
ECO
52
53128 across biomes or at the global scale. Empirical evidence for the link between GPP and R is
ECO
54
129 reported for most, if not all, ecosystems: grassland (e.g. Bahn et al., 2008, Bahn et al., 2009, Craine
55
56
130 et al., 1999, Hungate et al., 2002), crops (e.g. Kuzyakov & Cheng, 2001, Moyano et al., 2007),
57
58131 boreal forests (Gaumont-Guay et al., 2008, Hogberg et al., 2001) and temperate forests, both
59
60132 deciduous (e.g. Curiel-Yuste et al., 2004, Liu et al., 2006) and evergreen (e.g. Irvine et al., 2005).
4
Page 7 of 62 Global Change Biology
1
2
3 133 Moreover, several authors have found a time lag between productivity and respiration response.
4
5 134 This time lag depends to the vegetation structure it is related to the translocation time of assimilates
6
7 135 from aboveground to belowground organs through the phloem. Although the existence of a time lag
8
136 is still under debate, it has been found to be a few hours in grasslands, and croplands and a few
9
10
137 days in forests (Baldocchi et al., 2006, Knohl & Buchmann, 2005, Moyano et al., 2008, Savage et
11
12138 al., 2009).
13
14139 While the link between productivity and respiration appears to be clear, to our knowledge, few
15
140 model formulations include the effect of productivity or photosynthesis as a biotic driver of
16
17
141 respiration and these models are mainly developed for the simulation of soil respiration using a
18
19142 relatively small data set of soil respiration measurements (e.g. Hibbard et al., 2005, Reichstein et
20
F
21143 al., 2003a).
22
23144 In this context, the inocreasing availability of ecosystem carbon, water and energy flux
24 r
145 measurements collected by means of the eddy covariance technique (e.g. Baldocchi, 2008) over
25
26
146 different plant functional types (PFTs) at more than 400 research sites, represents an useful tool for
27 R
28147 understanding processes and interactions behind carbon fluxes and ecosystem respiration. These
29 e
30148 data serve as a backbone for bottom-up estimates of continental carbon balance components (e.g.
31 v
32149 Ciais et al., 2005, Papale & Valentini, 200i3, Reichstein et al., 2007) and for ecosystem model
33
150 development, calibration and validation (e.g. Baldocchi, 1997, Hanson et al., 2004, Law et al.,
e
34
35151 2000, Owen et al., 2007, Reichstein et al., 2003b, Reichstein et al., 2002, Verbeeck et al., 2006).
36 w
37152 The database includes a number of added products such as gap-filled net ecosystem exchange
38
153 (NEE), gross primary productivity (GPP), ecosystem respiration (R ) and meteorological drivers
39 ECO
O
40
154 (air temperature, radiation, precipitation etc) aggregated at different time-scale (e.g. half-hourly,
41
42 n
155 daily, annual) and consistent for data treatment (Papale et al., 2006, Reichstei et al., 2005)
43 l
44156 In this paper we analyze with a semi-empirical modeling approach the R at 104 different sites
yECO
45
46157 belonging to the FLUXNET database with the primary objective of synthesizing and identifying the
47
158 main factors controlling i) the temporal variability of R , ii) the between-site (spatial) variability
48 ECO
49
159 and iii) to provide a model which can be used for diagnostic up-scaling of R from eddy
50 ECO
51160 covariance flux sites to large spatial scales.
52
53161 Specifically, the analysis and the model development followed these two steps:
54
162 1. we developed a semi-empirical R model site by site (site-by-site analysis) with the aim of
55 ECO
56
163 clarifying if and how GPP should be included into a model for improving the description of
57
58164 R and which factors are best suited for describing the spatial variability of reference
ECO
59
60165 respiration (i.e. the daily R at the reference temperature without moisture limitations).
ECO
166 We follow these three steps:
5
Global Change Biology Page 8 of 62
1
2
3 167 o the analysis of R data was conducted by using a purely climate driven model: ‘TP
ECO
4
5 168 Model’ (Raich et al., 2002). The accuracy of the model and the main bias were
6
7 169 analyzed and discussed;
8
170 o we evaluated the inclusion of biotic factors (i.e. GPP) as drivers of R . A range of
9 ECO
10
171 different model formulations, which differ mainly in regard to the functional
11
12172 responses of R to photosynthesis, were tested in order to identify the best model
ECO
13
14173 formulation for the daily description of R at each site;
ECO
15
174 o we analyzed variability of the reference respiration estimated at each site with the
16
17
175 aim of identifying, among the different site characteristics, one or more predictors of
18
19176 the spatial variability of this crucial parameter. This can be extremely useful for the
20
F
21177 application of the model at large spatial scale;
22
23178 2. we optimized the deoveloped model for each PFT (PFT analysis) with the aim of generalizing
24 r
179 the model parameters in a way that can be useful for diagnostic, PFT-based, up-scaling of
25
26
180 R . The accuracy of the model was assessed by a cross-validation technique and the main
ECO
27 R
28181 weak points of model were critically evaluated and discussed.
29 e
30182
31 v
32183 Material and Methods i
33
184 e
34
35
185 Data set
36 w
37186
38
39187 The data used in this analysis is based on the dataset from the FLUXNET (www.fluxdata.org)
O
40
41188 eddy covariance network (Baldocchi, 2008, Baldocchi et al., 2001). The analysis was restricted to
42 n
43189 104 sites (cf. Table in Appendix I and II) on the basis of the alncillary data availability (i.e. only
44
190 sites containing at least both leaf area index (LAI) of understorey and overstorey were selected) and
y
45
46191 of the time series length (all sites containing at least one year of carbon fluxes and meteorological
47
48192 data of good quality data were used). Further, we only analyzed those sites for which the relative
49
193 standard error of the estimates of the model parameters E (activation energy) and reference
50 0
51
194 respiration (R ) (please see further sections for more details on the meaning of parameters) were
52 0
53195 less than 50% and where E estimates were within an acceptable range (0–450 K).
0
54
55
196 The latitude spans from 71.32° at the Alaska Barrow site (US-Brw) to -21.62° at the Sao Paulo
56
57
197 Cerrado (BR-Sp1). The climatic regions include tropical to arctic.
58
59
198 All the main PFTs as defined by the IGBP (International Geosphere-Biosphere Programme)
60
199 were included in this study: the selected sites included 28 evergreen needleleaf forests (ENF), 17
6
Page 9 of 62 Global Change Biology
1
2
3 200 deciduous broadleaf forests (DBF), 16 grasslands (GRA), 11 croplands (CRO), 8 mixed forests
4
5 201 (MF), 5 savannas (SAV), 9 shrublands (SHB), 7 evergreen broadleaved forests (EBF) and 3
6
7 202 wetlands (WET). Due to limited number of sites and their similarity, the class SAV included both
8
203 the sites classified as savanna (SAV) and woody savannas (WSA), while the class SHB included
9
10
204 both the open (OSH) and closed (CSH) shrubland sites. For abbreviations and symbols refer to
11
12205 Appendix III.
13
14
206 Daily R , GPP and the associated uncertainties of NEE data, together with daily
15 ECO
16207 meteorological data such as mean air temperature (T ) and 30-day precipitation running average
A
17
18208 (P), were downloaded from the FLUXNET database.
19
209 At each site data are storage corrected, spike filtered, u -filtered according to Papale et al. (2006)
20 *
F
21
210 and subsequently gap-filled and partitioned as described by Reichstein et al. (2005). Only days
22
o
23
211 containing both meteorological and daily flux data with a percentage of gap-filled half hours below
24 r
25212 15% were used for this analysis. The median of the u threshold applied in the FLUXNET database
*
26
27213 for the site-years used in the analRysis are listed in the Appendix II. The average of the median u*
28
214 values are lower for short canopies (e.g. for grasslands 0.075±0.047 ms-1) and higher for tall
29 e
30215 canopies (e.g. for evergreen needleleaf forests 0.221 ±0.115 ms-1).
31 v
32216 Along with fluxes and meteorological daita, main ancillary data such as maximum ecosystem
33
34217 LAI (overstory and understory for forest sitese) (LAI ), LAI of overstory (LAI ), stand age
MAX MAX,o
35
218 for forests (StandAge), total soil carbon stock (SoilC) and the main information about disturbance
36 w
37219 (date of cuts, harvesting) were also downloaded fro m the database. Total atmospheric nitrogen
38
39220 deposition (N ) is based on the atmospheric chemistry transport model TM3 (Rodhe et al., 2002)
depo O
40
41221 and calculated at 1°x1° resolution. These data are grid-average downward deposition velocities and
42 n
43222 do not account for vegetation effects. The data used for the seleclted sites are shown in the Appendix
44
223 II. y
45
46
224
47
48225 Development of the ecosystem respiration model
49
50226
51
227 Site-by-site analysis – TP Model description
52
53
228
54
55229 For the analysis of R we started from a widely used climate-driven model: ‘TP Model’ (Eq. 1)
ECO
56
57230 proposed by Raich et al. (2002) and further modified by (Reichstein et al., 2003a). Here we used the
58
59231 ‘TP Model’ for the simulation of RECO at the daily time-step using as abiotic drivers daily TA and P:
60
232
233 R = R ⋅ f (T )⋅ f (P) (1)
ECO ref A
7