1
Supporting Information for
2
3
China’s terrestrial carbon balance: Contributions from multiple
4
global change factors
5
Hanqin Tian,1,2 Jerry Melillo,3 Chaoqun Lu,1,2 David Kicklighter,3 Mingliang Liu,1,2 Wei Ren,1,2
6
Xiaofeng Xu,1,2 Guangsheng Chen,1,2 Chi Zhang,1 Shufen Pan,1,2 Jiyuan Liu4 and Steven
7
Running5
8
1
9
Sciences, Auburn University, Auburn, AL 36849, USA; 2 International Center for Climate and
Ecosystem Dynamics and Global Ecology Laboratory, School of Forestry and Wildlife
10
Global Change Research, Auburn University, Auburn, AL 36849, USA; 3 The Ecosystem Center,
11
Marine Biological Laboratory, Woods Hole, MA 02543, USA; 4 Institute of Geographical
12
Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
13
5
14
This file includes:
15
Development of input data
16
Simulation of land use- and fire-related effects on terrestrial carbon balance
17
Additional model validation
18
Relative importance of environmental factors on regional carbon balances
19
Model intercomparisons
20
Relative importance of interactions among environmental factors on carbon balance
21
References
22
Table S1
23
Figure S1 to S12
College of Forestry and Conservation, University of Montana, Missoula, MT 59812, USA
1
1
2
3
Development of input data
We developed three types of driving data sets for running the models: 1) data having
4
temporal change, but no spatial variability (atmospheric CO2 concentration); 2) data having
5
spatial variability, but no temporal change (elevation, soil depth, soil pH, and soil texture); and 3)
6
data having both temporal and spatial changes (air temperatures, precipitation, relative humidity,
7
cloudiness, an index of the accumulated hourly ozone concentrations above a threshold of 40 ppb
8
– AOT40, atmospheric nitrogen deposition, and land-use and land–cover type). Time-series data
9
sets were developed from 1901 to 2005 using several time steps. The climate data (maximum air
10
temperature, minimum air temperature, average air temperature, total precipitation, relative
11
humidity, and cloudiness) and the AOT40 ozone index data were organized at a daily time step
12
while the atmospheric CO2 concentration, atmospheric nitrogen deposition, and land-use and
13
land-cover change data sets were developed at an annual time step. As TEM uses monthly
14
climate inputs, the daily data were also aggregated to a monthly time step for TEM. All
15
geographically-referenced data sets were organized at a spatial resolution of 10 km × 10 km with
16
a total of 96,415 grid cells representing China.
17
18
19
The atmospheric CO2 concentration data from 1900 to 2005 was obtained from the
Carbon Dioxide Information Analysis Center (CDIAC, http://cdiac.ornl.gov/).
The soil depth, pH, and texture maps were generated from the 1:1 million soil type map
20
(Institute of Soil Science, Chinese Academy of Sciences) [Shi et al., 2004] and the second
21
national soil survey of China [National Soil Survey Office, 1993-1996, 1998]. The soils in China
22
are divided into 12 orders, 61 sub-orders and 227 third levels. The soil survey database includes
23
2473 typical soil profiles, each of which represents soil species in the Genetic Soil Classification
2
1
of China [Shi et al., 2004]. We averaged the recorded soil variables (soil depth, pH and texture)
2
for each soil profiles grouped by soil sub-types with weighted area, and linked the indexed
3
tabular results with the vector soil map to produce gridded data sets at a 10 km ×10 km
4
resolution [Wang et al., 2003; Zhang et al., 2005; Tian et al., 2006, 2009].
5
To develop the gridded climate time-series data sets, we collected daily climate data from
6
740 weather stations in mainland China, 6 stations in Taiwan, and 29 stations from neighboring
7
countries of China, which are the most complete monitoring database for daily climatic condition
8
in China so far. We adjusted an extrapolation algorithm of the mountain microclimate simulator
9
(MTCLIM) developed by Thornton et al. [1997] to extrapolate daily climate data including daily
10
maximum temperature, daily minimum temperature, daily precipitation, daily solar radiation and
11
daily relative air humidity data from 1961 to 2005 across China (Figure S1). Based on the
12
climate data from 1961 to 1990, we generated de-trended climate data for model spin-up and to
13
represent climate conditions from 1901 to 1960. The climate data used in this study agreed well
14
with the data from the Climate Research Unit (CRU) at the University of East Anglia in terms of
15
climate anomaly relative to long-term average during 1961-1990 (Figure S1A, B). However, our
16
data set has a better representation of spatial heterogeneity at a resolution of 10 km × 10 km
17
comparing to CRU climate data.
18
The Accumulated Ozone exposure over a Threshold of 40 parts per billion (AOT40)
19
index is a measure of ozone damage based on the accumulated hourly exposure to ozone
20
concentrations greater than an assumed no-damage threshold of 40 ppb [Fuhrer et al., 1997].
21
Thus, for example, an AOT40 value of 23 ppb-hr may be obtained either by the exposure of an
22
ecosystem to 63 ppb ozone for an hour; or exposure to 41 ppb ozone for 23 hours. The AOT40
23
dataset (Figure S2) was derived from the global historical AOT40 datasets constructed by Felzer
3
1
et al [2005], as described in Ren et al. [2007]. The dataset was validated against field
2
observations in China and showed a similar seasonal pattern and increasing trend as observed. A
3
comparison of our model-generated ozone data with the 10-year observations at Waliguan, a
4
WMO baseline monitoring site confirmed a linear increase of 9% in AOT40 from 1994 to 2001.
5
The atmospheric nitrogen deposition data (Figure S3) for China were developed based on
6
site monitoring data and the results of a three-dimensional chemical transport model [Dentener et
7
al., 2006]. The monitoring data include the quarterly average values of rainfall NO3- and NH4+
8
concentrations collected from 81 rural and remote sites during 1992-1993. Comparisons between
9
the Dentener et al.’s [2006] model results and the monitoring data indicated that the modeling
10
results significantly underestimated the actual deposition rates, especially for reduced N [Lu and
11
Tian, 2007]. In this study, we established an empirical relationship among gridded wet N
12
deposition data in China during 1992 -1993 from chemical transport model [Dentener et al.,
13
2006], monitoring wet N deposition data in the same period and gridded annual average
14
precipitation amount with resolution of 10 × 10 km2. Based on this empirical equation [Lu,
15
2009], modeled N deposition rate during 1901-2005 has been up-regulated to a level closer to
16
field observations (Figure S3c). In order to validate the newly-developed data, we compared the
17
dry deposition amount and distribution pattern among observation, data from Dentener et al.
18
(2006) and data from this study. Based on field surveys, we assume dry deposition accounts for
19
30% of bulk deposition in both our data and Dentener’s [2006]. The average values of dry
20
deposition for the year1993 and 1999 derived by our approach are much closer to the monitoring
21
values than Dentener’s values. The higher correlated coefficient R indicates our data can account
22
for more spatial variations in monitoring N deposition (Table S1).
4
1
The gridded time series data set of historical land-use and land-cover is from Liu and
2
Tian [2010]. Because we identified only one dominant plant functional type in each grid cell, we
3
used the Boolean data sets developed by these authors to represent the historical changes in the
4
distribution of croplands, built-up urban areas, and natural vegetation in our study (Figure S4).
5
For croplands, we developed spatially explicit data sets to describe the distributions of irrigated
6
croplands and different crop rotations systems, which were assumed to be static over the duration
7
of our simulations. In addition, we developed gridded time series data sets to describe historical
8
N fertilizer application rates and their distribution in China. The irrigation data set was derived
9
from the county-level census in 1990. The crop rotation data set includes six major crop
10
categories, three rotation types (one harvest, two harvests and three harvests) and crop phenology
11
information which was developed based on agricultural census data and observation data in 400
12
agro-ecosystem experiment stations in China. The main crop categories and their distribution in
13
each grid cell were identified using a data set of global crop distribution with a spatial resolution
14
of 5 minutes [Leff et al., 2004], that was modified according to regional agricultural census data
15
derived from FAOSTAT (http://faostat.fao.org/) and the Chinese Academy of Agricultural
16
Sciences (http://www.caas.net.cn). The nitrogen fertilizer application data set (Figure S5) was
17
developed based on the county-level census data from 1981 to 2005 and the provincial tabular
18
data for 1950-2005 from National Bureau of Statistics (NBS, http://www.stats.gov.cn/).
19
Simulation of land use- and fire-related effects on terrestrial carbon balance
20
Land conversion from natural vegetation to cropland:
21
When natural lands are converted to agriculture, part of the vegetation biomass is
22
assumed to be lost to the atmosphere from burning, part of the vegetation is left on the ground as
5
1
detritus to eventually decompose and, if woody vegetation, part of the biomass is converted to
2
either paper products with an assumed 1-year (for DLEM) or 10-year lifetime (for TEM and
3
DLEM) or wood products with an assumed 100-year lifetime (for TEM and DLEM). For both
4
models, the proportion allocated to each of these carbon transfers depend on vegetation type
5
[Houghton et al., 1983; McGuire et al., 2001]. The EC flux is then determined as the carbon lost
6
to the atmosphere from burning related to land conversion. The product decomposition fluxes
7
(EP) are determined by linear decomposition of annual cohorts within the two (TEM) or three
8
(DLEM) wood product pools and an agricultural product pool, described below, and are
9
influenced only by the pool size of these annual cohorts. Thus, the sum of the whole agricultural
10
pool, the whole 1-year wood product pool, one-tenth of an annual cohort in the 10-year product
11
pool and one-hundredth of an annual cohort in the 100-year product pool is assumed to be lost
12
each year. Further details on simulating the effects of land-use change on terrestrial carbon
13
balance are provided in McGuire et al. [2001].
14
When land is under cultivation after land conversion, the terrestrial carbon balance of the
15
agro-ecosystem is determined by the estimated net ecosystem production (NEP) of the croplands,
16
the decomposition of the woody product pools that originated from land conversion, the
17
decomposition of the agricultural product pool and methane emission from cropland. With crop
18
harvest, carbon is transferred from crop plants to an agricultural product pool with an assumed 1-
19
year lifetime where daily cohorts are assumed to decompose linearly over the year. The rest of
20
carbon left on the ground or in the soil is partly decomposed into dissolved organic carbon
21
(DOC), the only source of methane production considered by DLEM, and thus affects methane
22
emission from cropland.
23
Cropland abandonment:
6
1
When agricultural lands are abandoned, the models simulate the regrowth of the natural
2
vegetation based on the local conditions experienced by the grid cell. Carbon balance is
3
determined by estimates of the NEP of the regrowing vegetation, decomposition of any
4
remaining product pools, and, in TEM, leaching of DOC, and in DLEM, net emission of CH4.
5
Disturbance of wildfire:
6
We use an offline fire module to estimate the wildfire-caused CO2 and non-CO2 GHG
7
emissions from terrestrial ecosystems. The carbon emission (C) is based on the following
8
equation [Seiler and Crutzen, 1980; Thonicke et al., 2001; Lu et al., 2006]
9
10
where A is the total burnt area (ha); B is the average biomass density; β is the fraction of
11
biomass consumed (combustion efficiency or fire resistance); and f is the carbon fraction of the
12
biomass or organic matter; the subscript of a is the aboveground vegetation, g is the floor fuel
13
loads. The post-fire vegetation recovery, controlled by local climate and soil conditions, is also
14
accounted into the calculation of terrestrial carbon balance. Detailed information can be found in
15
[Lu et al., 2006].
16
Additional model validation
17
Site-level validation
18
Besides validation against annual net carbon exchange in six sites (Table 2 in main text),
19
the daily net carbon exchange (NCE) in forest and cropland sites estimated by DLEM and
20
monthly NCE in forest site estimated by TEM and DLEM are also compared against the site-
21
specific eddy covariance measurements where negative values of net ecosystem exchange (NEE)
22
are assumed to be equal to positive values of NCE (Figure S6). The comparisons confirm that the
7
1
DLEM is able to capture the magnitude and daily/seasonal variations of NCE between these
2
terrestrial ecosystems and the atmosphere. While TEM captures the seasonal dynamics, the
3
model overestimates the magnitude of the seasonal variations at the Qianyanzhou site in 2003
4
and 2004, but is in much better agreement with the observations in 2005 at this site.
5
In order to evaluate how well DLEM simulates the response of maize to nitrogen
6
additions, we have mimicked a set of nitrogen fertilizer trials on maize crop in Northern China
7
[Wang et al., 2005], where the fertilizer was applied at 100, 200 and 300 kg N ha-1 yr-1. Our
8
simulated responses agreed well with the observed responses, with the 100 kg N application
9
resulting in the most rapid increase in maize biomass; an observed 45% increase in maize
10
biomass compared to a modeled 38% increase, with additional amounts yielding much smaller
11
incremental changes in the observed and modeled biomass (Figure S7). Although similar studies
12
for natural ecosystems in China would have been extremely useful for evaluating the
13
performance of our models, we have found no studies in the literature that are documented well
14
enough for model evaluation.
15
Comparison of simulated net primary productivity (NPP) with MODIS-derived NPP
16
We also compared the NPP estimated from MODIS [Zhao et al., 2005] with that modeled
17
by DLEM and TEM. Although MODIS-estimated NPP is lower than that from both models, the
18
spatial distribution pattern is quite similar, with a decreasing NPP gradient from the Southeast to
19
Northwest China (Figure S8). As a result, we found that MODIS-derived NPP correlated very
20
well with the model-simulated NPP if based on a grid-to-grid comparison (Figure S9). The
21
algorithms of MODIS for estimating NPP are not calibrated for cropland. A comparison of NPP
22
estimated by China’s cropland inventory with MODIS-estimated NPP, indicated that MODIS has
8
1
underestimated the cropland NPP, which partly explained the higher national NPP estimated by
2
TEM and DLEM.
3
Relative importance of environmental factors on regional carbon balances
4
5
Different environmental factors appeared to be important in various regions of China.
6
For Northwest China, carbon sequestration in non-forests areas appeared to have little effect on
7
the relative importance of environmental factors on carbon balance. Carbon sequestration in this
8
region was still dominated by effects of nitrogen deposition (44%) and CO2 fertilization (33%).
9
LCLUC effects were less than fertilizer application effects and the climate effect led to a small
10
carbon sink rather than a carbon source as in other parts of the country. The fact that climate in
11
Northwest China has experienced a shift from warm-dry to warm-wet during the past decades
12
[Shi et al., 2007] might be partly responsible for the climate-stimulated carbon sink. For Mid-
13
North China, carbon sequestration in croplands appeared to have a much larger influence on the
14
relative importance of environmental factors with the effects distributed more evenly across
15
LCLUC, nitrogen deposition, fertilizer applications and CO2 fertilization.
16
The large influence of nitrogen deposition in Southeast China is a reflection of both the
17
higher deposition rates that occur in the region (Table 3 in the main text, Figure S3) and the
18
ability of forests to take up this nitrogen.
19
deposition levels exceed 40 kg N ha-1 yr-1, our simulations show that carbon storage in response
20
to nitrogen deposition either leveled off or even declined in recent years, although the nitrogen
21
input continued to increase (Figure 3 in the main text). This suggests that nitrogen saturation, a
22
condition in which nitrogen inputs to an ecosystem exceed the demands of plants and microbes
23
[Aber et al., 1998], may be occurring in these areas.
For parts of Southeast China, where nitrogen
9
1
Although atmospheric CO2 concentrations did not vary spatially across China in our
2
simulations, CO2 fertilization effects did vary from region to region. These effects reflect
3
inherent differences in the ability of different vegetation types take up CO 2 [Nowak et al., 2004]
4
and the interactive effects of nitrogen subsidies from nitrogen deposition and
5
applications on CO2 fertilization described earlier. Nitrogen stimulation of the CO2 fertilization
6
effect increased from the Northwest to the Southwest region of China following a spatial
7
gradient that is consistent with the distribution of atmospheric nitrogen deposition (Figure S3).
fertilizer
8
The relatively large LCLUC effects on carbon balance in Southeast and Mid-North China
9
(Figure 2 in the main text) are mainly a result of afforestation and reforestation efforts. Carbon
10
sequestration in the Southeast is mainly attributed to the creation of new forest plantation areas.
11
However, for the Mid-north region, most of this increase is due to the implementation of two
12
government policies - “returning of cropland to woodland and grassland” and “Grain-for-Green”
13
[Wang et al., 2007a]. Our simulations indicated that reforestation/afforestation in the Mid-North,
14
Northeast and Southwest (Table 3) contributed to net carbon storage of 0.15, 0.05 and 0.33 Pg C,
15
respectively, over the past 45 years, equivalent to 78%, 51% and 47% of the total forest carbon
16
sequestration in these three regions. By contrast, a large area of forest expansion occurred in the
17
Southeast, but appeared to be relatively less important in its regional carbon uptake, causing
18
carbon storage of 0.68 Pg C, around 36% of the regional forest carbon sink. The rest 64% of
19
carbon sequestration was derived from increased nitrogen deposition and CO2 enrichment in this
20
region.
21
While cropland abandonment supported forest expansion in the Northwest, the Mid-
22
North and Southeast, the area of both croplands and forests increased in the Northeast and
23
Southwest during the period 1961 to 2005 (Table 3 in the main text). Cropland expansion
10
1
caused carbon losses to offset the carbon gains associated with forest expansion and re-growth in
2
these regions and thereby reduced the positive LCLUC effects on carbon balance in these regions
3
(Figure 2 in the main text).
4
The relatively large effects of the nitrogen fertilizer application on carbon dynamics in
5
the Mid-North reflect both amount of croplands found in this region (covering around ¼ of entire
6
China’s cropland, [Liu et al., 2005a]) and the application rates of nitrogen fertilizer (Table 3 in
7
the main text and Figure S5 in the Supporting Information). In contrast to forests where most
8
carbon is stored in vegetation, carbon sequestration in croplands occurs in soils as a result of the
9
addition of crop residue (and manure which was not considered in our study) left on the fields.
10
As we assume a fixed proportion of crop biomass is harvested, increases in plant productivity
11
associated with the application of nitrogen fertilizers enhance crop biomass, which in turn,
12
enhances the inputs of crop residue to the soil. While not as important on the regional carbon
13
balance, the high application rates of nitrogen fertilizers to croplands in the Southeast also
14
contributed to large increases in carbon storage in cropland soils in this region.
15
16
Model intercomparisons
17
In the main text, we provided the general results on the relative importance of the six
18
environmental factors and biome characteristics on carbon balance in China simulated by both
19
TEM and DLEM. Here, we examine the similarities and differences in the simulated responses
20
by these models in more detail (Figure S10-12). TEM and DLEM generally showed consistent
21
results in quantifying the factorial contribution to decadal mean net ecosystem carbon balance,
22
however, the larger TEM-simulated carbon sink over decades and the whole study period is
23
primarily due to its higher response to CO2 and nitrogen deposition (Figure S10). The higher
11
1
sensitivity to CO2 and nitrogen deposition by TEM occurred in all regions. In addition, TEM
2
had a larger negative response to climate than DLEM in Mid-North and Northeast China (Figure
3
S11). Except grassland, the model estimates of cumulative net carbon storage change in each
4
biome derived from TEM were larger than those from DLEM. One notable issue is that TEM
5
estimated a positive land-cover and land-use change (LCLUC) effect in Northeast China during
6
1961-2005 while DLEM estimated a negative LCLUC effect in the same region. This difference
7
is largely caused by difference in cropland NECB estimates for Northeast China (Figure S12),
8
where the largest increase in cropland area took place over the past 45 years. Our simulations for
9
agricultural land include two components: conversion of natural systems to cropland and the
10
carbon dynamics associated with crop growth. Although DLEM is used to quantify the second
11
component for both models, the divergence in estimating carbon balance arisen from land
12
conversion between TEM and DLEM led to the small difference in carbon flux estimates. Future
13
studies are needed to examine the uncertainty resulting from parameterization, model structure
14
and assumptions through multi-model inter-comparison.
15
16
Relative importance of interactions among environmental factors on carbon balance
17
18
To further examine the uncertainty induced by interactions among various environmental
19
stresses, we compared the results of our single-factor experiments in this study (Experiments 8-
20
13, Table 1), where only one driving factor changes and the other factors are kept constant for
21
the whole study period, with the results of experiments where the specific factor was held
22
constant and all other factors were allowed to change (Experiments 2-7, Table 1). Our
23
comparisons indicate that consideration of interaction between CO2 and other factors could lead
12
1
to a larger CO2 fertilization effect (33%) than that resulting from CO2 alone (16%). Thus, CO2
2
fertilization effects may vary from 16% to 33%, which depends on the existence and magnitude
3
of its interactions with other drivers. In contrast, both nitrogen deposition and nitrogen
4
fertilization show less of an effect (46% and 15%, respectively) on carbon sequestration when
5
taking into account interactions with other factors, compared to direct effects (58% and 25%,
6
respectively). The interactive effects doubled the enhancement of the carbon sink induced by
7
land conversion from 9% to 18%, but did not significantly affect the impacts of climate and
8
ozone pollution on carbon balance. Our simulated results further confirm the importance of
9
carbon-nitrogen interactions on carbon dynamics, and also suggest the necessity of considering
10
multiple factors in the assessment of global changes. Looking to the future, multivariate dose-
11
response studies conducted in natural ecosystems should be encouraged, as they are imperative
12
for further advancing model development and testing process-based models in the future [Melillo
13
et al., 2003].
14
13
1
2
Reference
3
Collatz GJ, Berry JA, Clark JS (1998). Effects of climate and atmospheric CO2 partial pressure
4
on the global distribution of C4 grasses: present, past, and future. Oecologia 114, 441-454.
5
Dentener, F. J. (2006), Global Maps of Atmospheric Nitrogen Deposition, 1860, 1993, and 2050.
6
Data set. Available on-line [http://daac.ornl.gov/] from Oak Ridge National Laboratory
7
Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.
8
9
10
Felzer, B. S. et al. (2005), Future effects of ozone on carbon sequestration and climate change
policy using a global biochemistry model. Clim. Change 73, 345-373.
Hou X (ed.), (1982). Vegetation Map of China, 2nd edition, Science Press, Beijing. (in
11
Chinese)Leff, B., N. Ramankutty, and J. A. Foley (2004), Geographic distribution of
12
major crops across the world. Glob. Biogeochem. Cycles, 18, GB1009, doi:
13
10.1029/2003GB002108.
14
15
16
Liu, J. et al. (2005a), Current status and recent changes of cropland in China: an analysis based
on Landsat TM data. Remote Sens. Environ. 98, 442-456.
Liu, J. Y. et al. (2005b), China’s changing landscape during the 1990s: Large-scale land
17
transformation estimated with satellite data. Geophys. Res. Letts. 32, L02405,
18
doi:10.1029/2004GL021649.
19
Liu, M., and H.Q. Tian (2010), China's land-cover and land-use change from 1700 to 2005:
20
estimations from high-resolution satellite data and historical archives. Glob. Biogeochem.
21
Cycles (In press).
14
1
Lu, A., H.Q. Tian, M. Liu, J. Liu, and J. M. Melillo (2006), Spatial and temporal patterns of
2
carbon emissions from forest fires in China from 1950 to 2000, J. Geophysical Research,
3
111, D05313, doi:10.1029/2005JD006198.
4
5
6
7
Lu C.Q (2009), Study of atmospheric nitrogen deposition and terrestrial ecosystem carbon cycle
in China, Ph.D. Dissertation, 204 pp, Chinese Academy of Sciences, Beijing.
Lu, C.Q., and H.Q. Tian (2007), Spatial and temporal patterns of nitrogen deposition in China:
8
Synthesis of observational data. J. Geophys. Res. 112, D22S05,
9
doi:10.1029/2006JD007990.
10
National Soil Survey Office (1998), Soils of China, pp. 1-1253. Agriculture Press. (in Chinese).
11
National Soil Survey Office (1993-1996), Soil Series of China. Vol. I-VI, Agriculture Press (in
12
13
Chinese).
Ren, W. et al. (2007), Effects of Tropospheric Ozone Pollution on net Primary Productivity and
14
Carbon Storage in Terrestrial Ecosystems of China. J Geophys. Res. 112, D22S09, doi:
15
10.1029/2007JD008521.
16
17
18
19
20
Seiler, W., and P. J. Crutzen (1980), Estimates of gross and net fluxes of carbon between the
biosphere and the atmophere from biomass burning, Climate Change, 2, 207– 247.
Shi, Y.F. et al. (2007), Recent and future climate change in northwest China. Climatic change,
80, 379-393.
Shi, X. et al. (2004), 1:1,000,000 soil database of China and its application (in Chinese), in
21
Proceedings of 10th National Congress of Soil Science of China, pp. 142– 145, Science
22
Press, Beijing. China.
15
1
Thonicke K, Venevsky S, Sitch S, Cramer W (2001) The role of fire disturbance for global
2
vegetation dynamics: coupling fire into a Dynamic Global Vegetation Model. Global
3
Ecology and Biogeography, 10, 661–677.
4
Thornton, P.E., S.W. Running, and M.A. White (1997), Generating surfaces of daily
5
meteorological variables over large regions of complex terrain. Journal of Hydrology 190,
6
214-251.Tian, H. Q. et al. (2006), Patterns of soil nitrogen storage in China, Global
7
Biogeochem. Cycles 20, GB1001, doi:10.1029/2005GB002464.
8
9
10
11
12
13
14
15
16
17
Tian, H.Q., G.S. Chen, C. Zhang, J.M. Melillo, and C.A.S. Hall (2009), Pattern and variation of
C:N:P ratios in China’s soils: A synthesis of observational data. Biogeochem. DOI
10.1007/s10533-009-9382-0.
Wang, S., H.Q.Tian, J. Liu, and S. Pan (2003), Pattern and change in soil organic carbon storage
in China: 1960s-1980s. Tellus 55B, 416-427.
Wang, X.W., Q. Yu, G.L. Zhang, and Y. S. Li (2005), The yield and dynamics of soil nitrate for
summer maize on the plain of northwest Shandong province. Geograph. Res. 24, 140-150.
Zhang, C. et al. (2005), Pools and distributions of soil phosphorus in China. Global Biogeochem.
Cycles 19, GB1020, doi:10.1029/2004GB002296.
Zhao, M., F.A. Heinsch, R.R. Nemani, and S.W. Running, (2005), Improvements of the MODIS
18
terrestrial gross and net primary production global data set. Remote Sens. Environ. 95,
19
164–176.
20
21
22
16
1
2
3
Table S1. Comparison of dry deposition in the year 1993 and 1999 (kg N ha-1 yr-1) estimated by
our data and Dentener (2006) with the field monitoring.
4
5
Monitoring data
This study
Dentener (2006)
for specified year
mean
R
mean
R
1993
2.75
0.712
0.72
0.382
2.71
mean
R
mean
R
1999
2.78
0.668
0.79
0.382
2.76
Note: mean is the national average dry N deposition rate calculated from each data set, and R
indicates the correlation coefficient of our data or Dentener’s data to the observations.
6
7
17
1
2
3
4
Figure S1. Climate anomalies (A: precipitation in comparison with data from CRU; B: average
temperature in comparison with data from CRU; C: maximum temperature and minimum
temperature) in China’s terrestrial ecosystems (relative to 1961-1990 mean data)
5
6
Figure S2. (A) Spatial and temporal distribution of mean annual AOT40 (ppm-hr) for 1960,
1980, and 2000; (B) Annual mean monthly AOT40 (ppb-hr) from 1900 to 2005
7
8
9
10
Figure S3. Change in NHx_N (a) and NOy_N deposition rate (b) during 1961-2005 (difference
between annual mean rate during 1996-2005 and annual mean rate during 1961-1970, units: kg
N ha-1 yr-1); (c) Comparison of annual nitrogen deposition rate in China’s terrestrial ecosystems
during 1900-2005 estimated by this study and Dentener et al. (2006).
11
12
Figure S4. Land-use and land-cover conversions in China during 1900-2005 (A) and during
1961-2005 (B); Change of major land cover area in China during 1900-2005 (C).
13
14
15
Figure S5. Spatial pattern of nitrogen fertilizer input (g N/m2/year) to cropland of China in the
year of 1960, 1970, 1980, and 1990, respectively (A); Annual nitrogen fertilizer application rate
in China’s cropland from 1900 to 2005 (B).
16
17
18
19
20
Figure S6. Comparison of daily net ecosystem production simulated by DLEM (a, upper) and
monthly net ecosystem production simulated by TEM (b, middle) against observed data in
temperate evergreen needleleaf forest in Qianyanzhou, Southeastern China; (c, lower)
Comparison of simulated daily Net Ecosystem Production (NEP) against observed data in dry
farmland of Yucheng, northern China
21
22
Figure S7. Comparison of modeled response of biomass increment to nitrogen fertilizer
application with the maniputed experiment conducted on maize crop in Yucheng, northern China.
23
24
Figure S8. Spatial patterns of MODIS-derived NPP and the simulated NPP from TEM and
DLEM from 2000 to 2005 (a: DLEM-simulated NPP; b: TEM-simulated NPP; c: MODIS NPP).
25
26
Figure S9. Comparison of MODIS-derived NPP and Simulated NPP from TEM and DLEM
models
27
28
Figure S10. Relative importance of environmental factors on net carbon storage/loss in China
from 1961 to 2005 as simulated by TEM and DLEM.
29
30
31
32
33
Figure S11. Relative importance of environmental factors on cumulative net carbon storage (Pg
C) of regions within China from 1961 to 2005. Factors include climate, atmospheric CO2, ozone
pollution, nitrogen deposition, N fertilizer application, and land-cover and land-use change
(LCLUC). Regions include the Northwest (NW), Middle North (MN), Northeast (NE),
Southwest (SW), and Southeast (SE).
18
1
2
3
4
Figure S12. Relative importance of biomes on cumulative net carbon storage (Pg C) of 5 regions
within China (upper) and the entire country (lower) from 1961 to 2005. Biomes include forests,
woodland and shrub, grass, crop and others. Regions include the Northwest (NW), Middle North
(MN), Northeast (NE), Southwest (SW), and Southeast (SE).
5
6
7
19
1
A
2
B
3
4
Maximum Temperature (o C)
2.0
Tmax anomaly
1.5
Tmin anomaly
5
1.0
6
0.5
7
0.0
1960
-0.5
-1.0
1965
1970
1975
1980
1985
Year
1990
1995
2000
2005 8
9
10
11
12
13
Figure S1. Climate anomalies (A: precipitation in comparison with data from CRU; B: average
temperature in comparison with data from CRU; C: maximum temperature and minimum
temperature) in China’s terrestrial ecosystems (relative to 1961-1990 mean data)
14
20
A
B
1
2
3
4
Figure S2. (A) Spatial and temporal distribution of mean annual AOT40 (ppm-hr) for 1960,
1980, and 2000; (B) Annual mean monthly AOT40 (ppb-hr) from 1900 to 2005
21
1
c
2
3
4
5
6
Figure S3. Change in NHx_N (a) and NOy_N deposition rate (b) during 1961-2005 (difference
between annual mean rate during 1996-2005 and annual mean rate during 1961-1970, units: kg
N ha-1 yr-1); (c) Comparison of annual nitrogen deposition rate in China’s terrestrial ecosystems
during 1900-2005 estimated by this study and Dentener et al. (2006).
7
8
9
10
11
22
1
A
B
C
2
3
Figure S4. Land-use and land-cover conversions in China during 1900-2005 (A) and during
4
1961-2005 (B); Change of major land cover area in China during 1900-2005 (C).
5
23
1
A
B
2
3
4
5
Figure S5. Spatial pattern of nitrogen fertilizer input (g N/m2/year) to cropland of China
in the year of 1960, 1970, 1980, and 1990, respectively (A); Annual nitrogen fertilizer
application rate in China’s cropland from 1900 to 2005 (B).
6
24
1
2
3
4
5
Figure S6. Comparison of daily net ecosystem production simulated by DLEM (a, upper) and
monthly net ecosystem production simulated by TEM and DLEM (b, middle) against observed
data in temperate evergreen needleleaf forest in Qianyanzhou, Southeastern China; (c, lower)
Comparison of DLEM-simulated daily Net Ecosystem Production (NEP) against observed data in
dry farmland of Yucheng, northern China.
25
1
2
3
Figure S7. Comparison of modeled response of biomass increment to nitrogen fertilizer
application with the maniputed experiment conducted on maize crop in Yucheng, northern China.
4
26
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Figure S8. Spatial patterns of MODIS-derived NPP and the simulated NPP from TEM and
DLEM from 2000 to 2005 (a: DLEM-simulated NPP; b: TEM-simulated NPP; c: MODIS NPP).
25
27
1
2
3
Figure S9. Comparison of MODIS-derived NPP and Simulated NPP from TEM and DLEM
models
4
28
1
2
3
Figure S10. Relative importance of environmental factors on net carbon storage/loss in China
4
from 1961 to 2005 as simulated by TEM and DLEM.
5
6
29
1
2
3
4
5
6
7
Figure S11. Relative importance of environmental factors on cumulative net carbon storage (Pg
C) of regions within China from 1961 to 2005. Factors include climate, atmospheric CO2, ozone
pollution, nitrogen deposition, N fertilizer application, and land-cover and land-use change
(LCLUC). Regions include the Northwest (NW), Middle North (MN), Northeast (NE),
Southwest (SW), and Southeast (SE).
8
30
1
2
3
4
5
6
Figure S12. Relative importance of biomes on cumulative net carbon storage (Pg C) of 5 regions
within China (upper) and the entire country (lower) from 1961 to 2005. Biomes include forests,
woodland and shrub, grass, crop and others. Regions include the Northwest (NW), Middle North
(MN), Northeast (NE), Southwest (SW), and Southeast (SE).
7
8
31