Grassland aboveground biomass (AGB) is a critical component of the
global carbon cycle and reflects ecosystem productivity. Although it is
widely acknowledged that dynamics of grassland biomass is significantly
regulated by climate change, in situ evidence at meaningfully large spatiotemporal
scales is limited. Here, we combine biomass measurements from six long-term
(> 30 years) experiments and data in existing literatures to
explore the spatiotemporal changes in AGB in Inner Mongolian temperate
grasslands. We show that, on average, annual AGB over the past 4 decades
is 2561, 1496 and 835 kg ha-1,
respectively, in meadow steppe, typical steppe and desert steppe in Inner
Mongolia. The spatiotemporal changes of AGB are regulated by interactions of
climatic attributes, edaphic properties, grassland type and livestock. Using
a machine-learning-based approach, we map annual AGB (from 1981 to 2100)
across the Inner Mongolian grasslands at the spatial resolution of 1 km. We
find that on the regional scale, meadow steppe has the highest annual AGB,
followed by typical and desert steppe. Future climate change
characterized mainly by warming could lead to a general decrease in
grassland AGB. Under climate change, on average, compared
with the historical AGB (i.e. average of 1981–2019), the AGB at the end of
this century (i.e. average of 2080–2100) would decrease by 14 % under
Representative Concentration Pathway (RCP) 4.5 and 28 % under RCP8.5. If the carbon dioxide
(CO2) enrichment effect on AGB is considered, however, the estimated
decreases in future AGB can be reversed due to the growing atmospheric
CO2 concentrations under both RCP4.5 and RCP8.5. The projected changes
in AGB show large spatial and temporal disparities across different
grassland types and RCP scenarios. Our study demonstrates the accuracy of
predictions in AGB using a modelling approach driven by several readily
obtainable environmental variables and provides new data at a large scale and
fine resolution extrapolated from field measurements.
Introduction
Grassland occupies ∼ 40 % of the world land and is an
essential component of global terrestrial ecosystems (Hufkens et al.,
2016). Grassland provides plenty of ecosystem services such as supplying food
to livestock and therefore meat and milk to humans (Sattari et al., 2016)
and accumulating carbon from the atmosphere, thus mitigating global warming
(O'Mara, 2012). All of these functions are more or less directly
dependent on grassland biomass, which has been recognized to be significantly
influenced by environmental changes and anthropogenic activities
(Hovenden et al., 2019). Thus, quantifying the dynamics of grassland
biomass and revealing the underlying mechanisms are of fundamental
importance (Andresen et al., 2018).
Dynamics of grassland aboveground biomass (AGB) are driven by complex
interactions among a series of environmental attributes such as climate
variables (De Boeck et al., 2008; H. Wang et al., 2020). The magnitudes and
directions of climate change effects on AGB can vary across different local
environments as well. For example, climate warming can either contribute to AGB
accumulation through reduction of constraints on low temperature
(Gonsamo et al., 2018; Park et al., 2019) or go against AGB
formation by aggravating water stress on plant growth (Fan et al.,
2009; Hu et al., 2007). In addition, in most existing studies, the mean
annual climate attributes (e.g. temperature and precipitation) have widely
been treated as potential drivers on spatiotemporal variations in grassland
biomass (Fan et al., 2009; Ma et al., 2008). However, growing
evidence has demonstrated the importance of seasonality and intra-annual
variability of climate in regulating the biomass dynamics (Godde et
al., 2020; Grant et al., 2014). For example, Peng et al. (2013)
reported that variations in seasonal precipitation significantly alter the
annual net primary productivity (NPP) in Inner Mongolian grasslands. To date, climatic seasonality and
intra-annual variability have seldom been considered in assessing grassland
AGB, particularly at large extents of space and time. Moreover, recent
studies have suggested the possible co-regulating effects of soil properties
(Bhandari and Zhang, 2019; Jia et al., 2011), grassland type and grazing
intensity (Eldridge and Delgado-Baquerizo, 2017) on AGB, which have also
seldom been included in exploring the spatiotemporal changes in grassland
AGB. Comprehensively considering these covariates, rather than including
only a few mean annual climatic attributes, provides an opportunity to more
accurately predict grassland AGB dynamics and disentangle the response of
AGB to the complex interactions between environmental drivers.
Inner Mongolian grasslands account for more than half of China's northern
temperate grassland area and have the nation's largest grassland biomass carbon
stock (Piao et al., 2004). The annual productivity in this region
tends to vary in response to climate change (Bai et al., 2008). Since
the start of the 1980s, warming has been taking place in many parts of Inner
Mongolia (Wang et al., 2019). Under this temperature rise, the
spatiotemporal variation in grassland AGB, however, is still unclear.
Although efforts have been made to quantify AGB dynamics at the regional
scale, these studies used mainly remote-sensing approaches and generally
showed large disparities (Guo et al., 2016; Long et al., 2010; Ma et al.,
2010a). Evidence from datasets independent of remote-sensing products can
certainly contribute to the assessments of spatiotemporal dynamics of AGB at
the regional scale. In addition, the climate in the future is projected to
experience substantial changes (IPCC, 2007) and thus significantly
affect grassland AGB dynamics, while little is known about the fate of AGB
under future climate changes. Furthermore, it has been reported that carbon
dioxide (CO2) enrichment may increase plant productivity through
enhancing photosynthetic rates and reducing stomatal conductance, thereby
increasing water use efficiency (Fay et al., 2012; Pastore et al., 2019).
This might provide an opportunity to mitigate or even reverse the harmful
effects of other environmental changes on grassland AGB (Lee et al.,
2010), e.g. the enhanced water limitations resulting from climate warming.
The actual effects of CO2 enrichment on AGB, however, depend
substantially on local environmental factors such as water availability
(Brookshire and Weaver, 2015) and soil texture
(Polley et al., 2019).
In this study, we collate a comprehensive dataset of in situ measurements on plant
biomass and climatic records in Inner Mongolian grasslands from six
long-term experiments and those data from existing literature. We calibrate
and validate a machine-learning-based model for predicting the aboveground
biomass in the study region, by treating tens of environmental covariates
(climates, soils, livestock and grassland type) as predicting variables.
Then, we map the annual aboveground biomass at a spatial resolution of 1 km
over the periods of 1981–2019 (using historical climatic dataset) and
2020–2100 (using climate projections driven by two representative
concentration pathways (e.g. Representative Concentration Pathway (RCP) 4.5 and RCP8.5)). We also include the
possible effects of atmospheric CO2 enrichment on future grassland AGB
dynamics in the study region.
Materials and methodsStudy region and datasets of grassland aboveground biomass
The study region (i.e. Inner Mongolian grasslands) is characterized mainly
by a temperate climate (Q. Zhang et al., 2020) and thus is named Inner
Mongolian temperate grasslands as well, which can be generally classified
into three categories, i.e. meadow steppe, typical steppe and desert steppe
(National Research Council, 1992). In brief, meadow steppe
is distributed mainly in the eastern areas, typical steppe locates mostly in
central Inner Mongolia and desert steppe is found mainly to the west of
typical steppe (Fig. 1). In this study, we acquired two datasets of in situ
aboveground biomass (AGB) in Inner Mongolian grasslands. First, we obtained
the AGB at six long-term (i.e. more than 30 years) experimental sites
across the study region (Fig. 1 and Wang, 2020). These six sites were established
by the Inner Mongolia Meteorological Bureau of China in the early 1980s;
measurements of AGB at each site have been carried out year by year since
then. At each site, four fenced plots (i.e. four replicates) were set up to
collect plant biomass data during plant growing seasons (e.g. from May to
September). For each measurement replicate, the plants within a 1 m2 area were clipped and collected in a cloth bag. The samples were
further air-dried to constant weights (weighed once every 3 d until
the percent change in two consecutive weights is less than 2 %). It is
noted that plant growth rate could peak at different periods across time and
space. Following Scurlock et al. (2002), we determined the annual plant
biomass as the largest observed monthly biomass during a year (normally at
the end of August at Ergün and at the end of September at three other
sites). Apart from measurements of these six long-term field experiments, we
also retrieved a dataset of grassland AGB from Xu et al. (2018), who
recently conducted a thorough literature synthesis and established a
comprehensive dataset of plant biomass in the grasslands of northern China.
For the dataset constructed by Xu et al. (2018), we used only the
observations conducted in Inner Mongolian grasslands and with investigation
time and coordinates clearly reported (Fig. 1). In general, the grassland
AGB derived from these two different datasets (i.e. long-term experiments
and literature synthesis) is comparable (Fig. S1 in the Supplement). In total, we obtained
511 individual measurements across 247 locations in the study region (Fig. 1, Wang, 2020).
Spatial distribution of grassland aboveground biomass observations
and climatic stations in Inner Mongolia. The Inner Mongolian grasslands are
grouped into three categories (i.e. meadow steppe, typical steppe and
desert steppe). Observations of grassland biomass were both derived from the
six long-term experimental sites and data synthesis of existing studies. The
ground climatic records were obtained from the National Meteorological
Information Centre (NMIC) of China.
Environmental covariates
Environmental covariates including climate, soil, grassland type and
livestock were retrieved for both AGB driver assessment and machine-learning-based model fitting. For climatic covariates, we first obtained the
daily climatic records of 120 climatic stations established in Inner
Mongolia (Fig. 1) from the National Meteorological Information Centre (NMIC)
of China. The daily climatic attributes such as minimum, average and
maximum temperature and precipitation were transformed into monthly time
series data using the daily2monthly function in the R package hydroTSM. Based on these monthly
data, we calculated 23 bioclimatic variables (Table 1) with an annual time
step over the period of 1981–2019 by using the biovars function in the R package
dismo. By doing so, we aim to comprehensively consider the possible effects of
seasonality and intra- and inter-annual variability of climates
(Fick and Hijmans, 2017) on grassland AGB. By further
applying an interpolation algorithm (Thornton et al., 1997) to
these 23 bioclimatic variables at the 120 stations, we created the raster
layers of the climatic attributes with a spatial resolution of 1 km year by
year. For the edaphic covariates, we directly extracted 10 raster soil
layers representing key soil physical and chemical properties (Table 1) at a
1 km spatial resolution in the study region from the ISRIC-WISE soil profile
database (Batjes, 2016).
The environmental covariates used in this study.
CovariatesCodeDescriptionUnitEdaphic variablesCFRAGCoarse fragments (> 2 mm)%BULKBulk densityg cm-3ORGCOrganic carbong kg-1SDTOSand content%CLPCClay content%STPCSilt content%TAWCAvailable water capacitycm m-1TOTNTotal nitrogeng kg-1CNrtC:N ratio–PHAQpH measured in H2O–Climatic variablesT1Annual mean temperature∘CT2Mean diurnal range∘CT3Isothermality (T2/T7 × 100)%T4Temperature seasonality (standard deviation × 100)∘CT5Max temperature of warmest month∘CT6Min temperature of coldest month∘CT7Temperature annual range (T5–T6)∘CT8Mean temperature of wettest quarter∘CT9Mean temperature of driest quarter∘CT10Mean temperature of warmest quarter∘CT11Mean temperature of coldest quarter∘CP1Annual precipitationmmP2Precipitation of wettest monthmmP3Precipitation of driest monthmmP4Precipitation seasonality (coefficient of variation)%P5Precipitation of wettest quartermmP6Precipitation of driest quartermmP7Precipitation of warmest quartermmP8Precipitation of coldest quartermmMATGMean annual temperature during growing season∘CMATNGMean annual temperature during non-growing season∘CMAPGMean annual precipitation during growing seasonmmMAPNGMean annual precipitation during non-growing seasonmmGrassland type–Meadow, typical and desert steppe–Livestock–Cattle, sheep and goatshead km-2
The grazing intensity in this study was represented by the quantity of three
key livestock (i.e. cattle, goat and sheep; Table 1) because they make up the
majority of livestock in the Inner Mongolian grasslands (National Bureau of Statistics of
China, 1981–2019). Here, we first derived the regional distribution data for
cattle (Fig. S2a), goats (Fig. S2b) and sheep (Fig. S2c) in 2010 in
the study region from Gilbert et al. (2018). Then, we obtained the
yearly total of each livestock in the study region (Fig. S2d) from the
National Bureau of Statistics of China (1981–2019). By assuming a similar
spatial distribution of livestock over time, we generated raster layers of
each of the three livestock types year by year over the past 4 decades using
the above-mentioned two datasets. In addition, a spatial layer of grassland
type (i.e. meadow steppe, typical steppe and desert steppe; Fig. 1 and
Table 1) at 1 km resolution was derived from the Vegetation Map of China
(Zhang, 2007), the digital version of which is publicly obtainable
(http://data.casearth.cn/sdo/detail/5c19a5680600cf2a3c557b6b, last access: 29 May 2020).
Machine learning models to predict grassland AGB
To predict grassland aboveground biomass (AGB) across the region, we
generated a suite of machine-learning-based predictive models for AGB
treating edaphic and climatic variables, grassland type and livestock (Table 1) as candidate predictors. Here, data from the 511 measurements (Fig. 1 and
Wang, 2020) were used to fit the models. For the spatial layers of soil
properties and grassland type, which were assumed to be constant over time,
we retrieved the associated covariates using the geographical coordinates of
the 511 measurements. For those variables varying over time (e.g. climatic
variables and livestock), we extracted the associated attributes using both
the locations and investigation year of the 511 measurements. In fitting the
models, AGB is treated as a dependent variable, and the environmental
covariates (Table 1) are treated as independent variables. Before fitting
the models, we converted the categorical variables (i.e. grassland type) to
dummy variables. This is to avoid simply deducing the dependent variables in
a certain category using the independent variables (e.g. climate variables)
in other categories in building the models. Then, the function
findCorrelation in R package caret was used to exclude the environmental covariates with high
multicollinearities. Following Brownlee (2020), the remaining attributes
were further adopted in model training (80 % stratified samples) and
validation (the remaining 20 % stratified samples). We used a 10-fold
cross-validation to train a suite of machine learning models using three
algorithms (i.e. random forest (RF), cubist and support vector machines
(SVMs)), which are implemented in the R package caret. The amount of variance in
AGB explained by each model was assessed by the coefficient of determination
(R2). The root-mean-square error (RMSE, kg ha-1) was also
calculated (RMSE=∑i=1n(Pi-Oi)2n, where n is sample size and Pi and Oi are
the ith predicted and observed AGB, respectively) to compare the model
simulations and observations. Apart from the three individual machine-learning-based models, we also derived an ensemble model by adopting a
principal component analysis (PCA) approach based on the predictions of the
above-mentioned three models. In brief, the smaller an individual model's
RMSE, the more the model's outputs contribute to the ensemble
predictions.
Assessment of drivers on AGB
We used three approaches to explore the effects of environmental covariates
on grassland AGB. First, the machine learning models themselves provide
assessments of the relative importance (RI) of each independent variable in
predicting the dependent variable (e.g. grassland AGB in this study). In
general, the greater the RI of a variable, the larger its influence on
AGB. Second, we adopted the Mantel test (Mantel, 1967) to assess the
relationship between similarity of different grassland types and the
similarity of environmental covariates using the R package vegan. Here, the
standardized Mantel's r (ranges from 0 to 1) is used to represent the
strength of this relationship (the higher the Mantel's r, the stronger
the correlation), and the associated significance is indicated by the P
value determined from 999 randomizations (Legendre and Fortin, 1989).
Third, we conducted a path analysis by using three latent variables, i.e.
climate, soil and livestock, to evaluate their regulating effects on AGB.
For each latent variable of climate and soil, the specific indicators were
pre-identified using the above-mentioned R function findCorrelation to exclude those
attributes with high multicollinearities. In constructing the inner model
matrix of the path model, we hypothesized that all three latent variables
have direct effects on AGB, and climate may also indirectly affect the
dependent variable through influencing soil properties (Luo et al.,
2019). Here, we adopted the partial least-squares (PLS) approach
(Sanchez, 2013) and used the R package plspm to perform the path analysis. In
interpreting the path analysis results, it is noted that the loadings of an
indicator show the correlations between a latent variable and its
indicators. All the indicators were standardized before the path analysis
was performed.
Regional mapping and uncertainty analysis
Using the fitted machine-learning-based ensemble model, we mapped AGB in
Inner Mongolian grasslands (at a spatial resolution of 1 km) on an annual
time step in the history (1981–2019) and future (2020–2100). In mapping the
historical AGB, the model is run using environmental covariates extracted
from the regional data layers (see Sect. 2.2). Prediction uncertainty was quantified
using a Monte Carlo analysis to develop the probability density functions
(PDFs) for each edaphic, climatic and livestock variable within the ranges of
mean ±10 %. The ensemble machine learning model was then run for
200 times in each grid with each independent variable assigned from the
PDF. The average and coefficient variation (CV, calculated as the standard
deviation divided by the average) were then determined in each grid using
the 200 model outputs to represent the predicted AGB and the associated
uncertainty, respectively.
For predictions of AGB in the future (i.e. 2020–2100), we included the
climatic datasets projected by a typical CMIP5 global circulation model,
i.e. CESM1-BGC, which was run by the National Center for Atmospheric Research
(NCAR). Here, we directly obtained the processed climatic products
constructed by Karger et al. (2020), who recently generated
downscaled and bias-corrected temperature and precipitation datasets.
Specifically, these future climatic datasets were driven by two scenarios of
representative concentration pathways (RCP4.5 and RCP8.5) at a monthly step in
this century. According to the model projections, mean annual temperature
(MAT) under both RCPs will continue to increase in the following decades
(Fig. S3a). The extent of climate warming is generally higher under RCP8.5
than that under RCP4.5 (Fig. S3a). The mean annual precipitation under both
RCPs shows large inter-annual variabilities (Fig. S3b). After obtaining the
future climate datasets, we also use the biovars function in the R environment (see
Secxt. 2.2) to calculate the 23 bioclimatic attributes of interest (Table 1) for both
RCPs year by year from 2020 to 2100. In projecting the future AGB dynamics
using the ensemble machine learning model, we assume that the soil
properties will not significantly change over time and current grazing
intensity will keep relatively stable (i.e. the average number of livestock
during 2014–2019 is used in future predictions). In addition, the
uncertainty analysis for future AGB predictions was performed using the
same approach as that adopted in mapping the historical AGB. Moreover, the
CO2 concentrations have been projected to increase under the two RCPs
(i.e. RCP4.5 and RCP8.5) used in this study (Fig. S4a). The growing
CO2 concentrations can either increase AGB through enhanced
photosynthetic rates (Fay et al., 2012; Lee et al., 2010) or have limited
influences because of other environmental constraints on plant growth
(Brookshire and Weaver, 2015). In this study, we deduced future AGB
dynamics both including and not including the effect of CO2
enrichment on grassland AGB. In including CO2 enrichment effect, we
used the relationship between CO2 concentration and aboveground NPP (ANPP) based on
long-term experimental data derived from Polley et al. (2019). Here, we assumed a general linear response of AGB to increased
CO2 concentrations; i.e. an increase of 100 ppm in CO2 leads to
an increase of 850 kg ha-1 in grassland AGB (Fig. S4b). This linearly
positive effect of CO2 on AGB is further applied to the model-predicted
future AGB (i.e. the AGB not including CO2 enrichment effect). In
brief, we used the annual CO2 concentrations under each RCP scenario in
the future (Fig. S4a) and the average annual CO2 concentration over
2014–2019 as a baseline, together with the relationship between changes in
CO2 and AGB (Fig. S4b), to determine the increment in AGB in each year
from 2020 to 2100. All statistical analyses and graphical productions in
this study were performed in R v3.6.3 (R Development Core Team, 2020).
Results
The field measurements indicate that, on average, aboveground biomass (AGB)
in Inner Mongolian grasslands is 1700 kg ha-1, ranging from 220 kg ha-1 (2.5 % confidence interval (CI)) to 4827 kg ha-1
(97.5 % CI, Fig. 2). Across the three grassland types, meadow steppe has
the highest AGB (2561 Mg ha-1 ranging from 736 to 5537 Mg ha-1), followed by typical steppe (1496 Mg ha-1 ranging from
213 to 4418 Mg ha-1), and desert steppe has the lowest
AGB (835 Mg ha-1 ranging from 234 to 1928 Mg ha-1,
Fig. 2).
Aboveground biomass distribution across different grassland types
in Inner Mongolia. See Fig. 1 for the spatial distribution of the three
grassland types in Inner Mongolia.
The fitted three individual machine learning algorithms (i.e. RF, cubist
and SVM) can explain overall 32 %–48 % of the variance in observed AGB
(Fig. 3a, b and c). The ensemble model of the three algorithms can better
simulate the observations than any of those individual models (Fig. 3). On
average, 52 % of the variance in the observations can be explained by the
ensemble model (Fig. 3d). Although the variable importance differed among
the three algorithms, climatic and livestock variables seem to substantially
regulate the AGB dynamics (Fig. S5). After excluding the covariates with
high multilinearities, the remaining 10 climatic attributes, 5 edaphic
variables and 3 livestock predictors generally show small
autocorrelations (Fig. 4a). The Mantel test suggests that, compared to the
edaphic and livestock attributes, the climatic variables are in general
stronger correlators of AGB in the three grassland types (Fig. 4a).
Furthermore, the path analysis suggests that AGB shows small correlations
with climate (using the 10 climatic indicators shown in Fig. 4a) and soil
(reflected by the five edaphic properties shown in Fig. 4a) while
significantly and positively correlating with livestock (Fig. 4b). We also
found that climate can indirectly affect AGB via its influence on soil
(Fig. 4b). It should be noticed that the small average magnitude with large
variabilities of the loadings for climate (Fig. 4b) suggests the
corresponding indicators for climate may distinctly affect AGB dynamics. It
should also be noted that the overall performance of the fitted path model
(R2=0.22, Fig. 4b) in explaining the variability of AGB is much
poorer than that of the machine learning models (Fig. 3). This indicates
the complex interactions between the environmental drivers in regulating AGB
dynamics.
Performances of models to predict grassland aboveground biomass
(AGB). (a) Random forest (RF); (b) cubist; (c) support vector machines (SVM);
(d), the ensemble model of (a)–(c). For each individual model, 80 % of the
stratified samples of observations were used for model calibration, with the
other 20 % used for validation. R2 and RMSE show the coefficient of
determination and root-mean-square error of model validations. In model
calibrations, the R2 is 0.82, 0.66 and 0.43 for RF, cubist and SVM,
respectively, and RMSE is 359, 460 and 579 kg ha-1 for RF, cubist and SVM, respectively.
Environmental drivers of aboveground grassland biomass (AGB). (a) The correlation matrix of environmental drivers and Mantel test results. The
upper triangle shows the pairwise comparisons of predicting variables, with
a colour gradient denoting Spearman's correlation coefficient. Taxonomic
grassland type (i.e. meadow, typical and desert steppe) was related to each
environmental factor by a partial (geographic-distance-corrected) Mantel
test. Line colour represents the statistical significance, and line width
denotes Mantel's r statistic for the corresponding distance
correlations. (b) The path analysis results of the direction and magnitude of
the effects of latent variable climate (reflected by T2, T3, T5, T8, T9, P2,
P3, P4, P8 and MAPNG), soil (using CFRAG, BULK TAWC, CNrt and PHAQ as
indicators) and livestock (using cattle, goats and sheep as indicators) on
AGB. Numbers in parentheses represent the loadings (correlation
coefficients) of the indicators to the latent variables. See Table 1 for
descriptions of each variable, and see details in the Materials and methods
section for the statistical analysis.
The model-simulated average AGB during 1981–2019 (Fig. 5a) and under RCP4.5
(Fig. 5b) and RCP8.5 (Fig. 5c) in the future shows large spatial
variations. On average, the regional AGB during the past 4 decades is
1438 kg ha-1, and the corresponding lower and upper limits of the 95 %
CI are 479 and 2284 kg ha-1, respectively (Fig. 5a).
Across grassland types, meadow steppe has the highest average AGB (2194 Mg ha-1 ranging from 1153 to 2631 Mg ha-1), followed
by typical steppe (1552 Mg ha-1 ranging from 539 to 2200 Mg ha-1) and desert steppe (893 Mg ha-1 ranging from 405 to 1341 Mg ha-1, Fig. 5a). Spatially, the average
coefficient of variation (CV) in the predictions is lowest in meadow steppe
(10.5 %), followed by desert steppe (14.6 %) and typical steppe
(21.8 %, Fig. 5d). Over 1981–2019, the regional average AGB displayed a
decreasing trend (Fig. 6a). Among the three grassland types, the historical
changes in AGB (Fig. 6b, c and d) are in general consistent with that of the
total Inner Mongolian grassland AGB (Fig. 6a). Moreover, the long-term field
observations also show large inter-annual variabilities in the grassland
biomass (Fig. 7) and can support our predicted temporal biomass dynamics at
the regional scale (Fig. 6). For example, at four of the six sites, AGB
showed a general decreasing trend (Fig. 7).
Spatial patterns of Inner Mongolian grassland aboveground biomass
(AGB) and the uncertainties in terms of coefficient of variations (CV). The
upper panels show the average gridded AGB over 1981–2019 (a) and under two
climate change scenarios (RCP4.5 b and RCP8.5 c) over 2020–2100. The
lower panels (d, e, f) exhibit the associated CV of the upper panels.
Please note that these estimations were derived from simulations without
considering the atmospheric CO2 enrichment effects on AGB.
Temporal variations in the predicted average aboveground biomass
(AGB) in Inner Mongolian grasslands. Each year, data are averages of all
the 1 km × 1 km grids (a) and across a certain grassland type at the
regional scale (b, c, d). It should be noticed that these estimations
were derived from simulations without considering the atmospheric CO2
effects on AGB.
Temporal changes in aboveground biomass (AGB) in the six long-term
filed experiments in Inner Mongolian grasslands. The table inside shows the
linear trends (slope, kg ha-1 yr-1) in AGB and the significance
(reflected by P value).
If the CO2 enrichment effect on AGB is not considered, our predicting
results show that future AGB in general decreases under both
RCPs (i.e. RCP4.5 and RCP8.5, Fig. 6a and Table 2). Compared with the
historical AGB (i.e. average AGB during 1981–2019, hereafter the same), on
average, AGB at the end of this century (i.e. average of 2080–2100,
hereafter the same) would decrease by 14 % under RCP4.5 and 28 % under
RCP8.5, respectively (Table 2). The decreases in AGB under future climate
change show large disparities across different grassland types and climate
change scenarios. Compared with the historical average AGB, AGB at the end
of this century under RCP4.5 is estimated to decrease by a smaller extent
(i.e. 10 %) in meadow steppe than that in typical (16 %) and desert
steppe (21 %, Table 2). In general, AGB under RCP8.5 would decrease by larger
extents compared with those under RCP4.5. Under RCP8.5, the average AGB at
the end of this century is estimated to experience 24 % (in meadow
steppe), 30 % (in typical steppe) and 25 % (in desert steppe) reductions,
compared with the averages over 1981–2019 (Table 2). The magnitudes and
spatial patterns of CV in the simulations under both RCP4.5 (Fig. 5e) and
RCP8.5 (Fig. 5f) are comparable with those during the period of 1981–2019
(Fig. 5d).
Summary of Inner Mongolian grassland aboveground (AGB) biomass
during different periods.
If the CO2 enrichment effect on AGB is included, the predicted losses
in future AGB can be reversed under both RCP scenarios and over different
grassland types (Fig. 8). By the end of this century, the regional average
AGB is increased by 63 % under RCP4.5 and 232 % under RCP8.5 compared with the average AGB during 1981–2019 (Fig. 8a, Table 2). The magnitudes of increases in future AGB differ across different
grassland types. For example for RCP4.5, the average AGB at the end of this
century is estimated to increase by 40 % in meadow steppe, 55 % in
typical steppe and 102 % in desert steppe compared with
their counterparts during 1981–2019 (Fig. 8b, c and d, Table 2). The
increases in AGB are much larger under RCP8.5 than those under RCP4.5. On
average, under RCP8.5, the AGB at the end of this century is projected to
enhance by 147 %, 212 % and 394 % in meadow, typical and desert
steppe, respectively, compared with those over 1981–2019 (Fig. 8b, c and d,
Table 2).
Estimated future aboveground biomass (AGB) in Inner Mongolian
grasslands when the CO2 enrichment effects on AGB are considered. The
temporal changes in AGB of all Inner Mongolian grasslands (a), meadow (b),
typical (c) and desert (d) steppe are presented.
Discussion
Our results, based on AGB observations derived from six long-term field
experiments and literature synthesis, indicate the large spatial disparities
in aboveground biomass across different grassland types (Fig. 2). This
gradient spatial pattern in AGB is comparable with that of Ma et al. (2008), who carried out comprehensive field measurements and investigated
113 locations in Inner Mongolian temperate grasslands during 2002–2005. On
the regional scale, we mapped grassland AGB at high spatial resolution,
which shows that AGB generally decreases from north-eastern to south-western
areas in the study region (Fig. 5a). Such a spatial pattern is also
consistent with the maps generated from remote sensing derivations (Fig. S6). This demonstrates the accuracy of our data-driven predictions of AGB.
It should be noted that existing mapping products of grassland AGB use
mainly remote sensing approaches requiring inputs from satellite-based
datasets (Guo et al., 2016; Jiao et al., 2019; Ma et al., 2010a). Our
fitted machine learning model, however, uses only several readily obtainable
environmental covariates (Fig. 4 and Table 1). Our results demonstrate the
ability of machine learning approaches to effectively extrapolate grassland
AGB to much larger spatiotemporal extents (e.g. Figs. 5 and 6).
Our simulation results show that, under the climate warming over the past
4 decades (Fig. S3), the average AGB generally experienced a declining
trend in the study region (Fig. 6). This may partly support the possible
negative effects of temperature rise on AGB that have been widely reported
(De Boeck et al., 2008; H. Wang et al., 2020), particularly in arid and
semi-arid ecosystems (Ma et al., 2010b). This harmful
influence of warming on AGB is explainable. For example, in a system
restrained by water availability (e.g. temperate grassland), warming can
not only inhibit plant photosynthesis (Xu and Zhou, 2005) but also
enhance evaporation and further intensify water stress (De Boeck et al.,
2006), thereby decreasing grassland biomass. Precipitation has generally been
recognized to have positive effects on AGB in the temperate grasslands
(Hovenden et al., 2019; Ma et al., 2010a), which supports our findings
in this study. For example, the simulated average AGB is relatively higher
in the years with higher mean annual precipitation (e.g. 1998 and 2012) than that in other years
(Fig. 6a). The importance of precipitation on AGB can be more reflected by
the spatial patterns of these two attributes; e.g. AGB is much lower in the
more arid regions (Fig. 5a) where soils suffer more severe water
deficiencies. Apart from climatic factors, our results also demonstrate the
co-regulating effects of soil conditions and livestock on the dynamics of
grassland AGB as indicated by the machine learning models (Fig. S5) and the
path analysis model (Fig. 4b). For example, the increasing trend in
livestock over the past 4 decades (Fig. S2d) is generally in line with
the overall decreasing trend in the contemporary AGB (Fig. 6a). It should be
noted that the major drivers of the simulated temporal changes in AGB (Fig. 6) can vary during different periods in this study due to data
unavailability, particularly for livestock. Specifically, AGB dynamics over
1981–2019 is co-regulated by changes in both climate and livestock (Figs. S2, S3 and S5). In future scenario simulations (e.g. 2020–2100, Fig. 6);
however, AGB variations are predominantly controlled by climate since a
constant grazing intensity was adopted over time in future predictions (see
Materials and methods). We admit that the actual grazing intensity can vary
over time in the future under different RCP scenarios, and simply assuming a
stable grazing intensity over time can lead to substantial biases in AGB
estimations. We need novel approaches to derive the temporal variations in
grazing intensity at larger temporal extents.
Our estimations indicate that AGB can be substantially increased under
future CO2 enrichment (Fig. 8). Here, several uncertainties and
limitations should be noticed in interpreting our results. First, the
gradient of CO2 concentrations in Polley et al. (2019),
which is used to derive the effect of CO2 enrichment on AGB, has a
smaller range (i.e. 250 to 500 ppm) than those under RCP8.5 (i.e.
around 900 ppm by the end of this century, Fig. S4a). Here, extrapolations
of such a relationship between CO2 concentration and AGB to larger
extents of CO2 concentrations can lead to substantial uncertainties in
estimations of AGB. Second, the local soil (Fay et al., 2012)
and climatic (Brookshire and Weaver, 2015) factors can modify the
actual CO2 enrichment effect on AGB, which may also result in large
uncertainties in the quantified AGB. For example, any stimulation in plant
growth is constrained by the availability of other resources required by
plant growth (Reyes-Fox et al., 2014) such as soil water
availability (Brookshire and Weaver, 2015). Consequently, the
magnitude of the increases in AGB induced by CO2 enrichment estimated
in this study, particularly under RCP8.5, can be largely overestimated due
to possible deficiencies of either nutrients or water required by plant
growth (S. Wang et al., 2020).
We also notice that our model predictions show larger inter-annual
variations in AGB (Fig. 6a) than those in the estimations based on
remote-sensing approaches (Fig. S6). In fact, the remote-sensing-derived AGB
has also been bias-corrected by the field measurements
(Jiao et al., 2019). Consequently, this disparity could be
related to the difference of observed AGB datasets used in different
studies. Specifically, the measurements of biomass used to calibrate
remote sensing data (normalized difference vegetation index (NDVI)) in
Jiao et al. (2019) were generally conducted during
2001–2015. Extrapolations of these observations from the short term (e.g.
2001–2015) to the much longer term (e.g. 1982–2015) might lead to
underestimations in the long-term interannual variabilities. Our study,
however, integrates the in-situ-observed data from six long-term (1982–2015) field
experiments (Fig. 1), which can potentially better represent the AGB over
larger temporal scales. It is noteworthy that the accuracy of our
predictions on future grassland AGB relies substantially on the robustness
of future climate change projections simulated by the global circulation models (GCMs) (e.g.
CESM1-BGC). However, although CESM1-BGC (like all the other CMIP5 models)
can simulate changes in temperature reasonably well, it may not predict
precipitation well, particularly for eastern China which is affected by large-scale
atmospheric circulations (Huang et al., 2013). In addition, the
effects of solar radiation (Yu et al., 2021; J. Zhang et al., 2020) and its
complex responses to dust aerosol (Fu et al., 2009; Qi et al., 2013; Wang
et al., 2013) on plant photosynthesis and biomass formation were not
considered in this study, which can be another source of uncertainties in
the estimated AGB under future climate change. Last but not least, the
assumption of space-for-time substitution has been widely debated and
challenged (Johnson and Miyanishi, 2008; Walker et al., 2010). Although
grassland type across space is treated as an independent predictor of AGB in
this study, we admit that using the spatial gradients of observations to
predict AGB backward or forward in time may still lead to large
uncertainties. Consequently, caution should be exercised in interpreting the
modelled future grassland AGB in this study.
Conclusions
Our results demonstrate that the aboveground biomass in Inner Mongolian
grasslands shows large spatial and temporal variations during the past 4
decades, which is driven by a series of environmental covariates.
Particularly, current climate change characterized mainly by warming
together with an increased grazing intensity can have negative effects on
grassland AGB. The decreases in AGB, however, can potentially be reversed by
the positive effects of atmospheric CO2 enrichment. In addition, our
results demonstrate that adopting a machine learning model approach with
only a few readily obtainable environmental predictors can accurately
capture AGB dynamics, which enables extrapolations of AGB across larger
spatiotemporal extents. Moreover, our study provides new data on annual AGB
in Inner Mongolian grasslands at fine spatial (1 km) and temporal (yearly)
resolutions at large temporal scales (1981–2100).
Data availability
The data that support the findings of this study are openly available at 10.6084/m9.figshare.13108430 (Wang, 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-3059-2021-supplement.
Author contributions
GW and YH conceived this study. GW
conducted the data analysis with interpretations from ZL and YH.
GW and ZL prepared the article with contributions from all authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors acknowledge the people who conducted the filed
long-term experiments and collected the observed data.
Financial support
This study was financially supported by the National
Natural Science Foundation of China (grant nos. 41775156 and 41590875) and
the Strategic Priority Research Program of the Chinese Academy of Sciences
(grant no. XDA26010103).
Review statement
This paper was edited by Jianping Huang and reviewed by Tao Wang and one anonymous referee.
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