SESolid EarthSESolid Earth1869-9529Copernicus PublicationsGöttingen, Germany10.5194/se-8-545-2017Application of a new model using productivity coupled with hydrothermal factors (PCH)
for evaluating net primary productivity of grassland in southern
ChinaSunZheng-Guosunzg@njau.edu.cnLiuJieTangHai-YangCollege of Agro-grassland Science, Nanjing Agricultural University, 1 Weigang, Nanjing, Jiangsu 210095, People's Republic of ChinaDepartment of Environmental Science, Hokkaido University, Sapporo 060-0810, JapanZheng-Guo Sun (sunzg@njau.edu.cn)21April20178254555227October20168November20167February201729March2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://se.copernicus.org/articles/8/545/2017/se-8-545-2017.htmlThe full text article is available as a PDF file from https://se.copernicus.org/articles/8/545/2017/se-8-545-2017.pdf
Grassland
ecosystems play important roles in the global carbon cycle. The net primary
productivity (NPP) of grassland ecosystems has become the hot spot of
terrestrial ecosystems. To simulate grassland NPP in southern China, a new
model using productivity coupled with hydrothermal factors (PCH) was built and validated based on data recorded from 2003 to 2014.
The results show a logarithmic correlation between grassland NPP and mean
annual temperature and a linear positive correlation between grassland NPP
and mean annual precipitation in southern China, both highly significant
relationships. There was a highly significant correlation between simulated
and measured NPP (R2=0.8027). Both RMSE and relative root mean square
error (RRMSE) were relatively low, showing that the simulation results of the
model were reliable. The NPP values in the study area had a decreasing trend
from east to west and south to north. Mean NPP was 471.62 g C m-2
from 2003 to 2014. Additionally, the mean annual NPP of southern grassland
presented a rising trend, increasing 3.49 g C m-2 yr-1 during
the past 12 years. These results document performance and use of a new method
to estimate the grassland NPP in southern China.
Introduction
Grassland is one of the major biological communities in the world. It covers
more than 40 % of the total land area on the planet and plays an
important role in the global biogeochemical cycle and energy transformation
process (Chen and Zhang, 2000; Mosier et al., 1991). Meanwhile, grassland
also plays a role in water and soil conservation, wind breaking and sand
fixation, biodiversity maintenance, and shaping soil from surface to depth,
and it shows a close connection with human survival and development (Brevik
et al., 2015). The root system of grassland vegetation occurs in soil, and
thus the direct link between soil and vegetation can be discovered. Most soil
functions have strong ties to vegetation, including biomass production;
biodiversity pooling; and storing, filtering, and transforming nutrients,
substances, and water (Keesstra et al., 2016). In the soil – grassland
vegetation – atmosphere continuum, grassland acts as the center of
ecological functions on the ecosystem scale. The impacts of the climate on
grasslands are quite complicated. On the one hand, different types of
grasslands have their own spatial distributions controlled by temperature and
precipitation; on the other hand, a rise in temperature will alter some
processes in the ecosystem (such as evapotranspiration, decomposition, and
photosynthesis). Therefore, temperature exerts a significant effect on
biological community productivity (Douglas and Geoffrey, 1997). Net primary
productivity (NPP) is an indicator that measures the production capacity and
economically and socially significant products of the plant community under
natural conditions (Sun et al., 2013). Changes in NPP directly reflect the
response of ecosystems to climatic conditions; therefore, it can be used as a
research index in the relationship between ecosystem function and climate
change (Zhou et al., 2014). It also has an important theoretical and
practical significance for evaluating the environmental quality of
terrestrial ecosystems, regulating ecological processes, and estimating the
terrestrial carbon sink to master the interannual variation rule of
terrestrial NPP (Cao et al., 2013; Richardson et al., 2012;
Picard et al., 2005; Zhang et al., 2011; Xu et al., 2012).
Estimation methods, most based on models, to calculate grassland NPP were
discussed in previous research (Gill et al., 2002). Models demonstrate
advantages over other methods in global, regional, and other large-scale
studies, becoming an important tool in macro-ecological research of
grasslands. Grassland NPP estimation models have been used by some
researchers for dynamic monitoring and forecasting (Raich et al., 1991;
Matsushita and Tamura 2002), providing theoretical and technical support for
ecological improvement and recovery of grasslands (Christenson et al.,
2014). A large number of studies were conducted by domestic and foreign
scholars to understand the influence of climate change on
ecosystem processes, including grassland productivity and grassland C
circulation. Although many researchers have studied the influences on a
national or regional scale (Parton et al., 1995; Hall et al., 1995; Braswell
et al., 1997; Cao and Woodward, 1998; Fang et al., 2001; Ni, 2002; Mantgem
and Stephenson, 2007; Wunder et al., 2013; Gang et al., 2015), there has
been little research on relationships between grassland NPP and climate
factors in southern China. Grassland resources are abundant in China, with
an area of nearly 400 million ha, nearly one-sixth of which is in southern China. As
the grassland in northern areas continues to deteriorate and become desert,
the ecological system of grassy hills and slopes in southern China is
becoming increasingly important. Study of the relationship between NPP and
climatic factors, together with their dynamic simulation, will provide
insights on the effective management and reasonable utilization of
grasslands in southern China and the promotion of global change research.
Our objectives were the following: (1) to build a model using productivity coupled with hydrothermal factors (PCH)
based on the statistical analysis of the relationship between measured NPP,
precipitation, and temperature; (2) to modify the adjustment coefficient and
the parameter of the model based on the grassland types and their ecological
characteristics; (3) to simulate NPP using the PCH model and analyze its
changing trends in spatial and temporal patterns from 2003 to 2014; (4) to verify the accuracy of the PCH model by comparing it with field
observation data; and (5) to explore the dominant hydrothermal factor for
determining the NPP change in the study area.
Materials and methodsStudy area
The grassy hills and slopes of southern China, centered at 110∘0′ E, 27∘30′ N, were the focus of our research. The site
encompassed 17 provinces and an area of about 60 million ha (Fig. 1). The
grasslands of southern China are mainly composed of typical grassland,
wetland grassland, lowland meadow, and upland meadow. The southern grasslands
are scattered and distributed among areas of forest land and cultivated
land and are mostly located on slopes. Most regions of the southern grasslands are
managed with grazing and some regions with enclosure and cutting. The
climate characteristics in this area include hot and rainy summers and mild
and rainy winters, with the frost-free period being more than 300 days per
year. The annual mean precipitation is between 800 and 1600 mm and the annual
mean temperature is greater than 15 ∘C. These climate conditions
contribute to a suitable environment for grassland.
Study area and meteorological stations in southern China (the black
boundary lines indicate the provincial boundary; the red dots represent the
locations of the meteorological stations).
Data acquirement and processing
NPP data were acquired in July of 2011, 2012, and 2013, 66 sample plots were
investigated in several provinces of the study area. Five quadrats
(1 m × 1 m) were set on corners and in the center of each
representative sample plot (10 m × 10 m). Aboveground biomass and
the latitude and longitude information were recorded in each small quadrat,
with an average level calculated after sampling. Every 2.2 g of dry matter was
converted into 1 g carbon, leading to the grass NPP in each sample area,
represented in the form of carbon (grams of carbon per square meter) (Fang et al., 2001).
Climate data acquired includes temperature and precipitation data from the
years 2003 to 2014 from the ground stations of China Meteorological Data
Service center (http://data.cma.cn/site/index.html) (Fig. 1). Kriging
interpolation from the geographic information system (GIS) interpolation tool
was utilized to analyze meteorological data according to the latitude and
longitude of each station. Then the image projection transformation converted
data into a raster image with a latitude and longitude network and 1000 m
resolution. Finally, temperature and precipitation information was extracted
according to latitude and longitude corresponding to the investigation
points.
The 1980
Chinese grassland resource inventory and MOD12Q1 data acquired in 2004 were
used to generate the land cover, land use map and the grassland distribution
map (Fig. 2). Open shrubs, woody savannas, savannas, grasslands, and permanent
wetlands were included as the grassland of southern China based on the land
use and land cover classification project proposed by the International
Geosphere–Biosphere Programme (IGBP).
Model establishment and validation
Based on the statistical analysis of the relationship
between measured NPP, precipitation, and temperature, the preliminary
structure of the model was developed. Then the nonlinear fitting algorithm
was utilized to optimize and determine the parameters of the model.
In order to verify the reliability of the simulation
results, both RMSE and relative root mean square
errors (RRMSE) were applied to the model for testing and evaluating the
simulation effects. RMSE and RRMSE were expressed as
RMSE=1n∑i=1nOi-Si2RRMSE=1n∑i=1nOi-Si2Oa,
where Oi was the real value, Si was the simulated value, Oa
was the average of real value, and n was the total number of samples.
The distribution map of grasslands of southern China (the black
boundary lines indicate the provincial boundary, the green zone represents
the grassland area, and the colorless region represents non-grassland in
the study area).
ResultsRelationship between grassland NPP and temperature
Grassland NPP is a joint result of the regional light, temperature,
precipitation, soil, and other natural conditions, which reflects the ability
of using natural environmental resources (Gang et al., 2015). Under natural
conditions, temperature and precipitation were the two dominate influential
factors in grassland NPP in southern China (Sun et al., 2014). The results of the
analysis of the relationship between grassland NPP and temperature in
southern China showed that (1) between 10 and 20 ∘C there was a linear
positive correlation between temperature and the NPP and (2) a para-curve
relationship was found between 20 and 30 ∘C.
Generally, the relationship between temperature and grassland NPP was
logarithmic, with correlation coefficient r being 0.4629 and reaching a
significant level (P < 0.01). As a result, the relationship could be
presented as a logarithmic equation.
Relationship between grassland NPP and precipitation
Precipitation is a key factor in many NPP estimation models (Huston, 2012;
Yu et al., 2008). Mean monthly precipitation in the grassland ecological
system of southern China presented a large range throughout a year, with
minimum
precipitation being 40 mm and the maximum being over 200 mm. NPP also showed a
regular distribution according to the precipitation, with a typical linear
positive correlation. The correlation coefficient r was 0.7836, reaching a
very significant level (P < 0.01). Therefore, the influences of
precipitation on grassland NPP could be expressed as a linear equation.
Estimation model of grassland NPPModel establishment
According to the analysis results, a positive relationship existed between
grassland NPP and mean annual temperature and annual precipitation in
southern China. Thus, it is feasible to express the relationship with
logarithmic and linear equations, respectively. However, the results varied
greatly when temperature was directly used as the equation factor and any
data below 0 ∘C failed to be processed. Thus, it was necessary to
introduce a temperature adjustment coefficient, described here as
Ta=LnTt1+a1,
where Ta was the temperature adjustment coefficient, T was the mean
annual temperature (∘C), t1 was the model parameter, and a1
was a constant, set to 2.5 in the paper.
Precipitation showed a similar trend. Growth stopped when moisture was below a certain level. Thus, another
adjustment coefficient was introduced and expressed as the following:
Wa=SqrtWw1+a2,
where Wa was the adjustment coefficient, W was the mean annual
precipitation (mm), w1 was the model parameter, and a2 was a constant, set to 0.5 in the paper.
According to the information above, the estimation model of grassland NPP in
southern China could be written as the following:
NPP=Ta×Wa×T+W6.
The PCH model was built to simulate grassland NPP of southern China based on
the principle of grassland productivity coupled with hydrothermal factors. In
order to improve the applicability in the grassland of southern China and
the accuracy of the simulation results, adjustment coefficients related to
temperature and precipitation were introduced into the model. Though the PCH
model has not been applied to simulate the NPP of different types of
vegetation, the establishment and application of the model were based
on the specific spatial distribution of grassland and the complicated
hydrothermal conditions in southern China. Each model has its advantages and
limitations depending on different study targets and scales. The limitation
of the model is that fewer influential factors were introduced into the
model compared with other ecological models. The future analysis and
explication of this will be carried out in the discussion part of this paper.
The strength of the PCH model lies in the origin of the model establishment
and the focalization and directness of assessing the NPP of grassland in
southern China. The novelty of this model is mainly embodied in the process
of hydrothermal assimilation in comparison to other models. Understanding
controls over NPP will be crucial in developing models of these processes at
larger spatial scales. Thus, the PCH model combines the hydrothermal parameter
and ecosystem process approach to quantify the carbon flow of grassland in
southern China (Gill et al., 2002).
Calculation of model parameters
The acquisition of model parameters was a complicated process and
would directly affect the accuracy of the final results. Based on the
measured data from 2009 to 2010, by adopting the contraction expansion
algorithm of the nonlinear fitting and MATLAB programs (Conway and Wilcox,
1970), those parameters were calculated as t1=5.8 and w1=560.4.
Model validation
The measured grassland NPP data from 2014 in southern China were used to
validate the simulation results. The results indicated that there was a
strong and significant correlation between the simulated and measured NPP
(R2=0.802, P < 0.01). The RMSE of the simulation was 58.351 g C m-2, the RRMSE was 0.326, and both were small. All these results
indicate that the simulation of precipitation and temperature model for
southern grassland NPP was feasible. The trends of the simulated and
measured grassland NPP were similar (Fig. 3), which also indicated that
the results were reliable.
Spatiotemporal variations of grassland NPP from the years 2003
to 2014
The spatial distribution map of grassland NPP produced by the estimation
model was beneficial in monitoring the grassland resource. This paper built the
spatial distribution map of southern grassland NPP using the estimation
model of grassland NPP based on climatic conditions (Fig. 4). It showed
that the minimum of mean annual NPP of southern grassland was 57.83 g C m-2 and the maximum was 1328.06 g C m-2 in the last 12 years. The NPP
of southern grassland had an obvious zonal distribution. The NPP value was
lower in northwestern regions and higher in southeastern and southern regions,
especially in Jiangxi, Guangdong, and Hainan provinces.
The variation of mean annual NPP and the relevant statistical indices of
southern grassland in the last 12 years were shown in Fig. 5. The trend
of mean annual NPP presented an increasing tendency of the whole southern
grassland from 2003 to 2014. The variation range of the mean annual NPP was
from 430.31 to 519.82 g C m-2, and the mean was 471.62 g C m-2.
The minimum of the mean annual NPP appeared in 2006, and the maximum value
appeared in 2013. The tilt rate of the mean annual NPP of southern grassland
in the last 12 years was 3.49 g C m-2 yr-1, which indicated that the
NPP increased about 3.49 g C m-2 every year (P < 0.05).
Comparison between simulated and observed grassland NPP (net primary
productivity) in southern China.
Spatial characteristics of grassland NPP in southern China from 2003
to 2014.
The interannual variation of grassland NPP in southern China from
2003 to 2014.
Discussion
The parameters of the model were set to mediate the abnormal values from the
model inputs and thus keep the stability of the model results. They were
determined and constrained using multi-observation results. Hence, the model
parameters are not associated with specific grassland types or the
corresponding ecological characteristics. To incorporate remote sensing
information into this model, we propose applying a remote sensing dataset as a
spatially explicit scalar for the model parameterization, and thus enhancing the
prediction ability of the future version of the model.
Research on the relationships between the NPP and climate factors in global
or regional ecological systems started in the mid-1800s (Nemani et al., 2003;
Zhou et al., 2014). As revealed in these studies, the vegetation index
showed periodic variations with corresponding climate indices, including
temperature and precipitation, during the growth process of most plants.
Temporal and spatial variations were quite distinct in grassland NPP, since
climatic factors, especially precipitation and temperature, were factors
directly linked to periodic variations (Ronnenberg and Wesche, 2010). This
study showed that a temperature rise would cause a certain level of rise in the
grassland NPP in southern China, especially in the high-temperature zones.
However, these results differed from some previous reports (Mcguire et al.,
1993). In addition, there was a significant positive correlation between
precipitation and NPP. When mean annual precipitation increases, grassland
NPP would also increase significantly. This conclusion is consistent with
previous studies (Sala et al., 2000; Knapp and Smith, 2001; Mohamed et al.,
2004).
The ultimate goal of those studies regarding the relationship between climate and
terrestrial ecosystem NPP is to predict the possible impacts on climate
change and to take scientific countermeasures (Pablo et al., 2007), and
establishing a model is an efficient means to make these predictions.
Through modeling and simulation, one could reveal the quantitative change
and trend of NPP caused by climate change. That was why the research of the NPP
model attracted a vast amount of attention (Ren et al., 2011). This
study establishes an estimation model for the grassland NPP in southern
China by using the statistical analysis of the relationship between the
southern grassland NPP and precipitation and temperature, combined with
biological process. The relationship between simulated and observed values
reached a highly significant level. This and the low RMSE validated the
reliability of the model. Therefore, it was feasible to estimate the
grassland NPP in southern China by using the PCH model described in this
paper.
The estimation of grassland NPP is a complex process. It is not only
affected by climatic factors such as precipitation and temperature but also
by the grassland vegetation's own inner physiological processes, fire
severity, slope position and aspect, grazing, human activities, cutting
frequency, and grassland ecotypes (Pereira et al., 2016; Shaw et al., 2016;
Lu et al., 2015; Lin et al., 2015; Poeplau et al., 2016; Roosendaal et al.,
2016).
Grassy hills and slopes in southern China had a wide distribution with
various vegetation types; therefore, the NPP distribution was uneven.
Although the model estimation worked well, some imperfections exist.
Firstly, a classification for grass hills and slopes is needed, without
which the NPP estimation fell into a single type (Hu et al., 2016).
Secondly, the NPP estimation results were representative of the entire year,
while arbitrary NPP estimation for a single month has not been verified yet.
Thirdly, as an important ecological parameter, the MODIS normalized difference
vegetation index needs to be added into the model (Gong et al.,
2015). Then precision of the model could be improved in the process of
evaluating the changes of grassland in southern China. Fourth, grassland
soil coarseness needs to be taken into account as a result of nutrient
cycling and respiration in grassland (Lü et al., 2016). The last issue concerns sensitivity. The study indicated that the simulation results from the
PCH model were large in a small fraction of areas with relatively
low NPP, while they were small in an area with high NPP. This may be caused by
the limited time span and other factors, including the influences from
different types of grasslands. Hence, there might be some uncertainty in
estimating the lower or higher grassland NPP using the estimation model.
Further study is required to solve these problems.
Conclusion
In this study, a new model using productivity coupled with hydrothermal factors (PCH)
was built to simulate the NPP in southern China's grasslands. The PCH model
uses productivity coupled with hydrothermal factors that can be expressed
by the transformation of the model parameters, mean annual temperature and
mean annual precipitation, which are the two most critical factors affecting
the NPP of southern China's grasslands. The results show that there is a
logarithmic correlation between grassland NPP and mean annual temperature,
and there is a linear positive correlation between grassland NPP and mean annual
precipitation in southern China. There was a very significant correlation
between simulated and measured NPP (R2=0.8027). Meanwhile, both
RMSE and RRMSE stayed at a relatively low level, showing that the simulation
results of the model were reliable. The NPP values in the study area had a
decreasing trend from east to west and south to north. The mean
NPP was 471.62 g C m-2 from 2003 to 2014. Additionally, the mean annual
NPP of southern grassland presented a rising trend and the rate of change
was 3.49 g C m-2 yr-1 in the last 12 years.
The observation data from the sample plots in the study
area were collected by Zheng-Guo Sun and his graduates. The hydrothermal
data, including temperature and precipitation, were downloaded from the
ground stations of the China Meteorological Data Service center
(http://data.cma.cn/site/index.html). The Arc GIS 10.4.1 software was
downloaded from the website
http://www.esri.com/en/arcgis/products/arcgis-pro/overview. The authors
declare that all data used in this paper are authentic and credible. The
data of this study can be provided by the authors upon
request.
The authors declare that they have no conflict of
interest.
Acknowledgements
We are grateful to the chief editor and anonymous reviewers for their
illuminating comments. We would also like to thank Kenneth A. Albrecht
(Department of Agronomy, University of Wisconsin–Madison, WI 53706, USA) for
his helpful comments on the draft of this paper. This work was supported by
the project of Natural Science Fund of Jiangsu Province (BK20140413) and the
Key Project of the Chinese National Programs for Fundamental Research and
Development (973 Program, 2010CB950702).
Edited by: A. Jordán
Reviewed by: five anonymous referees
ReferencesBraswell, B. H., Schimel, D. S., Linder, E., and Moore III., B.: The response
of global terrestrial ecosystems to interannual temperature variability,
Science, 278, 870–872, 10.1126/science.278.5339.870, 1997.Brevik, E. C., Cerdà, A., Mataix-Solera, J., Pereg, L., Quinton, J. N.,
Six, J., and Van Oost, K.: The interdisciplinary nature of SOIL, SOIL, 1,
117–129, 10.5194/soil-1-117-2015, 2015.Cao, L., Xu, J., Chen, Y., Li, W., Yang, Y., Hong, Y., and Li, Z.:
Understanding the dynamic coupling between vegetation cover and climatic
factors in a semiarid region-a case study of Inner Mongolia, China,
Ecohydrology, 6, 917–926, 10.1002/eco.1245, 2013.Cao, M. K. and Woodward, F. I.: Dynamic responses of terrestrial ecosystem
carbon cycling to global climate change, Nature, 393, 249–252,
10.1038/30460, 1998.Chen, Z. X. and Zhang, X. S.: Value of ecosystem services in China, Chinese
Sci. Bull., 45, 17–22, 10.1007/BF02886190, 2000.Christenson, L. M., Mitchell, M. J., Groffman, P. M., and Lovett, G. M.:
Cascading effects of climate change on forest ecosystems: biogeochemical
links between trees and moose in the northeast USA, Ecosystems, 3, 1–16,
10.1007/s10021-013-9733-5, 2014.Conway, G. R. and Wilcox, J. C.: Fitting nonlinear models to biological data
by Marquardt's algorithm, Ecology, 3, 503–507, 10.2307/1935386, 1970.Douglas, G. G. and Geoffrey, M. H.: A technique for monitoring ecological
disturbance in tall grass prairie using seasonal NDVI trajectories and a
discriminate function mixture model, Remote Sens. Environ., 61, 270–278,
10.1016/S0034-4257(97)00043-6, 1997.Fang, J. Y., Chen, A. P., Peng, C. H., Zhao, S. Q., and Ci, L. J.: Changes in
forest biomass carbon storage in China between 1949 and 1998, Science, 292,
2320–2322, 10.1126/science.1058629, 2001.Gang, C., Zhou, W., Wang, Z., Chen, Y., Li, J., Chen, J., Qi, J., Odeh, I.,
and Groisman, P. Y.: Comparative Assessment of Grassland NPP Dynamics in
Response to Climate Change in China, North America, Europe and Australia from
1981 to 2010, J. Agron. Crop Sci., 1, 57–68, 10.1111/jac.12088, 2015.Gill, R. A., Kelly, R. H., Parton, W. J., Day, K. A., Jackson, R. B., Morgan,
J. A., Scurlock, J. M. O., Tieszen, L. L., Castle, J. V., Ojima, D. S., and
Zhang, X. S.: Using simple environmental variables to estimate below-ground
productivity in grasslands, Global Ecol. Biogeogr., 1, 79–86,
10.1046/j.1466-822X.2001.00267.x, 2002.Gong, Z., Kawamura, K., Ishikawa, N., Goto, M., Wulan, T., Alateng, D., Yin,
T., and Ito, Y.: MODIS normalized difference vegetation index (NDVI) and
vegetation phenology dynamics in the Inner Mongolia grassland, Solid Earth,
6, 1185–1194, 10.5194/se-6-1185-2015, 2015.Hall, D. O., Ojima, D. S., Parton, W. J., and Scurlock, J. M. O.: Response of
Temperate and Tropical Grasslands to CO2 and Climate Change, J.
Biogeogr., 22, 537–547, 10.2307/2845952, 1995.Hu, G., Liu, H., Yin, Y., and Song, Z.: The Role of Legumes in Plant
Community Succession of Degraded Grasslands in Northern China, Land Degrad.
Dev., 27, 366–372, 10.1002/ldr.2382, 2016.Huston, M. A.: Precipitation, soils, NPP, and biodiversity: resurrection of
albrecht's curve, Ecol. Monogr., 3, 277–296, 10.1890/11-1927.1, 2012.Keesstra, S. D., Bouma, J., Wallinga, J., Tittonell, P., Smith, P.,
Cerdà, A., Montanarella, L., Quinton, J. N., Pachepsky, Y., van der
Putten, W. H., Bardgett, R. D., Moolenaar, S., Mol, G., Jansen, B., and
Fresco, L. O.: The significance of soils and soil science towards realization
of the United Nations Sustainable Development Goals, SOIL, 2, 111–128,
10.5194/soil-2-111-2016, 2016.Knapp, A. K. and Smith, M. D.: Variation among biomes in temporal dynamics of
aboveground primary production, Science, 291, 481–484,
10.1126/science.291.5503.481, 2001.Lin, L., Li, Y. K., Xu, X. L., Zhang, F. W., Du, Y. G., Liu, S. L., Guo, X.
W., and Cao, G. M.: Predicting parameters of degradation succession processes
of Tibetan Kobresia grasslands, Solid Earth, 6, 1237–1246,
10.5194/se-6-1237-2015, 2015.Lü, L., Wang, R., Liu, H., Yin, J., Xiao, J., Wang, Z., Zhao, Y., Yu, G.,
Han, X., and Jiang, Y.: Effect of soil coarseness on soil base cations and
available micronutrients in a semi-arid sandy grassland, Solid Earth, 7,
549–556, 10.5194/se-7-549-2016, 2016.Lu, X., Yan, Y., Sun, J., Zhang, X., Chen, Y., Wang, X., and Cheng, G.:
Short-term grazing exclusion has no impact on soil properties and nutrients
of degraded alpine grassland in Tibet, China, Solid Earth, 6, 1195–1205,
10.5194/se-6-1195-2015, 2015.Mantgem, P. J. V. and Stephenson, N. L.: Apparent climatically induced
increase of tree mortality rates in a temperate forest, Ecol. Lett., 10,
909–916, 10.1111/j.1461-0248.2007.01080.x, 2007.Matsushita, B. and Tamura, M.: Integrating remotely sensed data with an
ecosystem model to estimate net primary productivity in East Asia, Remote
Sens. Environ., 81, 58–66, 10.1016/S0034-4257(01)00331-5, 2002.Mcguire, A. D., Joyce, L. A., Kicklighter, D. W., Melillo, J. M., Esser, G.,
and Vorosmarty, C. J.: Productivity response of climax temperate forests to
elevated temperature and carbon dioxide: a North American comparison between
two global models, Climatic Change, 4, 287–310, 10.1007/BF01091852,
1993.Mohamed, M. A., Babiker, I. S., Chen, Z. M., Ikeda, K., Ohta, K., and Kato,
K.: The role of climate variability in the inter-annual variation of
terrestrial net primary production (NPP), Sci. Total Environ., 332, 123–137,
10.1016/j.scitotenv.2004.03.009, 2004.Mosier, A. R., Schimel, D., Valentine, D., Bronson, K., and Parton, W.:
Methane and nitrous oxide fluxes in native, fertilized and cultivated
grasslands, Nature, 350, 330–332, 10.1038/350330a0, 1991.Nemani, R. R., Keeling, C. D., Hashimoto, H., Jolly, W. M., Piper, S. C.,
Tucker, C. J., Myneni, R. B., and Running, S. W.: Climate-driven increases in
global terrestrial net primary production from 1982 to 1999, Science, 300,
1560–1563, 10.1126/science.1082750, 2003.Ni, J.: Effects of climate change on carbon storage in boreal forests of
china: a local perspective, Climatic Change, 1–2, 61–75,
10.1023/A:1020291220673, 2002.Pablo, M., Thomas, H., David, P. R., Benjamin, S., and Martin, T. S.: Changes
in European ecosystem productivity and carbon balance driven by regional
climate model output, Glob. Change Biol., 1, 108–122,
10.1111/j.1365-2486.2006.01289.x, 2007.Parton, W. J., Scurlock, J. M. O., Ojima, D. S., Schimel, D. S., and Hall, D.
O.: Impact of climate change on grassland production and soil carbon
worldwide, Glob. Change Biol., 1, 13–22,
10.1111/j.1365-2486.1995.tb00002.x, 1995.Pereira, P., Cerdà, A., Lopez, A. J., Zavala, L. M., Mataix-Solera, J.,
Arcenegui, V., Misiune, I., Keesstra, S., and Novara, A.: Short-term
vegetation recovery after a grassland fire in Lithuania: the effects of fire
severity, slope position and aspect, Land Degrad. Dev., 27, 1523–1534,
10.1002/ldr.2498, 2016.Picard, G., Quegan, S. N., Lomas, M. R., Toan, T. L., and Woodward, F. I.:
Bud-burst modelling in Siberia and its impact on quantifying the carbon
budget, Glob. Change Biol., 12, 2164–2176,
10.1111/j.1365-2486.2005.01055.x, 2005.Poeplau, C., Marstorp, H., Thored, K., and Kätterer, T.: Effect of
grassland cutting frequency on soil carbon storage – a case study on public
lawns in three Swedish cities, SOIL, 2, 175–184,
10.5194/soil-2-175-2016, 2016.Raich, J. W., Rastetter, E. B., Melillo, J. M., Kicklighter, D. W., and
Steudler, P. B. J.: Potential net primary productivity in South America:
application of a global model, Ecol. Appl., 4, 399–429, 10.2307/1941899,
1991.Ren, W., Tian, H., Tao, B., Chappelka, A., Sun, G., Lu, C., Liu, M., Chen,
G., and Xu, X.: Impacts of tropospheric ozone and climate change on net
primary productivity and net carbon exchange of china's forest ecosystems,
Global Ecol. Biogeogr., 3, 391–406, 10.1111/j.1466-8238.2010.00606.x,
2011.Richardson, A. D., Anderson, R. S., Arain, M. A., Barr, A. G., Bohrer, G.,
and Chen, G.: Terrestrial biosphere models need better representation of
vegetation phenology: results from the north American carbon program site
synthesis, Glob. Change Biol., 2, 566–584,
10.1111/j.1365-2486.2011.02562.x, 2012.Ronnenberg, K. and Wesche, K.: Effects of fertilization and irrigation on
productivity, plant nutrient contents and soil nutrients in Southern
Mongolia, Plant Soil, 1–2, 239–251, 10.1007/s11104-010-0409-z, 2010.Roosendaal, D., Stewart, C. E., Denef, K., Follett, R. F., Pruessner, E.,
Comas, L. H., Varvel, G. E., Saathoff, A., Palmer, N., Sarath, G., Jin, V.
L., Schmer, M., and Soundararajan, M.: Switchgrass ecotypes alter microbial
contribution to deep-soil C, SOIL, 2, 185–197, 10.5194/soil-2-185-2016,
2016.Sala, O. E., Chapin III., F. S., Armesto, J. J., Berlow, E., Bloomfield, J.,
Dirzo, R., Huber-Sanwald, E., Huenneke, L. F., Jackson, R. B., Kinzig, A.,
Leemans, R., Lodge, D. M., Mooney, H. A., Oesterheld, M., Poff, N. L., Syke,
M. T., Walker, B. H., Walker, M., and Wall, D. H.: Global biodiversity
scenarios for the year 2100, Science, 287, 1770–1774,
10.1126/science.287.5459.1770, 2000.Shaw, E. A., Denef, K., Milano de Tomasel, C., Cotrufo, M. F., and Wall, D.
H.: Fire affects root decomposition, soil food web structure, and carbon flow
in tallgrass prairie, SOIL, 2, 199–210, 10.5194/soil-2-199-2016, 2016.Sun, Z. G., Long, X. H., Sun, C. M., Zhou, W., Ju, W. M., and Li, J. L.:
Evaluation of net primary productivity and its spatial and temporal patterns
in Southern China's grasslands, Rangeland J., 3, 331–338,
10.1071/RJ12061, 2013.Sun, Z. G., Sun, C. M., Zhou, W., Ju, W. M., and Li, J. L.: Classification
and Net Primary Productivity of the Southern China's Grasslands Ecosystem
Based on Improved Comprehensive and Sequential Classification System (CSCS)
Approach, J. Integr. Agr., 4, 893–903, 10.1016/S2095-3119(13)60415-3,
2014.Wunder, J., Fowler, A. M., Cook, E. R., Pirie, M., and Mccloskey, S. P. J.:
On the influence of tree size on the climate–growth relationship of New
Zealand Kauri (Agathis australis): insights from annual, monthly and daily
growth patterns, Trees, 4, 937–948, 10.1007/s00468-013-0846-4, 2013.
Xu, X., Niu, S. L., Sherry, R. A., Zhou, X. H., Zhou, J. Z., and Luo, Y. Q.:
Interannual variability in responses of belowground net primary productivity
(NPP) and NPP partitioning to long-term warming and clipping in a tall grass
prairie, Glob. Change Biol., 18, 1648–1656,
10.1111/j.1365-2486.2012.02651.x, 2012.Yu, D., Zhu, W., and Pan, Y.: The role of atmospheric circulation system
playing in coupling relationship between spring NPP and precipitation in East
Asia area, Environ. Monit. Assess., 1–3, 135–143,
10.1007/s10661-007-0023-6, 2008.Zhang, G. G., Kang, Y. M., Han, G. D., and Sakurai, K.: Effect of climate
change over the past half century on the distribution, extent and NPP of
ecosystems of Inner Mongolia, Glob. Change Biol., 17, 377–389,
10.1111/j.1365-2486.2010.02237.x, 2011.Zhou, W., Gang, C., Zhou, L., Chen, Y., Li, J., Ju, W., and Odeh, I.: Dynamic
of grassland vegetation degradation and its quantitative assessment in the
northwest China, Acta Oecol., 55, 86–96, 10.1016/j.actao.2013.12.006,
2014.