Articles | Volume 21, issue 4
https://doi.org/10.5194/acp-21-3059-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-3059-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Simulating the spatiotemporal variations in aboveground biomass in Inner Mongolian grasslands under environmental changes
LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, 100029, China
Zhongkui Luo
College of Environmental and Resource Sciences, Zhejiang University,
Hangzhou 310058, Zhejiang, China
Yao Huang
State Key Laboratory of Vegetation and Environmental Change, Institute
of Botany, Chinese Academy of Sciences, Beijing, 100093, China
Wenjuan Sun
State Key Laboratory of Vegetation and Environmental Change, Institute
of Botany, Chinese Academy of Sciences, Beijing, 100093, China
Yurong Wei
Inner Mongolia Ecology and Agrometeorology Centre, Hohhot, Inner
Mongolia 100051, China
Liujun Xiao
College of Environmental and Resource Sciences, Zhejiang University,
Hangzhou 310058, Zhejiang, China
School of Atmospheric Sciences and Guangdong Province Key Laboratory
for Climate Change and Natural Disaster Studies, Sun Yat-sen University,
Zhuhai, 519000, China
Jinhuan Zhu
LAOR, Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, 100029, China
Tingting Li
LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, 100029, China
Wen Zhang
LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, 100029, China
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Cited articles
Andresen, L. C., Yuan, N., Seibert, R., Moser, G., Kammann, C. I.,
Luterbacher, J., Erbs, M., and Müller, C.: Biomass responses in a
temperate European grassland through 17 years of elevated CO2, Global
Change Biol., 24, 3875–3885, 2018.
Bai, Y., Wu, J., Xing, Q., Pan, Q., Huang, J., Yang, D., and Han, X.:
Primary production and rain use efficiency across a precipitation gradient
on the Mongolia plateau, Ecology, 89, 2140–2153, 2008.
Batjes, N. H.: Harmonized soil property values for broad-scale modelling
(WISE30sec) with estimates of global soil carbon stocks, Geoderma, 269,
61–68, https://doi.org/10.1016/j.geoderma.2016.01.034, 2016.
Bhandari, J. and Zhang, Y.: Effect of altitude and soil properties on
biomass and plant richness in the grasslands of Tibet, China, and Manang
District, Nepal, Ecosphere, 10, e02915, https://doi.org/10.1002/ecs2.2915, 2019.
Brookshire, E. N. J. and Weaver, T.: Long-term decline in grassland
productivity driven by increasing dryness, Nat. Commun., 6, 7148,
https://doi.org/10.1038/ncomms8148, 2015.
Brownlee, J.: Machine Learning Mastery, Train-Test Split for Evaluating
Machine Learning Algorithms, available at: https://machinelearningmastery.com/train-test-split-for-evaluating-machinelearning-algorithms/, last access: 1 July 2020.
De Boeck, H. J., Lemmens, C. M., Bossuyt, H., Malchair, S., Carnol, M.,
Merckx, R., Nijs, I., and Ceulemans, R.: How do climate warming and plant
species richness affect water use in experimental grasslands?, Plant Soil,
288, 249–261, 2006.
De Boeck, H. J., Lemmens, C. M. H. M., Zavalloni, C., Gielen, B., Malchair, S., Carnol, M., Merckx, R., Van den Berge, J., Ceulemans, R., and Nijs, I.: Biomass production in experimental grasslands of different species richness during three years of climate warming, Biogeosciences, 5, 585–594, https://doi.org/10.5194/bg-5-585-2008, 2008.
Eldridge, D. J. and Delgado-Baquerizo, M.: Continental-scale impacts of
livestock grazing on ecosystem supporting and regulating services, Land
Degrad. Dev., 28, 1473–1481, 2017.
Fan, J., Wang, K., Harris, W., Zhong, H., Hu, Z., Han, B., Zhang, W., and
Wang, J.: Allocation of vegetation biomass across a climate-related gradient
in the grasslands of Inner Mongolia, J. Arid Environ., 73, 521–528, 2009.
Fay, P. A., Jin, V. L., Way, D. A., Potter, K. N., Gill, R. A., Jackson, R.
B., and Polley, H. W.: Soil-mediated effects of subambient to increased
carbon dioxide on grassland productivity, Nat. Clim. Change, 2, 742–746,
https://doi.org/10.1038/nclimate1573, 2012.
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1-km spatial resolution
climate surfaces for global land areas, Int. J. Climatol., 37, 4302–4315,
https://doi.org/10.1002/joc.5086, 2017.
Fu, Q., Thorsen, T., Su, J., Ge, J., and Huang, J.: Test of Mie-based
single-scattering properties of non-spherical dust aerosols in radiative
flux calculations, J. Quant. Spectrosc. Ra., 110, 1640–1653, 2009.
Gilbert, M., Nicolas, G., Cinardi, G., Van Boeckel, T. P., Vanwambeke, S.
O., Wint, G. W., and Robinson, T. P.: Global distribution data for cattle,
buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010,
Sci. Data, 5, 1–11, 2018.
Godde, C. M., Boone, R., Ash, A. J., Waha, K., Sloat, L., Thornton, P. K.,
and Herrero, M.: Global rangeland production systems and livelihoods at
threat under climate change and variability, Environ. Res. Lett.,
15, 044021, https://doi.org/10.1088/1748-9326/ab7395, 2020.
Gonsamo, A., Chen, J. M., and Ooi, Y. W.: Peak season plant activity shift
towards spring is reflected by increasing carbon uptake by extratropical
ecosystems, Global Change Biol., 24, 2117–2128, 2018.
Grant, K., Kreyling, J., Dienstbach, L. F., Beierkuhnlein, C., and Jentsch,
A.: Water stress due to increased intra-annual precipitation variability
reduced forage yield but raised forage quality of a temperate grassland,
Agr. Ecosyst. Environ., 186, 11–22, 2014.
Guo, L. H., Hao, C. Y., Wu, S. H., Zhao, D. S., and Gao, J. B.: Analysis of
changes in net primary productivity and its susceptibility to climate change
of Inner Mongolian grasslands using the CENTURY model, Geogr.
Res., 35, 271–284, 2016 (in Chinese with English abstract).
Hovenden, M. J., Leuzinger, S., Newton, P. C., Fletcher, A., Fatichi, S.,
Lüscher, A., Reich, P. B., Andresen, L. C., Beier, C., and Blumenthal,
D. M.: Globally consistent influences of seasonal precipitation limit
grassland biomass response to elevated CO2, Nat. Plants, 5, 167–173,
2019.
Hu, Z., Fan, J., Zhong, H., and Yu, G.: Spatiotemporal dynamics of
aboveground primary productivity along a precipitation gradient in Chinese
temperate grassland, Sci. China Ser. D, 50, 754–764,
https://doi.org/10.1007/s11430-007-0010-3, 2007.
Huang, D.-Q., Zhu, J., Zhang, Y.-C., and Huang, A.-N.: Uncertainties on the
simulated summer precipitation over Eastern China from the CMIP5 models,
J. Geophys. Res.-Atmos., 118, 9035–9047,
https://doi.org/10.1002/jgrd.50695, 2013.
Hufkens, K., Keenan, T. F., Flanagan, L. B., Scott, R. L., Bernacchi, C. J.,
Joo, E., Brunsell, N. A., Verfaillie, J., and Richardson, A. D.:
Productivity of North American grasslands is increased under future climate
scenarios despite rising aridity, Nat. Clim. Change, 6, 710–714, 2016.
IPCC: Climate change 2007: impacts, adaptation and vulnerability,
Contribution of working group II to the fourth assessment report of the
intergovernmental panel on climate change, Cambridge University Press,
Cambridge, UK, 2007.
Jia, X., Shao, M., Wei, X., Horton, R., and Li, X.: Estimating total net
primary productivity of managed grasslands by a state-space modeling
approach in a small catchment on the Loess Plateau, China, Geoderma, 160,
281–291, https://doi.org/10.1016/j.geoderma.2010.09.016, 2011.
Jiao, C. C., Yu, G. R., Chen, Z., and He, N. P.: A dataset for aboveground
biomass of the northern temperate and Tibetan Plateau alpine grasslands in
China, based on field investigation and remote sensing inversion
(1982–2015), China Sci. Data, 4, 63–75, https://doi.org/10.11922/csdata.2018.0029.zh,
2019.
Johnson, E. A. and Miyanishi, K.: Testing the assumptions of
chronosequences in succession, Ecol. Lett., 11, 419–431,
https://doi.org/10.1111/j.1461-0248.2008.01173.x, 2008.
Karger, D. N., Schmatz, D. R., Dettling, G., and Zimmermann, N. E.:
High-resolution monthly precipitation and temperature time series from 2006
to 2100, Sci. Data, 7, 248, https://doi.org/10.1038/s41597-020-00587-y, 2020.
Lee, M., Manning, P., Rist, J., Power, S. A., and Marsh, C.: A global
comparison of grassland biomass responses to CO2 and nitrogen enrichment,
Philos. T. R. Soc. B, 365,
2047–2056, https://doi.org/10.1098/rstb.2010.0028, 2010.
Legendre, P. and Fortin, M. J.: Spatial pattern and ecological analysis,
Vegetatio, 80, 107–138, 1989.
Long, L. H., Li, X. B., Wang, H., Wei, D. D., and Zhang, C.: Net primary
productivity (NPP) of grassland ecosystem and its relationship with climate
in Inner Mongolia, Acta Ecologica Sinica, 30, 1367–1378, 2010 (in Chinese with
English abstract).
Luo, Z., Wang, G., and Wang, E.: Global subsoil organic carbon turnover
times dominantly controlled by soil properties rather than climate, Nat.
Commun., 10, 3688, https://doi.org/10.1038/s41467-019-11597-9, 2019.
Ma, W., Yang, Y., He, J., Zeng, H., and Fang, J.: Above-and belowground
biomass in relation to environmental factors in temperate grasslands, Inner
Mongolia, Sci. China Ser. C, 51, 263–270, 2008.
Ma, W., Fang, J., Yang, Y., and Mohammat, A.: Biomass carbon stocks and
their changes in northern China's grasslands during 1982–2006, Sci.
China Life Sci., 53, 841–850, 2010a.
Ma, W., Liu, Z., Wang, Z., Wang, W., Liang, C., Tang, Y., He, J.-S., and
Fang, J.: Climate change alters interannual variation of grassland
aboveground productivity: evidence from a 22-year measurement series in the
Inner Mongolian grassland, J. Plant Res., 123, 509–517,
https://doi.org/10.1007/s10265-009-0302-0, 2010b.
Mantel, N.: The detection of disease clustering and a generalized regression
approach, Cancer research, 27, 209–220, 1967.
National Bureau of Statistics of China: China Statistical Yearbook (various issues 1981–2019), China Statistics Press, Beijing,
available at: https://data.stats.gov.cn/easyquery.htm?cn=E0103 (last access: 1 June 2020), 1981–2019 (in Chinese).
National Research Council: Grasslands and Grassland Sciences in Northern
China, The National Academies Press, Washington, DC, USA, 1992.
O'Mara, F. P.: The role of grasslands in food security and climate change,
Ann. Bot., 110, 1263–1270, 2012.
Park, T., Chen, C., Macias-Fauria, M., Tømmervik, H., Choi, S., Winkler,
A., Bhatt, U. S., Walker, D. A., Piao, S., and Brovkin, V.: Changes in
timing of seasonal peak photosynthetic activity in northern ecosystems,
Global Change Biol., 25, 2382–2395, 2019.
Pastore, M. A., Lee, T. D., Hobbie, S. E., and Reich, P. B.: Strong
photosynthetic acclimation and enhanced water-use efficiency in grassland
functional groups persist over 21 years of CO2 enrichment, independent of
nitrogen supply, Global Change Biol., 25, 3031–3044, https://doi.org/10.1111/gcb.14714,
2019.
Peng, S., Piao, S., Shen, Z., Ciais, P., Sun, Z., Chen, S., Bacour, C.,
Peylin, P., and Chen, A.: Precipitation amount, seasonality and frequency
regulate carbon cycling of a semi-arid grassland ecosystem in Inner
Mongolia, China: A modeling analysis, Agr. Forest Meteorol.,
178/179, 46–55, https://doi.org/10.1016/j.agrformet.2013.02.002, 2013.
Piao, S., Fang, J., He, J., and Xiao, Y.: Spatial distribution of grassland
biomass in China, Acta Phytoecologica Sinica, 28, 491–498, 2004 (in Chinese with
English Abstract).
Polley, H. W., Aspinwall, M. J., Collins, H. P., Gibson, A. E., Gill, R. A.,
Jackson, R. B., Jin, V. L., Khasanova, A. R., Reichmann, L. G., and Fay, P.
A.: CO2 enrichment and soil type additively regulate grassland productivity,
New Phytol., 222, 183–192, https://doi.org/10.1111/nph.15562, 2019.
Qi, Y., Ge, J., and Huang, J.: Spatial and temporal distribution of MODIS
and MISR aerosol optical depth over northern China and comparison with
AERONET, Chinese Sci. Bull., 58, 2497–2506, 2013.
R Development Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna,
Austria, 2020.
Reyes-Fox, M., Steltzer, H., Trlica, M. J., McMaster, G. S., Andales, A. A.,
LeCain, D. R., and Morgan, J. A.: Elevated CO2 further lengthens growing
season under warming conditions, Nature, 510, 259–262, https://doi.org/10.1038/nature13207,
2014.
Sanchez, G.: PLS path modeling with R, Trowchez Editions, Berkeley, CL, USA, 2013.
Sattari, S., Bouwman, A., Rodriguez, R. M., Beusen, A., and Van Ittersum,
M.: Negative global phosphorus budgets challenge sustainable intensification
of grasslands, Nat. Commun., 7, 1–12, 2016.
Scurlock, J. M., Johnson, K., and Olson, R. J.: Estimating net primary
productivity from grassland biomass dynamics measurements, Global Change
Biol., 8, 736–753, 2002.
Thornton, P. E., Running, S. W., and White, M. A.: Generating surfaces of
daily meteorological variables over large regions of complex terrain,
J. Hydrol., 190, 214–251, 1997.
Walker, L. R., Wardle, D. A., Bardgett, R. D., and Clarkson, B. D.: The use
of chronosequences in studies of ecological succession and soil development,
J. Ecol., 98, 725–736, https://doi.org/10.1111/j.1365-2745.2010.01664.x, 2010.
Wang, G.: Inner Mongolian grassland aboveground biomass measurements, figshare, Figshare, https://doi.org/10.6084/m9.figshare.13108430.v1, 2020.
Wang, G., Huang, Y., Wei, Y., Zhang, W., Li, T., and Zhang, Q.: Climate
Warming Does Not Always Extend the Plant Growing Season in Inner Mongolian
Grasslands: Evidence From a 30-Year In Situ Observations at Eight
Experimental Sites, J. Geophys. Res.-Biogeosci., 124,
2364–2378, https://doi.org/10.1029/2019jg005137, 2019.
Wang, H., Liu, H., Cao, G., Ma, Z., Li, Y., Zhang, F., Zhao, X., Zhao, X.,
Jiang, L., and Sanders, N. J.: Alpine grassland plants grow earlier and
faster but biomass remains unchanged over 35 years of climate change, Ecol.
Lett., 23, 701–710, https://doi.org/10.1111/ele.13474, 2020.
Wang, S., Zhang, Y., Ju, W., Chen, J. M., Ciais, P., Cescatti, A., Sardans,
J., Janssens, I. A., Wu, M., Berry, J. A., Campbell, E.,
Fernández-Martínez, M., Alkama, R., Sitch, S., Friedlingstein, P.,
Smith, W. K., Yuan, W., He, W., Lombardozzi, D., Kautz, M., Zhu, D.,
Lienert, S., Kato, E., Poulter, B., Sanders, T. G. M., Krüger, I., Wang,
R., Zeng, N., Tian, H., Vuichard, N., Jain, A. K., Wiltshire, A., Haverd,
V., Goll, D. S., and Peñuelas, J.: Recent global decline of CO2 fertilization effects on
vegetation photosynthesis, Science, 370, 1295–1300, https://doi.org/10.1126/science.abb7772,
2020.
Wang, W., Huang, J., Zhou, T., Bi, J., Lin, L., Chen, Y., Huang, Z., and Su,
J.: Estimation of radiative effect of a heavy dust storm over northwest
China using Fu-Liou model and ground measurements, J. Quant.
Spectrosc. Ra., 122, 114–126, 2013.
Xu, L., Yu, G., He, N., Wang, Q., Gao, Y., Wen, D., Li, S., Niu, S., and Ge,
J.: Carbon storage in China's terrestrial ecosystems: A synthesis,
Sci. Rep., 8, 1–13, 2018.
Xu, Z. and Zhou, G.: Effects of water stress and high nocturnal temperature
on photosynthesis and nitrogen level of a perennial grass Leymus chinensis,
Plant Soil, 269, 131–139, 2005.
Yu, L., Zhang, M., Wang, L., Lu, Y., and Li, J.: Effects of aerosols and
water vapour on spatial-temporal variations of the clear-sky surface solar
radiation in China, Atmos. Res., 248, 105162,
https://doi.org/10.1016/j.atmosres.2020.105162, 2021.
Zhang, J., Ding, J., Zhang, J., Yuan, M., Li, P., Xiao, Z., Peng, C., Chen,
H., Wang, M., and Zhu, Q.: Effects of increasing aerosol optical depth on
the gross primary productivity in China during 2000–2014, Ecol.
Indic., 108, 105761, https://doi.org/10.1016/j.ecolind.2019.105761, 2020.
Zhang, Q., Buyantuev, A., Fang, X., Han, P., Li, A., Li, F. Y., Liang, C.,
Liu, Q., Ma, Q., Niu, J., Shang, C., Yan, Y., and Zhang, J.: Ecology and
sustainability of the Inner Mongolian Grassland: Looking back and moving
forward, Landscape Ecol., 35, 2413–2432, https://doi.org/10.1007/s10980-020-01083-9,
2020.
Zhang, X.: Vegetation Map of China and Its Geographic Pattern: Illustration
of the Vegetation Map of the People's Republic China (1:10,000,000), Geological Press, Beijing, China, 296–326, 2007.
Short summary
We simulate the spatiotemporal dynamics of aboveground biomass (AGB) in Inner Mongolian grasslands using a machine-learning-based approach. Under climate change, on average, compared with the historical AGB (average of 1981–2019), the AGB at the end of this century (average of 2080–2100) would decrease by 14 % under RCP4.5 and 28 % under RCP8.5. The decrease in AGB might be mitigated or even reversed by positive carbon dioxide enrichment effects on plant growth.
We simulate the spatiotemporal dynamics of aboveground biomass (AGB) in Inner Mongolian...
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