Response of grassland net primary productivity to climate change in China
Yuting Zhao A , Huilong Lin A C , Rong Tang A , Yanfei Pu A , Xiaoyu Xiong A , Charles Nyandwi A , Jean de Dieu Nzabonakuze A , Yonghui Zhang B , Jiaming Jin B and Han Tianhu B
+ Author Affiliations
- Author Affiliations
A State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, P.R. China.
B Gansu Provincial Extension Station of Grassland Techniques, Lanzhou 730010, P.R. China.
C Corresponding author. Email: linhuilong@lzu.edu.cn
The Rangeland Journal 43(6) 339-352 https://doi.org/10.1071/RJ20111
Submitted: 9 November 2020 Accepted: 22 August 2021 Published: 20 January 2022
Abstract
To protect the grassland ecosystem’s security, and coordinate the utilisation of grassland resources, explicitly estimating the response of Net Primary Productivity (NPP) of 10 grassland groups to future climate change is necessary. Based on the Comprehensive and Sequential Classification System (CSCS) and modified Carnegie Ames Stanford Approach (CASA) model, in conjunction with four Representative Concentration Pathways (RCP 2.6, RCP 4.5, RCP 6.0, RCP 8.5) of the Intergovernmental Panel on Climate Change (IPCC), the response of China’s grassland NPP to climate change was simulated and estimated. Results showed that: (1) the simulation accuracy of modified CASA model (R2 = 0.65) is 34% higher than that of CASA model, indicating that the modified CASA was suitable for estimating grassland NPP in China; (2) annual mean NPP and total NPP (2001–18) were 138.4 g C m−2 year−1 and 495.7 T g C, respectively; compared with the period 2001–18, NPP during the 2050 under RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5 were predicted with increases of 34.8%, 35.9%, 34.9% and 35.8%, respectively; and (3) from the present-day to the 2050, the NPP of Warm desert exhibited the largest increase (73.2–76.3%), while that of Tundra and alpine grassland the smallest (1.3–1.6%). These differences in NPP increase likely lead to differences in grassland carrying capacity. To respond to the impact of future climate change on grassland, grassland classification management strategies according to different groups should be implemented as grassland NPP changes differently in different grassland groups.
Keywords: China’s grassland, model simulation, Coupled Model Intercomparison Project Phase Five (CMIP5).
References
Adams, B., White, A., and Lenton, T. M. (2004). An analysis of some diverse approaches to modelling terrestrial net primary productivity. Ecological Modelling 177, 353–391.| An analysis of some diverse approaches to modelling terrestrial net primary productivity.Crossref | GoogleScholarGoogle Scholar |
Chen, J., Luo, Y., Xia, J., Wilcox, K. R., Cao, J., Zhou, X., Jiang, L., Niu, S., Estera, K. Y., Huang, R., Wu, F., Hu, T., Liang, J., Shi, Z., Guo, J., and Wang, R. W. (2017). Warming effects on ecosystem carbon fluxes are modulated by plant functional types. Ecosystems 20, 515–526.
| Warming effects on ecosystem carbon fluxes are modulated by plant functional types.Crossref | GoogleScholarGoogle Scholar |
Dai, A., Trenberth, K. E., and Qian, T. (2004). A global dataset of Palmer drought severity index for 1870–2002: relationship with soil moisture and effects of surface warming. Journal of Hydrometeorology 5, 1117–1130.
| A global dataset of Palmer drought severity index for 1870–2002: relationship with soil moisture and effects of surface warming.Crossref | GoogleScholarGoogle Scholar |
Dergachev, V. A., and Volobuev, D. M. (2018). Solar radiation change and climatic effects on decennial–centennial scales. Geomagnetizm i Aèronomia 58, 1042–1049.
| Solar radiation change and climatic effects on decennial–centennial scales.Crossref | GoogleScholarGoogle Scholar |
Diffenbaugh, N. S., and Giorgi, F. (2012). Climate change hotspots in the CMIP5 global climate model ensemble. Climatic Change 114, 813–822.
| Climate change hotspots in the CMIP5 global climate model ensemble.Crossref | GoogleScholarGoogle Scholar | 24014154PubMed |
Goldberg, M. H., Linden, S. d., Maibach, E., and Leiserowitz, A. (2019). Discussing global warming leads to greater acceptance of climate science. Proceedings of the National Academy of Sciences of the United States of America 116, 14804–14805.
| Discussing global warming leads to greater acceptance of climate science.Crossref | GoogleScholarGoogle Scholar | 31285333PubMed |
Google Earth Engine (2019). Available at: https://code.earthengine.google.com/ (accessed 12 October 2019).
Guo, B., Zhang, J., Meng, X., Xu, T., and Song, Y. (2020). Long-term spatio-temporal precipitation variations in China with precipitation surface interpolated by ANUSPLIN. Scientific Reports 10, 81.
| Long-term spatio-temporal precipitation variations in China with precipitation surface interpolated by ANUSPLIN.Crossref | GoogleScholarGoogle Scholar | 31919374PubMed |
Han, P., Lin, X., Zhang, W., Wang, G., and Wang, Y. (2019). Projected changes of alpine grassland carbon dynamics in response to climate change and elevated CO2 concentrations under Representative Concentration Pathways (RCP) scenarios. PLoS One 14, e0215261.
| Projected changes of alpine grassland carbon dynamics in response to climate change and elevated CO2 concentrations under Representative Concentration Pathways (RCP) scenarios.Crossref | GoogleScholarGoogle Scholar | 31329592PubMed |
Hancock, P., and Hutchinson, M. F. (2006). Spatial interpolation of large climate data sets using bivariate thin plate smoothing splines. Environmental Modelling & Software 21, 1684–1694.
| Spatial interpolation of large climate data sets using bivariate thin plate smoothing splines.Crossref | GoogleScholarGoogle Scholar |
Li, C., Sun, H., Wu, X., and Han, H. (2019a). Dataset of the net primary production on the Qinghai-Tibetan Plateau using a soil water content improved Biome-BGC model. Data in Brief 27, 104740.
| Dataset of the net primary production on the Qinghai-Tibetan Plateau using a soil water content improved Biome-BGC model.Crossref | GoogleScholarGoogle Scholar | 31763397PubMed |
Li, D., Miller, J. E. D., and Harrison, S. (2019b). Climate drives loss of phylogenetic diversity in a grassland community. Proceedings of the National Academy of Sciences of the United States of America 116, 19989–19994.
| Climate drives loss of phylogenetic diversity in a grassland community.Crossref | GoogleScholarGoogle Scholar | 31527249PubMed |
Lin, H., Feng, Q., Liang, T., and Ren, J. (2013a). Modelling global-scale potential grassland changes in spatio-temporal patterns to global climate change. International Journal of Sustainable Development and World Ecology 20, 83–96.
| Modelling global-scale potential grassland changes in spatio-temporal patterns to global climate change.Crossref | GoogleScholarGoogle Scholar |
Lin, H., Wang, X., Zhang, Y., Liang, T., Feng, Q., and Ren, J. (2013b). Spatio-temporal dynamics on the distribution, extent, and net primary productivity of potential grassland in response to climate changes in China. The Rangeland Journal 35, 409–425.
| Spatio-temporal dynamics on the distribution, extent, and net primary productivity of potential grassland in response to climate changes in China.Crossref | GoogleScholarGoogle Scholar |
Lin, H., Ma, H., Nyandwi, C., Feng, Q., and Liang, T. (2021). A new net primary productivity model and new management strategy of grassland classification based on CSCS in China. The Rangeland Journal 43, .
| A new net primary productivity model and new management strategy of grassland classification based on CSCS in China.Crossref | GoogleScholarGoogle Scholar |
Liu, Y., Yang, Y., Wang, Q., Du, X., Li, J., Gang, C., Zhou, W., and Wang, Z. (2019). Evaluating the responses of net primary productivity and carbon use efficiency of global grassland to climate variability along an aridity gradient. The Science of the Total Environment 652, 671–682.
| Evaluating the responses of net primary productivity and carbon use efficiency of global grassland to climate variability along an aridity gradient.Crossref | GoogleScholarGoogle Scholar | 30380475PubMed |
Lu, J., Carbone, G. J., and Grego, J. M. (2019). Uncertainty and hotspots in 21st century projections of agricultural drought from CMIP5 models. Scientific Reports 9, 4922.
| Uncertainty and hotspots in 21st century projections of agricultural drought from CMIP5 models.Crossref | GoogleScholarGoogle Scholar | 30894624PubMed |
Myhre, G., Alterskjaer, K., Stjern, C. W., Hodnebrog, O., Marelle, L., Samset, B. H., Sillmann, J., Schaller, N., Fischer, E., Schulz, M., and Stohl, A. (2019). Frequency of extreme precipitation increases extensively with event rareness under global warming. Scientific Reports 9, 16063.
| Frequency of extreme precipitation increases extensively with event rareness under global warming.Crossref | GoogleScholarGoogle Scholar | 31690736PubMed |
Neelin, J. D., Sahanya, S., Stechmannb, S. N., and Bernsteina, D. N. (2017). Global warming precipitation accumulation increases above the current-climate cutoff scal. Proceedings of the National Academy of Sciences of the United States of America 114, 1258–1263.
| Global warming precipitation accumulation increases above the current-climate cutoff scal.Crossref | GoogleScholarGoogle Scholar | 28115693PubMed |
Piao, S., Fang, J., He, J., and Yu, X. (2004). Spatial distribution of grassland biomass in china. Acta Phytoecologica Sinica 28, 491–498.
| Spatial distribution of grassland biomass in china.Crossref | GoogleScholarGoogle Scholar |
Piao, S., Fang, J., Zhou, L., Tan, K., and Tao, S. (2007). Changes in biomass carbon stocks in China’s grasslands between 1982 and 1999. Global Biogeochemical Cycles 21, GB2002.
| Changes in biomass carbon stocks in China’s grasslands between 1982 and 1999.Crossref | GoogleScholarGoogle Scholar |
Potter, C. S., Randerson, J. T., Field, C. B., Matson, P. A., Vitousek, P. M., Mooney, H. A., and Klooster, S. A. (1993). Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochemical Cycles 7, 811–841.
| Terrestrial ecosystem production: a process model based on global satellite and surface data.Crossref | GoogleScholarGoogle Scholar |
Potter, C., Gross, P., Genovese, V., and Smith, M. L. (2007). Net primary productivity of forest stands in New Hampshire estimated from Landsat and MODIS satellite data. Carbon Balance and Management 2, 9.
| Net primary productivity of forest stands in New Hampshire estimated from Landsat and MODIS satellite data.Crossref | GoogleScholarGoogle Scholar | 17941989PubMed |
Ren, J. Z., Hu, Z. Z., Zhao, J., Zhang, D. G., Hou, F. J., Lin, H. L., and Mu, X. D. (2008). A grassland classification system and its application in China. The Rangeland Journal 30, 199–209.
| A grassland classification system and its application in China.Crossref | GoogleScholarGoogle Scholar |
Routson, C. C., McKay, N. P., Kaufman, D. S., Erb, M. P., Goosse, H., Shuman, B. N., Rodysill, J. R., and Ault, T. (2019). Mid-latitude net precipitation decreased with Arctic warming during the Holocene. Nature 568, 83–87.
| Mid-latitude net precipitation decreased with Arctic warming during the Holocene.Crossref | GoogleScholarGoogle Scholar | 30918401PubMed |
Ryu, Y., Jiang, C., Kobayashi, H., and Detto, M. (2018). MODIS-derived global land products of shortwave radiation and diffuse and total photosynthetically active radiation at 5 km resolution from 2000. Remote Sensing of Environment 204, 812–825.
| MODIS-derived global land products of shortwave radiation and diffuse and total photosynthetically active radiation at 5 km resolution from 2000.Crossref | GoogleScholarGoogle Scholar |
Seddon, A. W. R., Macias-Fauria, M., Long, P. R., Benz, D., and Willis, K. J. (2016). Sensitivity of global terrestrial ecosystems to climate variability. Nature 531, 229–232.
| Sensitivity of global terrestrial ecosystems to climate variability.Crossref | GoogleScholarGoogle Scholar |
Sheikholeslami, R., and Razavi, S. (2017). Progressive Latin hypercube sampling: an efficient approach for robust sampling-based analysis of environmental models. Environmental Modelling & Software 93, 109–126.
| Progressive Latin hypercube sampling: an efficient approach for robust sampling-based analysis of environmental models.Crossref | GoogleScholarGoogle Scholar |
Song, Q. N., Lu, H., Liu, J., Yang, J., Yang, G. Y., and Yang, Q. P. (2017). Accessing the impacts of bamboo expansion on NPP and N cycling in evergreen broadleaved forest in subtropical China. Scientific Reports 7, 40383.
| Accessing the impacts of bamboo expansion on NPP and N cycling in evergreen broadleaved forest in subtropical China.Crossref | GoogleScholarGoogle Scholar | 28067336PubMed |
Teng, H., Liang, Z., Chen, S., Liu, Y., Viscarra Rossel, R. A., Chappell, A., Yu, W., and Shi, Z. (2018). Current and future assessments of soil erosion by water on the Tibetan Plateau based on RUSLE and CMIP5 climate models. The Science of the Total Environment 635, 673–686.
| Current and future assessments of soil erosion by water on the Tibetan Plateau based on RUSLE and CMIP5 climate models.Crossref | GoogleScholarGoogle Scholar | 29680758PubMed |
Teng, M., Zeng, L., Hu, W., Wang, P., Yan, Z., He, W., Zhang, Y., Huang, Z., and Xiao, W. (2020). The impacts of climate changes and human activities on net primary productivity vary across an ecotone zone in Northwest China. The Science of the Total Environment 714, 136691.
| The impacts of climate changes and human activities on net primary productivity vary across an ecotone zone in Northwest China.Crossref | GoogleScholarGoogle Scholar | 31978773PubMed |
Wang, L., and Wang, D. (2019). Reply to Liang and Gornish: climate and livestock grazing jointly regulate grassland ecosystem multifunctionality. Proceedings of the National Academy of Sciences of the United States of America 116, 23889–23890.
| Reply to Liang and Gornish: climate and livestock grazing jointly regulate grassland ecosystem multifunctionality.Crossref | GoogleScholarGoogle Scholar | 31662472PubMed |
Wang, X., Tan, K., Chen, B., and Du, P. (2017). Assessing the spatiotemporal variation and impact factors of net primary productivity in China. Scientific Reports 7, 44415.
| Assessing the spatiotemporal variation and impact factors of net primary productivity in China.Crossref | GoogleScholarGoogle Scholar | 28281668PubMed |
Wang, Q., Yang, Y., Liu, Y., Tong, L., Zhang, Q. P., and Li, J. (2019). Assessing the impacts of drought on grassland net primary production at the global scale. Scientific Reports 9, 14041.
| Assessing the impacts of drought on grassland net primary production at the global scale.Crossref | GoogleScholarGoogle Scholar | 31575904PubMed |
Wang, H., Liu, H., Cao, G., Ma, Z., Li, Y., Zhang, F., Zhao, X., Zhao, X., Jiang, L., Sanders, N. J., Classen, A. T., and He, J. S. (2020). Alpine grassland plants grow earlier and faster but biomass remains unchanged over 35 years of climate change. Ecology Letters 23, 701–710.
| Alpine grassland plants grow earlier and faster but biomass remains unchanged over 35 years of climate change.Crossref | GoogleScholarGoogle Scholar | 32052555PubMed |
Xin, X., Yan, R., Yao, Y., and Tang, H. (2015). ‘Digital Grassland Theory, Technology and Practice.’ pp. 245–249. (Science Press: Beijing.)
Xiong, X., Grunwald, S., Myers, D. B., Kim, J., Harris, W. G., and Comerford, N. B. (2014). Holistic environmental soil-landscape modeling of soil organic carbon. Environmental Modelling & Software 57, 202–215.
| Holistic environmental soil-landscape modeling of soil organic carbon.Crossref | GoogleScholarGoogle Scholar |
Xu, D., and Guo, X. (2015). Some insights on grassland health assessment based on remote sensing. Sensors 15, 3070–3089.
| Some insights on grassland health assessment based on remote sensing.Crossref | GoogleScholarGoogle Scholar | 25643060PubMed |
Xu, H. j., Wang, X. P., and Zhang, X. X. (2016). Alpine grasslands response to climatic factors and anthropogenic activities on the Tibetan Plateau from 2000 to 2012. Ecological Engineering 92, 251–259.
| Alpine grasslands response to climatic factors and anthropogenic activities on the Tibetan Plateau from 2000 to 2012.Crossref | GoogleScholarGoogle Scholar |
Yang, C. E., Mao, J., Hoffman, F. M., Ricciuto, D. M., Fu, J. S., Jones, C. D., and Thurner, M. (2018a). Uncertainty quantification of extratropical forest biomass in CMIP5 models over the Northern Hemisphere. Scientific Reports 8, 10962.
| Uncertainty quantification of extratropical forest biomass in CMIP5 models over the Northern Hemisphere.Crossref | GoogleScholarGoogle Scholar | 30026558PubMed |
Yang, L., Jiang, J., Liu, T., Li, Y., Zhou, Y., and Gao, X. (2018b). Projections of future changes in solar radiation in China based on CMIP5 climate models. Global Energy Interconnection 1, 452–459.
You, Q., Wu, F., Shen, L., Pepin, N., and Kang, S. (2020). Tibetan Plateau amplification of climate extremes under global warming of 1.5°C, 2°C and 3°C. Global and Planetary Change 192, 103261.
| Tibetan Plateau amplification of climate extremes under global warming of 1.5°C, 2°C and 3°C.Crossref | GoogleScholarGoogle Scholar |
Zhang, M., Lal, R., Zhao, Y., Jiang, W., and Chen, Q. (2016). Estimating net primary production of natural grassland and its spatio-temporal distribution in China. The Science of the Total Environment 553, 184–195.
| Estimating net primary production of natural grassland and its spatio-temporal distribution in China.Crossref | GoogleScholarGoogle Scholar | 26925730PubMed |
Zheng, Z., Zhu, W., and Zhang, Y. (2020). Seasonally and spatially varied controls of climatic factors on net primary productivity in alpine grasslands on the Tibetan Plateau. Global Ecology and Conservation 21, e00814.
| Seasonally and spatially varied controls of climatic factors on net primary productivity in alpine grasslands on the Tibetan Plateau.Crossref | GoogleScholarGoogle Scholar |
Zhu, W. Q., Pan, Y., and Zhang, J. S. (2007). Estimation of net primary productivity of Chinese terrestrial vegetation based on remote sensing. Journal of Plant Ecology 31, 413–424.
| Estimation of net primary productivity of Chinese terrestrial vegetation based on remote sensing.Crossref | GoogleScholarGoogle Scholar |
Zou, L., Wang, L., Li, J., Lu, Y., Gong, W., and Niu, Y. (2019). Global surface solar radiation and photovoltaic power from Coupled Model Intercomparison Project Phase 5 climate models. Journal of Cleaner Production 224, 304–324.
| Global surface solar radiation and photovoltaic power from Coupled Model Intercomparison Project Phase 5 climate models.Crossref | GoogleScholarGoogle Scholar |
