Skip to main content

Advertisement

SpringerLink
Log in

Quantifying the effect of trend, fluctuation, and extreme event of climate change on ecosystem productivity

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Climate change comprises three fractions of trend, fluctuation, and extreme event. Assessing the effect of climate change on terrestrial ecosystem requires an understanding of the action mechanism of these fractions, respectively. This study examined 11 years of remotely sensed-derived net primary productivity (NPP) to identify the impacts of the trend and fluctuation of climate change as well as extremely low temperatures caused by a freezing disaster on ecosystem productivity in Hunan province, China. The partial least squares regression model was used to evaluate the contributions of temperature, precipitation, and photosynthetically active radiation (PAR) to NPP variation. A climatic signal decomposition and contribution assessment model was proposed to decompose climate factors into trend and fluctuation components. Then, we quantitatively evaluated the contributions of each component of climatic factors to NPP variation. The results indicated that the total contribution of the temperature, precipitation, and PAR to NPP variation from 2001 to 2011 in Hunan province is 85 %, and individual contributions of the temperature, precipitation, and PAR to NPP variation are 44 % (including 34 % trend contribution and 10 % fluctuation contribution), 5 % (including 4 % trend contribution and 1 % fluctuation contribution), and 36 % (including 30 % trend contribution and 6 % fluctuation contribution), respectively. The contributions of temperature fluctuation-driven NPP were higher in the north and lower in the south, and the contributions of precipitation trend-driven NPP and PAR fluctuation-driven NPP are higher in the west and lower in the east. As an instance of occasionally triggered disturbance in 2008, extremely low temperatures and a freezing disaster produced an abrupt decrease of NPP in forest and grass ecosystems. These results prove that the climatic trend change brought about great impacts on ecosystem productivity and that climatic fluctuations and extreme events can also alter the ecosystem succession process, even resulting in an alternative trajectory. All of these findings could improve our understanding of the impacts of climate change on the provision of ecosystem functions and services and can also provide a basis for policy makers to apply adaptive measures to overcome the unfavorable influence of climate change.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Bala, G., Caldeira, K., Wickett, M., Phillips, T. J., Lobell, D. B., Delire, C., et al. (2007). Combined climate and carbon-cycle effects of large-scale deforestation. Proc Natl Acad Sci U S A, 104(16), 6550–6555. doi:10.1073/pnas.0608998104.

    Article  CAS  Google Scholar 

  • Bellassen, V., Delbart, N., Le Maire, G., Luyssaert, S., Ciais, P., & Viovy, N. (2011). Potential knowledge gain in large-scale simulations of forest carbon fluxes from remotely sensed biomass and height. For Ecol Manage, 261(3), 515–530. doi:10.1016/j.foreco.2010.11.002.

    Article  Google Scholar 

  • Bradford, J. B. (2011). Divergence in forest-type response to climate and weather: evidence for regional links between forest-type evenness and net primary productivity. Ecosystems, 14(6), 975–986. doi:10.1007/s10021-011-9460-8.

    Article  Google Scholar 

  • Carpenter, S. R., & Brock, W. A. (2006). Rising variance: a leading indicator of ecological transition. Ecol Lett, 9(3), 311–318. doi:10.1111/j.1461-0248.2005.00877.x.

    Article  CAS  Google Scholar 

  • Coops, N. C., Waring, R. H., & Schroeder, T. A. (2009). Combining a generic process-based productivity model and a statistical classification method to predict the presence and absence of tree species in the Pacific Northwest, U.S.A. Ecol Model, 220(15), 1787–1796. doi:10.1016/j.ecolmodel.2009.04.029.

    Article  Google Scholar 

  • Craine, J. M., Nippert, J. B., Elmore, A. J., Skibbe, A. M., Hutchinson, S. L., & Brunsell, N. A. (2012). Timing of climate variability and grassland productivity. Proc Natl Acad Sci, 109(9), 3401–3405. doi:10.1073/pnas.1118438109.

    Article  CAS  Google Scholar 

  • Cramer, W., Kicklighter, D. W., Bondeau, A., Iii, B. M., Churkina, G., Nemry, B., et al. (1999). Comparing global models of terrestrial net primary productivity (NPP): overview and key results. Glob Chang Biol, 5(S1), 1–15. doi:10.1046/j.1365-2486.1999.00009.x.

    Article  Google Scholar 

  • Easterling, D. R., Evans, J. L., Groisman, P. Y., Karl, T. R., Kunkel, K. E., & Ambenje, P. (2000). Observed variability and trends in extreme climate events: a brief review. Bull Am Meteorol Soc, 81(3), 417–426.

    Article  Google Scholar 

  • Eastman, J. R., Sangermano, F., Ghimire, B., Zhu, H., Chen, H., Neeti, N., et al. (2009). Seasonal trend analysis of image time series. Int J Remote Sens, 30(10), 2721–2726. doi:10.1080/01431160902755338.

    Article  Google Scholar 

  • Evangelista, P. H., Kumar, S., Stohlgren, T. J., & Young, N. E. (2011). Assessing forest vulnerability and the potential distribution of pine beetles under current and future climate scenarios in the Interior West of the US. For Ecol Manage, 262(3), 307–316. doi:10.1016/j.foreco.2011.03.036.

    Article  Google Scholar 

  • Fan, J. W., Shao, Q. Q., Liu, J. Y., Wang, J. B., Harris, W., Chen, Z. Q., et al. (2010). Assessment of effects of climate change and grazing activity on grassland yield in the Three Rivers Headwaters Region of Qinghai-Tibet Plateau, China. Environ Monit Assess, 170(1–4), 571–584. doi:10.1007/s10661-009-1258-1.

    Article  Google Scholar 

  • Ferrez, J., Davison, A. C., & Rebetez, M. (2011). Extreme temperature analysis under forest cover compared to an open field. Agr Forest Meteorol, 151(7), 992–1001. doi:10.1016/j.agrformet.2011.03.005.

    Article  Google Scholar 

  • Geerken, R. A. (2009). An algorithm to classify and monitor seasonal variations in vegetation phenologies and their inter-annual change. ISPRS J Photogramm Remote Sens, 64(4), 422–431. doi:10.1016/j.isprsjprs.2009.03.001.

    Article  Google Scholar 

  • Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Anal Chim Acta, 185, 1–17. doi:10.1016/0003-2670(86)80028-9.

    Article  CAS  Google Scholar 

  • IPCC. (2007). Climate change 2007: Synthesis report. In Core Writing Team, R. K. Pachauri, & A. Reisinger (Eds.), Contribution of working groups I, II and III to the fourth assessment report of the intergovernmental panel on climate change (p. 104). Geneva, Switzerland: IPCC.

  • IPCC. (2013). Managing the risks of extreme events and disasters to advance climate change adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge, UK, and New York, NY, USA: Cambridge University Press.

  • Ito, A. (2011). A historical meta-analysis of global terrestrial net primary productivity: are estimates converging? Glob Chang Biol, 17(10), 3161–3175. doi:10.1111/j.1365-2486.2011.02450.x.

    Article  Google Scholar 

  • Jentsch, A., Kreyling, J., & Beierkuhnlein, C. (2007). A new generation of climate-change experiments: events, not trends. Front Ecol Environ, 5(7), 365–374. doi:10.1890/1540-9295(2007)5[365:angoce]2.0.co;2.

    Article  Google Scholar 

  • King, G., Fonti, P., Nievergelt, D., Büntgen, U., & Frank, D. (2013). Climatic drivers of hourly to yearly tree radius variations along a 6 °C natural warming gradient. Agr Forest Meteorol, 168, 36–46. doi:10.1016/j.agrformet.2012.08.002.

    Article  Google Scholar 

  • Knapp, A. K., Fay, P. A., Blair, J. M., Collins, S. L., Smith, M. D., Carlisle, J. D., et al. (2002). Rainfall variability, carbon cycling, and plant species diversity in a mesic grassland. Science, 298(5601), 2202–2205. doi:10.1126/science.1076347.

    Article  CAS  Google Scholar 

  • Li, Z. T., & Kafatos, M. (2000). Interannual variability of vegetation in the United States and its relation to El Nino/Southern Oscillation. Remote Sens Environ, 71(3), 239–247. doi:10.1016/s0034-4257(99)00034-6.

    Article  Google Scholar 

  • Lindner, M., Maroschek, M., Netherer, S., Kremer, A., Barbati, A., Garcia-Gonzalo, J., et al. (2010). Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. For Ecol Manage, 259(4), 698–709. doi:10.1016/j.foreco.2009.09.023.

    Article  Google Scholar 

  • Liu, Y. P., Yu, D. Y., Xun, B., Sun, Y., & Hao, R. F. (2014). The potential effects of climate change on the distribution and productivity of Cunninghamia lanceolata in China. Environ Monit Assess, 186(1), 135–149. doi:10.1007/s10661-013-3361-6.

    Article  Google Scholar 

  • McCallum, I., Wagner, W., Schmullius, C., Shvidenko, A., Obersteiner, M., Fritz, S., et al. (2009). Satellite-based terrestrial production efficiency modeling. Carbon Balance and Management, 4(1), 1–14. doi:10.1186/1750-0680-4-8.

    Article  Google Scholar 

  • Metzger, M. J., Rounsevell, M. D. A., Acosta-Michlik, L., Leemans, R., & Schröter, D. (2006). The vulnerability of ecosystem services to land use change. Agr Ecosyst Environ, 114(1), 69–85. doi:10.1016/j.agee.2005.11.025.

    Article  Google Scholar 

  • Mildrexler, D. J., Zhao, M., Heinsch, F. A., & Running, S. W. (2007). A new satellite-based methodology for continental-scale disturbance detection. Ecol Appl, 17(1), 235–250. doi:10.1890/1051-0761(2007)017[0235:ansmfc]2.0.co;2.

    Article  Google Scholar 

  • Mildrexler, D. J., Zhao, M., & Running, S. W. (2009). Testing a MODIS Global Disturbance Index across North America. Remote Sens Environ, 113(10), 2103–2117. doi:10.1016/j.rse.2009.05.016.

    Article  Google Scholar 

  • Milne, R., Bennett, L., & Hoyle, M. (2013). Weather variability permitted within amphibian monitoring protocol and affects on calling Hylidae. Environ Monit Assess, 185(11), 8879–8889. doi:10.1007/s10661-013-3221-4.

    Article  Google Scholar 

  • Murray, S. J., Foster, P. N., & Prentice, I. C. (2012). Future global water resources with respect to climate change and water withdrawals as estimated by a dynamic global vegetation model. Journal of Hydrology, 488–489(2), 14–29. doi:10.1016/j.jhydrol.2012.02.044.

    Article  Google Scholar 

  • Nayak, R. K., Patel, N. R., & Dadhwal, V. K. (2010). Estimation and analysis of terrestrial net primary productivity over India by remote-sensing-driven terrestrial biosphere model. Environ Monit Assess, 170(1–4), 195–213. doi:10.1007/s10661-009-1226-9.

    Article  Google Scholar 

  • Nemani, R. R., Keeling, C. D., Hashimoto, H., Jolly, W. M., Piper, S. C., Tucker, C. J., et al. (2003). Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300(5625), 1560–1563. doi:10.1126/science.1082750.

    Article  CAS  Google Scholar 

  • Potter, C. S., Klooster, S. A., Matson, P. A., Randerson, J. T., Field, C. B., Vitousek, P. M., et al. (1993). Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochem Cycles, 7(4), 811–841. doi:10.1029/93GB02725.

    Article  Google Scholar 

  • Running, S. W., Nemani, R. R., Heinsch, F. A., Zhao, M., Reeves, M., & Hashimoto, H. (2004). A continuous satellite-derived measure of global terrestrial primary production. BioScience, 54(6), 547–560.

    Article  Google Scholar 

  • Sasai, T., Saigusa, N., Nasahara, K. N., Ito, A., Hashimoto, H., Nemani, R., et al. (2011). Satellite-driven estimation of terrestrial carbon flux over Far East Asia with 1-km grid resolution. Remote Sens Environ, 115(7), 1758–1771. doi:10.1016/j.rse.2011.03.007.

    Article  Google Scholar 

  • Scheffer, M., Bascompte, J., Brock, W. A., Brovkin, V., Carpenter, S. R., Dakos, V., et al. (2009). Early-warning signals for critical transitions. Nature, 461(7260), 53–59. doi:10.1038/nature08227.

    Article  CAS  Google Scholar 

  • Scholze, M., Knorr, W., Arnell, N. W., & Prentice, I. C. (2006). A climate-change risk analysis for world ecosystems. Proc Natl Acad Sci, 103(35), 13116–13120.

    Article  CAS  Google Scholar 

  • Schröter, D., Cramer, W., Leemans, R., Prentice, I. C., Araújo, M. B., Arnell, N. W., et al. (2005). Ecosystem service supply and vulnerability to global change in Europe. Science, 310(5752), 1333–1337. doi:10.1126/science.1115233.

    Article  Google Scholar 

  • Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W., et al. (2003). Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob Chang Biol, 9(2), 161–185. doi:10.1046/j.1365-2486.2003.00569.x.

    Article  Google Scholar 

  • Tatarinov, F. A., & Cienciala, E. (2009). Long-term simulation of the effect of climate changes on the growth of main Central-European forest tree species. Ecol Model, 220(21), 3081–3088. doi:10.1016/j.ecolmodel.2009.01.029.

    Article  CAS  Google Scholar 

  • Twine, T. E., & Kucharik, C. J. (2009). Climate impacts on net primary productivity trends in natural and managed ecosystems of the central and eastern United States. Agr Forest Meteorol, 149(12), 2143–2161. doi:10.1016/j.agrformet.2009.05.012.

    Article  Google Scholar 

  • Verbesselt, J., Hyndman, R., Newnham, G., & Culvenor, D. (2010a). Detecting trend and seasonal changes in satellite image time series. Remote Sens Environ, 114(1), 106–115. doi:10.1016/j.rse.2009.08.014.

    Article  Google Scholar 

  • Verbesselt, J., Hyndman, R., Zeileis, A., & Culvenor, D. (2010b). Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sens Environ, 114(12), 2970–2980. doi:10.1016/j.rse.2010.08.003.

    Article  Google Scholar 

  • Verbesselt, J., Zeileis, A., & Herold, M. (2012). Near real-time disturbance detection using satellite image time series. Remote Sens Environ, 123, 98–108. doi:10.1016/j.rse.2012.02.022.

    Article  Google Scholar 

  • Wei, T., Yang, S., Moore, J. C., Shi, P., Cui, X., Duan, Q., et al. (2012). Developed and developing world responsibilities for historical climate change and CO2 mitigation. Proc Natl Acad Sci, 109(32), 12911–12915. doi:10.1073/pnas.1203282109.

    Article  CAS  Google Scholar 

  • Wen, L., Dong, S. K., Li, Y. Y., Sherman, R., Shi, J. J., Liu, D. M., et al. (2013). The effects of biotic and abiotic factors on the spatial heterogeneity of alpine grassland vegetation at a small scale on the Qinghai-Tibet Plateau (QTP), China. Environ Monit Assess, 185(10), 8051–8064. doi:10.1007/s10661-013-3154-y.

    Article  CAS  Google Scholar 

  • Wu, J. (1999). Hierarchy and scaling : extrapolating information along a scaling ladder. Can J Remote Sens, 25(4), 367–380.

    Article  Google Scholar 

  • Xu, C., Li, Y. T., Hu, J., Yang, X. J., Sheng, S., & Liu, M. S. (2012). Evaluating the difference between the normalized difference vegetation index and net primary productivity as the indicators of vegetation vigor assessment at landscape scale. Environ Monit Assess, 184(3), 1275–1286. doi:10.1007/s10661-011-2039-1.

    Article  Google Scholar 

  • Yu, D., Zhu, W., & Pan, Y. (2008). The role of atmospheric circulation system playing in coupling relationship between spring NPP and precipitation in East Asia area. Environ Monit Assess, 145(1–3), 135–143. doi:10.1007/s10661-007-0023-6.

    Google Scholar 

  • Yu, D., Shao, H., Shi, P., Zhu, W., & Pan, Y. (2009a). How does the conversion of land cover to urban use affect net primary productivity? A case study in Shenzhen city, China. Agr Forest Meteorol, 149(11), 2054–2060. doi:10.1016/j.agrformet.2009.07.012.

    Article  Google Scholar 

  • Yu, D., Shi, P., Shao, H., Zhu, W., & Pan, Y. (2009b). Modelling net primary productivity of terrestrial ecosystems in East Asia based on an improved CASA ecosystem model. Int J Remote Sens, 30(18), 4851–4866. doi:10.1080/01431160802680552.

    Article  Google Scholar 

  • Yuan, W., Liu, S., Zhou, G., Zhou, G., Tieszen, L. L., Baldocchi, D., et al. (2007). Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agr Forest Meteorol, 143(3–4), 189–207. doi:10.1016/j.agrformet.2006.12.001.

    Article  Google Scholar 

  • Zhang, C., Wu, J., Grimm, N. B., McHale, M., & Buyantuyev, A. (2013). A hierarchical patch mosaic ecosystem model for urban landscapes: model development and evaluation. Ecol Model, 250, 81–100. doi:10.1016/j.ecolmodel.2012.09.020.

    Article  Google Scholar 

  • Zurlini, G., Petrosillo, I., Jones, K. B., & Zaccarelli, N. (2013). Highlighting order and disorder in social–ecological landscapes to foster adaptive capacity and sustainability. Landsc Ecol, 28(6), 1161–1173. doi:10.1007/s10980-012-9763-y.

    Article  Google Scholar 

Download references

Acknowledgments

This research was funded by the Program of National Basic Research Program of China (973 Program) “Global change and environmental risk’s evolution process and its integrated assessment model” (No. 2012CB955402), the 111 project “Hazard and Risk Science Base at Beijing Normal University“ under Grant B08008, Ministry of Education and State Administration of Foreign Experts Affairs, People’s Republic of China, and the Project of State Key Laboratory of Earth Surface Processes and Resources Ecology. Special thanks are given to the referees and editors for their instructive comments and suggestions and editing the manuscript.

Author information

Authors and Affiliations

  1. Center for Human-Environment System Sustainability, Beijing Normal University, Beijing, 100875, People’s Republic of China

    Yupeng Liu, Deyong Yu, Yun Su & Ruifang Hao

  2. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing, 100875, People’s Republic of China

    Yupeng Liu, Deyong Yu, Yun Su & Ruifang Hao

Authors
  1. Yupeng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deyong Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yun Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ruifang Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deyong Yu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Y., Yu, D., Su, Y. et al. Quantifying the effect of trend, fluctuation, and extreme event of climate change on ecosystem productivity. Environ Monit Assess 186, 8473–8486 (2014). https://doi.org/10.1007/s10661-014-4031-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10661-014-4031-z

Keywords

Search

Navigation