Articles | Volume 21, issue 2
https://doi.org/10.5194/bg-21-625-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-21-625-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Jan 2024
Research article |
| 30 Jan 2024
| 30 Jan 2024
Evaluation of five models for constructing forest NPP–age relationships in China based on 3121 field survey samples
Peng Li
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
Academy of Carbon Neutrality, Fujian Normal University, Fuzhou, 350117, China
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
Academy of Carbon Neutrality, Fujian Normal University, Fuzhou, 350117, China
Jing M. Chen
CORRESPONDING AUTHOR
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
Department of Geography and Planning, University of Toronto, Ontario, ON M5S 3G3, Canada
Mingzhu Xu
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
Academy of Carbon Neutrality, Fujian Normal University, Fuzhou, 350117, China
Xudong Lin
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
Academy of Carbon Neutrality, Fujian Normal University, Fuzhou, 350117, China
Guirui Yu
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Nianpeng He
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Li Xu
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Related authors
Peng Li, Rong Shang, Jing M. Chen, Huiguang Zhang, Xiaoping Zhang, Guoshuai Zhao, Hong Yan, Jun Xiao, Xudong Lin, Lingyun Fan, Rong Wang, Jianjie Cao, and Hongda Zeng
EGUsphere, https://doi.org/10.5194/egusphere-2025-1062, https://doi.org/10.5194/egusphere-2025-1062, 2025
Short summary
Short summary
This study explored species-specific relationships between net primary productivity and forest age for seven forest species in subtropical China based on field data using the Semi-Empirical Model. Compared to nationwide relationships, these species-specific relationships improved simulations of aboveground biomass when using the process-based model. Our findings suggest that these species-specific relationships are crucial for accurate forest carbon modeling and management in subtropical China.
Rong Shang, Xudong Lin, Jing M. Chen, Yunjian Liang, Keyan Fang, Mingzhu Xu, Yulin Yan, Weimin Ju, Guirui Yu, Nianpeng He, Li Xu, Liangyun Liu, Jing Li, Wang Li, Jun Zhai, and Zhongmin Hu
Earth Syst. Sci. Data, 17, 3219–3241, https://doi.org/10.5194/essd-17-3219-2025, https://doi.org/10.5194/essd-17-3219-2025, 2025
Short summary
Short summary
Forest age is critical for carbon cycle modeling and effective forest management. Existing datasets, however, have low spatial resolutions or limited temporal coverage. This study introduces China's annual forest age dataset (CAFA), spanning 1986–2022 at a 30 m resolution. By tracking forest disturbances, we annually update ages. Validation shows small errors for disturbed forests and larger errors for undisturbed forests. CAFA can enhance carbon cycle modeling and forest management in China.
Peng Li, Rong Shang, Jing M. Chen, Huiguang Zhang, Xiaoping Zhang, Guoshuai Zhao, Hong Yan, Jun Xiao, Xudong Lin, Lingyun Fan, Rong Wang, Jianjie Cao, and Hongda Zeng
EGUsphere, https://doi.org/10.5194/egusphere-2025-1062, https://doi.org/10.5194/egusphere-2025-1062, 2025
Short summary
Short summary
This study explored species-specific relationships between net primary productivity and forest age for seven forest species in subtropical China based on field data using the Semi-Empirical Model. Compared to nationwide relationships, these species-specific relationships improved simulations of aboveground biomass when using the process-based model. Our findings suggest that these species-specific relationships are crucial for accurate forest carbon modeling and management in subtropical China.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
Short summary
Short summary
In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Wei Zhang, Xunhua Zheng, Siqi Li, Shenghui Han, Chunyan Liu, Zhisheng Yao, Rui Wang, Kai Wang, Xiao Chen, Guirui Yu, Zhi Chen, Jiabing Wu, Huimin Wang, Junhua Yan, and Yong Li
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-141, https://doi.org/10.5194/gmd-2024-141, 2024
Revised manuscript not accepted
Short summary
Short summary
Process-oriented biogeochemical models are promising tools for estimating the carbon fluxes of forest ecosystems. In this study, the hydro-biogeochemical model of CNMM-DNDC was improved by incorporating a new forest growth module derived from the Biome-BGC. The updated model was validated using the multiple-year observed carbon fluxes and showed better performance in capturing the daily dynamics and annual variations. The sensitive eco-physiological parameters were also identified.
Jiye Leng, Jing M. Chen, Wenyu Li, Xiangzhong Luo, Mingzhu Xu, Jane Liu, Rong Wang, Cheryl Rogers, Bolun Li, and Yulin Yan
Earth Syst. Sci. Data, 16, 1283–1300, https://doi.org/10.5194/essd-16-1283-2024, https://doi.org/10.5194/essd-16-1283-2024, 2024
Short summary
Short summary
We produced a long-term global two-leaf gross primary productivity (GPP) and evapotranspiration (ET) dataset at the hourly time step by integrating a diagnostic process-based model with dynamic parameterizations. The new dataset provides us with a unique opportunity to study carbon and water fluxes at sub-daily time scales and advance our understanding of ecosystem functions in response to transient environmental changes.
Yang Liu, Ronggao Liu, and Rong Shang
Earth Syst. Sci. Data, 14, 4505–4523, https://doi.org/10.5194/essd-14-4505-2022, https://doi.org/10.5194/essd-14-4505-2022, 2022
Short summary
Short summary
Surface water has been changing significantly with high seasonal variation and abrupt change, making it hard to capture its interannual trend. Here we generated a global annual surface water cover frequency dataset during 2000–2020. The percentage of the time period when a pixel is covered by water in a year was estimated to describe the seasonal dynamics of surface water. This dataset can be used to analyze the interannual variation and change trend of highly dynamic inland water extent.
Jing M. Chen, Rong Wang, Yihong Liu, Liming He, Holly Croft, Xiangzhong Luo, Han Wang, Nicholas G. Smith, Trevor F. Keenan, I. Colin Prentice, Yongguang Zhang, Weimin Ju, and Ning Dong
Earth Syst. Sci. Data, 14, 4077–4093, https://doi.org/10.5194/essd-14-4077-2022, https://doi.org/10.5194/essd-14-4077-2022, 2022
Short summary
Short summary
Green leaves contain chlorophyll pigments that harvest light for photosynthesis and also emit chlorophyll fluorescence as a byproduct. Both chlorophyll pigments and fluorescence can be measured by Earth-orbiting satellite sensors. Here we demonstrate that leaf photosynthetic capacity can be reliably derived globally using these measurements. This new satellite-based information overcomes a bottleneck in global ecological research where such spatially explicit information is currently lacking.
Fei Jiang, Weimin Ju, Wei He, Mousong Wu, Hengmao Wang, Jun Wang, Mengwei Jia, Shuzhuang Feng, Lingyu Zhang, and Jing M. Chen
Earth Syst. Sci. Data, 14, 3013–3037, https://doi.org/10.5194/essd-14-3013-2022, https://doi.org/10.5194/essd-14-3013-2022, 2022
Short summary
Short summary
A 10-year (2010–2019) global monthly terrestrial NEE dataset (GCAS2021) was inferred from the GOSAT ACOS v9 XCO2 product. It shows strong carbon sinks over eastern N. America, the Amazon, the Congo Basin, Europe, boreal forests, southern China, and Southeast Asia. It has good quality and can reflect the impacts of extreme climates and large-scale climate anomalies on carbon fluxes well. We believe that this dataset can contribute to regional carbon budget assessment and carbon dynamics research.
Fei Jiang, Hengmao Wang, Jing M. Chen, Weimin Ju, Xiangjun Tian, Shuzhuang Feng, Guicai Li, Zhuoqi Chen, Shupeng Zhang, Xuehe Lu, Jane Liu, Haikun Wang, Jun Wang, Wei He, and Mousong Wu
Atmos. Chem. Phys., 21, 1963–1985, https://doi.org/10.5194/acp-21-1963-2021, https://doi.org/10.5194/acp-21-1963-2021, 2021
Short summary
Short summary
We present a 6-year inversion from 2010 to 2015 for the global and regional carbon fluxes using only the GOSAT XCO2 retrievals. We find that the XCO2 retrievals could significantly improve the modeling of atmospheric CO2 concentrations and that the inferred interannual variations in the terrestrial carbon fluxes in most land regions have a better relationship with the changes in severe drought area or leaf area index, or are more consistent with the previous estimates about drought impact.
Yi Zheng, Ruoque Shen, Yawen Wang, Xiangqian Li, Shuguang Liu, Shunlin Liang, Jing M. Chen, Weimin Ju, Li Zhang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 2725–2746, https://doi.org/10.5194/essd-12-2725-2020, https://doi.org/10.5194/essd-12-2725-2020, 2020
Short summary
Short summary
Accurately reproducing the interannual variations in vegetation gross primary production (GPP) is a major challenge. A global GPP dataset was generated by integrating the regulations of several major environmental variables with long-term changes. The dataset can effectively reproduce the spatial, seasonal, and particularly interannual variations in global GPP. Our study will contribute to accurate carbon flux estimates at long timescales.
Cited articles
Alexandrov, G. A., Oikawa, T., and Esser, G.: Estimating terrestrial NPP: what the data say and how they may be interpreted?, Ecol. Modell., 117, 361–369, https://doi.org/10.1016/S0304-3800(99)00019-8, 1999.
Bond-Lamberty, B., Wang, C., and Gower, S. T.: Net primary production and net ecosystem production of a boreal black spruce wildfire chronosequence, Glob. Change Biol., 10, 473–487, https://doi.org/10.1111/j.1529-8817.2003.0742.x, 2004.
Burkes, E. C., Will, R. E., Barron-Gafford, G. A., Teskey, R. O., and Shiver, B.: Biomass partitioning and growth efficiency of intensively managed Pinus taeda and Pinus elliottii stands of different planting densities, Forest Sci., 49, 224–234, 2003.
Camenzind, T., Hättenschwiler, S., Treseder, K. K., Lehmann, A., and Rillig, M. C.: Nutrient limitation of soil microbial processes in tropical forests, Ecol. Monogr., 88, 4–21, https://doi.org/10.1002/ecm.1279, 2018.
Chapin, F. S., III, Woodwell, G. M., Randerson, J. T., Rastetter, E. B., Lovett, G. M., Baldocchi, D. D., Clark, D. A., Harmon, M. E., and Schimel, D. S.: Reconciling carbon-cycle concepts, terminology, and methods, Ecosystems, 9, 1041–1050, https://doi.org/10.1007/s10021-005-0105-7, 2006.
Chen, J. M., Ju, W., Cihlar, J., Price, D., Liu, J., Chen, W., Pan, J., Black, A., and Barr, A.: Spatial distribution of carbon sources and sinks in Canada's forests, Tellus B, 55, 622–641, https://doi.org/10.3402/tellusb.v55i2.16711, 2003.
Chen, J. M., Mo, G., Pisek, J., Liu, J., Deng, F., Ishizawa, M., and Chan, D.: Effects of foliage clumping on the estimation of global terrestrial gross primary productivity, Global Biogeochem. Cy., 26, GB1019, https://doi.org/10.1029/2010GB003996, 2012.
Chen, W., Chen, J., and Cihlar, J.: An integrated terrestrial ecosystem carbon-budget model based on changes in disturbance, climate, and atmospheric chemistry, Ecol. Modell., 135, 55–79, https://doi.org/10.1016/S0304-3800(00)00371-9, 2000.
Chen, W., Chen, J. M., Price, D. T., and Cihlar, J.: Effects of stand age on net primary productivity of boreal black spruce forests in Ontario, Canada, Can. J. Forest Res., 32, 833–842, https://doi.org/10.1139/x01-165, 2002.
Dai, L., Wang, Y., Su, D., Zhou, L., Yu, D., Lewis, B. J., and Qi, L.: Major forest types and the evolution of sustainable forestry in China, Environ. Manag., 48, 1066–1078, https://doi.org/10.1007/s00267-011-9706-4, 2011.
Dalgleish, S. A., Van Etten, E. J. B., Stock, W. D., and Knuckey, C.: Fuel dynamics and vegetation recovery after fire in a semiarid Australian shrubland, Int. J. Wildl. Fire, 24, 613–623, https://doi.org/10.1071/WF14128, 2015.
DesRochers, A. and Lieffers, V. J.: Root biomass of regenerating aspen (Populus tremuloides) stands of different densities in Alberta, Can. J. Forest Res., 31, 1012–1018, https://doi.org/10.1139/x01-037, 2001.
Ding, Z., Ji, B., Yao, H., Cheng, X., Yu, S., Sun, X., Liu, S., Xu, L., Zhou, Y., and Shi, Y.: An Analysis of the Factors Affecting Forest Mortality and Research on Forecasting Models in Southern China: A Case Study in Zhejiang Province, Forests, 14, 2199, https://doi.org/10.3390/f14112199, 2023.
Do, H. T. T., Zimmer, H. C., Vanclay, J. K., Grant, J. C., Trinh, B. N., Nguyen, H. H., and Nichols, J. D.: Site form classification – a practical tool for guiding site-specific tropical forest landscape restoration and management, Forestry, 95, 261–273, https://doi.org/10.1093/forestry/cpab046, 2022.
Drake, J. E., Davis, S. C., Raetz, L. M., and Delucia, E. H.: Mechanisms of age-related changes in forest production: The influence of physiological and successional changes, Glob. Change Biol., 17, 1522–1535, https://doi.org/10.1111/j.1365-2486.2010.02342.x, 2011.
Eggleston, S., Buendia, L., Miwa, K., Ngara, T., and Tanabe, K.: 2006 IPCC Guidelines for National Greenhouse Gas Inventories, V.4. Agriculture, Forestry and Other Land Use, Institute for Global Environmental Strategies (IGES), ISBN 4-88788-032-4, 2006.
Fang, J., Yu, G., Liu, L., Hu, S., and Stuart Chapin, F.: Climate change, human impacts, and carbon sequestration in China, P. Natl. Acad. Sci. USA, 115, 4015–4020, https://doi.org/10.1073/pnas.1700304115, 2018.
Fang, J., Chen, A., Peng, C., Zhao, S., and Ci, L.: Changes in forest biomass carbon storage in China between 1949 and 1998, Science, 292, 2320–2322, https://doi.org/10.1126/science.1058629, 2001a.
Fang, J., Ke, J., Tang, Z., and Chen, A.: Implications and estimations of four terrestrial productivity parameters, Acta Phytoecol. Sin., 25, 414–419, https://europepmc.org/article/cba/540929, 2001b.
Fisher, J. B., Badgley, G., and Blyth, E.: Global nutrient limitation in terrestrial vegetation, Global Biogeochem. Cy., 26, GB3007, https://doi.org/10.1029/2011GB004252, 2012.
Gao, B., Taylor, A. R., Searle, E. B., Kumar, P., Ma, Z., Hume, A. M., and Chen, H. Y. H.: Carbon storage declines in old boreal forests irrespective of succession pathway, Ecosystems, 21, 1168–1182, https://doi.org/10.1007/s10021-017-0210-4, 2018.
Gough, C. M., Vogel, C. S., Schmid, H. P., and Curtis, P. S.: Controls on annual forest carbon storage: lessons from the past and predictions for the future, Bioscience, 58, 609–622, https://doi.org/10.1641/B580708, 2008.
Gower, S. T., McMurtrie, R. E., and Murty, D.: Aboveground net primary production decline with stand age: potential causes, Trend. Ecol. Evol., 11, 378–382, https://doi.org/10.1016/0169-5347(96)10042-2, 1996.
Gower, S. T., Vogel, J. G., Norman, J. M., Kucharik, C. J., and Steele, S. J.: Carbon distribution and aboveground net primary production in aspen, jack pine, and black spruce stands in Saskatchewan and Manitoba, Canada, J. Geophys. Res.-Atmos., 102, 29029–29041, https://doi.org/10.1029/97JD02317, 1997.
Gundersen, P., Thybring, E. E., Nord-Larsen, T., Vesterdal, L., Nadelhoffer, K. J., and Johannsen, V. K.: Old-growth forest carbon sinks overestimated, Nature, 591, E21–E23, https://doi.org/10.1038/s41586-021-03266-z, 2021.
Guo, L., An, N., and Wang, K.: Journal of Geophysical Research, Nature, 175, 238, https://doi.org/10.1038/175238c0, 1955.
Harmon, M. E., Ferrell, W. K., and Franklin, J. F.: Effects on carbon storage of conversion of old-growth forests to young forests, Science, 247, 699–702, https://doi.org/10.1126/science.247.4943.699, 1990.
Hasenauer, S.: the significance of remote sensing in the good practice guidance for land use, land-use change and forestry as specified by the kyoto protocol, Diploma Thesis, https://publik.tuwien.ac.at/files/PubDat_119760.pdf (last access: 25 October 2023), 2004.
He, L., Chen, J. M., Pan, Y., Birdsey, R., and Kattge, J.: Relationships between net primary productivity and forest stand age in U.S. forests, Global Biogeochem. Cy., 26, 1–19, https://doi.org/10.1029/2010GB003942, 2012.
Hicke, J. A., Jenkins, J. C., and Ducey, O. M.: Spatial patterns of forest characteristics in the western United States derived from inventories, Ecol. Appl., 17, 2387–2402, https://doi.org/10.1890/06-1951.1, 2007.
Kira, T. and Shidei, T.: Primary production and turnover of organic matter in different forest ecosystems of the western Pacific, Japn. J. Ecol., 17, 70–87, https://doi.org/10.18960/seitai.17.2_70, 1967.
Ji, Y., Zhou, G., Luo, T., Dan, Y., Zhou, L., and Lv, X.: Variation of net primary productivity and its drivers in China's forests during 2000–2018, Forest Ecosyst., 7, 1–11, https://doi.org/10.1186/s40663-020-00229-0, 2020.
Ju, W., Chen, J. M., Black, T. A., Barr, A. G., Liu, J., and Chen, B.: Modelling multi-year coupled carbon and water fluxes in a boreal aspen forest, Agr. Forest Meteorol., 140, 136–151, https://doi.org/10.1016/j.agrformet.2006.08.008, 2006.
Li, Z. and Zhou, T.: Optimization of forest age-dependent light-use efficiency and its implications on climate-vegetation interactions in China, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. – ISPRS Arch. 40, 449–454, https://doi.org/10.5194/isprsarchives-XL-7-W3-449-2015, 2015.
Litton, C. M., Raich, J. W., and Ryan, M. G.: Carbon allocation in forest ecosystems, Glob. Change Biol., 13, 2089–2109, https://doi.org/10.1111/j.1365-2486.2007.01420.x, 2007.
Liu, H., Gong, P., Wang, J., Wang, X., Ning, G., and Xu, B.: Production of global daily seamless data cubes and quantification of global land cover change from 1985 to 2020 – iMap World 1.0, Remote Sens. Environ., 258, 112364, https://doi.org/10.1016/j.rse.2021.112364, 2021.
Liu, J., Chen, J. M., Cihlar, J., and Chen, W.: Net primary productivity distribution in the boreas region from a process model using satellite and surface data, J. Geophys. Res.-Atmos., 104, 27735–27754, https://doi.org/10.1029/1999JD900768, 1999.
Liu, J., Chen, J. M., Cihlar, J., and Chen, W.: Net primary productivity mapped for Canada at 1-km resolution, Glob. Ecol. Biogeogr., 11, 115–129, https://doi.org/10.1046/j.1466-822X.2002.00278.x, 2002.
Liu, Y., Liu, R., and Chen, J. M.: Retrospective retrieval of long-term consistent global leaf area index (1981–2011) from combined AVHRR and MODIS data, J. Geophys. Res.-Biogeo., 117, 1–14, https://doi.org/10.1029/2012JG002084, 2012a.
Liu Y., Yu G., Wang Q., and Zhang Y.: Huge carbon sequestration potential in global forests, J. Resour. Ecol., 3, 193–201, https://doi.org/10.5814/j.issn.1674-764x.2012.03.001, 2012b.
Luyssaert, S., Inglima, I., Jung, M., Richardson, A. D., Reichstein, M., Papale, D., Piao, S. L., Schulze, E.-D., Wingate, L., Matteucci, G., Aragao, L., Aubinet, M., Beer, C., Bernhofer, C., Black, K. G., Bonal, D., Bonnefond, J.-M., Chambers, J., Ciais, P., Cook, B., Davis, K. J., Dolman, A. J., Gielen, B., Goulden, M., Grace, J., Granier, A., Grelle, A., Griffis, T., Grünwald, T., Guidolotti, G., Hanson, P. J., Harding, R., Hollinger, D.Y., Hutyra, L. R., Kolari, P., Kruijt, B., Kutsch, W., Lagergren, F., Laurila, T., Law, B. E., Le maire, G., Lindroth, A., Loustau, D., Malhi, Y., Mateus, J., Migliavacca, M., Misson, L., Montagnani, L., Moncrieff, J., Moors, E., Munger, J. W., Nikinmaa, E., Ollinger, S. V, Pita, G., Rebmann, C., Roupsard, O., Saigusa, N., Sanz, M. J., Seufert, G., Sierra, C., Smith, M.-L., Tang, J., Valentini, R., Vesala, T., and Janssens, I. A.: CO2 balance of boreal, temperate, and tropical forests derived from a global database, Glob. Change Biol. 13, 2509–2537, https://doi.org/10.1111/j.1365-2486.2007.01439.x, 2007.
Ma, T., Liang, Y., Li, Z., Liu, Z., Liu, B., Wu, M. M., Lau, M. K., and Fang, Y.: Age-related patterns and climatic driving factors of drought-induced forest mortality in Northeast China, Agr. Forest Meteorol., 332, 109360, https://doi.org/10.1016/j.agrformet.2023.109360, 2023.
Mund, M., Kummetz, E., Hein, M., Bauer, G. A., and Schulze, E.-D.: Growth and carbon stocks of a spruce forest chronosequence in central Europe, Forest Ecol. Manag. 171, 275–296, https://doi.org/10.1016/S0378-1127(01)00788-5, 2002.
Odum, E. P.: The strategy of ecosystem development, Science, 164, 262–270, https://doi.org/10.1126/science.164.3877.262, 1969.
Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., Kauppi, P. E., Kurz, W. A., Phillips, O. L., Shvidenko, A., Lewis, S. L., Canadell, J. G., Ciais, P., Jackson, R. B., Pacala, S. W., McGuire, A. D., Piao, S., Rautiainen, A., Sitch, S., and Hayes, D.: A large and persistent carbon sink in the world's forests, Science, 333, 988–993, https://doi.org/10.1126/science.1201609, 2011.
Peper, P. J., McPherson, G. E., and Mori, S. M.: Predictive equations for dimensions and leaf area of coastal southern California street trees, J. Arboricul., 27, 169–180, https://doi.org/10.48044/jauf.2001.019, 2001.
Ryan, M. G. and Waring, R. H.: Maintenance respiration and stand development in a subalpine Lodgepole pine forest, Ecology, 73, 2100–2108, https://doi.org/10.2307/1941458, 1992.
Ryan, M. G., Binkley, D., and Fownes, J. H.: Age-Related Decline in Forest Productivity: Pattern and Process, Adv. Ecol. Res., 27, 213–262, https://doi.org/10.1016/S0065-2504(08)60009-4, 1997.
Ryan, M. G., Binkley, D., Fownes, J. H., Giardina, C. P., and Senock, R. S.: An experimental test of the causes of forest growth decline with stand age, Ecol. Monogr., 74, 393–414, https://doi.org/10.1890/03-4037, 2004.
Salas-Eljatib, C.: An approach to quantify climate–productivity relationships: an example from a widespread nothofagus forest, Ecol. Appl., 31, 1–14, https://doi.org/10.1002/eap.2285, 2021.
Semenzato, P., Cattaneo, D., and Dainese, M.: Growth prediction for five tree species in an Italian urban forest, Urban For. Urban Gree., 10, 169–176, https://doi.org/10.1016/j.ufug.2011.05.001, 2011.
Shang, R., Zhu, Z., Zhang, J., Qiu, S., Yang, Z., Li, T., and Yang, X.: Near-real-time monitoring of land disturbance with harmonized Landsats 7–8 and Sentinel-2 data, Remote Sens. Environ., 278, 113073, https://doi.org/10.1016/j.rse.2022.113073, 2022.
Shang, R., Chen, J. M., Xu, M., Lin, X., Li, P., Yu, G., He, N., Xu, L., Gong, P., Liu, L., Liu, H., and Jiao, W.: China's current forest age structure will lead to weakened carbon sinks in the near future, Innov., 4, 100515, https://doi.org/10.1016/j.xinn.2023.100515, 2023.
Sillett, S. C., Van Pelt, R., Koch, G. W., Ambrose, A. R., Carroll, A. L., Antoine, M. E., and Mifsud, B. M.: Increasing wood production through old age in tall trees, For. Ecol. Manage., 259, 976–994, https://doi.org/10.1016/j.foreco.2009.12.003, 2010.
Song, X., Zeng, X., and Tian, D.: Allocation of forest net primary production varies by forest age and air temperature, Ecol. Evol., 8, 12163–12172, https://doi.org/10.1002/ece3.4675, 2018.
Tang, J., Luyssaert, S., Richardson, A. D., Kutsch, W., and Janssens, I. A.: Steeper declines in forest photosynthesis than respiration explain age-driven decreases in forest growth, P. Natl. Acad. Sci. USA, 111, 8856–8860, https://doi.org/10.1073/pnas.1320761111, 2014.
Tang, X., Wang, Y. P., Zhou, G., Zhang, D., Liu, S., Liu, S., Zhang, Q., Liu, J., and Yan, J.: Different patterns of ecosystem carbon accumulation between a young and an old-growth subtropical forest in Southern China, Plant Ecol., 212, 1385–1395, https://doi.org/10.1007/s11258-011-9914-2, 2011.
Tang, X., Zhao, X., Bai, Y., Tang, Z., Wang, W., Zhao, Y., Wan, H., Xie, Z., Shi, X., Wu, B., Wang, G., Yan, J., Ma, K., Du, S., Li, S., Han, S., Ma, Y., Hu, H., He, N., Yang, Y., Han, W., He, H., Yu, G., Fang, J., and Zhou, G.: Carbon pools in China's terrestrial ecosystems: new estimates based on an intensive field survey, P. Natl. Acad. Sci. USA, 115, 4021–4026, https://doi.org/10.1073/pnas.1700291115, 2018.
Van Tuyl, S., Law, B. E., Turner, D. P., and Gitelman, A. I.: Variability in net primary production and carbon storage in biomass across Oregon forests – an assessment integrating data from forest inventories, intensive sites, and remote sensing, Forest Ecol. Manag., 209, 273–291, https://doi.org/10.1016/j.foreco.2005.02.002, 2005.
Wang, B., Li, M., Fan, W., Yu, Y., and Chen, J. M.: Relationship between net primary productivity and forest stand age under different site conditions and its implications for regional carbon cycle study, Forests, 9, f9010005, https://doi.org/10.3390/f9010005, 2018.
Wang, S., Chen, J. M., Ju, W. M., Feng, X., Chen, M., Chen, P., and Yu, G.: Carbon sinks and sources in China's forests during 1901–2001, J. Environ. Manag., 85, 524–537, https://doi.org/10.1016/j.jenvman.2006.09.019, 2007.
Wang, S., Zhou, L., Chen, J., Ju, W., Feng, X., and Wu, W.: Relationships between net primary productivity and stand age for several forest types and their influence on China's carbon balance, J. Environ. Manag., 92, 1651–1662, https://doi.org/10.1016/j.jenvman.2011.01.024, 2011.
White, M. A., Thornton, P. E., Running, S. W., and Nemani, R. R.: Parameterization and sensitivity analysis of the BIOME–BGC terrestrial ecosystem model: net primary production controls, Earth Interact., 4, 1–85, https://doi.org/10.1175/1087-3562(2000)004<0003:pasaot>2.0.co;2, 2000.
Xia, J., Yuan, W., Lienert, S., Joos, F., Ciais, P., Viovy, N., Wang, Y., Wang, X., Zhang, H., Chen, Y., and Tian, X.: Global patterns in net primary production allocation regulated by environmental conditions and forest stand age: a model-data comparison, J. Geophys. Res.-Biogeo., 124, 2039–2059, https://doi.org/10.1029/2018JG004777, 2019.
Xiaoyun, Z., Minghang, G., and Tibin, Z.: Joint control of net primary productivity by climate and soil nitrogen in the forests of eastern China, Forests, 9, f9060322, https://doi.org/10.3390/f9060322, 2018.
Xu, B., Guo, Z. Di, Piao, S. L., and Fang, J. Y.: Biomass carbon stocks in China's forests between 2000 and 2050: A prediction based on forest biomass-age relationships, Sci. China Life Sci., 53, 776–783, https://doi.org/10.1007/s11427-010-4030-4, 2010.
Xu, C. Y., Turnbull, M. H., Tissue, D. T., Lewis, J. D., Carson, R., Schuster, W. S. F., Whitehead, D., Walcroft, A. S., Li, J., and Griffin, K. L.: Age-related decline of stand biomass accumulation is primarily due to mortality and not to reduction in NPP associated with individual tree physiology, tree growth or stand structure in a Quercus-dominated forest, J. Ecol., 100, 428–440, https://doi.org/10.1111/j.1365-2745.2011.01933.x, 2012.
Yan, E. R., Wang, X. H., and Huang, J. J.: Shifts in plant nutrient use strategies under secondary forest succession, Plant Soil, 289, 187–197, https://doi.org/10.1007/s11104-006-9128-x, 2006.
Yu, G., Chen, Z., Piao, S., Peng, C., Ciais, P., Wang, Q., Lia, X., and Zhu, X.: High carbon dioxide uptake by subtropical forest ecosystems in the East Asian monsoon region, P. Natl. Acad. Sci. USA, 111, 4910–4915, https://doi.org/10.1073/pnas.1317065111, 2014.
Yu, Y., Chen, J. M., Yang, X., Fan, W., Li, M., and He, L.: Influence of site index on the relationship between forest net primary productivity and stand age, PLoS One, 12, 1–20, https://doi.org/10.1371/journal.pone.0177084, 2017.
Zaehle, S., Sitch, S., Prentice, I. C., Liski, J., Cramer, W., Erhard, M., Hickler, T., and Smith, B.: The importance of age-related decline in forest NPP for modeling regional carbon balances, Ecol. Appl., 16, 1555–1574, https://doi.org/10.1890/1051-0761(2006)016[1555:TIOADI]2.0.CO;2, 2006.
Zerihun, A. and Montagu, K. D.: Belowground to aboveground biomass ratio and vertical root distribution responses of mature pinus radiata stands to phosphorus fertilization at planting, Can. J. Forest Res., 34, 1883–1894, https://doi.org/10.1139/X04-069, 2004.
Zha, T. S., Barr, A. G., Bernier, P. Y., Lavigne, M. B., Trofymow, J. A., Amiro, B. D., Arain, M. A., Bhatti, J. S., Black, T. A., and Margolis, H. A.: Gross and aboveground net primary production at Canadian forest carbon flux sites, Agr. Forest Meteorol., 174/175, 54–64, https://doi.org/10.1016/j.agrformet.2013.02.004, 2013.
Zhang, F., Chen, J. M., Pan, Y., Birdsey, R. A., Shen, S., Ju, W., and He, L.: Attributing carbon changes in conterminous U.S. forests to disturbance and non-disturbance factors from 1901 to 2010, J. Geophys. Res.-Biogeo., 117, 1–18, https://doi.org/10.1029/2011JG001930, 2012.
Zhang, X., Zhang, X., Han, H., Shi, Z., and Yang, X.: Biomass accumulation and carbon sequestration in an age-sequence of Mongolian pine plantations in Horqin sandy land, China, Forests, 10, 1–18, https://doi.org/10.3390/f10020197, 2019.
Zhang, Y., Yao, Y., Wang, X., Liu, Y., and Piao, S.: Mapping spatial distribution of forest age in China, Earth Sp. Sci., 4, 108–116, https://doi.org/10.1002/2016EA000177, 2017.
Zhao, M. and Zhou, G. S.: Estimation of biomass and net primary productivity of major planted forests in China based on forest inventory data, Forest Ecol. Manag., 207, 295–313, https://doi.org/10.1016/j.foreco.2004.10.049, 2005.
Zhao, M. and Zhou, G. S.: Estimating net primary productivity of Chinese pine forests based on forest inventory data, Forestry, 79, 231–239, https://doi.org/10.1093/forestry/cpl002, 2006.
Zheng, J., Mao, F., Du, H., Li, X., Zhou, G., Dong, L., Zhang, M., Han, N., Liu, T., and Xing, L.: Spatiotemporal simulation of net ecosystem productivity and its response to climate change in subtropical forests, Forests, 10, f10080708, https://doi.org/10.3390/f10080708, 2019.
Short summary
The amount of carbon that forests gain from the atmosphere, called net primary productivity (NPP), changes a lot with age. These forest NPP–age relationships could be modeled from field survey data, but we are not sure which model works best. Here we tested five different models using 3121 field survey samples in China, and the semi-empirical mathematical (SEM) function was determined as the optimal. The relationships built by SEM can improve China's forest carbon modeling and prediction.
The amount of carbon that forests gain from the atmosphere, called net primary productivity...
Altmetrics
Final-revised paper
Preprint