Articles | Volume 15, issue 18
https://doi.org/10.5194/gmd-15-7075-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-7075-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
20 Sep 2022
Model evaluation paper |
| 20 Sep 2022
| 20 Sep 2022
Impact of changes in climate and CO2 on the carbon storage potential of vegetation under limited water availability using SEIB-DGVM version 3.02
Shanlin Tong
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan, 430072, PR China
State Key Laboratory of Hydrology–Water Resources and Hydraulic
Engineering, Hohai University, Nanjing, 210098, PR China
Key Laboratory of Water Big Data Technology of Ministry of Water
Resources, Hohai University, Nanjing, 210098, PR China
Weiguang Wang
CORRESPONDING AUTHOR
State Key Laboratory of Hydrology–Water Resources and Hydraulic
Engineering, Hohai University, Nanjing, 210098, PR China
Key Laboratory of Water Big Data Technology of Ministry of Water
Resources, Hohai University, Nanjing, 210098, PR China
Jie Chen
CORRESPONDING AUTHOR
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan, 430072, PR China
Chong-Yu Xu
Department of Geosciences, University of Oslo, Oslo, 0316, Norway
Hisashi Sato
Research Institute for Global Change, Japan Agency for Marine–Earth Science and Technology, Yokohama, 236-0001, Japan
Guoqing Wang
Nanjing Hydraulic Research Institute, Nanjing, 210029, PR China
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Zitong Jia, Shouzhi Chen, Yongshuo H. Fu, David Martín Belda, David Wårlind, Stefan Olin, Chongyu Xu, and Jing Tang
EGUsphere, https://doi.org/10.5194/egusphere-2025-4064, https://doi.org/10.5194/egusphere-2025-4064, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Groundwater sustains vegetation and regulates land-atmosphere exchanges, but most Earth system models oversimplify its movement. Our study develops an integrated framework coupling LPJ-GUESS with the 3D hydrological model ParFlow to explicitly represent groundwater-vegetation interactions. Our results add to the evidence that three-dimensional groundwater flow strongly regulates water exchanges, and provides a powerful tool to improve simulations of water cycles in Earth system models.
Jiaoyang Wang, Dedi Liu, Shenglian Guo, Lihua Xiong, Pan Liu, Hua Chen, Jie Chen, Jiabo Yin, and Yuling Zhang
Hydrol. Earth Syst. Sci., 29, 3315–3339, https://doi.org/10.5194/hess-29-3315-2025, https://doi.org/10.5194/hess-29-3315-2025, 2025
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The unclear feedback loops of water supply–hydropower generation–environmental conservation (SHE) nexuses with inter-basin water diversion projects (IWDPs) increase the uncertainty in the rational scheduling of water resources for water receiving and water donation areas. To address the different impacts of IWDPs on dynamic SHE nexuses and explore synergies, a framework is proposed to identify these effects across the different temporal and spatial scales in a reservoir group.
Kun Xie, Lu Li, Hua Chen, Stephanie Mayer, Andreas Dobler, Chong-Yu Xu, and Ozan Mert Göktürk
Hydrol. Earth Syst. Sci., 29, 2133–2152, https://doi.org/10.5194/hess-29-2133-2025, https://doi.org/10.5194/hess-29-2133-2025, 2025
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We compared hourly and daily extreme precipitation across Norway from HARMONIE Climate models at convection-permitting 3 km (HCLIM3) and 12 km (HCLIM12) resolutions. HCLIM3 more accurately captures the extremes in most regions and seasons (except in summer). Its advantages are more pronounced for hourly extremes than for daily extremes. The results highlight the value of convection-permitting models in improving extreme-precipitation predictions and in helping the local society brace for extreme weather.
Qiumei Ma, Chengyu Xie, Zheng Duan, Yanke Zhang, Lihua Xiong, and Chong-Yu Xu
EGUsphere, https://doi.org/10.5194/egusphere-2025-679, https://doi.org/10.5194/egusphere-2025-679, 2025
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We propose a method to estimate the reservoir WLS curve based on the capacity loss induced by sediment accumulation and further assess the potential negative impact caused by outdated design WLS curve on flood regulation risks. The findings highlight that when storage capacity is considerably reduced, continued use of the existing design WLS curve may significantly underestimate, thus posing potential safety hazards to the reservoir itself and downstream flood protection objects.
Tian Lan, Tongfang Li, Hongbo Zhang, Jiefeng Wu, Yongqin David Chen, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 29, 903–924, https://doi.org/10.5194/hess-29-903-2025, https://doi.org/10.5194/hess-29-903-2025, 2025
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This study develops an integrated framework based on the novel Driving index for changes in Precipitation–Runoff Relationships (DPRR) to explore the controlling changes in precipitation–runoff relationships in non-stationary environments. According to the quantitative results of the candidate driving factors, the possible process explanations for changes in the precipitation–runoff relationships are deduced. The main contribution offers a comprehensive understanding of hydrological processes.
Tian Lan, Xiao Wang, Hongbo Zhang, Xinghui Gong, Xue Xie, Yongqin David Chen, and Chong-Yu Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-384, https://doi.org/10.5194/hess-2024-384, 2025
Preprint under review for HESS
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Hydrological models are vital for water management but often fail to predict water flow in dynamic catchments due to model simplification. This study tackles it by developing an optimized calibration framework that considers dynamic catchment characteristics. To overcome potential difficulties, multiple schemes were tested on over 200 U.S. catchments. The results enhanced our understanding of simulation in dynamic catchments and provided a practical solution for improving future forecasting.
Ruikang Zhang, Dedi Liu, Lihua Xiong, Jie Chen, Hua Chen, and Jiabo Yin
Hydrol. Earth Syst. Sci., 28, 5229–5247, https://doi.org/10.5194/hess-28-5229-2024, https://doi.org/10.5194/hess-28-5229-2024, 2024
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Flash flood warnings cannot be effective without people’s responses to them. We propose a method to determine the threshold of issuing warnings based on a people’s response process simulation. The results show that adjusting the warning threshold according to people’s tolerance levels to the failed warnings can improve warning effectiveness, but the prerequisite is to increase forecasting accuracy and decrease forecasting variance.
Reza Kusuma Nurrohman, Tomomichi Kato, Hideki Ninomiya, Lea Végh, Nicolas Delbart, Tatsuya Miyauchi, Hisashi Sato, Tomohiro Shiraishi, and Ryuichi Hirata
Biogeosciences, 21, 4195–4227, https://doi.org/10.5194/bg-21-4195-2024, https://doi.org/10.5194/bg-21-4195-2024, 2024
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SPITFIRE (SPread and InTensity of FIRE) was integrated into a spatially explicit individual-based dynamic global vegetation model to improve the accuracy of depicting Siberian forest fire frequency, intensity, and extent. Fires showed increased greenhouse gas and aerosol emissions in 2006–2100 for Representative Concentration Pathways. This study contributes to understanding fire dynamics, land ecosystem–climate interactions, and global material cycles under the threat of escalating fires.
Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 28, 2809–2829, https://doi.org/10.5194/hess-28-2809-2024, https://doi.org/10.5194/hess-28-2809-2024, 2024
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Ensemble forecasting facilitates reliable flood forecasting and warning. This study couples the copula-based hydrologic uncertainty processor (CHUP) with Bayesian model averaging (BMA) and proposes the novel CHUP-BMA method of reducing inflow forecasting uncertainty of the Three Gorges Reservoir. The CHUP-BMA avoids the normal distribution assumption in the HUP-BMA and considers the constraint of initial conditions, which can improve the deterministic and probabilistic forecast performance.
Jinghua Xiong, Shenglian Guo, Abhishek, Jiabo Yin, Chongyu Xu, Jun Wang, and Jing Guo
Hydrol. Earth Syst. Sci., 28, 1873–1895, https://doi.org/10.5194/hess-28-1873-2024, https://doi.org/10.5194/hess-28-1873-2024, 2024
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Temporal variability and spatial heterogeneity of climate systems challenge accurate estimation of probable maximum precipitation (PMP) in China. We use high-resolution precipitation data and climate models to explore the variability, trends, and shifts of PMP under climate change. Validated with multi-source estimations, our observations and simulations show significant spatiotemporal divergence of PMP over the country, which is projected to amplify in future due to land–atmosphere coupling.
Qian Lin, Jie Chen, and Deliang Chen
EGUsphere, https://doi.org/10.5194/egusphere-2024-826, https://doi.org/10.5194/egusphere-2024-826, 2024
Preprint archived
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Glaciers of the Tibetan Plateau (TP) have experienced widespread retreat in recent decades, but impacts of glacier changes that have occurred on regional climate, including precipitation, is still unknown. Thus, this study addressed this knowledge gap, and found that glacier changes exert a more pronounced impact on summer extreme precipitation events than mean precipitation over the TP. This provides a certain theoretical reference for the further improvement of long-term glacier projection.
Hisashi Sato
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-106, https://doi.org/10.5194/bg-2023-106, 2024
Preprint withdrawn
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Modelling potential natural biome distribution is one of the most classical issues in biogeoscience. This study shows how accurate models can be constructed without simplifying climate data by employing machine-learning techniques. While extreme climate data enhance predictions, their inclusion can significantly reduce model reliability. With the convolutional neural network algorithm emerging as the preferred choice, this research paves the way for more robust global-scale biome predictions.
Danielle M. Barna, Kolbjørn Engeland, Thomas Kneib, Thordis L. Thorarinsdottir, and Chong-Yu Xu
EGUsphere, https://doi.org/10.5194/egusphere-2023-2335, https://doi.org/10.5194/egusphere-2023-2335, 2023
Preprint archived
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Estimating flood quantiles at data-scarce sites often involves single-duration regression models. However, floodplain management and reservoir design, for example, need estimates at several durations, posing challenges. Our flexible generalized additive model (GAM) enhances accuracy and explanation, revealing that single-duration models may underperform elsewhere, emphasizing the need for adaptable approaches.
Pengxiang Wang, Zuhao Zhou, Jiajia Liu, Chongyu Xu, Kang Wang, Yangli Liu, Jia Li, Yuqing Li, Yangwen Jia, and Hao Wang
Hydrol. Earth Syst. Sci., 27, 2681–2701, https://doi.org/10.5194/hess-27-2681-2023, https://doi.org/10.5194/hess-27-2681-2023, 2023
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Considering the impact of the special geological and climatic conditions of the Qinghai–Tibet Plateau on the hydrological cycle, this study established the WEP-QTP hydrological model. The snow cover and gravel layers affected the temporal and spatial changes in frozen soil and improved the regulation of groundwater on the flow process. Ignoring he influence of special underlying surface conditions has a great impact on the hydrological forecast and water resource utilization in this area.
Jinghua Xiong, Shenglian Guo, Abhishek, Jie Chen, and Jiabo Yin
Hydrol. Earth Syst. Sci., 26, 6457–6476, https://doi.org/10.5194/hess-26-6457-2022, https://doi.org/10.5194/hess-26-6457-2022, 2022
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Although the "dry gets drier, and wet gets wetter (DDWW)" paradigm is prevalent in summarizing wetting and drying trends, we show that only 11.01 %–40.84 % of the global land confirms and 10.21 %–35.43 % contradicts the paradigm during 1985–2014 from a terrestrial water storage change perspective. Similar proportions that intensify with the increasing emission scenarios persist until the end of the 21st century. Findings benefit understanding of global hydrological responses to climate change.
Wei Li, Jie Chen, Lu Li, Yvan J. Orsolini, Yiheng Xiang, Retish Senan, and Patricia de Rosnay
The Cryosphere, 16, 4985–5000, https://doi.org/10.5194/tc-16-4985-2022, https://doi.org/10.5194/tc-16-4985-2022, 2022
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Snow assimilation over the Tibetan Plateau (TP) may influence seasonal forecasts over this region. To investigate the impacts of snow assimilation on the seasonal forecasts of snow, temperature and precipitation, twin ensemble reforecasts are initialized with and without snow assimilation above 1500 m altitude over the TP for spring and summer in 2018. The results show that snow assimilation can improve seasonal forecasts over the TP through the interaction between land and atmosphere.
Xiongpeng Tang, Guobin Fu, Silong Zhang, Chao Gao, Guoqing Wang, Zhenxin Bao, Yanli Liu, Cuishan Liu, and Junliang Jin
Hydrol. Earth Syst. Sci., 26, 5315–5339, https://doi.org/10.5194/hess-26-5315-2022, https://doi.org/10.5194/hess-26-5315-2022, 2022
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In this study, we proposed a new framework that considered the uncertainties of model simulations in quantifying the contribution rate of climate change and human activities to streamflow changes. Then, the Lancang River basin was selected for the case study. The results of quantitative analysis using the new framework showed that the reason for the decrease in the streamflow at Yunjinghong station was mainly human activities.
Kang Xie, Pan Liu, Qian Xia, Xiao Li, Weibo Liu, Xiaojing Zhang, Lei Cheng, Guoqing Wang, and Jianyun Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-217, https://doi.org/10.5194/essd-2022-217, 2022
Revised manuscript not accepted
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There are currently no available common datasets of the Soil moisture storage capacity (SMSC) on a global scale, especially for hydrological models. Here, we produce a dataset of the SMSC parameter for global hydrological models. The global SMSC is constructed based on the deep residual network at 0.5° resolution. SMSC products are validated on global grids and typical catchments from different climatic regions.
Hisashi Sato and Takeshi Ise
Geosci. Model Dev., 15, 3121–3132, https://doi.org/10.5194/gmd-15-3121-2022, https://doi.org/10.5194/gmd-15-3121-2022, 2022
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Accurately predicting global coverage of terrestrial biome is one of the earliest ecological concerns, and many empirical schemes have been proposed to characterize their relationship. Here, we demonstrate an accurate and practical method to construct empirical models for operational biome mapping via a convolutional neural network (CNN) approach.
Jiacheng Chen, Jie Chen, Xunchang John Zhang, Peiyi Peng, and Camille Risi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-460, https://doi.org/10.5194/essd-2021-460, 2022
Manuscript not accepted for further review
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To make full use of the advantages of isotope observations and simulations, this study generates a new dataset by integrating multi-GCM data based on data fusion and bias correction methods. This dataset contains monthly δ18Op over mainland China for the 1870–2017 period with a spatial resolution of 50–60 km. The built isoscape shows similar spatial and temporal distribution characteristics to observations, which is reliable and useful to extend the time and space of observations in China.
Jinghua Xiong, Shenglian Guo, Jie Chen, and Jiabo Yin
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-645, https://doi.org/10.5194/hess-2021-645, 2022
Manuscript not accepted for further review
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Although the “dry gets drier and wet gets wetter” (DDWW) paradigm is widely used to describe the trends in wetting and drying globally, we show that 27.1 % of global land agrees with the paradigm, while 22.4 % shows the opposite pattern during the period 1985–2014 from the perspective of terrestrial water storage change. Similar percentages are discovered under different scenarios during the future period. Our findings will benefit the understanding of hydrological responses under climate change.
Pengxiang Wang, Zuhao Zhou, Jiajia Liu, Chongyu Xu, Kang Wang, Yangli Liu, Jia Li, Yuqing Li, Yangwen Jia, and Hao Wang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-538, https://doi.org/10.5194/hess-2021-538, 2021
Manuscript not accepted for further review
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Combining the geological characteristics of the thin soil layer on the thick gravel layer and the climate characteristics of the long-term snow cover of the Qinghai-Tibet Plateau, the WEP-QTP hydrological model was constructed by dividing a single soil structure into soil and gravel. In contrast to the general cold area, the special environment of the Qinghai–Tibet Plateau affects the hydrothermal transport process, which can not be ignored in hydrological forecast and water resource assessment.
Qifen Yuan, Thordis L. Thorarinsdottir, Stein Beldring, Wai Kwok Wong, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 25, 5259–5275, https://doi.org/10.5194/hess-25-5259-2021, https://doi.org/10.5194/hess-25-5259-2021, 2021
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Localized impacts of changing precipitation patterns on surface hydrology are often assessed at a high spatial resolution. Here we introduce a stochastic method that efficiently generates gridded daily precipitation in a future climate. The method works out a stochastic model that can describe a high-resolution data product in a reference period and form a realistic precipitation generator under a projected future climate. A case study of nine catchments in Norway shows that it works well.
Wei Li, Lu Li, Jie Chen, Qian Lin, and Hua Chen
Hydrol. Earth Syst. Sci., 25, 4531–4548, https://doi.org/10.5194/hess-25-4531-2021, https://doi.org/10.5194/hess-25-4531-2021, 2021
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Reforestation can influence climate, but the sensitivity of summer rainfall to reforestation is rarely investigated. We take two reforestation scenarios to assess the impacts of reforestation on summer rainfall under different reforestation proportions and explore the potential mechanisms. This study concludes that reforestation increases summer rainfall amount and extremes through thermodynamics processes, and the effects are more pronounced in populated areas than over the whole basin.
Tian Lan, Kairong Lin, Chong-Yu Xu, Zhiyong Liu, and Huayang Cai
Hydrol. Earth Syst. Sci., 24, 5859–5874, https://doi.org/10.5194/hess-24-5859-2020, https://doi.org/10.5194/hess-24-5859-2020, 2020
Cited articles
Ahlstrom, A., Raupach, M. R., Schurgers, G., Smith, B., Arneth, A., Jung,
M., Reichstein, M., Canadell, J. G., Friedlingstein, P., Jain, A. K., Kato,
E., Poulter, B., Sitch, S., Stocker, B. D., Viovy, N., Wang, Y. P.,
Wiltshire, A., Zaehle, S., and Zeng, N.: The dominant role of semi-arid
ecosystems in the trend and variability of the land CO2 sink, Science, 348,
895–899, https://doi.org/10.1126/science.aaa1668, 2015.
Ajtay, G. L., Ketner, P., and Duvigneaud, P.: Terrestrial primary production and phytomass, in: The Global Carbon Cycle, edited by: Bolin, B., Degens, E. T., Kempe, S., and Ketner, P., New York, USA, John Wiley & Sons, 129–181, https://www.osti.gov/biblio/6540487 (last access: 7 September 2022), 1979.
Bartholome, E. and Belward, A. S.: GLC2000: a new approach to global land
cover mapping from Earth observation data, Int. J. Remote Sens., 26, 1959–1977, https://doi.org/10.1080/01431160412331291297, 2005.
Bayer, A. D., Pugh, T. A. M., Krause, A., and Arneth, A.: Historical and
future quantification of terrestrial carbon sequestration from a
Greenhouse-Gas-Value perspective, Global Environ. Chang., 32, 153–164, https://doi.org/10.1016/j.gloenvcha.2015.03.004, 2015.
Bazilevich, N. I., Rodin, L. Y., and Rozov, N. N.: Geographical Aspects of
Biological Productivity, Soviet Geograpgy Review and Translation, 5, 293–317, 1971.
Bloom, A. A., Exbrayat, J. F., van der Velde, I. R., Feng, L., and Williams,
M.: The decadal state of the terrestrial carbon cycle: Global retrievals of
terrestrial carbon allocation, pools, and residence times, P. Natl. Acad. Sci. USA, 113,
1285–1290, https://doi.org/10.1073/pnas.1515160113, 2016.
Chen, J., Ju, W., Ciais, P., Viovy, N., Liu, R. G., Liu, Y., and Lu, X. H.:
Vegetation structural change since 1981 significantly enhanced the
terrestrial carbon sink, Nat. Commun., 10, 4259, https://doi.org/10.1038/S41467-019-12257-8,
2019.
Chen, L.-P., Zhao, N.-X., Zhang, L.-H., and Gao, Y.-B.: Responses of two
dominant plant species to drought stress and defoliation in the Inner
Mongolia Steppe of China, Plant Ecol., 214, 221–229, https://doi.org/10.1007/s11258-012-0161-y, 2013.
Cheng, L., Zhang, L., Wang, Y. P., Canadell, J. G., Chiew, F. H. S.,
Beringer, J., Li, L. H., Miralles, D. G., Piao, S. L., and Zhang, Y. Q.:
Recent increases in terrestrial carbon uptake at little cost to the water
cycle, Nat. Commun., 8, 110, https://doi.org/10.1038/s41467-017-00114-5, 2017.
Dirmeyer, P., Dolman, A., and Sato, N.: The Global Soil Wetness Project: A pilot project for global land surface modeling and validation, B. Am. Meteorol. Soc., 80, 851–878, https://doi.org/10.1175/1520-0477(1999)080<0851:TPPOTG>2.0.CO;2, 1999 (data available at: http://cola.gmu.edu/gswp/, last access: 7 September 2022).
Erb, K.-H., Gaube, V., Krausmann, F., Plutzar, C., Bondeau, A., and Haberl,
H.: A comprehensive global 5min resolution land-use data set for the year
2000 consistent with national census data, J. Land Use Sci., 2,
191–224, https://doi.org/10.1080/17474230701622981, 2007.
Erb, K.-H., Gingrich, S., Krausmann, F., and Haberl, H.: Industrialization,
Fossil Fuels, and the Transformation of Land Use, J. Ind.
Ecol., 12, 686–703, https://doi.org/10.1111/j.1530-9290.2008.00076.x, 2008.
Erb, K.-H., Fetzel, T., Plutzar, C., Kastner, T., Lauk, C., Mayer, A.,
Niedertscheider, M., Körner, C., and Haberl, H.: Biomass turnover time
in terrestrial ecosystems halved by land use, Nat. Geosci., 9, 674–678, https://doi.org/10.1038/ngeo2782, 2016.
Erb, K.-H., Kastner, T., Plutzar, C., Bais, A. L. S., Carvalhais, N.,
Fetzel, T., Gingrich, S., Haberl, H., Lauk, C., Niedertscheider, M.,
Pongratz, J., Thurner, M., and Luyssaert, S.: Unexpectedly large impact of
forest management and grazing on global vegetation biomass, Nature, 553,
73–76, https://doi.org/10.1038/nature25138, 2018.
Fan, L., Wigneron, J. P., Ciais, P., Chave, J., Brandt, M., Fensholt, R.,
Saatchi, S. S., Bastos, A., Al-Yaari, A., Hufkens, K., Qin, Y. W., Xiao, X.
M., Chen, C., Myneni, R. B., Fernandez-Moran, R., Mialon, A.,
Rodriguez-Fernandez, N. J., Kerr, Y., Tian, F., and Penuelas, J.:
Satellite-observed pantropical carbon dynamics, Nat. Plants, 5, 944–951, https://doi.org/10.1038/s41477-019-0478-9, 2019.
Fang, J., Yang, Y., Ma, W., Mohammat, A., and Shen, H.: Ecosystem carbon
stocks and their changes in China's grasslands, Science China, Life
Sci., 53, 757–765, https://doi.org/10.1007/s11427-010-4029-x, 2010.
Friedlingstein, P., Joel, G., Field, C. B., and Fung, I. Y.: Toward an
allocation scheme for global terrestrial carbon models, Glob. Change Biol.,
5, 755–770, https://doi.org/10.1046/j.1365-2486.1999.00269.x, 1999.
Gentine, P., Green, J. K., Guérin, M., Humphrey, V., Seneviratne, S. I.,
Zhang, Y., and Zhou, S.: Coupling between the terrestrial carbon and water
cycles – a review, Environ. Res. Lett., 14, 083003, https://doi.org/10.1088/1748-9326/ab22d6,
2019.
Gill, R. and Jackson, R.: Global patterns of root turnover for terrestrial
ecosystems, New Phytol., 147, 13–31, https://doi.org/10.1046/j.1469-8137.2000.00681.x, 2000.
Gocic, M. and Trajkovic, S.: Analysis of changes in meteorological variables
using Mann-Kendall and Sen's slope estimator statistical tests in Serbia,
Global Planet. Change, 100, 172–182, https://doi.org/10.1016/j.gloplacha.2012.10.014,
2013.
Gulbeyaz, O., Bond-Lamberty, B., Akyurek, Z., and West, T. O.: A new
approach to evaluate the MODIS annual NPP product (MOD17A3) using forest
field data from Turkey, Int. J. Remote Sens., 39, 2560–2578, https://doi.org/10.1080/01431161.2018.1430913, 2018.
Haberl, H., Erb, K. H., and Krausmann, F.: Human Appropriation of Net
Primary Production: Patterns, Trends, and Planetary Boundaries, Annu. Rev. Env.
Resour., 39, 363–391, https://doi.org/10.1146/annurev-environ-121912-094620, 2014.
Harper, A. B., Wiltshire, A. J., Cox, P. M., Friedlingstein, P., Jones, C. D., Mercado, L. M., Sitch, S., Williams, K., and Duran-Rojas, C.: Vegetation distribution and terrestrial carbon cycle in a carbon cycle configuration of JULES4.6 with new plant functional types, Geosci. Model Dev., 11, 2857–2873, https://doi.org/10.5194/gmd-11-2857-2018, 2018.
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU
TS monthly high-resolution gridded multivariate climate dataset, Sci.
Data, 7, 109, https://doi.org/10.1038/s41597-020-0453-3, 2020 (data available at: https://crudata.uea.ac.uk/cru/data/hrg/, last access: 7 September 2022).
Hovenden, M. J., Newton, P. C., and Wills, K. E.: Seasonal not annual
rainfall determines grassland biomass response to carbon dioxide, Nature,
511, 583–586, https://doi.org/10.1038/nature13281, 2014.
Humphrey, V., Zscheischler, J., Ciais, P., Gudmundsson, L., Sitch, S., and
Seneviratne, S. I.: Sensitivity of atmospheric CO2 growth rate to observed
changes in terrestrial water storage, Nature, 560, 628–631, https://doi.org/10.1038/s41586-018-0424-4, 2018.
Humphrey, V., Berg, A., Ciais, P., Gentine, P., Jung, M., Reichstein, M.,
Seneviratne, S. I., and Frankenberg, C.: Soil moisture–atmosphere feedback
dominates land carbon uptake variability, Nature, 592, 65-69, https://doi.org/10.1038/s41586-021-03325-5, 2021.
Hurtt, G. C., Chini, L. P., Frolking, S., Betts, R. A., Feddema, J.,
Fischer, G., Fisk, J. P., Hibbard, K., Houghton, R. A., Janetos, A., Jones,
C. D., Kindermann, G., Kinoshita, T., Goldewijk, K. K., Riahi, K.,
Shevliakova, E., Smith, S., Stehfest, E., Thomson, A., Thornton, P., van
Vuuren, D. P., and Wang, Y. P.: Harmonization of land-use scenarios for the
period 1500-2100: 600 years of global gridded annual land-use transitions,
wood harvest, and resulting secondary lands, Climate Change, 109, 117–161, https://doi.org/10.1007/s10584-011-0153-2, 2011.
Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Klein Goldewijk, K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenöder, F., Jungclaus, J., Kaplan, J. O., Kennedy, J., Krisztin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pongratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Stehfest, E., Thornton, P., Tubiello, F. N., van Vuuren, D. P., and Zhang, X.: Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6, Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, 2020 (data available at: https://luh.umd.edu/, last access: 7 September 2022).
IPCC: Impacts, Adaptation and Vulnerability, Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, https://www.ipcc.ch/site/assets/uploads/2018/03/ar4_wg2_full_report.pdf (last access: 7 September 2022), 2007.
Jung, M., Reichstein, M., Schwalm, C. R., Huntingford, C., Sitch, S.,
Ahlstrom, A., Arneth, A., Camps-Valls, G., Ciais, P., Friedlingstein, P.,
Gans, F., Ichii, K., Jain, A. K., Kato, E., Papale, D., Poulter, B., Raduly,
B., Rodenbeck, C., Tramontana, G., Viovy, N., Wang, Y. P., Weber, U.,
Zaehle, S., and Zeng, N.: Compensatory water effects link yearly global land
CO2 sink changes to temperature, Nature, 541, 516–520, https://doi.org/10.1038/nature20780,
2017.
Kaplan, J. O., Krumhardt, K. M., Ellis, E. C., Ruddiman, W. F., Lemmen, C.,
and Goldewijk, K. K.: Holocene carbon emissions as a result of anthropogenic
land cover change, Holocene, 21, 775–791, https://doi.org/10.1177/0959683610386983, 2011.
Keenan, T. F., Prentice, I. C., Canadell, J. G., Williams, C. A., Wang, H.,
Raupach, M., and Collatz, G. J.: Recent pause in the growth rate of
atmospheric CO2 due to enhanced terrestrial carbon uptake, Nat. Commun., 7, 13428, https://doi.org/10.1038/Ncomms13428, 2016.
Kindermann, G. E., Mcallum, I., Fritz, S., and Obersteiner, M.: A global
forest growing stock, biomass and carbon map based on FAO statistics, Silva
Fenn., 42, 387–396, https://doi.org/10.14214/Sf.244, 2008.
Le Noë, J., Matej, S., Magerl, A., Bhan, M., Erb, K. H., and Gingrich,
S.: Modeling and empirical validation of long-term carbon sequestration in
forests (France, 1850–2015), Glob. Change Biol., 26, 2421–2434, https://doi.org/10.1111/gcb.15004, 2020.
Ma, H. Z., Mo, L. D., Crowther, T. W., Maynard, D. S., van den Hoogen, J.,
Stocker, B. D., Terrer, C., and Zohner, C. M.: The global distribution and
environmental drivers of aboveground versus belowground plant biomass, Nat.
Ecol. Evol., 5, 1110–1122, https://doi.org/10.1038/s41559-021-01485-1, 2021.
Madani, N., Parazoo, N. C., Kimball, J. S., Ballantyne, A. P., Reichle, R.
H., Maneta, M., Saatchi, S., Palmer, P. I., Liu, Z., and Tagesson, T.:
Recent Amplified Global Gross Primary Productivity Due to Temperature
Increase Is Offset by Reduced Productivity Due to Water Constraints, AGU
Adv., 2, e2020AV000180, https://doi.org/10.1029/2020AV000180, 2020.
Magerl, A., Le Noë, J., Erb, K.-H., Bhan, M., and Gingrich, S.: A
comprehensive data-based assessment of forest ecosystem carbon stocks in the
U.S. 1907–2012, Environ. Res. Lett., 14, 125015, https://doi.org/10.1088/1748-9326/ab5cb6,
2019.
McConnaughay, K. D. M. and Coleman, J. S.: Biomass allocation in plants:
ontogeny or optimality? A test along three resource gradients, Ecology, 80,
2581–2593, https://doi.org/10.1890/0012-9658(1999)080[2581:BAIPOO]2.0.CO;2, 1999.
Monteith, J. L. and Unsworth, M. H.: Principles of Environmental Physics, 4th Edn., London, https://denning.atmos.colostate.edu/readings/Monteith.and.Unsworth.4thEd.pdf (last access: 7 September 2022), 1990.
Olson, J., Watts, J., and Allison, L.: Carbon in Live Vegetation of Major
World Ecosystems, Oak Ridge National Laboratory, https://technicalreports.ornl.gov/1983/3445603599988.pdf (last access: 7 September 2022), 1983.
Olson, R., Scurlock, J., Prince, S., Zheng, D., and Johnson, K.: NPP Multi-Biome: NPP and Driver Data for Ecosystem Model-data Intercomparison, R2, ORNL DAAC [data set], Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/615, 2013.
Pan, Y. D., Birdsey, R. A., Phillips, O. L., and Jackson, R. B.: The
Structure, Distribution, and Biomass of the World's Forests, Annu. Rev. Ecol.
Evol. S., 44, 593–622, https://doi.org/10.1146/annurev-ecolsys-110512-135914, 2013.
Pan, Y. D., Birdsey, R. A., Fang, J. Y., 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. L., 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.
Piao, S. L., Friedlingstein, P., Ciais, P., Zhou, L. M., and Chen, A. P.:
Effect of climate and CO2 changes on the greening of the Northern Hemisphere
over the past two decades, Geophys. Res. Lett., 33, L23402, https://doi.org/10.1029/2006GL028205, 2006.
Piao, S. L., Wang, X., Wang, K., Li, X., Bastos, A., Canadell, J. G., Ciais,
P., Friedlingstein, P., and Sitch, S.: Interannual variation of terrestrial
carbon cycle: Issues and perspectives, Glob. Change Biol., 26, 300–318, https://doi.org/10.1111/gcb.14884, 2020.
Poorter, H.: Construction costs and payback time of biomass: a whole plant perspective, A Whole-Plant Perspective on Carbon-Nitrogen Interactions, SPB Academic Publishing, The Hague, http://science.poorter.eu/1994_Poorter_C&Nrelations.pdf (last access: 7 September 2022), 1994.
Poulter, B., Frank, D., Ciais, P., Myneni, R. B., Andela, N., Bi, J.,
Broquet, G., Canadell, J. G., Chevallier, F., Liu, Y. Y., Running, S. W.,
Sitch, S., and van der Werf, G. R.: Contribution of semi-arid ecosystems to
interannual variability of the global carbon cycle, Nature, 509, 600–603, https://doi.org/10.1038/nature13376, 2014.
Prentice, I. C., Harrison, S. P., and Bartlein, P. J.: Global vegetation and
terrestrial carbon cycle changes after the last ice age, New Phytol., 189,
988–998, https://doi.org/10.1111/j.1469-8137.2010.03620.x, 2011.
Ruesch, A. and Gibbs, H. K.: New IPCC Tier-1 global biomass carbon map for the year 2000, Oak Ridge National Laboratory, https://cdiac.ess-dive.lbl.gov/epubs/ndp/global_carbon/carbon_documentation.html (last access: 7 September 2022), 2008.
Running, S. W., Nemani, R., Glassy, J. M., and Thornton, P. E.: MODIS Daily Photosynthesis (PSN) and Annual net primary production (NPP) Product (MOD17) Algorithm Theoretical Basis Document, https://modis.gsfc.nasa.gov/data/atbd/atbd_mod16.pdf (last access: 7 September 2022), 1999 (data available at: https://lpdaac.usgs.gov/products/mod17a3hgfv006/, last access: 7 September 2022).
Ryan, M. G.: Effects of Climate Change on Plant Respiration, Ecol.
Appl., 1, 157–167, https://doi.org/10.2307/1941808, 1991.
Sato, H., Itoh, A., and Kohyama, T.: SEIB–DGVM: A new Dynamic Global
Vegetation Model using a spatially explicit individual-based approach,
Ecol. Model., 200, 279–307, https://doi.org/10.1016/j.ecolmodel.2006.09.006, 2007.
Sato, H., Kobayashi, H., Beer, C., and Fedorov, A.: Simulating interactions
between topography, permafrost, and vegetation in Siberian larch forest,
Environ. Res. Lett., 15, 095006, https://doi.org/10.1088/1748-9326/Ab9be4, 2020 (data available at: http://seib-dgvm.com/, last access: 7 September 2022).
Saugier, B., Roy, J., and Mooney, H. A.: Estimations of global terrestrial
productivity: converging toward a single number?, in: Terrestrial Global
Productivity, Academic Press, San Diego, https://www.researchgate.net/publication/284993456_Estimations_of_global_terrestrial_productivity_Converging_toward_a_single_number (last access: 7 September 2022), 2001.
Schimel, D., Stephens, B. B., and Fisher, J. B.: Effect of increasing CO2 on
the terrestrial carbon cycle, P. Natl. Acad. Sci. USA, 112, 436–441, https://doi.org/10.1073/pnas.1407302112, 2015.
Shevliakova, E., Pacala, S. W., Malyshev, S., Hurtt, G. C., Milly, P. C. D.,
Caspersen, J. P., Sentman, L. T., Fisk, J. P., Wirth, C., and Crevoisier,
C.: Carbon cycling under 300 years of land use change: Importance of the
secondary vegetation sink, Global Biogeochem. Cy., 23, GB2022, https://doi.org/10.1029/2007gb003176,
2009.
Sulla-Menasha, D. and Friedl, M.: User Guide to Collection 6 MODIS Land Cover (MCD12Q1 and MCD12C1) Product, https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf (last access: 7 September 2022), 2018 (data available at: https://ladsweb.modaps.eosdis.nasa.gov/search/order, last access: 7 September 2022).
Tei, S., Sugimoto, A., Liang, M. C., Yonenobu, H., Matsuura, Y., Osawa, A.,
Sato, H., Fujinuma, J., and Maximov, T.: Radial Growth and Physiological
Response of Coniferous Trees to Arctic Amplification, J. Geophys. Res.-Biogeo.,
122, 2786–2803, https://doi.org/10.1002/2016JG003745, 2017.
Terrer, C., Phillips, R. P., Hungate, B. A., Rosende, J., Pett-Ridge, J.,
Craig, M. E., van Groenigen, K. J., Keenan, T. F., Sulman, B. N., Stocker,
B. D., Reich, P. B., Pellegrini, A. F. A., Pendall, E., Zhang, H., Evans, R.
D., Carrillo, Y., Fisher, J. B., Van Sundert, K., Vicca, S., and Jackson, R.
B.: A trade-off between plant and soil carbon storage under elevated CO2,
Nature, 591, 599–603, https://doi.org/10.1038/s41586-021-03306-8, 2021.
Tharammal, T., Bala, G., Devaraju, N., and Nemani, R.: A review of the major
drivers of the terrestrial carbon uptake: model-based assessments,
consensus, and uncertainties, Environ. Res. Lett., 14, 093005, https://doi.org/10.1088/1748-9326/Ab3012, 2019.
Tong, S.: Code and Data Availability (SEIB_DGVM_V1), Zenodo [data set], https://doi.org/10.5281/zenodo.5811832, 2021.
Tong, X. W., Brandt, M., Yue, Y. M., Ciais, P., Jepsen, M. R., Penuelas, J.,
Wigneron, J. P., Xiao, X. M., Song, X. P., Horion, S., Rasmussen, K.,
Saatchi, S., Fan, L., Wang, K. L., Zhang, B., Chen, Z. C., Wang, Y. H., Li,
X. J., and Fensholt, R.: Forest management in southern China generates short
term extensive carbon sequestration, Nat. Commun., 11, 129, https://doi.org/10.1038/s41467-019-13798-8, 2020.
West, P. C., Gibbs, H. K., Monfreda, C., Wagner, J., Barford, C. C.,
Carpenter, S. R., and Foley, J. A.: Trading carbon for food: Global
comparison of carbon stocks vs. crop yields on agricultural land,
P. Natl. Acad. Sci. USA, 107, 19645–19648, https://doi.org/10.1073/pnas.1011078107, 2010.
Yang, Y., Fang, J., Ma, W., Guo, D., and Mohammat, A.: Large-scale pattern
of biomass partitioning across China's grasslands, Global Ecol.
Biogeogr., 19, 268-277, https://doi.org/10.1111/j.1466-8238.2009.00502.x, 2010.
Zhang, H., Song, T. Q., Wang, K. L., Yang, H., Yue, Y. M., Zeng, Z. X.,
Peng, W. X., and Zeng, F. P.: Influences of stand characteristics and
environmental factors on forest biomass and root-shoot allocation in
southwest China, Ecol. Eng., 91, 7–15, https://doi.org/10.1016/j.ecoleng.2016.01.040, 2016.
Zhu, Z. C., Piao, S. L., Myneni, R. B., Huang, M. T., Zeng, Z. Z., Canadell,
J. G., Ciais, P., Sitch, S., Friedlingstein, P., Arneth, A., Cao, C. X.,
Cheng, L., Kato, E., Koven, C., Li, Y., Lian, X., Liu, Y. W., Liu, R. G.,
Mao, J. F., Pan, Y. Z., Peng, S. S., Penuelas, J., Poulter, B., Pugh, T. A.
M., Stocker, B. D., Viovy, N., Wang, X. H., Wang, Y. P., Xiao, Z. Q., Yang,
H., Zaehle, S., and Zeng, N.: Greening of the Earth and its drivers, Nat.
Clim. Change, 6, 791–795, https://doi.org/10.1038/Nclimate3004, 2016.
Short summary
Plant carbon storage potential is central to moderate atmospheric CO2 concentration buildup and mitigation of climate change. There is an ongoing debate about the main driver of carbon storage. To reconcile this discrepancy, we use SEIB-DGVM to investigate the trend and response mechanism of carbon stock fractions among water limitation regions. Results show that the impact of CO2 and temperature on carbon stock depends on water limitation, offering a new perspective on carbon–water coupling.
Plant carbon storage potential is central to moderate atmospheric CO2 concentration buildup and...
