Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
Journal topic
Volume 14, issue 23
Biogeosciences, 14, 5455-5470, 2017
https://doi.org/10.5194/bg-14-5455-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Biogeosciences, 14, 5455-5470, 2017
https://doi.org/10.5194/bg-14-5455-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 04 Dec 2017

Research article | 04 Dec 2017

Modeling impacts of climate change and grazing effects on plant biomass and soil organic carbon in the Qinghai–Tibetan grasslands

Wenjuan Zhang1,2, Feng Zhang3, Jiaguo Qi4, and Fujiang Hou1 Wenjuan Zhang et al.
  • 1State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, 730020, China
  • 2Grassland Management Administration of Qinghai Province, Xining, Qinghai, 810008, China
  • 3State Key Laboratory of Grassland Agro-ecosystems, Institute of Arid Agroecology, School of Life Sciences, Lanzhou University, Lanzhou, Gansu, 730000, China
  • 4Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48823, USA

Abstract. The Qinghai Province supports over 40% of the human population of the Qinghai–Tibetan Plateau (QTP) but occupies about 29% of its land area, and thus it plays an important role in the plateau. The dominant land cover is grassland, which has been severely degraded over the last decade due to a combination of increased human activities and climate change. Numerous studies indicate that the plateau is sensitive to recent global climate change, but the drivers and consequences of grassland ecosystem change are controversial, especially the effects of climate change and grazing patterns on the grassland biomass and soil organic carbon (SOC) storage in this region. In this study, we used the DeNitrification-DeComposition (DNDC) model and two climate change scenarios (representative concentration pathways: RCP4.5 and RCP8.5) to understand how the grassland biomass and SOC pools might respond to different grazing intensities under future climate change scenarios. More than 1400 grassland biomass sampling points and 46 SOC points were used to validate the simulated results. The simulated above-ground biomass and SOC concentrations were in good agreement with the measured data (R2 0.71 and 0.73 for above-ground biomass and SOC, respectively). The results showed that climate change may be the major factor that leads to fluctuations in the grassland biomass and SOC, and it explained 26.4 and 47.7% of biomass and SOC variation, respectively. Meanwhile, the grazing intensity explained 6.4 and 2.3% variation in biomass and SOC, respectively. The project average biomass and SOC between 2015 and 2044 was significantly smaller than past 30 years (1985–2014), and it was 191.17gCm−2, 63.44gCkg−1 and 183.62gCm−2, 63.37gCkg−1 for biomass and SOC under RCP4.5 and RCP8.5, respectively. The RCP8.5 showed the more negative effect on the biomass and SOC compared with RCP4.5. Grazing intensity had a negative relationship with biomass and positive relationship with SOC. Compared with the baseline, the biomass and SOC changed by 12.56 and −0.19% for G0, 7.23 and 0.23 for G−50, and −5.17 and 1.19% for G+50. In the future, more human activity and management practices should be coupled into the model simulation.

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Climate change disturbances are the main factor that affects the grassland on a large scale in long-term impact assessments. Here, the total grassland biomass had a negative relationship with the grazing, and the SOC had a positive relationship with the grazing intensity. The total grassland biomass and average SOC in QTP grassland were reduced significantly under the future climate change projection. The change in the biomass and SOC had significant differences in the spatial distribution.
Climate change disturbances are the main factor that affects the grassland on a large scale in...
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