Biogeosciences (Jan 2017)

Soil CO<sub>2</sub> efflux from two mountain forests in the eastern Himalayas, Bhutan: components and controls

  • N. Wangdi,
  • M. Mayer,
  • M. P. Nirola,
  • N. Zangmo,
  • K. Orong,
  • I. U. Ahmed,
  • A. Darabant,
  • R. Jandl,
  • G. Gratzer,
  • A. Schindlbacher

DOI
https://doi.org/10.5194/bg-14-99-2017
Journal volume & issue
Vol. 14, no. 1
pp. 99 – 110

Abstract

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The biogeochemistry of mountain forests in the Hindu Kush Himalaya range is poorly studied, although climate change is expected to disproportionally affect the region. We measured the soil CO2 efflux (Rs) at a high-elevation (3260 m) mixed forest and a lower-elevation (2460 m) broadleaf forest in Bhutan, eastern Himalayas, during 2015. Trenching was applied to estimate the contribution of autotrophic (Ra) and heterotrophic (Rh) soil respiration. The temperature (Q10) and the moisture sensitivities of Rh were determined under controlled laboratory conditions and were used to model Rh in the field. The higher-elevation mixed forest had a higher standing tree stock, reflected in higher soil C stocks and basal soil respiration. Annual Rs was similar between the two forest sites (14.5 ± 1.2 t C ha−1 for broadleaf; 12.8 ± 1.0 t C ha−1 for mixed). Modelled annual contribution of Rh was ∼ 65 % of Rs at both sites with a higher heterotrophic contribution during winter and lower contribution during the monsoon season. Rh, estimated from trenching, was in the range of modelled Rh but showed higher temporal variability. The measured temperature sensitivity of Rh was similar at the mixed and broadleaf forest sites (Q10 2.2–2.3) under intermediate soil moisture but decreased (Q10 1.5 at both sites) in dry soil. Rs closely followed the annual course of field soil temperature at both sites. Covariation between soil temperature and moisture (cold dry winters and warm wet summers) was likely the main cause for this close relationship. Under the prevailing weather conditions, a simple temperature-driven model was able to explain more than 90 % of the temporal variation in Rs. A longer time series and/or experimental climate manipulations are required to understand the effects of eventually occurring climate extremes such as monsoon failures.