Impacts of inadequate historical disturbance data in the early twentieth century on modeling recent carbon dynamics (1951–2010) in conterminous U.S. forests Journal Articles uri icon

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abstract

  • AbstractStand age and disturbance data have become more available in recent years and can facilitate modeling studies that integrate and quantify effects of disturbance and nondisturbance factors on carbon dynamics. Since high‐quality disturbance and forest age data to support forest dynamic modeling are lacking before 1950, we assumed dynamic equilibrium (carbon neutrality) for the starting conditions of forests with unknown historical disturbance and forest age information. The impacts of this assumption on forest carbon cycle estimation for recent decades have not been systematically examined. In this study, we tested an assumption of disequilibrium conditions for forests with unknown disturbance and age data by randomly assigning ages to them in the model initial year (1900) and analyzed uncertainties for 1951–2010 carbon dynamic simulations compared with the equilibrium assumption. Results show that with the dynamic equilibrium assumption, the total net biome productivity (NBP) of conterminous U.S. forests was 188 ± 60 Tg C yr−1 with 185 ± 56 Tg C yr−1 in living biomass and 3 ± 23 Tg C yr−1 in soil. The C release due to disturbance on average was about 68 ± 55 Tg C yr−1. The disequilibrium assumption causes annual NBP from 1951 to 2010 in conterminous U.S. forests to vary by an average of 13% with the largest impact on the soil carbon component. Uncertainties related to nondisturbance factors have relatively small impacts on NBP estimation (within 10%), while uncertainties related to disturbances cause biases in NBP of 4 to 28%. We conclude that the dynamic equilibrium assumption for the model initialization in 1900 is acceptable for simulating 1951–2010 forest carbon dynamics as long as disturbance and age data are reliable for this later period, although caution should be taken regarding the prior‐1950 simulation results because of their greater uncertainties.

authors

  • Zhang, Fangmin
  • Chen, Jing
  • Pan, Yude
  • Birdsey, Richard A
  • Shen, Shuanghe
  • Ju, Weimin
  • Dugan, Alexa J

publication date

  • March 2015