Miyauchi Tatsuya, Machimura Takashi, Saito Makoto
Center for Global Environmental Research National Institute for Environmental Studies Tsukuba Japan.
Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering Osaka University Osaka Japan.
Ecol Evol. 2019 Jun 26;9(14):8025-8041. doi: 10.1002/ece3.5328. eCollection 2019 Jul.
Afforestation projects for mitigating CO emissions require to monitor the carbon fixation and plant growth as key indicators. We proposed a monitoring method for predicting carbon fixation in afforestation projects, combining a process-based ecosystem model and field data and addressed the uncertainty of predicted carbon fixation and ecophysiological characteristics with plant growth. Carbon pools were simulated using the Biome-BGC model tuned by parameter optimization using measured carbon density of biomass pools on an 11-year-old plantation on Loess Plateau, China. The allocation parameters fine root carbon to leaf carbon (FRC:LC) and stem carbon to leaf carbon (SC:LC), along with specific leaf area (SLA) and maximum stomatal conductance ( ) strongly affected aboveground woody (AC) and leaf carbon (LC) density in sensitivity analysis and were selected as adjusting parameters. We assessed the uncertainty of carbon fixation and plant growth predictions by modeling three growth phases with corresponding parameters: (i) before afforestation using default parameters, (ii) early monitoring using parameters optimized with data from years 1 to 5, and (iii) updated monitoring at year 11 using parameters optimized with 11-year data. The predicted carbon fixation and optimized parameters differed in the three phases. Overall, 30-year average carbon fixation rate in plantation (AC, LC, belowground woody parts and soil pools) was ranged 0.14-0.35 kg-C m y in simulations using parameters of phases (i)-(iii). Updating parameters by periodic field surveys reduced the uncertainty and revealed changes in ecophysiological characteristics with plant growth. This monitoring method should support management of afforestation projects by carbon fixation estimation adapting to observation gap, noncommon species and variable growing conditions such as climate change, land use change.
用于减少碳排放的造林项目需要将碳固定和植物生长作为关键指标进行监测。我们提出了一种结合基于过程的生态系统模型和实地数据来预测造林项目中碳固定的监测方法，并解决了预测碳固定和植物生长过程中生态生理特征的不确定性问题。利用中国黄土高原一个11年生人工林生物量库的实测碳密度，通过参数优化对Biome - BGC模型进行校准，从而模拟碳库。在敏感性分析中，细根碳与叶碳的分配参数（FRC:LC）、茎碳与叶碳的分配参数（SC:LC），以及比叶面积（SLA）和最大气孔导度（ ）对地上木质部碳（AC）和叶碳（LC）密度有强烈影响，并被选为调整参数。我们通过对三个生长阶段使用相应参数进行建模来评估碳固定和植物生长预测的不确定性：（i）造林前使用默认参数；（ii）早期监测使用1至5年数据优化的参数；（iii）在第11年更新监测，使用11年数据优化的参数。三个阶段预测的碳固定和优化参数有所不同。总体而言，在使用阶段（i） - （iii）的参数进行模拟时，人工林30年平均碳固定率（AC、LC、地下木质部和土壤碳库）在0.14 - 0.35 kg-C m² y范围内。通过定期实地调查更新参数降低了不确定性，并揭示了随着植物生长生态生理特征的变化。这种监测方法应通过适应观测差距、非常见物种和气候变化、土地利用变化等可变生长条件的碳固定估算，来支持造林项目的管理。
