Application of BIOME-BGC model to managed forests

Cienciala, Emil; Tatarinov, Fyodor

doi:10.1016/j.foreco.2006.09.086

Cited by 43 publications

(11 citation statements)

References 47 publications

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“…The model BIOME-BGC (Running and Hunt, 1993;Thornton, 1998Thornton, , 2000 is a process-based model, which operates with a daily time step and describes the dynamics of energy, water, carbon and nitrogen in a defined type of terrestrial ecosystem. Spatial variability within the simulated biome is neglected; the model operates with the pools per unit ground area.…”

Section: Biome-bgc Model Descriptionmentioning

confidence: 99%

Application of BIOME-BGC model to managed forests

Tatarinov

Cienciala

2006

Forest Ecology and Management

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Section: Biome-bgc Model Descriptionmentioning

confidence: 99%

Application of BIOME-BGC model to managed forests

Tatarinov

Cienciala

2006

Forest Ecology and Management

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“…Several studies have investigated validation schemes for prognostic biosphere models (e.g., Braswell, Sacks, Linder, & Schimel, 2005;Fox et al, 2009;Mo, Chen, Ju, & Black, 2008;Santaren, Peylin, Viovy, & Ciais, 2007;Trudinger et al, 2007), although these studies were designed to examine carbon exchange rates between the atmosphere and the biosphere, not carbon pools and growth in plant organs and soil. Cienciala and Tatarinov (2006) estimated aboveground woody biomass in managed forests using adjusted ecophysiological and nitrogen parameters. Zhao, Xiang, Peng, and Tian (2009) performed sensitivity analysis and prediction for each plant organ in a managed forest, comparing long-term fir-stand data.…”

Section: Afforestation Projects Under Market Mechanisms In the Kyotomentioning

confidence: 99%

“…In Biome-BGC, 39 ecophysiological parameters are used to represent photosynthetic production based on A and its allocation to each plant organ according to specified allocation parameters. We conducted a literature search for these parameters (e.g., Chiesi et al, 2007;Cienciala & Tatarinov, 2006;Hidy et al, 2012;Jarvis, 1976;Jarvis & McNaughton, 1986;Leuning, 1995;Pietsch et al, 2005;Warren, Livingston, & Turpin, 2004;White et al, 2000). We then arbitrarily selected seven parameters for sensitivity analysis to determine the critical parameters for variability in the carbon pools of each plant organ.…”

Section: Sensitivity Of Carbon Pool Simulations To Ecophysiologicalmentioning

confidence: 99%

Estimating carbon fixation of plant organs for afforestation monitoring using a process‐based ecosystem model and ecophysiological parameter optimization

Miyauchi

Machimura

Saito

2019

Ecology and Evolution

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Afforestation projects for mitigating CO 2 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 Eucommia ulmoides 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 ( g smax ) 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 −2 y −1 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.

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“…In general, Biome-BGC is a process-based model widely used for estimating ecosystem productivity under current and changed environmental conditions [23,[26][27][28][29][30]. Major improvements in BBGCMuSo include introducing a figURe 1.…”

Section: Biome-bgcmuso Modelmentioning

confidence: 99%