Mixed Forest Carbon Storage Modeling with Thinning and Phenology
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Solution Overview
Problem
The Biome-BGC model is inadequate for accurately simulating carbon storage in mixed forests due to limitations in phenology modeling and insufficient consideration of eco-physiological parameters, leading to inaccuracies in carbon cycling processes, especially under management practices like thinning.
Innovation Solution
An improved Biome-BGC model is developed by enhancing the phenology module to incorporate deciduous vegetation dynamics and adding a thinning operation management module, while optimizing eco-physiological parameters using a flower pollination algorithm to better simulate carbon storage in mixed forests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the Biome-BGC model uses traditional phenology models set as evergreen or deciduous separately with weighted averaging, then the model structure is preserved, but the accuracy of carbon cycling simulation deteriorates due to inaccurate description of mixed forest carbon cycling processes
Solution Approach 1:
The phenology model is segmented into separate evergreen and deciduous components, each with its own phenological parameters and carbon cycling processes. The model calculates carbon cycling for each vegetation type independently and then aggregates the results, rather than using a single weighted average phenology model. This segmentation allows accurate representation of the distinct phenological behaviors of evergreen and deciduous trees in mixed forests.
2Ease of manufacture
If the Biome-BGC model reduces fixed values by defining leaf area index after thinning, then the model implementation is simplified, but the accuracy deteriorates because only leaf changes are considered while biomass loss of various vegetation organs is not fully accounted for
Solution Approach 1:
The thinning impact is segmented into separate components: leaf area index reduction, biomass loss of stems, branches, and roots, and carbon allocation adjustments. Each component is calculated and applied separately to the model, ensuring comprehensive representation of thinning effects on vegetation carbon cycling rather than simplifying to only leaf changes.
Solution Approach 2:
Multiple phenological and biomass parameters are changed to reflect thinning impacts, including leaf area index, vegetation biomass, carbon allocation ratios, and phenological timing. These parameter changes are applied systematically based on observed thinning effects, improving the model's ability to quantify carbon cycling under management practices.
3Ease of manufacture
If eco-physiological parameters are determined using literature data, then the parameterization process is simplified, but the accuracy deteriorates due to insufficient prior knowledge and high uncertainty of parameters for specific mixed forests
Solution Approach 1:
Field measurements of eco-physiological parameters are conducted before model calibration to establish baseline values specific to the mixed forest site. These preliminary measurements include leaf area index, biomass, and other key parameters, providing a foundation for subsequent model calibration and reducing reliance on uncertain literature data.
Solution Approach 2:
The model undergoes iterative calibration where simulated carbon cycling outputs are compared with observed field data, and eco-physiological parameters are adjusted accordingly. This feedback loop continues until the model accurately reproduces observed carbon storage and cycling patterns, ensuring high model accuracy for the specific mixed forest ecosystem.
Data Source
AI summary
A method for calculating carbon storage in a mixed forest ecosystem is provided. The method includes: acquiring basic geographic data, meteorological data, eco-physiological parameter, thinning management history data, and validation data; proposing an improved biome-biogeochemical cycles (Biome-BGC) model suitable for simulating carbon storage of a mixed forest ecosystem under management by improving a phenology module, adding a thinning operation management module, and optimizing the eco-physiological parameter, based on an existing Biome-BGC model; simulating, by taking a pine-oak mixed forest as a research object, the carbon storage based on the improved Biome-BGC model; validating the improved model; analyzing sensitivity of the eco-physiological parameter by an extended Fourier amplitude sensitivity test (EFAST) method; and selecting a highly sensitive parameter, and analyzing an effect of the highly sensitive parameter on the carbon storage by a path analysis method. The improved model exhibits good performance in calculating the carbon storage of the mixed forest.