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A time-stepping scheme to simulate leaf area index, phenology, and gross primary production across deciduous broadleaf forests in eastern United States

Preprint published in 2018 by Qinchuan Xin, Yongjiu Dai, Xiaoping Liu
This paper is available in a repository.
This paper is available in a repository.

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Preprint: policy unknown
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Postprint: policy unknown
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Abstract

Terrestrial plants play a key role in regulating the exchange of energy and materials between the land surface and the atmosphere. Robust terrestrial biosphere models that simulate both time series of leaf dynamics and canopy photosynthesis are required to understand the vegetation-climate interactions. This study proposes a time stepping scheme to simulate leaf area index (LAI), phenology, and gross primary production (GPP) simultaneously via only climate variables based on an ecological assumption that plants allocate leaf biomass till an environment could sustain to maximize photosynthetic reproduction. The method establishes a linear function between the steady-state LAI and the corresponding GPP, which is used to track the suitability of environmental conditions for plant photosynthesis, and applies the MOD17 algorithm to form simultaneous equations together, which can be solved numerically. To account for the time lag in plant responses of leaf allocation to environment variation, a time stepping scheme is developed to simulate the LAI time series based on the solved steady-state LAI. The simulated LAI time series is then used to derive the timing of key phenophases and simulate canopy GPP with the MOD17 algorithm. The developed method is applied to deciduous broadleaf forests in eastern United States and has found to perform well on simulating canopy LAI and GPP at the site scale as evaluated using both flux tower and satellite data. The method could also capture the spatiotemporal variation of vegetation LAI and phenology across eastern United States as compared with satellite observations. The developed time-stepping scheme provides a simplified and improved version of our previous modeling approach and forms a potential basis for regional to global applications in future studies.

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