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Biogeosciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/bg-2016-165
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-2016-165
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Submitted as: research article 29 Apr 2016

Submitted as: research article | 29 Apr 2016

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This discussion paper is a preprint. It has been under review for the journal Biogeosciences (BG). The manuscript was not accepted for further review after discussion.

Improving vegetation phenological parameterization of a land surface model

Baozhang Chen1,2,3 and Mingliang Che3 Baozhang Chen and Mingliang Che
  • 1Key Laboratory of Soil and Water Conservation and Desertification Combating, Ministry of Education, Beijing Forestry University, Beijing 100083, China
  • 2China University of Mining and Technology, Xuzhou, and Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, China
  • 3State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing

Abstract. The growing degree day (GDD) model and the growing season index (GSI) model are two common approaches used in various land surface models (LSMs) for simulating phenophases. The capacity of these two models for simulating phenolphases was evaluated by coupling them to a LSM (DLM: Dynamic Land Model) and validated by observation data from the 22 selected eddy covariance flux towers representing six typical plant functional types. The main findings are threefold: (i) the simulated phenophases using DLM-GSI were much closer to the observations derived from the green chromatic coordinate data than using DLM-GDD. The start of the growing season (SGS) was estimated to be earlier by DLM-GSI and later by DLM-GDD. Meanwhile, the end of growing season (EGS) was estimated to be later by DLM-GSI and earlier by DLM-GDD; (ii) compared to the GDD model, the GSI model significantly decreased the absolute bias of the phenophases simulated by DLM for all sites. The DLM-GSI model simulated biases for SGS and EGS decreased by 48.2 % and by 39 % on average, respectively; and (iii) the accuracy of modeled GPP using the DLM-GSI model is much higher than using the DLM-GDD model for all sites. The DLM-GSI model reduced the root mean square error of simulated GPP by 8.0 % and increased the corresponding index of agreement by 7.5 %.

Baozhang Chen and Mingliang Che
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Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Baozhang Chen and Mingliang Che
Baozhang Chen and Mingliang Che
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Short summary
Most studies of phenological estimates focusing on the phenology (RSP) retrieval algorithms based on remote sensing data, however, published studies that comparing process-based phenology models are limited. In this study, we evaluated two common used phenological algorithms in a land surface model (LSM) with selected eddy covariance flux tower measurements. We concluded the growing season index algorithm has good performance and can reasonably capture vegetation phenological changes in LSMs.
Most studies of phenological estimates focusing on the phenology (RSP) retrieval algorithms...
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