Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 3.480
IF3.480
IF 5-year value: 4.194
IF 5-year
4.194
CiteScore value: 6.7
CiteScore
6.7
SNIP value: 1.143
SNIP1.143
IPP value: 3.65
IPP3.65
SJR value: 1.761
SJR1.761
Scimago H <br class='widget-line-break'>index value: 118
Scimago H
index
118
h5-index value: 60
h5-index60
Preprints
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.

  29 Apr 2016

29 Apr 2016

Review status
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

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Baozhang Chen and Mingliang Che

Baozhang Chen and Mingliang Che

Viewed

Total article views: 727 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
396 294 37 727 11 36
  • HTML: 396
  • PDF: 294
  • XML: 37
  • Total: 727
  • BibTeX: 11
  • EndNote: 36
Views and downloads (calculated since 29 Apr 2016)
Cumulative views and downloads (calculated since 29 Apr 2016)

Cited

Saved

Discussed

No discussed metrics found.
Latest update: 25 Oct 2020
Publications Copernicus
Download
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...
Citation
Altmetrics