Preprints
https://doi.org/10.5194/essd-2021-44
https://doi.org/10.5194/essd-2021-44
20 May 2021
 | 20 May 2021
Status: this preprint was under review for the journal ESSD but the revision was not accepted.

A biomass equation dataset for common shrub species in China

Yang Wang, Wenting Xu, Zhiyao Tang, and Zongqiang Xie

Abstract. Shrub biomass equations provide an accurate, efficient and convenient method in estimating biomass of shrubland ecosystems and biomass of the shrub layer in forest ecosystems at various spatial and temporal scales. In recent decades, many shrub biomass equations have been reported mainly in journals, books and postgraduate's dissertations. However, these biomass equations are applicable for limited shrub species with respect to a large number of shrub species widely distributed in China, which severely restricted the study of terrestrial ecosystem structure and function, such as biomass, production, and carbon budge. Therefore, we firstly carried out a critical review of published literature (from 1982 to 2019) on shrub biomass equations in China, and then developed biomass equations for the dominant shrub species using a unified method based on field measurements of 738 sites in shrubland ecosystems across China. Finally, we constructed the first comprehensive biomass equation dataset for China’s common shrub species. This dataset consists of 822 biomass equations specific to 167 shrub species and has significant representativeness to the geographical, climatic and shrubland vegetation features across China. The dataset is freely available at https://doi.org/10.11922/sciencedb.00641 for noncommercial scientific applications, and this dataset fills a significant gap in woody biomass equations and provides key parameters for biomass estimation in studies on terrestrial ecosystem structure and function.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Yang Wang, Wenting Xu, Zhiyao Tang, and Zongqiang Xie

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on essd-2021-44', Dayong Fan, 06 Aug 2021
    • AC1: 'Reply on CC1', Yang Wang, 17 Oct 2021
  • CC2: 'Comment on essd-2021-44', Xiangping Wang, 07 Aug 2021
    • AC2: 'Reply on CC2', Yang Wang, 17 Oct 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on essd-2021-44', Dayong Fan, 06 Aug 2021
    • AC1: 'Reply on CC1', Yang Wang, 17 Oct 2021
  • CC2: 'Comment on essd-2021-44', Xiangping Wang, 07 Aug 2021
    • AC2: 'Reply on CC2', Yang Wang, 17 Oct 2021
Yang Wang, Wenting Xu, Zhiyao Tang, and Zongqiang Xie

Data sets

A biomass equation dataset for common shrub species in China Yang Wang, Wenting Xu, Zhiyao Tang, Zongqiang Xie https://doi.org/10.11922/sciencedb.00641

Yang Wang, Wenting Xu, Zhiyao Tang, and Zongqiang Xie

Viewed

Total article views: 1,595 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,133 376 86 1,595 60 86
  • HTML: 1,133
  • PDF: 376
  • XML: 86
  • Total: 1,595
  • BibTeX: 60
  • EndNote: 86
Views and downloads (calculated since 20 May 2021)
Cumulative views and downloads (calculated since 20 May 2021)

Viewed (geographical distribution)

Total article views: 1,510 (including HTML, PDF, and XML) Thereof 1,510 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 06 Dec 2024
Download
Short summary
A dataset consists of 822 biomass equations specific to 167 shrub species in China was developed based on field measurement and literature review. The equations featured excellent goodness-of-fit (mean value of R2 and Fitness Index are larger than 0.8) and prediction precision (mean value of slope, R2 and Relative Error of the simple linear regression between predicted and measured data are 0.96, 0.85 and −4.1%). The dataset provides key parameters for terrestrial ecosystem biomass estimation.
Altmetrics