the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluation of five models for constructing forest NPP-age relationships in China based on 3121 field survey samples
Peng Li
Jing M. Chen
Mingzhu Xu
Xudong Lin
Guirui Yu
Nianpeng He
Li Xu
Abstract. Forest net primary productivity (NPP), representing the biomass carbon gain from the atmosphere, varies significantly with forest age. Reliable forest NPP-age relationships are essential for forest carbon cycle modelling and prediction. These relationships can be derived from forest inventory or field survey data, but it is unclear which model is the most effective for simulating forest NPP variation with age. Here, we aim to establish NPP-age relationships for China’s forests based on 3121 field survey samples. Five models, including the Semi-Empirical Mathematical (SEM) function, the Second-Degree Polynomial (SDP) function, the Logarithmic (L) function, the Michaelis-Menten (M) function, and the Γ function, were compared against field data. Results of the comparison showed that the SEM and the Γ function performed much better than the other three models. SEM also outperformed the Γ function in tracking forest NPP-age curves at old ages and therefore is regarded as the best NPP-age model. The finalized forest NPP-age curves for five forest types in six regions of China can facilitate forest carbon modelling and future carbon projections in China and may also be useful for other regions.
- Preprint
(1991 KB) - Metadata XML
- BibTeX
- EndNote
Peng Li et al.
Status: open (until 29 Oct 2023)
-
RC1: 'Comment on bg-2023-137', Liming He, 07 Sep 2023
reply
Reliable NPP-age relationships are critical for carbon flux simulations and forest management. In this study, 10 NPP-age curves for different regions and forest types in China were derived based on field and satellite data. The authors also compared the performance of five different math models in deriving these relationships. The results were clearly described. The authors also compared their results to existing models in the same region and differences were interpreted. In my opinion, this study is unique in making use of the large amount of field data and satellite LAI time-series, and credible and up-to-date results provided. I found no major problems in this study. A list of minor comments is provided below for the authors’ reference.
It is my interest to see a discussion on how the CO2 fertilization could have affected in the collected datasets (i. e., biomass inventory and LAI), and correspondingly, how the trend changes in these datasets could be propagated into these derived curves. This is important because of the potentially uneven fertilization effects in different periods of time-series, compared to a scenario in the pre-industrial era. However, this might already be out of the scope of this study since a focus of this study is to compare different math models.
Specific comments:
L27: is not it GPP the largest flux (component)?
L29: a gradual decline is not always seen, especially for some mixed forests.
Table 1: turnover rate for EBF (evergreen) is “one”, is it true?
L99: LAI data in 1981-2022 were used – did you use the LAI in a specific year to calculate the corresponding Ll (age) in the NPP-age curve? LAI(age) may not be available for the earlier stage of old forest (42 yr+), did you use spatial surrogate? This could be clear in the revision.
L191: “consistent” - not sure for mixed forests
L208: “the rectangle in each line…” – no rectangles are seen.
Figure 4, panel 8 (DNF …) – for Delta_Bc (biomass increment): any interpretation for the “increase” pattern in 120-180 yrs? Slightly increasing pattern is also observed for panel 1 (EBF).
Figure 4 again: increase, stable, and decrease patterns of LAI (therefore for leave and fine-root biomass) are seen. It would be interesting to see the interpretation of these patterns (trends).
Figure 5: this figure can be replaced by a Table, with max/min numbers bolded, but this is up to the authors. Unit for RMSE needs to be added.
Table 2: either coefficient “a” has a unit, or the unit of derived total NPP needs to be indicated.
Table 3: for the “Source – This study”, are these peak-ages derived from NPP-age curves, or from measured data (Fig. 3)? It will be useful to show/interpret any differences (e.g. 39 yrs vs 50 ys for panel 10 – MF in Fig. 3?).
Citation: https://doi.org/10.5194/bg-2023-137-RC1
Peng Li et al.
Peng Li et al.
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 144 | 51 | 9 | 204 | 5 | 4 |
- HTML: 144
- PDF: 51
- XML: 9
- Total: 204
- BibTeX: 5
- EndNote: 4
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1