Scientia Silvae Sinicae ›› 2024, Vol. 60 ›› Issue (7): 28-39.doi: 10.11707/j.1001-7488.LYKX20230296
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Chongfan Guan1,2,3,Xiang Gao1,2,3,Zhipeng Li1,2,3,Xiaochuang Hu1,2,3,Meijun Hu1,2,3,Jinsong Zhang1,2,3,Ping Meng1,2,3,Jinfeng Cai2,Shoujia Sun1,2,3,*()
Received:
2023-07-06
Online:
2024-07-25
Published:
2024-08-19
Contact:
Shoujia Sun
E-mail:.ssj1011@163.com
CLC Number:
Chongfan Guan,Xiang Gao,Zhipeng Li,Xiaochuang Hu,Meijun Hu,Jinsong Zhang,Ping Meng,Jinfeng Cai,Shoujia Sun. Response and Prediction of Productivity and Water Use Efficiency of Pinus sylvestris var. mongolica Plantations in Western Liaoning Province to Climate Change[J]. Scientia Silvae Sinicae, 2024, 60(7): 28-39.
Table 1
Comparison of BIOME-BGC model parameter optimization"
参数 Parameters | 初始值 Initial value | 优化值 Optimization values | 优化 Optimization |
叶片和细根年周转率 Annual leaf and fine root turnover fraction (LFRT) | 0.25 | 0.039 | 是Yes |
细根与叶片碳分配比 New fine root C∶new leaf C (FRC∶LC) | 1.0 | 0.434 | 是Yes |
茎与叶片碳分配比 New stem C∶new leaf C (SC∶LC) | 2.2 | 0.01 | 是Yes |
叶片碳氮比C∶N of leaves (Cleaf∶Nleaf) | 42.0 | 100.0 | 是Yes |
细根碳氮比 C∶N of fine roots (Cfr∶Nfr) | 42.0 | 93.67 | 是Yes |
冠层截留系数 Canopy water interception coefficient (wint) | 0.041 | 0.500 | 是Yes |
冠层消光系数 Canopy light extinction coefficient (k) | 0.5 | 3.7 | 是Yes |
冠层比叶面积 Canopy average specific leaf area ( projected area basis) (SLA) | 12.0 | 0.01 | 是Yes |
阴生叶和阳生叶的比叶面积比例 Ratio of shaded SLA∶sunlit SLA (SLAshd:sun) | 2.0 | 2.0 | 是Yes |
酮糖二磷酸羧化酶中氮含量与叶氮含量 Fraction of leaf N in Rubisco (FLNR) | 0.04 | 是Yes | |
转换生长占生长季比例 Transfer growth period as fraction of growing season (TFG) | 0.3 | 否No | |
落叶时段占生长季比例 Litterfall as fraction of growing season (LFG) | 0.3 | 否No | |
活立木年周转率 Annual live wood turnover fraction (LWT) | 0.70 | 否No | |
整株植物死亡率 Annual whole-plant mortality fraction (WPM) | 0.005 | 否No | |
植物火烧死亡率 Annual fire mortality fraction (FM) | 0.000 | 否No | |
活木与木质组织碳分配比 New live wood C∶new total wood C (LWC∶TWC) | 0.1 | 否No | |
粗根与茎分配比 New croot C∶new stem C (CRC∶SC) | 0.3 | 否No | |
当前生长比例 Current growth proportion (CGP) | 0.5 | 否No | |
凋落物碳氮比 C∶N of leaf litter, after retranslocation (C∶Nlit) | 93.0 | 否No | |
活木质组织碳氮比 C∶N of live wood (Clw∶Nlw) | 50.0 | 否No | |
死木质组织碳氮比C∶N of dead wood (Cdw∶Ndw) | 729.0 | 否No | |
叶面积与投影叶面积指数比 All-sided to projected leaf arera ratio (LAIall:proj) | 2.6 | 否No | |
最大气孔导度 Maximum stomatal conductance ( projected area basis) (gsmax) | 0.003 | 否No | |
表皮层导度 Cuticular conductance ( projected area basis) (gcut) | 否No | ||
边界层导度Boundary layer conductance ( projected area basis) (gbl) | 0.08 | 否No | |
气孔开始缩小时的叶片水势 Leaf water potential:start of conductance reduction (LWPi) | ?0.6 | 否No | |
气孔完全闭合时的叶片水势 Leaf water potential:complete conductance reduction (LWPf) | ?2.3 | 否No | |
气孔开始缩小时的饱和水汽压差 Vapor pressure deficit:start of conductance reduction (VPDi) | 930.0 | 否No | |
气孔完全闭合时的饱和水汽压差 Vapor pressure deficit:complete conductance reduction (VPDf) | 否No |
Table 3
Future climate scenario settings"
情景模式 Scenarios | 年份 Years | 温度变化 Temperature change/℃ | 降水变化 Precipitation change (%) | CO2浓度 CO2 concentration/ (μmol·mol-1) |
RCP2.6 | 2011—2040 | 0.8~1.0 | 0~4 | 540 |
2040—2070 | 1.2~1.4 | 4~6 | 510 | |
2071—2100 | 1.2~1.4 | 4~8 | 490 | |
RCP4.5 | 2011—2040 | 0.8~1.0 | 0~2 | 560 |
2040—2070 | 1.8~2.0 | 4~8 | 650 | |
2071—2100 | 2.0~2.4 | 4~8 | 660 | |
RCP8.5 | 2011—2040 | 1.0~1.2 | 0~4 | 640 |
2040—2070 | 2.4~2.8 | 6~10 | 960 | |
2071—2100 | 4.5~5.0 | 15~20 |
Fig.2
The annual variation characteristics of GPP in Pinus sylvestris var. mongolica plantations observed in 2022 and simulated by the BIOME-BGC model GPPm represents gross primary productivity (GPP) after model calibration, GPPs represents GPP calculated using vorticity correlation method, and GPPq represents GPP before model calibration. R2 represents the degree to which the independent variable explains the variation of the dependent variable, RMSE represents the root mean square error."
Fig.3
Comparison chart of Pinus sylvestris var. mongolica plantation GPP between the observations in 2021 and BIOME-BGC model simulations R2 represents the degree to which the independent variable explains the variation of the dependent variable, and RMSE represents the root mean square error. GPPm represents gross primary productivity (GPP) after model calibration, GPPs represents GPP calculated using vorticity correlation method."
Fig.6
Effects of different scenarios on GPPm and iWUEm in Pinus sylvestris var. mongolica plantation D: control group, T: warming, P: rain enhancement, C: CO2 concentration doubled, TP: warming + rain enhancement, PC: rain enhancement + CO2 concentration doubled, TC: warming + CO2 concentration doubled, TPC: warming + rain enhancement + CO2 concentration doubled. The different lowercase letters on the error bars indicate significant differences at the P<0.05."
Fig.7
Changes in GPPm and iWUEm of Pinus sylvestris var. mongolica in the period (1980—2020) and in the predicted period (2020—2100) under different emission scenarios The projections are based on BIOME-BGC, taking into account the RCP 2.6 (red), RCP 4.5 (green) and RCP 8.5 (blue) emission scenarios. The black dashed line indicates the baseline. Shaded areas represent mean ± SD."
Table 4
Differences between GPP and iWUE simulated values and baseline under different climate scenarios in near future, far future and total future"
差异Difference | RCPs | 近未来均值 Mean near future | 远未来均值 Mean far future | 总未来均值 Mean total future |
ΔGPPm (%) | RCP2.6 | 2.96±1.48a | 3.44±0.24A | 3.22±1.71A |
RCP4.5 | 5.71±2.84b | 7.03±0.66 B | 6.37±3.50B | |
RCP8.5 | 6.83±3.40c | 13.07±3.11C | 9.95±6.51C | |
ΔiWUEm (%) | RCP2.6 | 2.73±11.89a | 3.32±2.55 A | 3.03±14.45A |
RCP4.5 | 4.70±20.49a | 4.49±0.93 A | 4.60±19.55A | |
RCP8.5 | 6.18±26.92a | 16.44±44.70 B | 11.31 ±71.62B |
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