辽西地区樟子松人工林生产力和水分利用效率对气候变化的响应及预测

doi:10.11707/j.1001-7488.LYKX20230296

林业科学 ›› 2024, Vol. 60 ›› Issue (7): 28-39.doi: 10.11707/j.1001-7488.LYKX20230296

辽西地区樟子松人工林生产力和水分利用效率对气候变化的响应及预测

管崇帆^1,^2,³,高翔^1,^2,³,李志鹏^1,^2,³,胡晓创^1,^2,³,胡美均^1,^2,³,张劲松^1,^2,³,孟平^1,^2,³,蔡金峰²,孙守家^1,^2,^3,*()

1. 中国林业科学研究院林业研究所　国家林草局林木培育重点实验室　北京 100091
2. 南京林业大学　南方现代林业协同创新中心　南京 210037
3. 河南黄河小浪底关键带国家野外科学观测研究站　济源454650

收稿日期:2023-07-06 出版日期:2024-07-25 发布日期:2024-08-19
通讯作者: 孙守家 E-mail:.ssj1011@163.com
基金资助:
国家重点研发计划项目（2020YFA0608101）；中央级公益性科研院所基本科研业务费专项资金（CAFYBB2022ZA00102）。

Response and Prediction of Productivity and Water Use Efficiency of Pinus sylvestris var. mongolica Plantations in Western Liaoning Province to Climate Change

Chongfan Guan^1,^2,³,Xiang Gao^1,^2,³,Zhipeng Li^1,^2,³,Xiaochuang Hu^1,^2,³,Meijun Hu^1,^2,³,Jinsong Zhang^1,^2,³,Ping Meng^1,^2,³,Jinfeng Cai²,Shoujia Sun^1,^2,^3,*()

1. Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration　Research Institute of Forestry, Chinese Academy of Forestry　Beijing 100091
2. Collaborative Innovation Center of Sustainable Forestry in Southern China　Nanjing Forest University　Nanjing 210037
3. Henan Xiaolangdi Earth Critical Zone National Research Station on the Middle Yellow River　Jiyuan 454650

Received:2023-07-06 Online:2024-07-25 Published:2024-08-19
Contact: Shoujia Sun E-mail:.ssj1011@163.com

摘要/Abstract

摘要：

目的: 预测未来气候背景下辽宁西部黑水林场樟子松人工林总初级生产力（GPP）和生态系统内禀水分利用效率（iWUE）的变化趋势及差异，为樟子松人工林的可持续管理与科学经营提供科学参考。方法: 对BIOME-BGC模型中影响GPP和蒸散（ET）的参数进行敏感性分析，利用参数估计（PEST）结合涡度数据对模型进行参数调整，获得模拟生态系统总初级生产力（GPP_m）和模拟生态系统内禀水分利用效率（iWUE_m），以稳定同位素和遥感数据进行比对，预测本世纪末黑水林场樟子松GPP_m和iWUE_m对气候变化和CO₂浓度增加的响应。结果: 细根与叶片碳分配比是同时影响BIOME-BGC模型中GPP和ET的高敏感性参数，叶片和细根年周转率是中敏感性参数。对敏感性参数进行校正后，模型输出的GPP模拟值更趋近于实测值。iWUE_m与遥感生态系统内禀水分利用效率（iWUE_y）、单株内禀水分利用效率（iWUE_d）均呈显著相关（P<0.05）。在增温情景下，樟子松GPP_m升高，但在降水和CO₂增加情景下GPP_m变化不显著，不同情景下的iWUE_m与GPP_m变化相似但变幅更大。在RCP2.6、RCP4.5和RCP8.5情景下，樟子松GPP_m与基线相比呈上升趋势且差异显著，但在RCP2.6和RCP4.5情景下iWUE_m与基线差异不显著，仅在RCP8.5情景下极显著升高（P<0.01）。结论: 参数校正后的BIOME-BGC模型能够准确模拟黑水林场樟子松GPP_m和iWUE_m，未来GPP_m与iWUE_m变化趋势的差异暗示iWUE的气候变化响应机制比GPP更复杂，其不仅受气候变化影响，还可能与当地立地条件有关。

关键词: BIOME-BGC模型, 总初级生产力, 内禀水分利用效率, 气候响应, 情景模式

Abstract:

Objective: This study aims to predict the changes in gross primary productivity (GPP) and inherent water use efficiency (iWUE) of Pinus sylvestris var. mongolica plantations in Heishui forest station in western Liaoning Province under future different climatic conditions. Method: A sensitivity analysis was conducted on the parameters affecting GPP and evapotranspiration (ET) in the BIOME-BGC model. Parameter estimation (PEST) combined with eddy covariance data of GPP was used to adjust the model parameters, to obtain the simulated gross primary productivity (GPP_m) and simulated inherent water use efficiency (iWUE_m). The result of stable isotope measurement was compared with remote sensing data. The responses of GPP_m and iWUE_m of P. sylvestris var. mongolica in the Heishui forest station to climate change and increasing CO₂ concentration by the end of this century were also predicted. Result: The carbon allocation ratio between fine roots and leaves was a highly sensitive parameter affecting both GPP and ET in the BIOME-BGC model, while the turnover rates of leaves and fine roots were moderately sensitive parameters. After calibration of the sensitive parameters, the simulated values of GPP were closer to the observed values. The simulated iWUE_m was significantly correlated with the inherent water use efficiency (iWUE_y) obtained by remote sensing and the individual inherent water use efficiency (iWUE_d) with isotopic measurement (P<0.05). Under warming, GPP_m increased, but under reduced precipitation and increased CO₂, GPP_m did not change significantly. The iWUE_m showed similar trends in different scenarios, but with larger variations. Under the RCP2.6, RCP4.5, and RCP8.5 scenarios, GPP_m of P. sylvestris var. mongolica significantly increased compared to the baseline, with significant differences among scenarios. However, iWUE_m did not differ significantly from the baseline under RCP2.6 and RCP4.5, but increased significantly under RCP8.5 (P<0.01). Conclusion: The BIOME-BGC model, after calibration, can accurately simulate GPP_m and iWUE_m of P. sylvestris var. mongolica in the Heishui forest station. The differences in the future trends of GPP_m and iWUE_m suggest that the response mechanism of iWUE to climate change is more complex than that of GPP, and iWUE is not only influenced by climate change, but also by local site conditions.

Key words: BIOME-BGC model, gross primary productivity (GPP), inherent water use efficiency (iWUE), climate response, scene mode

中图分类号:

S718.45

管崇帆,高翔,李志鹏,胡晓创,胡美均,张劲松,孟平,蔡金峰,孙守家. 辽西地区樟子松人工林生产力和水分利用效率对气候变化的响应及预测[J]. 林业科学, 2024, 60(7): 28-39.

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.

图/表 11

表1

BIOME-BGC模型参数优化对照"

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 (C_leaf∶N_leaf)	42.0	100.0	是Yes
细根碳氮比 C∶N of fine roots (C_fr∶N_fr)	42.0	93.67	是Yes
冠层截留系数 Canopy water interception coefficient (w_int)	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 (SLA_shd: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	0.0011	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∶N_lit)	93.0		否No
活木质组织碳氮比 C∶N of live wood (C_lw∶N_lw)	50.0		否No
死木质组织碳氮比C∶N of dead wood (C_dw∶N_dw)	729.0		否No
叶面积与投影叶面积指数比 All-sided to projected leaf arera ratio (LAI_all:proj)	2.6		否No
最大气孔导度 Maximum stomatal conductance ( projected area basis) (g_smax)	0.003		否No
表皮层导度 Cuticular conductance ( projected area basis) (g_cut)	0.00001		否No
边界层导度Boundary layer conductance ( projected area basis) (g_bl)	0.08		否No
气孔开始缩小时的叶片水势 Leaf water potential:start of conductance reduction (LWP_i)	?0.6		否No
气孔完全闭合时的叶片水势 Leaf water potential:complete conductance reduction (LWP_f)	?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 (VPD_i)	930.0		否No
气孔完全闭合时的饱和水汽压差 Vapor pressure deficit:complete conductance reduction (VPD_f)	4100.0		否No

表1

表2

表3

图1

图2

图3

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图7

表4

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情景模式 Scenarios	温度变化 Temperature change/℃	降水变化 Precipitation change (%)	CO₂浓度变化 CO₂ concentration change
D	0	0	不加倍Not doubling
T	4	0	不加倍Not doubling
P	0	20	不加倍Not doubling
C	0	0	加倍Doubling
TP	4	20	不加倍Not doubling
PC	0	20	加倍Doubling
TC	4	0	加倍Doubling
TPC	4	20	加倍Doubling

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	1370

差异Difference	RCP_s	近未来均值 Mean near future	远未来均值 Mean far future	总未来均值 Mean total future
ΔGPP_m (%)	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
ΔiWUE_m (%)	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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