Carbon exchange characteristics and their environmental effects in the northern forest ecosystem of the Greater Khingan Mountains in China

Zhang, Xin; Zhang, Qiuliang; Shi, Shuyan; Xu, Zhihong; Jian, Yanan; Yang, Yue; Tian, Yuan; Sa, Rula; Wang, Bing; Wang, Fei

doi:10.1016/j.scitotenv.2022.156056

Cited by 2 publications

(1 citation statement)

References 57 publications

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“…Based on this technique, measurements of high-frequency NEE allow us to investigate the dynamics of gross primary productivity (GPP), as well as estimations of ecosystem water use efficiency (WUE, the ratio of GPP to ET) [10,11]. In recent decades, the Global Flux Observation Network (FLUXNET) has provided a large amount of data for the study of ecosystem water-carbon flux and its response to climate change [12], covering almost all major terrestrial ecosystems, including forests [13][14][15][16], grasslands [17][18][19], and farmlands [20][21][22][23]. Most previous studies have focused only on the water flux [20] or carbon budget [21,22,24] of single types of ecosystem separately, while ignoring the possible effects of parts outside the observational range on the ecosystem.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of Carbon and Water Fluxes over Cropland and Agroforest Ecosystems on the Southern Chinese Loess Plateau

Han,

Fang,

Bai

et al. 2024

Forests

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Studies on the spatiotemporal dynamics in ecosystem carbon and water exchanges are essential in predicting the effects of climate change on regional carbon and energy budgets. Using the eddy covariance technique, carbon and water fluxes were observed in a typical winter wheat ecosystem (WWE) and an agroforest ecosystem (AFE) in the southern Loess Plateau from 2004 to 2010. The seasonal and inter-annual variability in gross primary productivity (GPP), net ecosystem exchange (NEE), evapotranspiration (ET), and water use efficiency (WUE) were examined and the main influencing factors were identified using the Pearson correlation. The results indicate that the seasonal GPP and NEE showed a bimodal distribution in WWE, while this was unimodal in AFE. The sinusoidal function did well in the characterization of seasonal ET dynamics for both ecosystems, with the determination coefficients being 0.85 and 0.94, respectively. In WWE and AFE, the annual mean GPP were 724.33 and 723.08 g C m−2 a−1, respectively, and the corresponding ET were 392.22 and 410.02 mm a−1. However, the difference in NEE between the two ecosystems was obvious, NEE were −446.28 and −549.08 g C m−2 a−1, respectively, showing a stronger carbon sink in AFE. There were strong coupling relationships between the GPP and ET of both ecosystems; the overall slopes were 1.71 and 1.69, respectively. The seasonal trend of WUE was bimodal in WWE, with peak values of 3.94 and 3.65 g C kg−1 H2O, occurring in November and April, respectively. However, the monthly WUE in AFE had one single peak of 4.07 g C kg−1 H2O in January. Photosynthetically active radiation (PAR) and soil temperature (Ts) were most positively correlated with GPP, net radiation (Rn) and Ts were the major factors influencing ET, while vapor pressure deficit (VPD) and soil water content (SWC) were the major influencing factors for WUE. These results provide observational support for regional carbon neutrality simulations.

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Section: Introductionmentioning

confidence: 99%

Dynamics of Carbon and Water Fluxes over Cropland and Agroforest Ecosystems on the Southern Chinese Loess Plateau

Han,

Fang,

Bai

et al. 2024

Forests

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Forest Carbon Flux Simulation Using Multi-Source Data and Incorporation of Remotely Sensed Model with Process-Based Model

Zhang

Liu

et al. 2022

Remote Sensing

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Forest carbon flux is critical to climate change, and the accurate modeling of forest carbon flux is an extremely challenging task. The remote sensing model (the MODIS MOD_17 gross primary productivity (GPP) model (MOD_17)) has strong practicability and is widely used around the world. The ecological process (the Biome-BioGeochemical Cycles Multilayer Soil Module model (Biome-BGCMuSo)) model can describe most of the vegetation’s environmental and physiological processes on fine time scales. Nevertheless, complex parameters and calibrations pose challenges to the application and development of models. In this study, we optimized all the input parameters of the MOD_17 model for the calibration of the Biome-BGCMuSo model to obtain GPP with continuous spatiality. To determine the contribution of input parameters to the GPP of different forest types, an Extended Fourier Amplitude Sensitivity Test (EFAST) was performed on the Biome-BGCMuSo model firstly. Then, we selected the sample points of each forest type and its different ecological gradients (30 for each type), using the GPP simulation value of the optimized MOD_17 model corresponding to the time and space scale to calibrate the Biome-BGCMuSo model, to drive the calibrated Biome-BGCMuSo, and we simulated the different forest types’ net primary productivity (NPP). According to dendrochronological measurements, the NPP simulation results were verified on the whole regional scale. The results showed that the GPP values of different forest types were highly sensitive to C:Nleaf (C:N of leaf), SLA1 (canopy average specific leaf area in phenological phase 1), and FLNR (fraction of leaf N in Rubisco). The coefficient of determination (R2) between the simulated forest NPP and the measured NPP was 0.64, and the root-mean-square (RMSE) was 26.55 g/C/m2/year. Our study aims to reduce uncertainty in forest carbon fluxes simulated by the Biome-BGCMuSo model, providing feedback for understanding forest ecosystem carbon cycling, vegetation productivity, and climate change.

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Carbon exchange characteristics and their environmental effects in the northern forest ecosystem of the Greater Khingan Mountains in China

Cited by 2 publications

References 57 publications

Dynamics of Carbon and Water Fluxes over Cropland and Agroforest Ecosystems on the Southern Chinese Loess Plateau

Dynamics of Carbon and Water Fluxes over Cropland and Agroforest Ecosystems on the Southern Chinese Loess Plateau

Forest Carbon Flux Simulation Using Multi-Source Data and Incorporation of Remotely Sensed Model with Process-Based Model

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