Contribution of Climate Change and Grazing on Carbon Dynamics in Central Asian Pasturelands

Li, Chaofan; Han, Qifei; Xu, Wenqiang

doi:10.3390/rs14051210

Cited by 3 publications

(3 citation statements)

References 42 publications

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“…As a first step, the application of the EMBE model was suitable for GPP simulations at this site. Based on the previously successful development of EMBE in this forest to other typical forests in China [27], further development of EMGPP for other ecological systems in China, such as the grass ecosystem, is necessary and beneficial, [95,96].…”

Section: Performance Of Emgppmentioning

confidence: 99%

An Empirical Model of Gross Primary Productivity (GPP) and Relations between GPP and Its Driving Factors, Biogenic Volatile Organic Compounds in a Subtropical Conifer Plantation in China

et al. 2023

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Measurements of net ecosystem exchange (NEE), solar global radiation, photosynthetically active radiation (PAR) and meteorological parameters were carried out on a subtropical conifer plantation in China from 2013 to 2016. These observations were used to develop and evaluate an empirical model of gross primary production (GPP) (EMGPP) with 3-factor and 2-factor models. Using a 3-factor model, the simulated hourly GPP values were consistent with observations with a relative bias of 9.96% and normalized mean square error values of 0.07 mg CO2 m−2 s−1 for the scattering factor S/Q (S and Q are diffuse and global solar radiation) < 0.5 and 15.52% and 0.15 mg CO2 m−2 s−1 for S/Q ≥ 0.5. Validations of the EMGPP for hourly, daily, monthly, and annual GPP values were carried out and showed that both 3-factor and 2-factor EMGPP models can accurately capture diurnal, seasonal and interannual variations in GPP, but most simulated GPP overestimated the observed value. When the scattering factor is not available, the 2-factor EMGPP can be used. The EMGPP using 3-factor and 2-factor models was applied to simulate GPP under all sky conditions from 2013–2016, and the estimated GPP were in reasonable agreement with the measured values and showed systematic overestimations of 31% and 29% for mean hourly GPP and 41% and 29% for annual amounts, respectively. The sensitivity test demonstrated that GPP values were more sensitive to changes in PAR than to changes in water vapor and scattering factor at low S/Q, but were more sensitive to changes in water vapor than to PAR and S/Q at high S/Q. The sensitivity test revealed some mechanisms of GPP and its related processes, including the relationships between GPP and scattering of PAR, GPP and water vapor, which were in good agreement with other observations and model studies. An empirical model based on PAR energy balance can better describe the multiple interactions between GPP and its driving factors (PAR, water vapor, S/Q). The ratio of the emissions of biogenic volatile organic compounds (BVOCs) to net ecosystem exchange clearly varied between forests in different climate zones.

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Section: Performance Of Emgppmentioning

confidence: 99%

An Empirical Model of Gross Primary Productivity (GPP) and Relations between GPP and Its Driving Factors, Biogenic Volatile Organic Compounds in a Subtropical Conifer Plantation in China

et al. 2023

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“…But limited by the operating time of satellite platforms, most satellite products have a short time span and cannot provide long‐term climate information. In comparison, reanalysis data, which generated based on model simulations and data assimilation, enjoy extensive global coverage and decades of temporal range and are preferred in climate studies (Avila‐Diaz et al, 2021; Ellis et al, 2022; Frauenfeld et al, 2005; Krikken et al, 2021; Li et al, 2022a; Storto et al, 2021; Wang et al, 2020b; Yan et al, 2020; Zhao et al, 2020; Zhou et al, 2017). However, reanalysis products with spatial resolutions of ten to several tens km are relatively coarse for regional studies and suffer from large uncertainties in highland areas with complex topography (Kim & Hong, 2012; Nicholas & Battisti, 2012), which limits their ability to provide reliable climate information for the MP at finer scales.…”

Section: Introductionmentioning

confidence: 99%

One‐kilometre monthly air temperature and precipitation product over the Mongolian Plateau for 1950–2020

Xin

Yang

Chen

et al. 2023

Intl Journal of Climatology

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The fragile ecological environment of the Mongolian Plateau (MP) is sensitive to climate change. It is necessary to fully understand the temperature and precipitation changes on the MP to ensure regional sustainable development. Most existing studies are conducted based on station observations which suffer from sparse distribution and limited spatial representativeness and cannot perfectly represent the climatic conditions of the whole region. By contrast, the long‐term, spatially continuous reanalysis products offer new opportunities for MP climate change research. However, the coarse spatial resolution and large uncertainties of reanalysis data limit their applicability to provide reliable climate information at finer scales. Within the delta downscaling framework, we downscale and correct the state‐of‐the‐art European Centre for Medium‐range Weather Forecasts ReAnalysis 5 land portion (ERA5‐Land) using a high‐resolution WorldClim reference climatology and generate a long‐term (1950–2020) 1‐km monthly air temperature and precipitation downscaled dataset. During the downscaling process, the results obtained by nearest neighbour, bilinear and bicubic interpolation methods are evaluated and compared. Air temperature result of bilinear method and precipitation result of nearest neighbour method, which have the best accuracy, are taken as the final downscaled data. The evaluation shows that the downscaled data has acceptable accuracy (overall MBE = −0.41°C, Corr = 0.997, RMSE = 1.51°C for air temperature, and MBE = 1.58 mm·month−1, Corr = 0.747, RMSE = 24.24 mm·month−1 for precipitation), and outperforms the original ERA5‐Land and widely used Climatic Research Unit (CRU) data. In addition, the downscaled data can also provide accurate and detailed temperature and precipitation change trends over the MP. With fine spatial resolution, long‐time span and good spatial continuity, the downscaled air temperature and precipitation will be a useful data source to explore climate change over the MP.

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“…Among them, the Biome-BioGeochemical Cycles (Biome-BGC) model is widely used because it is driven by traditional meteorological data [ 12 ]. The model has good scalability and can not only simulate the carbon sink changes of biological communities under different environmental factors and human management modes through structural adjustment [ 13 , 14 ], but also can be combined with remote sensing data to explore the spatiotemporal dynamics of vegetation at the regional scale [ 15 , 16 , 17 ]. However, this model was originally designed for temperate forests, and the phenological differences of different vegetation types were ignored in its structure, so the application of the model in tropical regions has been relatively rare and shown some simulation errors.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Biome-BGC for Gross Primary Production of a Rubber Plantation Ecosystem: A Case Study of Hainan Island, China

Liu

Yang

et al. 2022

IJERPH

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Accurate monitoring of forest carbon flux and its long-term response to meteorological factors is important. To accomplish this task, the model parameters need to be optimized with respect to in situ observations. In the present study, the extended Fourier amplitude sensitivity test (eFAST) method was used to optimize the sensitive ecophysiological parameters of the Biome BioGeochemical Cycles model. The model simulation was integrated from 2010 to 2020. The results showed that using the eFAST method quantitatively improved the model output. For instance, the R2 increased from 0.53 to 0.72. Moreover, the root-mean-square error was reduced from 1.62 to 1.14 gC·m−2·d−1. In addition, it was reported that the carbon flux outputs of the model were highly sensitive to various parameters, such as the canopy average specific leaf area and canopy light extinction coefficient. Moreover, long-term meteorological factor analysis showed that rainfall dominated the trend of gross primary production (GPP) of the study area, while extreme temperatures restricted the GPP. In conclusion, the eFAST method can be used in future studies. Furthermore, eFAST could be applied to other biomes in response to different climatic conditions.

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Contribution of Climate Change and Grazing on Carbon Dynamics in Central Asian Pasturelands

Cited by 3 publications

References 42 publications

An Empirical Model of Gross Primary Productivity (GPP) and Relations between GPP and Its Driving Factors, Biogenic Volatile Organic Compounds in a Subtropical Conifer Plantation in China

An Empirical Model of Gross Primary Productivity (GPP) and Relations between GPP and Its Driving Factors, Biogenic Volatile Organic Compounds in a Subtropical Conifer Plantation in China

One‐kilometre monthly air temperature and precipitation product over the Mongolian Plateau for 1950–2020

Sensitivity Analysis of Biome-BGC for Gross Primary Production of a Rubber Plantation Ecosystem: A Case Study of Hainan Island, China

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