Evaluation of forest carbon uptake in South Korea using the national flux tower network, remote sensing, and data-driven technology

Cho, Sungsik; Kang, Minseok; Ichii, Kazuhito; Kim, Joon; Lim, Jong-Hwan; Chun, Jung-Hwa; Park, Chanwoo; Choi, Seong Hee; Lee, Seung‐Hoon; Indrawati, Yohana Maria; Kim, Jong-Ho

doi:10.1016/j.agrformet.2021.108653

Cited by 20 publications

(5 citation statements)

References 119 publications

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“…Optical properties of throughfall DOM 2.7 also suggests that canopy leaching is the dominant process controlling throughfall DOC 2.7 . Both SUVA 254 and HIX were the highest in summer (Figure 3 and Table S1 in Supporting Information ), coinciding with the period of the lowest atmospheric PM 2.5 concentrations and highest biogenic activities among the seasons in South Korea (Cho et al., 2021; He et al., 2003). These findings indicate that the presence of freshly produced biomolecules could contribute to the aromatic fraction of throughfall DOM.…”

Section: Resultsmentioning

confidence: 90%

Canopy Leaching Rather than Desorption of PM_2.5 From Leaves Is the Dominant Source of Throughfall Dissolved Organic Carbon in Forest

Cha,

Lee,

Lee

et al. 2023

Geophysical Research Letters

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Dissolved organic carbon (DOC) in throughfall plays a vital role in providing carbon and energy to organisms in forests. We utilized carbon isotope analysis to allocate the sources of throughfall DOC, including leaching from plant tissues, PM2.5 deposition on plant foliage, and precipitation. Rainwater, PM2.5, and throughfall samples were collected from pine and oak forests between March and November 2021. The mean concentration of throughfall DOC was 7.9 mg L−1, approximately six times higher than that of rainfall. The mean Δ14C of throughfall DOC was −38.2‰, ∼200‰ higher than that of rainwater or PM2.5. Mass balance estimates revealed that canopy leaching contributed to ∼83% of throughfall DOC, while desorption of PM2.5 and rainwater accounted for only ∼3% and ∼14% of throughfall DOC, respectively. These results clearly highlight canopy leaching as the primary source of carbon input to the forest floor, with a relatively minor contribution from PM2.5 desorption on leaves.

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Section: Resultsmentioning

confidence: 90%

Canopy Leaching Rather than Desorption of PM_2.5 From Leaves Is the Dominant Source of Throughfall Dissolved Organic Carbon in Forest

Cha,

Lee,

Lee

et al. 2023

Geophysical Research Letters

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“…Alterations in terrestrial ecosystem carbon sinks and sources influence atmospheric CO 2 concentrations, thereby modulating the global carbon cycle. Carbon sources and sinks across diverse vegetation types and eco-climatic zones were examined using various methods, such as inventories [2][3][4][5], in situ observations [6][7][8], satellite inversions [9][10][11], spaceborne observations [12][13][14], and model simulations [15][16][17][18][19]. Under the conditions of long-term warming, the carbon assimilation capacity on the Tibetan Plateau (TP) increased [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Original and Water Stress-Incorporated Modified Weather Research and Forecasting Vegetation Photosynthesis and Respiration Model in Simulating CO2 Flux and Concentration Variability over the Tibetan Plateau

Niu,

Hu,

Shang

et al. 2023

Remote Sensing

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Terrestrial carbon fluxes are crucial to the global carbon cycle. Quantification of terrestrial carbon fluxes over the Tibetan Plateau (TP) has considerable uncertainties due to the unique ecosystem and climate and scarce flux observations. This study evaluated our recent improvement of terrestrial flux parameterization in the weather research and forecasting model coupled with the vegetation photosynthesis and respiration model (WRF-VPRM) in terms of reproducing observed net ecosystem exchange (NEE), gross ecosystem exchange (GEE), and ecosystem respiration (ER) over the TP. The improvement of VPRM relative to the officially released version considers the impact of water stress on terrestrial fluxes, making it superior to the officially released model due to its reductions in bias, root mean square error (RMSE), and ratio of standard deviation (RSD) of NEE to 0.850 μmol·m−2·s−1, 0.315 μmol·m−2·s−1, and 0.001, respectively. The improved VPRM also affects GEE simulation, increasing its RSD to 0.467 and decreasing its bias and RMSE by 1.175 and 0.324 μmol·m−2·s−1, respectively. Furthermore, bias and RMSE for ER were lowered to −0.417 and 0.954 μmol·m−2·s−1, with a corresponding increase in RSD by 0.6. The improved WRF-VPRM simulation indicates that eastward winds drive the transfer of lower CO2 concentrations from the eastern to the central and western TP and the influx of low-concentration CO2 inhibits biospheric CO2 uptake. The use of an improved WRF-VPRM in this study helps to reduce errors, improve our understanding of the role of carbon flux cycle over the TP, and ultimately reduce uncertainty in the carbon flux budget.

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“…Subsequently, considerable efforts have been made by researchers to improve such predictive models. Many studies have demonstrated the effectiveness of their proposed improvements (i.e., using predictors with a higher spatial resolution - Reitz et al, 2021 -and using data from the local flux site network - Cho et al, 2021) by comparing them with previous studies. However, the improvements achieved in these studies may be limited to smaller areas and specific conditions and may not be generalizable (Cleverly et al, 2020;Reed et al, 2021;Cho et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have demonstrated the effectiveness of their proposed improvements (i.e., using predictors with a higher spatial resolution - Reitz et al, 2021 -and using data from the local flux site network - Cho et al, 2021) by comparing them with previous studies. However, the improvements achieved in these studies may be limited to smaller areas and specific conditions and may not be generalizable (Cleverly et al, 2020;Reed et al, 2021;Cho et al, 2021). We are more interested in guidelines with universal applicability that improve model accuracy, such as the selection of appropriate predictors and algorithms under different conditions.…”

Section: Introductionmentioning

confidence: 99%

Variability and uncertainty in flux-site-scale net ecosystem exchange simulations based on machine learning and remote sensing: a systematic evaluation

et al. 2022

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Abstract. Net ecosystem exchange (NEE) is an important indicator of carbon cycling in terrestrial ecosystems. Many previous studies have combined flux observations and meteorological, biophysical, and ancillary predictors using machine learning to simulate the site-scale NEE. However, systematic evaluation of the performance of such models is limited. Therefore, we performed a meta-analysis of these NEE simulations. A total of 40 such studies and 178 model records were included. The impacts of various features throughout the modeling process on the accuracy of the model were evaluated. Random forests and support vector machines performed better than other algorithms. Models with larger timescales have lower average R2 values, especially when the timescale exceeds the monthly scale. Half-hourly models (average R2 = 0.73) were significantly more accurate than daily models (average R2 = 0.5). There are significant differences in the predictors used and their impacts on model accuracy for different plant functional types (PFTs). Studies at continental and global scales (average R2 = 0.37) with multiple PFTs, more sites, and a large span of years correspond to lower R2 values than studies at local (average R2 = 0.69) and regional (average R2 = 0.7) scales. Also, the site-scale NEE predictions need more focus on the internal heterogeneity of the NEE dataset and the matching of the training set and validation set.

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Evaluation of forest carbon uptake in South Korea using the national flux tower network, remote sensing, and data-driven technology

Cited by 20 publications

References 119 publications

Canopy Leaching Rather than Desorption of PM_2.5 From Leaves Is the Dominant Source of Throughfall Dissolved Organic Carbon in Forest

Canopy Leaching Rather than Desorption of PM_2.5 From Leaves Is the Dominant Source of Throughfall Dissolved Organic Carbon in Forest

Evaluation of Original and Water Stress-Incorporated Modified Weather Research and Forecasting Vegetation Photosynthesis and Respiration Model in Simulating CO2 Flux and Concentration Variability over the Tibetan Plateau

Variability and uncertainty in flux-site-scale net ecosystem exchange simulations based on machine learning and remote sensing: a systematic evaluation

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Evaluation of forest carbon uptake in South Korea using the national flux tower network, remote sensing, and data-driven technology

Cited by 20 publications

References 119 publications

Canopy Leaching Rather than Desorption of PM2.5 From Leaves Is the Dominant Source of Throughfall Dissolved Organic Carbon in Forest

Canopy Leaching Rather than Desorption of PM2.5 From Leaves Is the Dominant Source of Throughfall Dissolved Organic Carbon in Forest

Evaluation of Original and Water Stress-Incorporated Modified Weather Research and Forecasting Vegetation Photosynthesis and Respiration Model in Simulating CO2 Flux and Concentration Variability over the Tibetan Plateau

Variability and uncertainty in flux-site-scale net ecosystem exchange simulations based on machine learning and remote sensing: a systematic evaluation

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Canopy Leaching Rather than Desorption of PM_2.5 From Leaves Is the Dominant Source of Throughfall Dissolved Organic Carbon in Forest

Canopy Leaching Rather than Desorption of PM_2.5 From Leaves Is the Dominant Source of Throughfall Dissolved Organic Carbon in Forest