• Journal of Environmental Impact Assessment
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• v.26 no.2
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• pp.127-139
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• 2017

Measurements of net ecosystem exchange (NEE) of $CO_2$ based on the eddy covariance technique provide reasonable carbon balance estimates in response to local environmental conditions. In South Korea, the forest ecosystems cover approximately 64% of the total area, thereby strongly affecting regional carbon balances. Cultivated croplands that cover about 17% of the total area should also be considered when calculating the carbon balance of the country. In this study, our objectives were (a) to quantify the range and seasonal variation of NEE at forest ecosystems, including deciduous, coniferous, and mixed forests, and agricultural ecosystems, including rice paddies and a potato field, in South Korea and (b) to compare NEE at ten Fluxnet sites that have the same or similar ecosystems as found in South Korea. The results showed that the forest and agricultural ecosystems were carbon sinks. In Korea, NEE at the forest ecosystems varied between -31 and $-362gC/m^2/yr$, and NEE at the croplands ranged from -210 to $-248gC/m^2/growing$ season. At the deciduous forest, NEE reached low values in late spring, early summer, and early autumn, while at the coniferous forest, it reached low values in spring, early summer, and mid autumn. The young mixed forest was a much stronger carbon sink than the old-growth deciduous and coniferous forests. During each crop growing season, beet had the lowest NEE value within six crops, followed by wither wheat, maize, rice, potato, and soybean. These results will be useful for designing and applying management strategies for the reduction of $CO_2$ emissions.

• Journal of the Korean Society for Marine Environment & Energy
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• v.17 no.4
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• pp.306-317
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• 2014

This study has been executed to understand the additional and removal processes of nutrients in the Saemangeum Salt-water Lake, and discussed with other monthly-collected environmental parameters such as water temperature, salinity, dissolved oxygen, suspended solids, and Chl-a from 2008 to 2010. $NO_3$-N, TP, $PO_4$-P, and DISi showed the removal processes along with the salinity gradients at the surface water of the lake, whereas $NO_2$-N, $NH_4$-N, and Chl-a showed addition trend. In the bottom water all water quality parameters except $NO_3$-N appeared addition processes indicating evidence of continuous nutrients suppliance into the bottom layer. The mixing modelling approach revealed that the biogeochemical processes in the lake consume $NO_3$-N and consequently added $NH_4$-N and $PO_4$-P to the bottom water during the summer seasons. The $NH_4$-N and $PO_4$-P appeared strong increase at the bottom water of the river-side of the lake and strong concentration gradient difference of dissolved oxygen also appeared in the same time. DISi exhibited continuous seasonal supply from spring to summer. Internal addition of $NH_4$-N and $PO_4$-P in the river-side of the lake were much higher than the dike-side, while the increase of DISi showed similar level both the dike and river sides. The temporal distribution of benthic flux for DISi indicates that addition of nutrients in the bottom water was strongly affected by other sources, for example, submarine ground-water discharge (SGD) through bottom sediment.

• Korean Journal of Agricultural and Forest Meteorology
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• v.5 no.2
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• pp.61-69
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• 2003

Surface energy and $CO_2$ fluxes have been measured over a farmland in Haenam, Korea since July 2002. Eddy covariance technique, which is the only direct flux measurement method, was employed to quantitatively understand the interaction between the farmland ecosystem and the atmospheric boundary layer. Maintenance of eddy covariance system was the main concern during the early stage of measurement to minimize gaps and uncertainties in the dataset. Half-hourly averaged $CO_2$ concentration showed distinct diurnal and seasonal variations, which were closely related to changes in net ecosystem exchange (NEE) of $CO_2$. Daytime maximum $CO_2$ uptake was about -1.0 mg $CO_2$ m$^{-2}$ s$^{-1}$ in August whereas nighttime $CO_2$ release was up to 0.3 mg $CO_2$ m$^{-2}$ s$^{-1}$ during the summer. Both daytime $CO_2$ uptake and nighttime release decreased gradually with season. During the winter season, NEE was from near zero to 0.05 mg $CO_2$ m$^{-2}$ s$^{-1}$ . FK site was a moderate sink of atmospheric $CO_2$ until September with daily NEE of 22 g $CO_2$ m$^{-2}$ d$^{-1}$ . In October, it became a weak source of $CO_2$ with an emission rate of 2 g $CO_2$ m$^{-2}$ d$^{-1}$ . Long-term flux measurements will continue at FK site to further investigate inter-annual variability in NEE. to better understand these exchange mechanism and in-depth analysis, process-level field experiments and intensive short-term intercomparisons are also expected to be followed.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.6
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• pp.190-197
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• 2012

The purpose of this study was to evaluate thermal environment and heat budget of light thin layer green roof through an experiment in order to quantify its heat budget. Two concrete model boxes($1.2m(W){\times}1.2m(D){\times}1.0m(H)$) were constructed: One experiment box with Zoysia japonica planted on substrate depth of 10cm and one control box without any plant. Between June 6th and 7th, 2012, outside climatic conditions(air temperature, relative humidity, wind direction, wind speed), evapotranspiration, surface and ceiling temperature, heat flux, and heat budget of the boxes were measured. Daily maximum temperature of those two days was $29.4^{\circ}C$ and $30^{\circ}C$, and daily evapotranspiration was $2,686.1g/m^2$ and $3,312.8g/m^2$, respectively. It was found that evapotranspiration increased as the quantity of solar radiation increased. A surface and ceiling temperature of those two boxes was compared when outside air temperature was the greatest. and control box showed a greater temperature in both cases. Thus it was found that green roof was effective in reducing temperature. As results of heat budget analysis, heat budget of a green roof showed a greater proportion of net radiation and latent heat while heat budget of the control box showed a greater proportion of sensible heat and conduction heat. The significance of this study was to analyze heat budget of green roof temperature reduction. As substrate depth and types, species and seasonal changes may have influences on temperature reduction of green roof, further study is necessary.

• Journal of Climate Change Research
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• v.5 no.1
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• pp.71-81
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• 2014

Using the Eddy Covariance technique, we analyzed seasonal variation in net ecosystem $CO_2$ exchange (NEE) and investigated the effects of environmental factors and aboveground biomass of rice on the $CO_2$ fluxes in a rice-barley double cropping paddy field of Gimje, Korea. Quality control and gap-filling were conducted before this investigation of the effects. The results have been showed that NEE, gross primary production (GPP), and ecosystem respiration (Re) during the rice growing period were -215.6, 763.9, and $548.3g\;C\;m^{-2}$, respectively. Relation between NEE and net radiation (Rn) could be described by a quadratic equation, and about 65 % of variation in NEE was explained by changes in Rn. On the other hand, an exponential function relating Re to soil temperature accounted for approximately 43 % of variation in Re under the flooded condition of paddy field. Aboveground biomass showed significant linear relationships with NEE ($r^2=0.93$), GPP ($r^2=0.96$), and Re ($r^2=0.95$), respectively.