• Journal of Environmental Science International
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• v.26 no.6
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• pp.729-739
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• 2017

The Weather Research and Forecasting (WRF) model and Vegetation Photosynthesis and Respiration Model (VPRM) were coupled to simulate atmospheric $CO_2$ concentrations. The performance of the WRF-VPRM to simulate regional scale $CO_2$ concentration was estimated over coastal basin areas. Either Hestia 2011(HST) or Vulcan 2002(VUL) anthropogenic $CO_2$ emission data were used in two numerical experiments for the study regions. Simulated meteorological variables were validated with ground and background $CO_2$ measurement data, and the results show that the model captured temporal variations of $CO_2$ concentration on a daily basis. $CO_2$ directional analysis revealed that the dominant $CO_2$ emission sources are located S and SW. The simulated Net Ecosystem Exchange (NEE) agreed relatively well with measured $CO_2$ fluxes at each vegetation class site, showing approximately 40% at max improvement at shrub areas.

• Journal of Environmental Science International
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• v.26 no.6
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• pp.741-750
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• 2017

A model coupling a meteorological predictive model and a vegetation photosynthesis and respiration model was used to simulate $CO_2$ concentrations over coastal basin areas, and modeling results were estimated with aircraft observations during a massive sampling campaign. Along with the flight tracks, the model captured the meteorological variables of potential temperature and wind speed with mean bias results of $0.8^{\circ}C$, and 0.2 m/s, respectively. These results were statistically robust, which allowed for further estimation of the model's performance for $CO_2$ simulations. Two high-resolution emission data sets were adopted to determine $CO_2$ concentrations, and the results show that the model underestimated by 1.8 ppm and 0.9 ppm at higher altitude over the study areas during daytime and nighttime, respectively, on average. Overall, it was concluded that the model's $CO_2$ performance was fairly good at higher altitude over the study areas during the study period.

• Korean Journal of Environmental Agriculture
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• v.13 no.3
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• pp.346-358
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• 1994

In this paper, the current knowledge on the formation of acid rain and its effect on vegetation are reviewed. The pollutants which were emitted into the air are oxidized by photochemical reaction and affect the vegetation by dry and wet deposition. Acid rain at pH 4.0 affected sensitive plants and when it was below pH 3.0, visible symptoms developed in most of the crops. The acid rain treatment at pH 2.0 decreased dry weight, leaf area and chlorophyll contents in soybean but it increased rate of photosynthesis and respiration rate. Rain treatment at pH 2.8 increased ethylene production, but it’s not a suitable indicator of sensitivity to acid rain. At pH 2.0 treatment, the contents of soluble Mn and Al were increased but the cultivated soil pH at upper layer(0-5cm) was significantly decreased. The pertubation of glandular trichome which is existed along the vein was developed at all treatment except the control(pH 6.0) and non-treatment. Histological pertubation of spiked trichome and disintegration of chloroplast were developed only on the leaves of sesame treated with SAR(simulated acid rain) of pH 2.0.

• Atmosphere
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• v.28 no.4
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• pp.393-402
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• 2018

This study investigated future changes in the Arctic permafrost features and related biogeochemical alterations under global warming. The Community Land Model (CLM) with biogeochemistry (BGC) was run for the period 2005 to 2099 with projected future climate based on the Special Report on Emissions Scenarios (SRES) A2 scenario. Under global warming, over the Arctic land except for the permafrost region, the rise in soil temperature led to an increase in soil liquid and decrease in soil ice. Also, the Arctic ground obtained carbon dioxide from the atmosphere due to the increase in photosynthesis of vegetation. On the other hand, over the permafrost region, the microbial respiration was increased due to thawing permafrost, resulting in increased carbon dioxide emissions. Methane emissions associated with total water storage have increased over most of Arctic land, especially in the permafrost region. Methane releases were predicted to be greatly increased especially near the rivers and lakes associated with an increased chance of flooding. In conclusion, at the end of $21^{st}$ century, except for permafrost region, the Arctic ground is projected to be the sink of carbon dioxide, and only permafrost region the source of carbon dioxide. This study suggests that thawing permafrost can further to accelerate global warming significantly.

• Journal of Korea Water Resources Association
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• v.56 no.11
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• pp.721-728
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• 2023

Agricultural drought is triggered by a depletion of moisture content in the soil, which hinders photosynthesis and thus increases carbon dioxide (CO₂) concentrations in the atmosphere. The aim of this study is to analyze the relationship between soil moisture (SM) and vegetation activity toward quantifying CO₂ concentration in the atmosphere. To this end, the MODerate resolution imaging spectroradiometer (MODIS), an optical multispectral sensor, was used to evaluate two regions in South Korea for validation. Vegetation activity was analyzed through MOD13A1 vegetation indices products, and MODIS gross primary productivity (GPP) product was used to calculate the CO₂ flux based on its relationship with respiration. In the case of SM, it was calculated through the method of applying apparent thermal inertia (ATI) in combination with land surface temperature and albedo. To validate the SM and CO₂ flux, flux tower data was used which are the observed measurement values for the extreme drought period of 2014 and 2015 in South Korea. These two variables were analyzed for temporal variation on flux tower data as daily time scale, and the relationship with vegetation index (VI) was synthesized and analyzed on a monthly scale. The highest correlation between SM and VI (correlation coefficient (r) = 0.82) was observed at a time lag of one month, and that between VI and CO₂ (r = 0.81) at half month. This regional study suggests a potential capability of MODIS-based SM, VI, and CO₂ flux, which can be applied to an assessment of the global view of the agricultural drought by using available satellite remote sensing products.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.5
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• pp.27-43
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• 2019

This study applied the LANDIS-II model to the forest vegetation of the study area in Yeongdong-gun, Korea to identify climate effects on ecosystems of forest vegetation. The main purpose of the study is to examine the long-term changes in forest aboveground biomass(AGB) under three different climate change scenarios; The baseline climate scenario is to maintain the current climate condition; the RCP 4.5 scenario is a stabilization scenario to employ of technologies and strategies for reducing greenhouse gas emissions; the RCP 8.5 scenario is increasing greenhouse gas emissions over time representative with 936ppm of $CO_2$ concentration by 2100. The vegetation survey and tree-ring analysis were conducted to work out the initial vegetation maps and data for operation of the LANDIS model. Six types of forest vegetation communities were found including Quercus mongolica - Pinus densiflora community, Quercus mongolica community, Pinus densiflora community, Quercus variabilis-Quercus acutissima community, Larix leptolepis afforestation and Pinus koraiensis afforestation. As for changes in total AGB under three climate change scenarios, it was found that RCP 4.5 scenario featured the highest rate of increase in AGB whereas RCP 8.5 scenario yielded the lowest rate of increase. These results suggest that moderately elevated temperatures and $CO_2$ concentrations helped the biomass flourish as photosynthesis and water use efficiency increased, but huge increase in temperature ($above+4.0^{\circ}C$) has resulted in the increased respiration with increasing temperature. Consequently, Species productivity(Biomass) of trees decrease as the temperature is elevated drastically. It has been confirmed that the dominant species in all scenarios was Quercus mongolica. Like the trends shown in the changes of total AGB, it revealed the biggest increase in the AGB of Quercus mongolica under the RCP 4.5 scenario. AGB of Quercus mongolica and Quercus variabilis decreased in the RCP 4.5 and RCP 8.5 scenarios after 2050 but have much higher growth rates of the AGB starting from 2050 under the baseline scenario. Under all scenarios, the AGB of coniferous species was eventually perished in 2100. In particular they were extinguished in early stages of the RCP 4.5 and RCP 8.5 scenarios. This is because of natural selection of communities by successions and the failure to adapt to climate change. The results of the study could be expected to be effectively utilized to predict changes of the forest ecosystems due to climate change and to be used as basic data for establishing strategies for adaptation climate changes and the management plans for forest vegetation restoration in ecological restoration fields.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.2
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• pp.95-106
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• 2010

The performance of Community Land Model version 3.5 - Dynamic Global Vegetation Model (CLM-DGVM) was evaluated through a comparison with the observation over temperate deciduous forest in Gwangneung, Korea. Influence of plant phenology, composition of plant functional type, and climate variability on carbon exchanges was also examined through sensitivity test. To get equilibrium carbon storage, the model was run for 400 years driven by the observed atmospheric data at the deciduous forest of the year 2006. We run the model for 2006 with the equilibrium carbon storage at Gwangneung forest and compared the model output with the observation. A comparison of leaf area index (LAI) between the model and observation indicated that the simulated phenology poorly represented the timing of budburst, leaf-fall, and evolution of LAI. Senescence of the phenology was delayed about four weeks and the simulated maximum LAI (of 5.8 $m^2$ $m^{-2}$) was greater than the observed value (of 4.5 $m^2$ $m^{-2}$). The overestimated LAI contributed to overestimation of both gross primary productivity (GPP) and ecosystem respiration $(R_e)$ through increased photosynthesis and foliar autotropic respiration $(R_a)$, respectively. Despite the discrepancy between the simulated and observed LAI, the simulated tree carbon storage amounts were comparable with the reported values at the site. Change in plant phenology from the simulated to the observed reduced more than six weeks of the plant growth period, resulting in the decreased amount of GPP and $R_e$. These values, however, were still higher (~10% of GPP and 40% of $R_e$) than the observed values. The effect of change in plant functional type composition (from dominant temperate deciduous forest to the coexistence of temperate deciduous and needle leaf forests) on the estimated amount of GPP and $R_e$ was marginal. The influence of climate variability on carbon storage amounts was not significant. The simulated inter-annual variation of GPP and $R_e$ from 1994 to 2003 depended on annual mean air temperature and total radiation but not on precipitation. Other deficiencies of CLM3.5-DGVM have been discussed.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.251-267
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• 2021

Remotely sensed vegetation indices (VIs) are empirically related with gross primary productivity (GPP) in various spatio-temporal scales. The uncertainties in GPP-VI relationship increase with temporal resolution. Uncertainty also exists in the eddy covariance (EC)-based estimation of GPP, arising from the partitioning of the measured net ecosystem CO2 exchange (NEE) into GPP and ecosystem respiration (RE). For two forests and two agricultural sites, we correlated the EC-derived GPP in various time scales with three different near-surface remotely sensed VIs: (1) normalized difference vegetation index (NDVI), (2) enhanced vegetation index (EVI), and (3) near infrared reflectance from vegetation (NIRv) along with NIRvP (i.e., NIRv multiplied by photosynthetically active radiation, PAR). Among the compared VIs, NIRvP showed highest correlation with half-hourly and monthly GPP at all sites. The NIRvP was used to test the reliability of GPP derived by two different NEE partitioning methods: (1) original KoFlux methods (GPP_Ori) and (2) machine-learning based method (GPP_ANN). GPP_ANN showed higher correlation with NIRvP at half-hourly time scale, but there was no difference at daily time scale. The NIRvP-GPP correlation was lower under clear sky conditions due to co-limitation of GPP by other environmental conditions such as air temperature, vapor pressure deficit and soil moisture. However, under cloudy conditions when photosynthesis is mainly limited by radiation, the use of NIRvP was more promising to test the credibility of NEE partitioning methods. Despite the necessity of further analyses, the results suggest that NIRvP can be used as the proxy of GPP at high temporal-scale. However, for the VIs-based GPP estimation with high temporal resolution to be meaningful, complex systems-based analysis methods (related to systems thinking and self-organization that goes beyond the empirical VIs-GPP relationship) should be developed.

• Journal of Ecology and Environment
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• v.42 no.4
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• pp.210-218
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• 2018

Background: Soil respiration (Rs) is a major factor of the absorption and accumulation of carbon through photosynthesis in the ecosystem carbon cycle. This directly affects the amount of net ecosystem productivity, which affects the stability and sustainability of the ecosystem. Understanding the characteristics of Rs is indispensable to scientifically understand the carbon cycle of ecosystems. It is very important to study Rs characteristics through analysis of environmental factors closely related to Rs. Rs is affected by various environmental factors, such as temperature, precipitation, soil moisture, litter supply, organic matter content, dominant plant species, and soil disturbance. This study was conducted to analyze the effects of micro-topographical differences on Rs in forest vegetation by measuring the Rs on the ridge and southern slope sites of the broadly established Quercus mongolica forest in the central Korean area. Method: Rs, Ts, and soil moisture data were collected at the southern slope and ridge of the Q. mongolica forest in the Mt. Jeombong area in order to investigate the effects of topographical differences on Rs. Rs was collected by the closed chamber method, and data collection was performed from May 2011 to October 2013, except Winter seasons from November to April or May. For collecting the raw data of Rs in the field, acrylic collars were placed at the ridge and southern slope of the forest. The accumulated surface litter and the soil organic matter content (SOMC) were measured to a 5 cm depth. Based on these data, the Rs characteristics of the slope and ridge were analyzed. Results: Rs showed a distinct seasonal variation pattern in both the ridge and southern slope sites. In addition, Rs showed a distinct seasonal variation with high and low Ts changes. The average Rs measurements for the two sites, except for the Winter periods that were not measured, were $550.1\;mg\;CO_2m^{-2}h^{-1}$ at the ridge site and $289.4\;mg\;CO_2m^{-2}h^{-1}$ at the southern slope, a difference of 52.6%. There was no significant difference in the Rs difference between slopes except for the first half of 2013, and both sites showed a tendency to increase exponentially as Ts increased. In addition, although the correlation is low, the difference in Rs between sites tended to increase as Ts increased. SMC showed a large fluctuation at the southern slope site relative to the ridge site, as while it was very low in 2013, it was high in 2011 and 2012. The accumulated litter of the soil surface and the SOMC at the depth range of 0~5 cm were $874g\;m^{-2}$ and 23.3% at the ridge site, and $396g\;m^{-2}$ and 19.9% at the southern slope site. Conclusions: In this study, Rs was measured for the ridge and southern slope sites, which have two different results where the surface litter layer is disturbed by strong winds. The southern slope site shows that the litter layer formed in autumn due to strong winds almost disappeared, and while in the ridge site, it became thick due to the transfer of litter from the southern slope site. The mean Rs was about two times higher in the ridge site compared to that in the southern slope site. The Rs difference seems to be due to the difference in the amount of litter accumulated on the soil surface. As a result, the litter layer supplied to the soil surface is disturbed due to the micro-topographical difference, as the slope and the change of the community structure due to the plant season cause heterogeneity of the litter layer development, which in turn affects SMC and Rs. Therefore, it is necessary to introduce and understand these micro-topographical features and mechanisms when quantifying and analyzing the Rs of an ecosystem.