• KOREAN JOURNAL OF CROP SCIENCE
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• v.33 no.4
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• pp.353-359
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• 1988

Variations in photosynthetic capacities of leaves differing in thickness were explained on the basis of relationships between gas exchange and internal leaf structure. The relative importance of gas diffusion and of biochemical processes as limiting for leaf photosynthesis was also determined. Mesophyll cell surface was considered to be the limiting internal site for gas diffusion. and cell volume to be indicative of the sink capacity for CO$_2$ fixation. Increases in cell surface area were assumed to reduce proportionately mesophyll resistance to the liquid phase diffusion of CO$_2$. Increased cell volume was thought to account for a proportional increase in reaction rates for carboxylation, oxygenation. and dark respiration. This assumption was tested using chamber-grown Glycine max (L.) Merr. cv. Amsoy plants. Plants were grown under 200, 400, and 600 ${\mu}$mol photons m$\^$-2/ s$\^$-1/ of PAR to induce development of various leaf thickness. Photosynthetic CO$_2$ uptake rates were measured on the 3rd and 4th trifoliolate leaves under 1000 ${\mu}$mol photons m$\^$-2/ s$\^$-1/ of PAR and at the air temperature of 28 C. A pseudo -mechanistic photosynthesis model was modified to accommodate the concept of cell surface area as well as both cell volume and surface area. Both versions were used to simulate leaf photosynthesis. Computations based on volume and surface area showed slightly better agreement with experimental data than did those based on the surface area only. This implies that any single factor, whether it is photosynthetic model utilized in this study was suitable for relating leaf thickness to leaf productivity.

• Korean Journal of Soil Science and Fertilizer
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• v.37 no.3
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• pp.131-135
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• 2004

This study was carried out for the analysis of temperature characteristics on soil surface using soil heat flux which is one of the important parameters forming soil temperature. Soil surface temperature was estimated by using the soil temperature measured at 10 cm soil depth and the soil heat flux measured by flux plate at 5 cm soil depth. There was time lag of two hours between soil temperature and soil heat flux. Temperature changes over time showed a positive correlation with soil heat flux. Soil surface temperature was estimated by the equation using variable separation method for soil surface temperature. Arithmetic mean using temperatures measured at soil surface and 10 cm depth, and soil temperature measured at 5 cm depth were compared for accuracy of the value. To validate the regression model through this comparison, F-validation was used. Usefulness of deductive regression model was admitted because intended F-value was smaller than 0.001 and the determination coefficient was 0.968. It can be concluded that the estimated surface soil temperatures obtained by variable separation method were almost equal to the measured surface soil temperature.

• Korean Journal of Environment and Ecology
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• v.32 no.5
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• pp.469-477
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• 2018

Hanabusaya asiatica is an endemic species whose distribution is limited in the mid-eastern part of the Korean peninsula. Due to its narrow range and small population, it is necessary to protect its habitats by identifying it as Key Biodiversity Areas (KBAs) adopted by the International Union for Conservation of Nature (IUCN). In this paper, we estimated potential natural habitats for H. asiatica using maximum entropy model (MaxEnt) and identified candidate sites for KBA based on the model results. MaxEnt is a machine learning algorithm that can predict habitats for species of interest unbiasedly with presence-only data. This property is particularly useful for the study area where data collection via a field survey is unavailable. We trained MaxEnt using 38 locations of H. asiatica and 11 environmental variables that measured climate, topography, and vegetation status of the study area which encompassed all locations of the border region between South and North Korea. Results showed that the potential habitats where the occurrence probabilities of H. asiatica exceeded 0.5 were $778km^2$, and the KBA candidate area identified by taking into account existing protected areas was $1,321km^2$. Of 11 environmental variables, elevation, annual average precipitation, average precipitation in growing seasons, and the average temperature in the coldest month had impacts on habitat selection, indicating that H. asiatica prefers cool regions at a relatively high elevation. These results can be used not only for identifying KBAs but also for the reference to a protection plan for H. asiatica in preparation of Korean reunification and climate change.

• Journal of Korea Water Resources Association
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• v.56 no.11
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• pp.737-749
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• 2023

In this study, we analyzed the hydrological impacts of future climate change on Jeju Island using SSP-based climate change scenarios from 18 climate models and watershed modeling (SWAT-K). Despite discrepancies among climate models, we generally observed an increase in evapotranspiration due to rising future temperatures. Furthermore, a significant increase in runoff and recharge was noted due to increased precipitation. These increasing trends were particularly pronounced in the SSP5-8.5 scenario, and differences among GCM models became more significant in the late 21 century. When compared to the historical period (1981-2010), the projected changes for the far-future period (2071-2100) in the SSP5-8.5 scenario showed a 21.4% increase in precipitation, a 19.2% increase in evapotranspiration, a 40.9% increase in runoff, and a 16.6% increase in recharge on an annual average basis. On a monthly basis in the SSP5-8.5 scenario, precipitation was expected to increase by 24.5% in September, evapotranspiration by 34.1% in April, runoff by 58.1% in October, and recharge by 33.8% in September. To further assess projections based on extreme climate scenarios, we selected two models, CanESM5 and ACCESS-ESM1-5, which represented the maximum and minimum future precipitation forecasts, and compared the hydrological changes in the future scenarios. The results indicated that runoff and recharge rates were relatively higher in the CanESM5 model with the highest precipitation forecast, while evapotranspiration rates were higher in the ACCESS-ESM1-5 model with the lowest precipitation forecast. Based on the climate change scenarios used in this study, the overall available water resources on Jeju Island are more likely to increase. However, since results vary by season and region depending on the climate model and scenario, it is considered necessary to conduct a comprehensive analysis and develop response measures using various scenarios.

• Atmosphere
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• v.24 no.3
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• pp.303-315
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• 2014

Terrestrial ecosystem plays the important role as carbon sink in the global carbon cycle. Understanding of interactions of terrestrial carbon cycle with climate is important for better prediction of future climate change. In this paper, terrestrial carbon cycle is investigated by Hadley Centre Global Environmental Model, version 2, Carbon Cycle (HadGEM2-CC) that considers vegetation dynamics and an interactive carbon cycle with climate. The simulation for future projection is based on the three (8.5/4.5/2.6) representative concentration pathways (RCPs) from 2006 to 2100 and compared with historical land carbon uptake from 1979 to 2005. Projected changes in ecological features such as production, respiration, net ecosystem exchange and climate condition show similar pattern in three RCPs, while the response amplitude in each RCPs are different. For all RCP scenarios, temperature and precipitation increase with rising of the atmospheric $CO_2$. Such climate conditions are favorable for vegetation growth and extension, causing future increase of terrestrial carbon uptakes in all RCPs. At the end of 21st century, the global average of gross and net primary productions and respiration increase in all RCPs and terrestrial ecosystem remains as carbon sink. This enhancement of land $CO_2$ uptake is attributed by the vegetated area expansion, increasing LAI, and early onset of growing season. After mid-21st century, temperature rising leads to excessive increase of soil respiration than net primary production and thus the terrestrial carbon uptake begins to fall since that time. Regionally the NEE average value of East-Asia ($90^{\circ}E-140^{\circ}E$, $20^{\circ}N{\sim}60^{\circ}N$) area is bigger than that of the same latitude band. In the end-$21^{st}$ the NEE mean values in East-Asia area are $-2.09PgC\;yr^{-1}$, $-1.12PgC\;yr^{-1}$, $-0.47PgC\;yr^{-1}$ and zonal mean NEEs of the same latitude region are $-1.12PgC\;yr^{-1}$, $-0.55PgC\;yr^{-1}$, $-0.17PgC\;yr^{-1}$ for RCP 8.5, 4.5, 2.6.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.218-233
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• 2022

The long-term (1986~2020) predictability of the number of days of heat and cold damages for each growth stage of soybean is evaluated using the daily maximum and minimum temperature (Tmax and Tmin) data produced by Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF). The Predictability evaluation methods for the number of days of damages are Normalized Standard Deviations (NSD), Root Mean Square Error (RMSE), Hit Rate (HR), and Heidke Skill Score (HSS). First, we verified the simulation performance of the Tmax and Tmin, which are the variables that define the heat and cold damages of soybean. As a result, although there are some differences depending on the month starting with initial conditions from January (01RUN) to May (05RUN), the result after a systematic bias correction by the Variance Scaling method is similar to the observation compared to the bias-uncorrected one. The simulation performance for correction Tmax and Tmin from March to October is overall high in the results (ENS) averaged by applying the Simple Composite Method (SCM) from 01RUN to 05RUN. In addition, the model well simulates the regional patterns and characteristics of the number of days of heat and cold damages by according to the growth stages of soybean, compared with observations. In ENS, HR and HSS for heat damage (cold damage) of soybean have ranged from 0.45~0.75, 0.02~0.10 (0.49~0.76, -0.04~0.11) during each growth stage. In conclusion, 01RUN~05RUN and ENS of PNU CGCM-WRF Chain have the reasonable performance to predict heat and cold damages for each growth stage of soybean in South Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.4
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• pp.274-284
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• 2014

Crops and varieties are mostly affected by temperature, the amount of precipitation, and duration of sunshine. This study aims to identify the weather factors that directly influence to apple yield among the series of daily measured weather variables during growing seasons. In order to identify them, 1) a priori natural scientific knowledge with respect to the growth stage of apples and 2) pure statistical approaches to minimize bias due to the subject selection of variables are considered. Each result estimated by the Panel regression using fixed/random effect models is evaluated through suitability (i.e., Akaike information criterion and Bayesian information criterion) and predictability (i.e., mean absolute error, root mean square error, mean absolute percentage). The Panel data of apple yield and weather factors are collected from fifteen major producing areas of apples from 2006 to 2013 in Korea for the case study. The result shows that variable selection using factor analysis, which is one of the statistical approaches applied in the analysis, increases predictability and suitability most. It may imply that all the weather factors are important to predict apple yield if statistical problems, such as multicollinearity and lower degree of freedom due to too many explanatory variables used in the regression, can be controlled effectively. This may be because whole growth stages, such as germination, florescence, fruit setting, fatting, ripening, coloring, and harvesting, are affected by weather.

• Korean Journal of Environmental Biology
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• v.34 no.4
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• pp.240-245
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• 2016

This study was conducted to predict the changes of potential distribution for invasive alien plant, Amaranthus viridis in Korea. The habitats of A. viridis were roadside, bare ground, farm area, and pasture, where the interference by human was severe. We used maximum entropy modeling (MaxEnt) for analyzing the environmental influences on A. viridis distribution and projecting on two different representative concentration pathways (RCP) scenarios, RCP 4.5 and RCP 8.5. The results of our study indicated annual mean temperature, elevation and precipitation of coldest month had higher contribution for A. viridis potential distribution. Projected potential distribution of A. viridis will be increased by 110% on RCP 4.5, 470% on RCP 8.5.

• Journal of Korean Society on Water Environment
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• v.28 no.1
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• pp.137-147
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• 2012

Land surface temperature in ecohydrology is a variable that links surface structure to soil processes and yet its spatial prediction across landscapes with variable surface structure is poorly understood. And there are an insufficient number of soil temperature monitoring stations. In this study, a grid-based land surface temperature prediction model is proposed. Target sites are Andong and Namgang dam region. The proposed model is run in the following way. At first, geo-referenced site specific air temperatures are estimated using a kriging technique from data collected from 60 point weather stations. Then surface soil temperature is computed from the estimated geo-referenced site-specific air temperature and normalized difference vegetation index. After the model is calibrated with data collected from observed remote-sensed soil temperature, a soil temperature map is prepared based on the predictions of the model for each geo-referenced site. The daily and monthly simulated soil temperature shows that the proposed model is useful for reproducing observed soil temperature. Soil temperatures at 30 and 50 cm of soil depth are also well simulated.

• Atmosphere
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• v.25 no.2
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• pp.353-366
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• 2015

The impact of land and ocean initial condition on coupled general circulation model seasonal predictability is assessed in this study. The CGCM used here is Pusan National University Couple General Circulation Model (PNU CGCM). The seasonal predictability of the surface air temperature and ocean potential temperature for boreal winter are evaluated with 4 different experiments which are combinations of 2 types of land initial conditions (AMI and CMI) and 2 types of ocean initial conditions (DA and noDA). EXP1 is the experiment using climatological land initial condition and ocean initial condition to which the data assimilation technique is not applied. EXP2 is same with EXP1 but used ocean data assimilation applied ocean initial condition. EXP3 is same with EXP1 but AMIP-type land initial condition is used for this experiment. EXP4 is the experiment using the AMIP-type land initial condition and data assimilated ocean initial condition. By comparing these 4 experiments, it is revealed that the impact of data assimilated ocean initial is dominant compared to AMIP-type land initial condition for seasonal predictability of CGCM. The spatial and temporal patterns of EXP2 and EXP4 to which the data assimilation technique is applied were improved compared to the others (EXP1 and EXP3) in boreal winter 2m temperature and sea surface temperature prediction.