• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.1
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• pp.73-87
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• 2018

A variety of micro meteorological variables such as air temperature, wind, solar radiation and latent heat at Gwangneung forests (conifer and broadleaved forests) and AWS (Automated Weather Station) of Pocheon urban area were used to quantify the air temperature reduction effect of forests, which is considered to be an eco-friendly solution for reducing the urban heat island intensity during summer. In June, July and August of 2016 and 2017, the average maximum air temperature differences between above and below canopy of forests, and between the forests and urban areas were $-1.9^{\circ}C$ and $-3.4^{\circ}C$ respectively, and they occurred at 17:00. However, there was no difference between conifer and broadleaved forests. The effect of air temperature reduction by the forests was positively correlated with accumulated evapotranspiration and solar radiation from 14:00 to 17:00 and showed a negative correlation with wind speed. We have developed a model to quantify the effect of air temperature reduction by forests using these variables. The nighttime air temperature reduction effect by forests was due to the generation of cold air from radiative cooling and the air temperature inversion phenomenon that occurs when the generated cold air moves down the side of mountain. The model was evaluated in Seoul by using 28 AWSs. The evaluation shows that the air temperature of each district in Seoul was negatively correlated with the area and size of the surrounding tall vegetation that drives vegetation evapotranspiration during the day. During the night, however, the size of the surrounding tall vegetation and the elevations of nearby mountains were the main influencing factors on the air temperature. Our research emphasizes the importance of the establishment and management of urban forests and the composition of wind roads from mountains for urban air temperature reduction.

• Journal of Korean Society of Forest Science
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• v.110 no.4
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• pp.504-515
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• 2021

To examine the relationship between environmental factors influencing the habitat of Aconitum austrokoreense Koidz., this study employed the MexEnt model to evaluate 21 environmental factors. Fourteen environmental factors having an AUC of at least 0.6 were found to be the age of stand, growing stock, altitude, topography, topographic wetness index, solar radiation, soil texture, mean temperature in January, mean temperature in April, mean annual temperature, mean rainfall in January, mean rainfall in August, and mean annual rainfall. Based on the response curves of the 14 descriptive factors, Aconitum austrokoreense Koidz. on the Baekun Mountain were deemed more suitable for sites at an altitude of 600 m or lower, and habitats were not significantly affected by the inclination angle. The preferred conditions were high stand density, sites close to valleys, and distribution in the northwestern direction. Under the five-age class system, the species were more likely to be observed for lower classes. The preferred solar radiation in this study was 1.2 MJ/m². The species were less likely to be observed when the topographic wetness index fell below the reference value of 4.5, and were more likely observed above 7.5 (reference of threshold). Soil analysis showed that Aconitum austrokoreense Koidz. was more likely to thrive in sandy loam than clay. Suitable conditions were a mean January temperature of - 4.4℃ to -2.5℃, mean April temperature of 8.8℃-10.0℃, and mean annual temperature of 9.6℃-11.0℃. Aconitum austrokoreense Koidz. was first observed in sites with a mean annual rainfall of 1,670- 1,720 mm, and a mean August rainfall of at least 350 mm. Therefore, sites with increasing rainfall of up to 390 mm were preferred. The area of potential habitats having distributive significance of 75% or higher was 202 ha, or 1.8% of the area covered in this study.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.146-157
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• 2019

One of the most distinctive features of the South Korean rural environment is that the variation of weather or climate is large even within a small area due to complex terrains. The Geospatial Schemes based on Topo-Climatology (GSTP) was developed to simulate such variations effectively. In the present study, we reviewed the progress of the geospatial schemes for production of farm-scale agricultural weather data. Efforts have been made to improve the GSTP since 2000s. The schemes were used to provide climate information based on the current normal year and future climate scenarios at a landscape scale. The digital climate maps for the normal year include the maps of the monthly minimum temperature, maximum temperature, precipitation, and solar radiation in the past 30 years at 30 m or 270 m spatial resolution. Based on these digital climate maps, future climate change scenario maps were also produced at the high spatial resolution. These maps have been used for climate change impact assessment at the field scale by reprocessing them and transforming them into various forms. In the 2010s, the GSTP model was used to produce information for farm-specific weather conditions and weather forecast data on a landscape scale. The microclimate models of which the GSTP model consists have been improved to provide detailed weather condition data based on daily weather observation data in recent development. Using such daily data, the Early warning service for agrometeorological hazard has been developed to provide weather forecasts in real-time by processing a digital forecast and mid-term weather forecast data (KMA) at 30 m spatial resolution. Currently, daily minimum temperature, maximum temperature, precipitation, solar radiation quantity, and the duration of sunshine are forecasted as detailed weather conditions and forecast information. Moreover, based on farm-specific past-current-future weather information, growth information for various crops and agrometeorological disaster forecasts have been produced.

• Korean Journal of Soil Science and Fertilizer
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• v.23 no.2
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• pp.87-93
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• 1990

A statistical model to predict soil temperature from the ambient meteorological factors including mean, maximum and minimum air temperatures, precipitation, wind speed and snow depth combined with Fourier time series expansion was developed with the data measured at the Suwon Meteorolical Service from 1979 to 1988. The stepwise elimination technique was used for statistical analysis. For the yearly oscillation model for soil temperature with 8 terms of Fourier expansion, the mean square error was decreased with soil depth showing 2.30 for the surface temperature, and 1.34-0.42 for 5 to 500-cm soil temperatures. The $r^2$ ranged from 0.913 to 0.988. The number of lag days of air temperature by remainder analysis was 0 day for the soil surface temperature, -1 day for 5 to 30-cm soil temperature, and -2 days for 50-cm soil temperature. The number of lag days for precipitaion, snow depth and wind speed was -1 day for the 0 to 10-cm soil temperatures, and -2 to -3 days for the 30 to 50-cm soil teperatures. For the statistical soil temperature prediction model combined with the yearly oscillation terms and meteorological factors as remainder terms considering the lag days obtained above, the mean square error was 1.64 for the soil surfac temperature, and ranged 1.34-0.42 for 5 to 500cm soil temperatures. The model test with 1978 data independent to model development resulted in good agreement with $r^2$ ranged 0.976 to 0.996. The magnitudes of coeffcicients implied that the soil depth where daily meteorological variables night affect soil temperature was 30 to 50 cm. In the models, solar radiation was not included as a independent variable ; however, in a seperated analysis on relationship between the difference(${\Delta}Tmxs$) of the maximum soil temperature and the maximum air temperature and solar radiation(Rs ; $J\;m^{-2}$) under a corn canopy showed linear relationship as $${\Delta}Tmxs=0.902+1.924{\times}10^{-3}$$ Rs for leaf area index lower than 2 $${\Delta}Tmxs=0.274+8.881{\times}10^{-4}$$ Rs for leaf area index higher than 2.

• Korean Journal of Environmental Agriculture
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• v.8 no.1
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• pp.30-36
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• 1989

To determine irrigated water temperature under the rice plant canopy, micrometeorological elements air temperature, relative humidity, water temperature, solar radiation, and the rice leaf area index the rice plant canopywere measured. Water temperature under the canopy was also estimated from these data. The results are as follows ; 1. Maximum and minimum temperatures of water in the paddy field were higher about $1-2^{\circ}C$ than those of air temperature. 2. Mean water temperature under the canopy became lower than mean air temperature when the leaf area indices were greater than 4, because of decreased light penetration rates 3. Penetration amounts of net radiation under the canopy can be estimated by an exponential equation 4. Estimated water temperatures under the canopy by a combination method model was adaptable in Suweon, a plain area, but its accuracy was lower in Jinbu, an alpine area.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.461-476
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• 2013

Carnegie-Ames-Stanford Approach (CASA) model is one of the most quick, convenient and accurate models to estimate the NPP (Net Primary Productivity) of vegetation. The purposes of this study are (1) to examine the spatial and temporal patterns of vegetation NPP of the paddy field area in Korea from 2002 to 2012, and (2) to investigate how the rice productivity responded to inter-annual NPP variability, and (3) to estimate rice yield in Korea using CASA model applied to MOderate Resolution Imaging Spectroradiometer (MODIS) products and solar radiation. MODIS products; MYD09 for NIR and SWIR bands, MYD11 for LST, MYD15 for FPAR, respectively from a NASA web site were used. Finally, (4) its applicability is to be reviewed. For those purposes, correlation coefficients (linear regression for monthly NPP and accumulated NPP with rice yield) were examined to evaluate the spatial and temporal patterns of the relations. As a result, the total accumulated NPP and Sep. NPP tend to have high correlation with rice yield. The rice yield in 2012 was estimated to be 526.93kg/10a by accumulated NPP and 520.32 kg/10a by Sep. NPP. RMSE were 9.46kg/10a and 12.93kg/10a, respectively, compared with the yield forecast of the National Statistical Office. This leads to the conclusion that NPP changes in the paddy field were well reflected rice yield in this study.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.295-304
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• 2020

The IPCC (Intergovernmental Panel on Climate Change) recommended the importance of preventive measures against extreme weather, and heat waves are one of the main themes for establishing preventive measures. In this study, we tried to analyze the heat vulnerable areas by considering not only spatial characteristics but also social characteristics. Energy consumption, popu lation density, normalized difference vegetation index, waterfront distance, solar radiation, and road distribution were examined as variables. Then, by selecting a suitable model, SLM (Spatial Lag Model), available variables were extracted. Then, based on the Fuzzy theory, the degree of vulnerability to heat waves was analyzed for each variable, and six variables were superimposed to finally derive the heat vulnerable area. The study site was selected as the Daegu area where the effects of the heat wave were high. In the case of vulnerable areas, it was confirmed that the existing urban areas are mainly distributed in Seogu, Namgu, and Dalseogu of Daegu, which are less affected by waterside and vegetation. It was confirmed that both spatial and social characteristics should be considered in policy support for reducing heat waves in Daegu.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.311-325
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• 2023

This study was conducted to analyze the habitat suitability of the major honey trees including Kalopanax septemlobus Koidz., Prunus spp., Tilia spp., and Styrax obassia Siebold & Zucc. indigenous to mountain Gariwang and Yumeong using the machine learning approach (i.e., MaxEnt model). The AUC values of the model predictions were mostly above 0.7, and the results of the response curves showed that the environmental drivers that had effects on the habitat suitability of the major honey trees were elevation, mean annual precipitation, and mean annual temperature. These results indicate that climatic drivers along the elevation gradient are the main environmental drivers in explaining the distribution patterns of the major honey trees. In addition, the results of the response curves of Prunus spp. and Styrax obassia Siebold & Zucc. differed slightly in terms of slope and mean annual solar radiation as the main environmental drivers. The results of this study will be valuable for the establishment of honey tree forests and management plans for the natural and artificial forests in South Korea, as well as for the mapping the distribution of honey trees. Further studies at different regional levels, reflecting biotic drivers, will be needed to expand the production of honey and pollen at different strata and to produce honey annually.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.404-414
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• 2023

In this study, digital climate maps with high-resolution (1km, daily) for the period of 1981 to 2020 were produced for the use as reference data within the procedures for statistical downscaling of climate change scenarios. Grid data for the six climate variables including maximum temperature, minimum temperature, precipitation, wind speed, relative humidity, solar radiation was created over Korean Peninsula using statistical downscaling model, so-called IGISRM (Improved GIS-based Regression Model), using global reanalysis data and in-situ observation. The digital climate data reflects topographical effects well in terms of representing general behaviors of observation. In terms of Correlation Coefficient, Slope of scatter plot, and Normalized Root Mean Square Error, temperature-related variables showed satisfactory performance while the other variables showed relatively lower reproducibility performance. These digital climate maps based on observation will be used to downscale future climate change scenario data as well as to get the information of gridded agricultural weather data over the whole Korean Peninsula including North Korea.

• Journal of the Korean earth science society
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• v.29 no.2
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• pp.128-139
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• 2008

Intercomparison among the three radiative transfer models (RTMs) which have been used in the studies for COMS, was carried out on the condition of aerosol-laden atmospheres. Also the role of aerosols in the atmospheric radiation budget was analyzed. The results (hereafter referred to as H15) from Halthore et al.'s study (2005) were used as a benchmark to examine the models. Aerosol Radiative Forcing (ARF) values from the three RTMs, calculated under two conditions of Aerosol Optical Thickness (AOT=0.08, 0.24), were systematically underestimated in comparison to H15 in the following shortwave components; 1) direct and diffuse irradiance at the surface, 2) diffuse upward fluxes at the surface and the top of the atmosphere, and 3) atmospheric absorbance. The ARF values for the direct and diffuse fluxes at the surface was $-10{\sim}-40Wm^{-2}$. The diffuse upward values became larger with increasing both AOT and Solar Zenith Angle (SZA). Diffuse upward/downward fluxes at the surface were more sensitive to the SZA than to the atmospheric type. The diffuse downward values increased with increasing AOT and decreasing SZA. The larger AOT led to surface cooling by exceeding the reduction of direct irradiance over the enhancement of diffuse one at the surface. The extinction of direct solar irradiance was due mainly to water vapor in tropical atmospheres, and to both ozone and water vapor in subarctic atmospheres. The effect of water vapor in the tropics was $3{\sim}4$ times larger than that of the ozone. The absorbance values from the three RTMs agree with those from H15 within ${\pm}10%$.