• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.4
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• pp.280-292
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• 2017

Hail is not a frequently occurring weather event, and there are even fewer reports of hail damages to forest stands. Since the 2000s, an increase in hail incidence has been documented in Europe and the United States. In Korea, severe hails occurred in Jeollanam-do province on May 31 and in Gyeongsangbuk-do province on June 1, 2017. Hail size was ranged from 0.5 to 5.0 cm in diameter in Jeollanam-do, and from 1.5 to 3.0 cm in Gyeongsangbuk-do. This study was aimed to analyze the hail damages to forests by species and topography based on damage-categorized maps created by using drones and aerial photographs, and to analyze relationships of the damages with meteorological factors. The total damaged forest area was 1,163.1ha in Jeollanam-do, and 2,942.3ha in Gyeongsangbuk-do. Among the 'severe' damaged area 326.7ha, 91% was distributed in Jeollanam-do, and concentrated in the city of Hwasun which covers 57.2% of the total 'severe' damaged area. The most heavily damaged species was Korean red pine(Pinus densiflora S. & Z.) followed by P. rigida. Most broad-leaved trees species including oaks were recovered without any dead trees found. Liliodendron tulipifera was the most severely damaged in terms of the rate of 'severe' degree individuals which are needed to be checked whether they will die or be recovered. Cause of the death of pines was considered as the combination of physical damage caused by the hail and long-lasting drought with high air temperature that occurred before and after the hail event. No pathogens and insects were found which might have affected to tree deaths. We suggested a dieback mechanism of the pine trees damaged by hail and drought.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.835-848
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• 2021

In Jeju Island which has peculiarity for its geological features and hydrology system, hydrological factor analysis for the effective water management is necessary. Because in-situ hydro-meteorological data is affected by surrounding environment, the in-situ dataset could not be the spatially representative for the study area. For this reason, remote sensing data may be used to overcome the limit of the in-situ data. In this study, applicability assessment of MOD16 evapotranspiration data, Globas Land Data Assimilation System (GLDAS) based evapotranspiration/soil moisture data, and Advanced SCATterometer (ASCAT) soil moisture product which were evaluated their applicability on other study areas was conducted. In the case of evapotranspiration, comparison with total precipitation and flux-tower based evapotranspiration were conducted. And for soil moisture, 6 in-situ data and ASCAT soil moisture product were compared on each site. As a result, 57% of annual precipitation was calculated as evapotranspiration, and the correlation coefficient between MOD16 evapotranspiration and GLDAS evapotranspiration was 0.759, which was a robust value. The correlation coefficient was 0.434, indicating a relatively low fit. In the case of soil moisture, in the case of the GLDAS data, the RMSE value was less than 0.05 at all sites compared to the in-situ data, and a statistically significant result was obtained as a result of the significance test of the correlation coefficient. However, for satellite data, RMSE over than 0.05 were found at Wolgak and there was no correlation at Sehwa and Handong points. It is judged that the above results are due to insufficient quality control and spatial representation of the evapotranspiration and soil moisture sensors installed in Jeju Island. It is estimated as the error that appears when adjacent to the coast. Through this study, the necessity of improving the existing ground observation data of hydrometeorological factors is emphasized.

• Economic and Environmental Geology
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• v.54 no.2
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• pp.161-176
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• 2021

In this study, one of the distributed hydrologic models, VELAS, was used to analyze the variation of hydrologic elements based on water balance analysis to evaluate the groundwater recharge in more detail than the annual time scale for the past and future. The study area is located in Yanggok-ri, Seobu-myeon, Hongseong-gun, Chungnam-do, which is very vulnerable to drought. To implement the VELAS model, spatial characteristic data such as digital elevation model (DEM), vegetation, and slope were established, and GIS data were constructed through spatial interpolation on the daily air temperature, precipitation, average wind speed, and relative humidity of the Korea Meteorological Stations. The results of the analysis showed that annual precipitation was 799.1-1750.8 mm, average 1210.7 mm, groundwater recharge of 28.8-492.9 mm, and average 196.9 mm over the past 18 years from 2001 to 2018 in the study area. Annual groundwater recharge rate compared to annual precipitation was from 3.6 to 28.2% with a very large variation and average 14.9%. By the climate change RCP 8.5 scenario, the annual precipitation from 2019 to 2100 was 572.8-1996.5 mm (average 1078.4 mm) and groundwater recharge of 26.7-432.5 mm (average precipitation 16.2%). The annual groundwater recharge rates in the future were projected from 2.8% to 45.1%, 18.2% on average. The components that make up the water balance were well correlated with precipitation, especially in the annual data rather than the daily data. However, the amount of evapotranspiration seems to be more affected by other climatic factors such as temperature. Groundwater recharge in more detailed time scale rather than annual scale is expected to provide basic data that can be used for groundwater development and management if precipitation are severely varied by time, such as droughts or floods.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.374-390
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• 2021

Growth and yield of field vegetable crops would be affected by climate conditions, which cause a relatively large fluctuation in crop production and consumer price over years. The yield prediction system for these crops would support decision-making on policies to manage supply and demands. The objectives of this study were to compile literatures related to onion and garlic and to perform data-mining analysis, which would shed lights on the development of crop models for these major field vegetable crops in Korea. The literatures on crop growth and yield were collected from the databases operated by Research Information Sharing Service, National Science & Technology Information Service and SCOPUS. The keywords were chosen to retrieve research outcomes related to crop growth and yield of onion and garlic. These literatures were analyzed using text mining approaches including word cloud and semantic networks. It was found that the number of publications was considerably less for the field vegetable crops compared with rice. Still, specific patterns between previous research outcomes were identified using the text mining methods. For example, climate change and remote sensing were major topics of interest for growth and yield of onion and garlic. The impact of temperature and irrigation on crop growth was also assessed in the previous studies. It was also found that yield of onion and garlic would be affected by both environment and crop management conditions including sowing time, variety, seed treatment method, irrigation interval, fertilization amount and fertilizer composition. For meteorological conditions, temperature, precipitation, solar radiation and humidity were found to be the major factors in the literatures. These indicate that crop models need to take into account both environmental and crop management practices for reliable prediction of crop yield.

• Korean Journal of Environment and Ecology
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• v.35 no.6
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• pp.669-678
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• 2021

This study surveyed three land cover types in Chungju City in Chungcheongbuk Province to check the distribution of cultured bacteria in bio-aerosols according to land cover type. It was possible to compare and analyze the distribution of bacteria according to microclimatic changes at each measurement point by examining meteorological factors and bio-aerosols according to land cover. The microclimate temperature in each measurement point was 8.7℃ for the urban forest, 10.8℃ for the waterside green area, and 10.2℃ for the urban area, indicating the urban forest had the lowest temperature among the measurement points. The relative humanity was 61.8% fin the urban forest, 59.3% in the waterside green area, and 55.7% in the urban area, indicating that the urban forest was the most humid among the measurement points. The identified bacteria were found to be 43 genera and 99 species. In terms of species diversity of cultured bacteria, 22 genera were found in the waterside green area, 21 genera in the urban forest, and 17 genera in the urban area, 37 species were found in the waterside green area, 31 species in the urban area, and 31 species in the urban forest. Bacillus toyonensis and Pseudarthrobacter oxydan were the species present in all three types of measurement sites, and Herbiconiux flava was confirmed to inhabit green areas such as urban forests and waterside green areas. The analysis result of the bacterial concentration according to the microclimatic environment in each measurement point was 333 CFU/m³ in the urban forest, 287 CFU/m³, in the waterside green area, and 173 CFU/m³ in the downtown area. The relative humidity and wind speed were analyzed to show a similar trend as the concentration. This study is expected to provide basic data for healthy urban management and green area creation by identifying the distribution of cultured bacteria in bio-aerosols according to land cover type and comparing and analyzing the traits of bio-aerosol in each measurement point.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.723-736
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• 2022

In this study, monthly precipitation forecasting models that can predict up to 12 months in advance were constructed for the Geum River basin, and two statistical techniques, multiple linear regression (MLR) and artificial neural network (ANN), were applied to the model construction. As predictor candidates, a total of 47 climate indices were used, including 39 global climate patterns provided by the National Oceanic and Atmospheric Administration (NOAA) and 8 meteorological factors for the basin. Forecast models were constructed by using climate indices with high correlation by analyzing the teleconnection between the monthly precipitation and each climate index for the past 40 years based on the forecast month. In the goodness-of-fit test results for the average value of forecasts of each month for 1991 to 2021, the MLR models showed -3.3 to -0.1% for the percent bias (PBIAS), 0.45 to 0.50 for the Nash-Sutcliffe efficiency (NSE), and 0.69 to 0.70 for the Pearson correlation coefficient (r), whereas, the ANN models showed PBIAS -5.0~+0.5%, NSE 0.35~0.47, and r 0.64~0.70. The mean values predicted by the MLR models were found to be closer to the observation than the ANN models. The probability of including observations within the forecast range for each month was 57.5 to 83.6% (average 72.9%) for the MLR models, and 71.5 to 88.7% (average 81.1%) for the ANN models, indicating that the ANN models showed better results. The tercile probability by month was 25.9 to 41.9% (average 34.6%) for the MLR models, and 30.3 to 39.1% (average 34.7%) for the ANN models. Both models showed long-term predictability of monthly precipitation with an average of 33.3% or more in tercile probability. In conclusion, the difference in predictability between the two models was found to be relatively small. However, when judging from the hit rate for the prediction range or the tercile probability, the monthly deviation for predictability was found to be relatively small for the ANN models.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.2
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• pp.1-22
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• 2022

The modification effects of street trees on outdoor thermal comfort in summertime according to tree planting types and road direction were analyzed using a computer simulation program, ENVI-met. With trees, the air temperature and wind speed decreased, and the relative humidity increased. In the case of mean radiant temperature (T_mrt) and human thermal sensation, physiological equivalent temperature (PET) and universal thermal climate index (UTCI), there was a decrease during the daytime. The greatest change among the meteorological factors by trees happened in Tmrt, and PET and UTCI showed similar patterns with T_mrt·The most effective tree planting type on thermal comfort modification was low tree height, wide tree crown, high leaf area index, and narrow planting interval (LWDN). T_mrt, PET and UTCI showed a large difference depending on shadow patterns of buildings and trees according to solar altitude and azimuth angles, and building locations. When the building shade areas increased, the thermal modification effect by trees decreased. In particular, results on the east and west sidewalks showed a large deviation over time. When applying the LWDN, the northwest, west and southwest sidewalks showed a significant reduction of 8.6-12.3℃ PET and 4.2-4.5℃ UTCI at 10:00, and the northeast, east and southeast sidewalks showed 8.1-11.8℃ PET and 4.4-5.0℃ UTCI at 16:00. On the other hand, when the least effective type (high tree height, narrow tree crown, low leaf area index, and wide planting interval) was applied, the maximum reduction was up to 1.8℃ PET and 0.9℃ UTCI on the eastern sidewalks, and up to 3.0℃ PET and 0.9℃ UTCI on the western ones. In addition, the difference in modification effects on T_mrt, PET and UTCI between the tree planting types was not significant when the tree effects were reduced by the effects of buildings. These results can be used as basic data to make the most appropriate street tree planting model for thermal comfort improvement in urban areas in summer.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.485-496
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• 2022

Modern agriculture is being transformed into smart agriculture to maximize production efficiency along with changes in the 4th industrial revolution. However, rural areas in Korea are facing challenges of aging, low fertility, and population outflow, making it difficult to transition to smart agriculture. Among ICT technologies, simulation allows users to observe or experience the results of their choices through imitation or reproduction of reality. The combination of the three-dimension (3D) model and the greenhouse simulator enable a 3D experience by virtual greenhouse for fruits and vegetable cultivation. At the same time, it is possible to visualize the greenhouse under various cultivation or climate conditions. The objective of this study is to apply the greenhouse climate management model for simulation development that can visually see the state of the greenhouse environment under various micrometeorological properties. The numerical solution with the mathematical model provided a dynamic change in the greenhouse environment for a particular greenhouse design. Light intensity, crop transpiration, heating load, ventilation rate, the optimal amount of CO₂ enrichment, and daily light integral were calculated with the simulation. The results of this study are being built so that users can be linked through a web page, and software will be designed to reflect the characteristics of cladding materials and greenhouses, cultivation types, and the condition of environmental control facilities for customized environmental control. In addition, environmental information obtained from external meteorological data, as well as recommended standards and set points for each growth stage based on experiments and research, will be provided as optimal environmental factors. This simulation can help growers, students, and researchers to understand the ICT technologies and the changes in the greenhouse microclimate according to the growing conditions.

• Ecology and Resilient Infrastructure
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• v.9 no.1
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• pp.36-58
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• 2022

In this study, the weather conditions corresponding to the increase in the environmental concentration of fine dust (PM₁₀) and ultrafine dust (PM_2.5) from 2001 to 2019 in Jeju and Seogwipo cities were analyzed. The increase in the levels of PM₁₀ and PM_2.5 was observed in the order: spring > winter > autumn > summer. In both cities, PM₁₀ and PM_2.5 levels increased more frequently during the day in spring and summer and at night in autumn and winter, with PM_2.5 showing a greater increase in concentration than PM₁₀. The air temperature and wind speed corresponding with increased levels of PM₁₀ were higher than their respective seasonal averages in spring and winter, but lower in summer and autumn. Relative humidity was lower than the seasonal average during all seasons. The air temperature variation corresponding with increased levels of PM_2.5 showed the same seasonal trend as that observed for PM₁₀. The relative humidity was higher than the respective seasonal averages in spring and summer, and lower in winter. The wind speed was lower than the seasonal average in both the cities. When the PM₁₀ and PM_2.5 levels increased, the wind direction was from the north and the west during the day and varied according to the season at night. The rate of the increase in the PM₁₀ concentration was the highest in both cities at the wind speed of 1.6 - 3.4 ms^-1 during the day and night except during night in the summer. The highest concentration of PM_2.5 was observed with the wind speed range of 1.6 - 3.4 ms^-1 in Jeju, and 0.3 - 1.6 ms^-1 in Seogwipo. The results of this study applied to urban and landscape planning will aid in the formulation of strategies to reduce the adverse effects of fine particular matter.