• Korean Journal of Soil Science and Fertilizer
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• v.19 no.3
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• pp.211-216
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• 1986

To establish a suitability grouping system of paddy soils for double or multiple cropping with rice which is intensively practiced in the southern parts of Korea, a few basic experiments were carried out for two years. The results are summarized as follows; 1. The potential productivities of the paddy soils which were tested without any fertilizer in the pots of subsoil samples by the double cropping of rice and other upland crops were resulted that the soils of "Moderately well drained" fine silty textured were the highest while the soils of "Poorly drained" sandy were the lowest, and the productivities could be clearly comparable according to the differences of soil conditions. 2. The decomposability of organic matter also was higher in the soils of "Moderately well drained" than the "Imperfectly drained". The coarse loamy and coarse silty textured soils were high in the upland condition and in the early stages of submerging while the fine loamy and fine silty textured soils were high at the late stage of submerging in the rates of organic matter decomposition. 3. The days to be reached to tillable condition after rainfall in fine loamy textured soils were about 5 days earlier than the clayey soils. The period of tillable condition of fine clayey soils with "Moderately well drained" was the longest and that of the fine loamy textured soils was the shortest. But the soils with "Imperfectly drained" were not clear among soil textural classes. 4. The lower the ground water table the higher was the productivity indices. The variation of ground water table in the medium textured soils was higher than the both of coarse and fine textured soils among "Moderately well drained". But it was observed the opposite in the soils of "Imperfectly drained".

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.65-81
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• 2021

In this study, soil moisture and evapotranspiration were calculated throughout South Korea using the Korea Land Data Assimilation System(KLDAS) of the Korea-Land Surface Information System(K-LIS) built on the basis of the Land Information System (LIS). The hydrometeorological data sets used to drive K-LIS and build KLDAS are MERRA-2(Modern-Era Retrospective analysis for Research and Applications, version 2) GDAS(Global Data Assimilation System) and ASOS(Automated Synoptic Observing System) data. Since ASOS is a point-based observation, it was converted into grid data with a spatial resolution of 0.125° for the application of KLDAS(ASOS-S, ASOS-Spatial). After comparing the hydrometeorological data sets applied to KLDAS against the ground-based observation, the mean of R² ASOS-S, MERRA-2, and GDAS were analyzed as temperature(0.994, 0.967, 0.975), pressure(0.995, 0.940, 0.942), humidity (0.993, 0.895, 0.915), and rainfall(0.897, 0.682, 0.695), respectively. For the hydrologic output comparisons, the mean of R² was ASOS-S(0.493), MERRA-2(0.56) and GDAS (0.488) in soil moisture, and the mean of R² was analyzed as ASOS-S(0.473), MERRA-2(0.43) and GDAS(0.615) in evapotranspiration. MERRA-2 and GDAS are quality-controlled data sets using multiple satellite and ground observation data, whereas ASOS-S is grid data using observation data from 103 points. Therefore, it is concluded that the accuracy is lowered due to the error from the distance difference between the observation data. If the more ASOS observation are secured and applied in the future, the less error due to the gridding will be expected with the increased accuracy.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.1
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• pp.47-56
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• 2018

Providing high-quality meteorological observation data at sites that represent actual farming environments is essential for useful agrometeorological services. The Automated Agricultural Observing System (AAOS) of the Korean Meteorological Administration, however, has been deployed on lawns rather than actual farm land. In this study, we show the inaccuracies that arise in AAOS data by analyzing temporal and vertical variation and by comparing them with data recorded by the National Center for AgroMeteorology (NCAM) tower that is located at an actual farming site near the AAOS tower. The analyzed data were gathered in August and October (before and after harvest time, respectively). Observed air temperature and water vapor pressure were lower at AAOS than at NCAM tower before and after harvest time. Observed reflected shortwave radiation tended to be higher at AAOS than at NCAM tower. Soil variables showed bigger differences than meteorological observation variables. In August, observed soil temperature was lower at NCAM tower than at AAOS with smaller diurnal changes due to irrigation. The soil moisture observed at NCAM tower continuously maintained its saturation state, while the one at AAOS showed a decreasing trend, following an increase after rainfall. The trend changed in October. Observed soil temperature at NCAM showed similar daily means with higher diurnal changes than at AAOS. The soil moisture observed at NCAM was continuously higher, but both AAOS and NCAM showed similar trends. The above results indicate that the data gathered at the AAOS are inaccurate, and that ground surface cover and farming activities evoke considerable differences within the respective meteorological and soil environments. We propose to shift the equipment from lawn areas to actual farming sites such as rice paddies, farms and orchards, so that the gathered data are representative of the actual agrometeorological observations.

• Journal of the Korean earth science society
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• v.37 no.7
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• pp.434-447
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• 2016

This study analyzed mass concentrations of PM10 and PM2.5, as measured at Tae-ahn and Gang-nae, Cheongju in central Korea over the period from 2011 to 2015. Higher mass concentrations of PM10, with the exception of dustfall cases during the period of winter and spring, reflected the influence of a prevailing westerly airflow, while the level of PM10 stayed at a low level in summer, reflecting the influence of North Pacific air mass and frequent rainfall. Accordingly, cases where a daily PM10 average of $81{\mu}gm^{-3}$ or over (exceeding the status of fine dust particles being 'a little bit bad') were often observed during the period of winter and spring, with more cases occurring in parts of Tae-ahn that are located close to the sources of pollutant emission in eastern China. Dustfall usually originated from dust storms made up of particles $2.5{\mu}m$ or over in diameter. However, anthropogenic haze displayed a high composition ratio of particulate less than $2.5{\mu}m$ in diameter. Accordingly, brightness temperature difference (BTD) values from the Communication, Ocean and Meteorological Satellite (COMS) were $-0.5^{\circ}K$ or over in haze with fine particulate. PM10 mass concentrations and NOAA 19 satellite BTD for haze cases were analyzed. Though PM10 mass concentrations were found to be lower than $200{\mu}g\;m^{-3}$, the mass concentration ratio of PM2.5/PM10 was measured as higher than 0.4 and BTD was found to be distributed in the range from -0.3 to $0.5^{\circ}K$. However, the BTD of dustfall cases exceeding $190{\mu}g\;m^{-3}$, were found to be less than 0.4 and BTD was found to be distributed in the range less than $-0.7^{\circ}K$. The result of applying BTD threshold values of the large-scale transport of haze proved to fall into line with the range over which aerosols of MODIS AOD and OMI AI were distributed.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.525-536
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• 2014

In this study, a method to assess and monitor hydrological drought using remote sensing was investigated for use in regions with limited observation data, and was applied to the Upper Namhangang basin in South Korea, which was seriously affected by the 2008-2009 drought. Drought information may be obtained more easily from meteorological data based on water balance than hydrological data that are hard to estimate. Air temperature data at 2 m above ground level (AGL) were estimated using remotely sensed data, evapotranspiration was estimated from the air temperature, and the correlations between precipitation minus evapotranspiration (P-PET) and streamflow percentiles were examined. Land Surface Temperature data with $1{\times}1km$ spatial resolution as well as Atmospheric Profile data with $5{\times}5km$ spatial resolution from MODIS sensor on board Aqua satellite were used to estimate monthly maximum and minimum air temperature in South Korea. Evapotranspiration was estimated from the maximum and minimum air temperature using the Hargreaves method and the estimates were compared to existing data of the University of Montana based on Penman-Monteith method showing smaller coefficient of determination values but smaller error values. Precipitation was obtained from TRMM monthly rainfall data, and the correlations of 1-, 3-, 6-, and 12-month P-PET percentiles with streamflow percentiles were analyzed for the Upper Namhan-gang basin in South Korea. The 1-month P-PET percentile during JJA (r = 0.89, tau = 0.71) and SON (r = 0.63, tau = 0.47) in the Upper Namhan-gang basin are highly correlated with the streamflow percentile with 95% confidence level. Since the effect of precipitation in the basin is especially high, the correlation between evapotranspiration percentile and streamflow percentile is positive. These results indicate that remote sensing-based P-PET estimates can be used for the assessment and monitoring of hydrological drought. The high spatial resolution estimates can be used in the decision-making process to minimize the adverse impacts of hydrological drought and to establish differentiated measures coping with drought.

• Journal of Korea Water Resources Association
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• v.51 no.3
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• pp.247-261
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• 2018

The purpose of this study is to evaluate the groundwater level behavior of Geum river basin ($9,645.5km^2$) under future climate change scenario projection periods (2020s: 2010~2039, 2050s: 2040~2069, 2080s: 2070~2099) using SWAT (Soil and Water Assessment Tool). Before future evaluation, the SWAT was calibrated and validated using 11 years (2005~2015) daily multi-purpose dam inflow at 2 locations (DCD, YDD), ground water level data at 5 locations (JSJS, OCCS, BEMR, CASS, BYBY), and three years (2012~2015) daily multi-function weir inflow at 3 locations (SJW, GJW, BJW). For the two dam inflow and dam storage, the Nash-Sutcliffe efficiency (NSE) was 0.57~0.67 and 0.87~0.94, and the coefficient of determination ($R^2$) was 0.69~0.73 and 0.63~0.73 respectively. For the three weir inflow and storage, the NSE was 0.68~0.70 and 0.94~0.99, and the $R^2$ was 0.83~0.86 and 0.48~0.61 respectively. The average $R^2$ for groundwater level was from 0.53 to 0.61. Under the future temperature increase of $4.3^{\circ}C$ and precipitation increase of 6.9% in 2080s (2070~2099) based on the historical periods (1976~2005) from HadGEM3-RA RCP 8.5 scenario, the future groundwater level shows decrease of -13.0 cm, -5.0 cm, -9.0 cm at 3 upstream locations (JSJS, OCCS, BEMR) and increase of +3.0 cm, +1.0 cm at 2 downstream locations (CASS, BYBY) respectively. The future groundwater level was directly affected by the groundwater recharge by the future seasonal spatial variation of rainfall in the watershed.

• Korean Journal of Organic Agriculture
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• v.19 no.3
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• pp.357-372
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• 2011

We carried out the experiment to select the suitable sprout soybean varieties for environment-friendly cultivation in paddy field of southern part area, compares of excess moisture injury degree and yield ability among 29 sprout soybean varieties. Plant growth of sprout soybean was generally low in beginning and recovered after flowering due to rainfall. In paddy field cultivation, number of pod per individual and number of seed per individual were less in difference than upland cultivation, and maturing date was delayed 5-14 days than upland cultivation in most species. When environment-friendly cultivation, pest injury was not caused major problem for the growth during the vegetative period of soybean due to ground spider as natural enemy to insect pest. However, damage of stink bugs showed severe during grain filling period, and Dawonkong, Anpyeongkong, Dachaekong and Wonhwangkong showed susceptible to sting bug. SMV infection was weak and showed some necrosis symptoms in Sokangkong, but black root rot was not infected at all. Bacterial pustule began to be infected slowly from pod enlargement stage in most species, displayed severe symptoms in Dawonkong, Pungsannamulkong, Seonamkong and Sobaeknamulkong. The symptoms of pod anthracnose, pod blight and purple spot were greatly appeared after flowering. Disease resistance varieties was Paldokong, Kwangankong, Doremikong, Somyeongkong, Pungsannamulkong, Iksa-namulkong, Seonamkong, Sojinkong, Pureunkong, Bosugkong, Namhaekong and Sorokkong. Lodging index showed 3 in Saebyeolkong, and other species displayed slight lodging in 0-3 degree. 100-seed weight is 9.8-17.2g extent and increased 0.1-3.7g than upland cultivation in most species, but decreased in some species. Government purchase standard, species correspond to small-seed-size namulkong (Sizing screen diameter 4.0-5.6 mm) was Dawonkong, Dachaekong, Bosugkong, Seonamkong, Sokangkong, Hannamkong, Somyeongkong and Wonhwangkong. Species which seed yield was higher than Pungsannamulkong (266kg/10a) were Sorokkong, Hannamkong, Bosugkong and Sowonkong. Considering sprout soybean species, disease endurance, insect resistance, lodging resistance, 100-seed weight, yield ability and excess moisture tolerances synthetically, Seonamkong, Hannamkong, Doremikong, Bosugkong, Pungwonkong, Kwangankong, Sowonkong, Dagikong, Paldokong, Eunhakong and Pungsannamulkong were promising for environment-friendly cultivation in paddy field.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.65-73
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• 2010

To observe and analyze the characteristics of cloud and precipitation properties, the Cloud physics Observation System (CPOS) has been operated from December 2003 at Daegwallyeong ($37.4^{\circ}N$, $128.4^{\circ}E$, 842 m) in the Taebaek Mountains. The major instruments of CPOS are follows: Forward Scattering Spectrometer Probe (FSSP), Optical Particle Counter (OPC), Visibility Sensor (VS), PARSIVEL disdrometer, Microwave Radiometer (MWR), and Micro Rain Radar (MRR). The former four instruments (FSSP, OPC, visibility sensor, and PARSIVEL) are for the observation and analysis of characteristics of the ground cloud (fog) and precipitation, and the others are for the vertical cloud characteristics (http://weamod.metri.re.kr) in real time. For verification of CPOS products, the comparison between the instrumental products has been conducted: the qualitative size distributions of FSSP and OPC during the hygroscopic seeding experiments, the precipitable water vapors of MWR and radiosonde, and the rainfall rates of the PARSIVEL(or MRR) and rain gauge. Most of comparisons show a good agreement with the correlation coefficient more than 0.7. These reliable CPOS products will be useful for the cloud-related studies such as the cloud-aerosol indirect effect or cloud seeding. The visibility value is derived from the droplet size distribution of FSSP. The derived FSSP visibility shows the constant overestimation by 1.7 to 1.9 times compared with the values of two visibility sensors (SVS (Sentry Visibility Sensor) and PWD22 (Present Weather Detect 22)). We believe this bias is come from the limitation of the droplet size range ($2{\sim}47\;{\mu}m$) measured by FSSP. Further studies are needed after introducing new instruments with other ranges.

• Journal of the Korean earth science society
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• v.42 no.3
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• pp.264-277
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• 2021

This study analyzed the relationship between the total precipitable water vapor in the atmosphere and the moving speed of recent typhoons. This study used ground observation data of air temperature, precipitation, and wind speed from the Korea Meteorological Administration (KMA) as well as total rainfall data and Red-Green-Blue (RGB) composite images from the U.S. Meteorological and Satellite Research Institute and the KMA's Cheollian Satellite 2A (GEO-KOMPSAT-2A). Using the typhoon location and moving speed data provided by the KMA, we compared the moving speeds of typhoon Bavi, Maysak, and Haishen from 2020, typhoon Tapah from 2019, and typhoon Kong-rey from 2018 with the average typhoon speed by latitude. Tapah and Kong-rey moved at average speed with changing latitude, while Bavi and Maysak showed a significant decrease in moving speed between approximately 25°N and 30°N. This is because a water vapor band in the atmosphere in front of these two typhoons induced frontogenesis and prevented their movement. In other words, when the water vapor band generated by the low-level jet causes frontogenesis in front of the moving typhoon, the high pressure area located between the site of frontogenesis and the typhoon develops further, inducing as a blocking effect. Together with the tropical night phenomenon, this slows the typhoon. Bavi and Maysak were accompanied by copious atmospheric water vapor; consequently, a water vapor band along the low-level jet induced frontogenesis. Then, the downdraft of the high pressure between the frontogenesis and the typhoon caused the tropical night phenomenon. Finally, strong winds and heavy rains occurred in succession once the typhoon landed.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.