• Journal of Korea Water Resources Association
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• v.55 no.9
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• pp.679-686
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• 2022

As the occurrences of flash floods have increased due to climate change, faster and more accurate precipitation observation using X-band radar has become important. Therefore, the Ministry of Environment installed two dual-pol X-band radars at Samcheok and Uljin. The radar data used in this study were obtained from two different elevation angles and composed to reduce the shielding effect. To obtain quantitative rainfall, quality control (QC), K_DP retrieval, and Hybrid Surface Rainfall (HSR) methods were sequentially applied. To improve the accuracy of the quantitative precipitation estimation (QPE) of the X-band radar, we retrieved parameters for the relationship between rainfall rate and specific differential phase, which is commonly called the R-K_DP relationship; hence, an empirical approach was developed using multiple rain gauges for those two radars. The newly suggested relationship, R = 27.4K^0.81_DP, slightly increased the correlation coefficient by 1% more than the relationship suggested by the previous study. The root mean square error significantly decreased from 3.88 mm/hr to 3.68 mm/hr, and the bias of the estimated precipitation also decreased from -1.72 mm/hr to -0.92 mm/hr for overall cases, showing the improvement of the new method.

• Journal of Korea Water Resources Association
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• v.56 no.11
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• pp.765-774
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• 2023

Due to climatechange, precipitation variability has increased, leading to more frequentoccurrences of droughts and floods. To establish measures for managing waterresources in response to the increasing uncertainties of climate conditions, itis necessary to understand the variability of natural river discharge and theimpact of reservoir operation modeling considering dam inflow and artificialwater supply. In this study, an integrated rainfall-runoff and reservoiroperation modeling was applied to analyze the water supply reliability andflood risk for a multipurpose dam catchment under climate change conditions. Therainfall-runoff model employed was the modèle du Génie Rural à 4 paramètresJournalier (GR4J) model, and the reservoir operation model used was an R-basedmodel with the structure of HEC-Ressim. Applying the climate change scenariosuntil 2100 to the established integrated model, the changes in water supplyreliability and flood risk of the Happcheon Dam were quantitatively analyzed.The results of the water supply reliability analysis showed that under SSP2-4.5conditions, the water supply reliability was higher than that under SSP5-8.5conditions. Particularly, in the far-future period, the range of flood risk widened,and both SSP2-4.5 and SSP5-8.5 scenarios showed the highest median flood riskvalues. While precipitation and runoff were expected to increase by less than10%, dam-released flood discharge was projected to surge by over 120% comparedto the baseline

• Journal of The Korean Society of Grassland and Forage Science
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• v.44 no.1
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• pp.6-13
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• 2024

This study was conducted to determine the optimal spring seeding dates for alfalfa yield and feed value. The experiment was conducted annually for three years (2021~2023) at the field in the Department of Animal Resources Development, NIAS, located in Cheonan. The treatments involved six seeding dates ranging from February 24 to April 14, with 10days intervals. Alfalfa was harvested four times a year at the early flowering stage. Dry matter yield showed a tendency to decrease with delayed the seeding date. However, depending on the climatidc condisions in the seeding year, the dry matter yield on March 14 or 24 was comparable to that on February 24. Annual dry matter yield varied, influenced by the daylight conditions each year. The average feed value did not significantly differ within in the same year with delayed seeding dates (p>0.05). Therefore, the most stable period for alfalfa spring seeding in the central area of South Korea is considered to be from February 24 to April 4, with February 24 indentified as the optimal date.

• Journal of Korea Water Resources Association
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• v.40 no.9
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• pp.697-707
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• 2007

In this study, we estimated the PMP(Probable Maximum Precipitation) and its transition in case of the typhoon Rusa which happened the biggest damage of all typhoons in the Korea. Specially, we analysed the moisture maximizing rate under the consideration of meteorological condition based on the orographic property when it hits in Gangneung region. The PMP is calculated by the rate of the maximum persisting 12 hours 1000 hPa dew points and representative persisting 12 hours 1000 hPa dew point. The former is influenced by the moisture inflow regions. These regions are determined by the surface wind direction, 850 hPa moisture flux and streamline, which are the critically different aspects compared to that of previous study. The latter is calculated using statistics program (FARD2002) provided by NIDP(National Institute for Disaster Prevention). In this program, the dew point is calculated by reappearance period 50-year frequency analysis from 5% of the level of significant when probability distribution type is applied extreme type I (Gumbel distribution) and parameter estimation method is used the Moment method. So this study indicated for small basin$(3.76km^2)$ the difference the PMP through new method and through existing result of established storm transposition and DAD(Depth-Area-Duration). Consequently, the moisture maximizing rate is calculated in the moisture inflow regions determined by meteorological fields is higher $0.20{\sim}0.40$ range than that of previous study. And the precipitation is increased $16{\sim}31%$ when this rate is applied for calculation.

• Journal of Korean Society of Forest Science
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• v.88 no.4
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• pp.498-509
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• 1999

This study aims to clarify the effect of forest management practices(thinning and pruning) in forest hydrological processes on electrical conductivity to get the fundamental information on the facilitation of purifying water quality after forestry practices. Rainfall, throughfall, stemflow, soil and stream water were sampled at the study sites which consist of Abies holophylla and Pinus koraiensis in Kwangnung Experimental Forest for 6 months from March 1 to August 4, 1998. In case of deviding into forest hydrological processes, multiple regression equations of electrical conductivity and total amount of anion, $NO{_3}^-$ of throughfall, stemflow, soil water of management site in Abies holophylla shows high significance. And multiple regression equations of electrical conductivity and total amount of anion, $SO{_4}^{2-}$, $Cl^-$ of throughfall, stemflow, soil water of non-management site in Abies holophylla shows high significance. Multiple regression equations of electrical conductivity and $NO{_3}^-$, before non-rain days of throughfall, stemflow, soil water of management site in Pinus koraiensis shows high significance. And multiple regression equations of electrical conductivity and total amount of ion, $NO{_3}^-$, $K^+$, pH, total amount of anion of throughfall, stemflow, soil water of non-management site in Plinus koraiensis shows high significance. Multiple regression equations of electrical conductivity and pricipitation, total amount of ion, $Na^+$ of stream water in Abies holophylla and Pinus koraiensis shows high significance. In case of combining into forest hydrological processes, multiple regression equations of electrical conductivity and total amount of cation and anion, $Na^+$, $Cl^-$, and pH in rainfall, throughfall, stemflow, soil and stream water shows high significance.

• Korean Journal of Remote Sensing
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• v.33 no.5_3
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• pp.855-866
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• 2017

Several research cases using remote sensing methods to analyze changes of storage and dynamics of groundwater aquifer were reviewed in this paper. The status of groundwater storage, in an area with regional scale, could be qualitatively inferred from geological feature, surface water altimetry and topography, distribution of vegetation, and difference between precipitation and evapotranspiration. These qualitative indicators could be measured by geological lineament analysis, airborne magnetic survey, DEM analysis, LAI and NDVI calculation, and surface energy balance modeling. It is certain that GRACE and InSAR have received remarkable attentions as direct utilization from satellite data for quantification of groundwater storage and dynamics. GRACE, composed of twin satellites having acceleration sensors, could detect global or regional microgravity changes and transform them into mass changes of water on surface and inside of the Earth. Numerous studies in terms of groundwater storage using GRACE sensor data were performed with several merits such that (1) there is no requirement of sensor data, (2) auxiliary data for quantification of groundwater can be entirely obtained from another satellite sensors, and (3) algorithms for processing measured data have continuously progressed from designated data management center. The limitations of GRACE for groundwater storage measurement could be defined as follows: (1) In an area with small scale, mass change quantification of groundwater might be inaccurate due to detection limit of the acceleration sensor, and (2) the results would be overestimated in case of combination between sensor and field survey data. InSAR can quantify the dynamic characteristics of aquifer by measuring vertical micro displacement, using linear proportional relation between groundwater head and vertical surface movement. However, InSAR data might now constrain their application to arid or semi-arid area whose land cover appear to be simple, and are hard to apply to the area with the anticipation of loss of coherence with surface. Development of GRACE and InSAR sensor data preprocessing algorithms optimized to topography, geology, and natural conditions of Korea should be prioritized to regionally quantify the mass change and dynamics of the groundwater resources of Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.4
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• pp.264-272
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• 2013

Climate changes have caused not only changes in the frequency and intensity of extreme climate events, but also temperature and precipitation. The damages on agricultural production system will be increased by heavy rainfall and snow. In this study we assessed vulnerability of crop cultivation facility and animal husbandry facility by heavy rain in 232 agricultural districts. The climate data of 2000 years were used for vulnerability analysis on present status and the data derived from A1B scenario were used for the assessment in the years of 2020, 2050 and 2100, respectively. Vulnerability of local districts was evaluated by three indices such as climate exposure, sensitivity and adaptive capacity, and each index was determined from selected alternative variables. Collected data were normalized and then multiplied by weight value that was elicited in delphi investigation. Jeonla-do and Gangwon-do showed higher climate exposures than the other provinces. The higher sensitivity to abnormal weather was observed from the regions that have large-scale cultivation facility complex compared to the other regions and vulnerability to abnormal weather also was higher at these provinces. In the projected estimation based on the SRES A1B, the vulnerability of controlled agricultural facility in Korea totally increased, especially was dramatic between 2000's and 2020 year.

• 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.