• Atmosphere
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• v.34 no.2
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• pp.83-95
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• 2024

Atmospheric rivers (ARs) are closely related to local precipitation which can be both beneficial and destructive. Although several studies have evaluated their predictability, there is a lack of studies on East Asian ARs. This study evaluates the prediction skill of East Asian ARs in the Korean Integrated Model (KIM) for 2020~2022 summer. The spatial distribution of AR frequency in KIM is qualitatively similar to the observation but overestimated. In particular, the model errors greatly increase along the boundary of the western North Pacific subtropical high as the forecast lead time increases. When the prediction skills are quantitatively verified by computing the Anomaly Correlation Coefficient and Mean Square Skill Score, the useful prediction skill of daily AR around the Korean Peninsula is found up to 5 days. Such prediction limit is primarily set by the wind field errors with a minor contribution of moisture distribution errors. This result suggests that the improved prediction of atmospheric circulation field can improve the prediction of East Asian summer ARs and the associated precipitation.

• Atmosphere
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• v.26 no.4
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• pp.495-508
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• 2016

In order to determine the prediction possibility of heavy rainfall, a variety of analyses was conducted by using three-dimensional data obtained from Korea Local Analysis and Prediction System (KLAPS) re-analysis data. Strong moisture convergence occurring around the time of the heavy rainfall is consistent with the results of previous studies on such continuous production. Heavy rainfall occurred in the cloud system with a thick convective clouds. The moisture convergence, temperature and potential temperature advection showed increase into the heavy rainfall occurrence area. The distribution of integrated liquid water content tended to decrease as rainfall increased and was characterized by accelerated convective instability along with increased buoyant energy. In addition, changes were noted in the various characteristics of instability indices such as K-index (KI), Showalter Stability Index (SSI), and lifted index (LI). The meteorological variables used in the analysis showed clear increases or decreases according to the changes in rainfall amount. These rapid changes as well as the meteorological variables changes are attributed to the surrounding and meteorological conditions. Thus, we verified that heavy rainfall can be predicted according to such increase, decrease, or changes. This study focused on quantitative values and change characteristics of diagnostic variables calculated by using numerical models rather than by focusing on synoptic analysis at the time of the heavy rainfall occurrence, thereby utilizing them as prognostic variables in the study of the predictability of heavy rainfall. These results can contribute to the identification of production and development mechanisms of heavy rainfall and can be used in applied research for prediction of such precipitation. In the analysis of various case studies of heavy rainfall in the future, our study result can be utilized to show the development of the prediction of severe weather.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.490-501
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• 2023

Laboratory column tests were conducted to analyze infiltration and water redistribution processes on the basis of rainfall. To efficiently measure moisture content within soil layers, this research developed a predictive model grounded in a convolutional neural network (CNN), a deep learning technique. The digital images obtained during the column tests were incorporated into the established CNN. The moisture content of each soil layer over time was effectively measured. The measured values were also in relatively good agreement with the moisture content determined using the moisture sensors installed for each soil layer. The use of CNN enabled a comprehensive understanding of continuous moisture distribution within the soil layers, as well as the infiltration process according to soil texture and initial moisture content conditions.

• Korean Journal of Agricultural Science
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• v.43 no.4
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• pp.507-521
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• 2016

Landslides are one of the most common natural hazards causing significant damage and casualties every year. In Korea, the increasing trend in landslide occurrence in recent decades, caused by climate change, has set off an alarm for researchers to find more reliable methods for landslide prediction. Therefore, an accurate landslide-susceptibility assessment is fundamental for preventing landslides and minimizing damages. However, analyzing the stability of a natural slope is not an easy task because it depends on numerous factors such as those related to vegetation, soil properties, soil moisture distribution, the amount and duration of rainfall, earthquakes, etc. A variety of different methods and techniques for evaluating landslide susceptibility have been proposed, but up to now no specific method or technique has been accepted as the standard method because it is very difficult to assess different methods with entirely different intrinsic and extrinsic data. Landslide prediction methods can fall into three categories: empirical, statistical, and physical approaches. This paper reviews previous research and surveys three groups of landslide prediction methods.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.09a
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• pp.1151-1155
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• 2010

Frost depth is one of important factors to design roadway structure, and it can be estimated with numerical simulation on thermal distribution through subgrade soils. Thermal conductivity is a key parameter for accurate prediction on thermal distribution, but there are few studies on thermal conductivity of subgrade soils in Korea. Thermal conductivity can be affected by several factors such as dry density, moisture content, and saturation degree based on previous researches. Two empirical equations to estimate thermal conductivity are applied to access the accuracy of these equations with experimental data. Results indicate that the equation can be used to estimate thermal conductivity with proper quartz fraction.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.2
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• pp.59-63
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• 2008

The surface runoff is one of the important components for the surface water balance. However, most Land Surface Models(LSMs), coupled to climate models at a large scale for the prediction and prevention of disasters caused by climate changes, simplistically estimate surface runoff from the soil water budget. Ignoring the role of surface flow depth on the infiltration rate causes errors in both surface and subsurface flow calculations. Therefore, for the comprehensive terrestrial water and energy cycle predictions in LSMs, a conjunctive surface-subsurface flow model at a large scale is developed by coupling a 1-D diffusion wave model for surface flow with the 3-D Volume Averaged Soil-moisture Transport(VAST) model for subsurface flow. This paper describes the new conjunctive surface-subsurface flow formulation developed for improvement of the prediction of surface runoff and spatial distribution of soil water by topography, along with basic schemes related to the terrestrial hydrologic system in Common Land Model(CLM), one of the state-of-the-art LSMs.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 1997.11a
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• pp.305-310
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• 1997

There are many parameters in prediction of forest fire spread. The variables such as fuel moisture, fuel loading, wind velocity, wind direction, relative humidity, slope, and solar aspect have important effects on fire. Particularly, wind and slope factors are considered to be the most important parameters in propagation of forest fire. Generally, slope effect cause different wind distribution in mountain area. However, this effect is disregarded in complex geometry. In this paper, wind is estimated by applying computational fluid dynamics to the forest geometry. Wind velocity data is obtained by using CFD code with Newtonian model and slope is calculated with geometrical data. These data are applied fer 2-dimentional forest fire spreading algorithm with Korean ROS(Rate Of Spread). Finally, the comparison between the simulation and the real forest fire is made. The algorithm spread of forest fire will help fire fighter to get the basic data far fire suppression and the prediction to behavior of forest fire.

• Korean Journal of Agricultural and Forest Meteorology
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• v.10 no.2
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• pp.35-46
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• 2008

Soil moisture is one of the important components in hydrological processes and also controls the subsurface flow mechanism at a hillslope scale. In this study, time series of soil moisture were measured at a hillslope located in Gwangneung National Arboretum, Korea using a multiplex Time Domain Reflectometry(TDR) system measuring soil moisture with bi-hour interval. The Box-Jenkins transfer function and noise model was used to estimate spatial distributions of soil moisture histories between May and September, 2007. Rainfall was used as an input parameter and soil moisture at 10 cm depth was used as an output parameter in the model. The modeling process consisted of a series of procedures(e.g., data pretreatment, model identification, parameter estimation, and diagnostic checking of selected models), and the relationship between soil moisture and rainfall was assessed. The results indicated that the patterns of soil moisture at different locations and slopes along the hillslope were similar with those of rainfall during the measurment period. However, the spatial distribution of soil moisture was not associated with the slope of the monitored location. This implies that the variability of the soil moisture was determined more by rainfall than by the slope of the site. Due to the influence of vegetation activity on soil moisture flow in spring, the soil moisture prediction in spring showed higher variability and complexity than that in early autumn did. This indicates that vegetation activity is an important factor explaining the patterns of soil moisture for an upland forested hillslope.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.5
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• pp.112-120
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• 2012

A numerical model considering the internal vaporization and the creep effect, in the form of a analytical program, for tracing the behavior of high strength concrete(HSC) members exposed to fire is presented. The two stages, i.e., spalling procedure and fire resistance time, associated with the thermal, moisture flow, creep and structural analysis, for the prediction of fire resistance behavior are explained. The use of the analytical program for tracing the response of HSC member from the initial pre-loading stage to collapse, due to fire, is demonstrated. Moisture evaporates, when concrete is exposed to fire, not only at concrete surface but also at inside the concrete to adjust the equilibrium and transfer properties of moisture. Finite element method is employed to facilitate the moisture diffusion analysis for any position of member, so that the prediction method of the moisture distribution inside the concrete members at fire is developed. The validity of the numerical model used in this program is established by comparing the predictions from this program with results from others fire resistance tests. The analytical program can be used to predict the fire resistance of HSC members for any value of the significant parameters, such as load, sectional dimensions, member length, and concrete strength.

• Journal of Korea Water Resources Association
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• v.36 no.6
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• pp.961-969
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• 2003

Formulation of quasi-dynamic wetness index was derived to predict the spatial and temporal distribution of the soil moisture. The algorithm of dynamic wetness index was developed through introducing the convolution integral with the rainfall input. The spatial and temporal behaviors of the wetness index of the Sulmachun Watershed was calculated using the digital elevation model(DEM) and the rainfall data for two years. The spatial distribution of the dynamic wetness index shows most dispersive feature of flow generation among the three assumptions of steady, quasi-dynamic and dynamic. The statistical distribution of the quasi-dynamic wetness index and the dynamic wetness index approximate to the steady state wetness index as the time step is increased. The dynamic wetness index shows mixed distribution of the normalized probability density function.