• Journal of Korean Society of Forest Science
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• v.99 no.5
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• pp.755-761
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• 2010

This study analyzed water quality data from a coniferous forest catchment in order to quantify the contributions of runoff components to stormflow, and to understand the effects of antecedent moisture conditions within catchment on the contributions of runoff components. Hydrograph separation by the twocomponent mixing model analysis was used to partition stormflow discharge into pre-event and event components for total 10 events in 2005 and 2008. To simplify the analysis, this study used single geochemical tracer with Na+. The result shows that the average contributions of event water and pre-event water were 34.8% and 65.2% of total stormflow of all 10 events, respectively. The event water contributions for each event varied from 18.8% to 47.9%. As the results of correlation analysis between event water contributions versus some storm event characteristics, 10 day antecedent rainfall and 1 day antecedent streamflow are significantly correlated with event water contributions. These results can provide insight which will contribute to understand the importance of antecedent moisture conditions in the generation of event water, and be used basic information to stormflow generation process in forest catchment.

• Journal of Ocean Engineering and Technology
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• v.38 no.3
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• pp.103-114
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• 2024

Remote sensing wave observation data are crucial when analyzing ocean waves, the main external force of coastal disasters. Nevertheless, it has limitations in accuracy when used in low-wind environments. Therefore, this study collected the raw data from MIROS Wave and Current Radar (MWR) and wave radar at the Ieodo Ocean Research Station (IORS) and applied the optimal filter by combining filters provided by MIROS software. The data were validated by a comparison with South Jeju ocean buoy data. The results showed it maintained accuracy for significant wave height, but errors were observed in significant wave periods and extreme waves. Hence, this study used an artificial neural network (ANN) to improve these errors. The ANN was generalized by separating the data into training and test datasets through stratified sampling, and the optimal model structure was derived by adjusting the hyperparameters. The application of ANN effectively improved the accuracy in significant wave periods and high wave conditions. Consequently, this study reproduced past wave data by enhancing the reliability of the MWR, contributing to understanding wave generation and propagation in storm conditions, and improving the accuracy of wave prediction. On the other hand, errors persisted under high wave conditions because of wave shadow effects, necessitating more data collection and future research.

• Journal of the Korean Society for Marine Environment & Energy
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• v.19 no.1
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• pp.62-73
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• 2016

The variation of groundwater by wave, tide and precipitation conditions is closely related to the vegetation environment at the natural vegetation and sandy based beach, and it has a significant impact on the vegetation development and ground stabilization. In this study, the water temperature, electrical conductivity, and pressure were monitored at five observational stations normal to the Jinu-do(Island) shoreline of Nakdong river estuary from March 2012 to September 2014 (approximately 799 days) with the aim of measuring the variation in groundwater-table characteristics. The purpose of the study was to identify factors (tide, wave etc.) affecting groundwater-table variation using time series and correlation analysis, and to record spatial variations in the groundwater level and electrical conductivity as a result of storm events. The observational station in the intertidal zone was strongly affected by wave period and tide level. During the storm period, the groundwater-table and electrical conductivity were stabilized at the edge of sand dunes, vegetation, and areas of transition between freshwater and seawater.

• Journal of Astronomy and Space Sciences
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• v.32 no.4
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• pp.335-340
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• 2015

Using the Total Electron Content (TEC) data from the Global Navigation Service System (GNSS) site in Jeju, operated by the Korea Astronomy and Space Science Institute (geographic location: $33.3^{\circ}N$, $126.5^{\circ}E$; geomagnetic location: $23.6^{\circ}N$) for 2002-2014 in Korea, the results of the statistical analysis of positive and negative ionospheric storms are presented for the first time. In this paper, ionospheric storms are defined as turbulences that exceed 50% of the percentage differential Global Positioning System (GPS) TEC ratio (${\Delta}TEC$) with monthly median GPS TEC. During the period of observations, the total number of positive ionospheric storms (${\Delta}TEC$ > 50%) was 170, which is greater than five times the number of negative ionospheric storms (${\Delta}TEC$ < - 50%) of 33. The numbers of ionospheric storms recorded during solar cycles 23 and 24 were 134 and 69, respectively. Both positive and negative ionospheric storms showed yearly variation with solar activity during solar cycle 23, but during solar cycle 24, the occurrence of negative ionospheric storms did not show any particular trend with solar activity. This result indicates that the ionosphere is actively perturbed during solar cycle 23, whereas it is relatively quiet during solar cycle 24. The monthly variations of the ionospheric storms were not very clear although there seems to be stronger occurrence during solstice than during equinox. We also investigated the variations of GPS positioning accuracy caused by ionospheric storms during November 7-10, 2004. During this storm period, the GPS positioning accuracies from a single frequency receiver are 3.26 m and 2.97 m on November 8 and 10, respectively, which is much worse than the quiet conditions on November 7 and 9 with the accuracy of 1.54 m and 1.69 m, respectively.

• Journal of Astronomy and Space Sciences
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• v.31 no.2
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• pp.149-157
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• 2014

As the prediction of geomagnetic storms is becoming an important and practical problem, conditions in the Earth's magnetosphere have been studied rigorously in terms of those in the interplanetary space. Another approach to space weather forecast is to deal with it as a probabilistic geomagnetic storm forecasting problem. In this study, we carry out detailed statistical analysis of solar wind parameters and geomagnetic indices examining the dependence of the distribution on the solar cycle and annual variations. Our main findings are as follows: (1) The distribution of parameters obtained via the superimposed epoch method follows the Gaussian distribution. (2) When solar activity is at its maximum the mean value of the distribution is shifted to the direction indicating the intense environment. Furthermore, the width of the distribution becomes wider at its maximum than at its minimum so that more extreme case can be expected. (3) The distribution of some certain heliospheric parameters is less sensitive to the phase of the solar cycle and annual variations. (4) The distribution of the eastward component of the interplanetary electric field BV and the solar wind driving function BV2, however, appears to be all dependent on the solar maximum/minimum, the descending/ascending phases of the solar cycle and the equinoxes/solstices. (5) The distribution of the AE index and the Dst index shares statistical features closely with BV and $BV^2$ compared with other heliospheric parameters. In this sense, BV and $BV^2$ are more robust proxies of the geomagnetic storm. We conclude by pointing out that our results allow us to step forward in providing the occurrence probability of geomagnetic storms for space weather and physical modeling.

• Journal of the Korean Society of Hazard Mitigation
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• v.7 no.3
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• pp.51-58
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• 2007

The present study surveyed 750 graduate and undergraduate students who were living or not living in Gangwon province concerning the characteristics of disasters in Gangwon province, people's consciousness of disaster prevention, etc. According to the results of analysis, all the respondents thought that the possibility of disasters is higher in Gangwon province(74.0%) than in any other province. Compared to non-residents, Gangwon province residents tended to perceive that the possibility of storm and flood disasters and forest fires is high in Gangwon province. As to reasons for frequent disasters in Gangwon province, the respondents mentioned disadvantageous natural conditions, the shortage of disaster prevention facilities and local residents' low consciousness of disaster prevention. As to methods for enhancing people's consciousness of disaster prevention in Gangwon province, they considered essential the expansion of disaster prevention facilities and education on disaster prevention. In particular, 62.1% of the respondents did not have experiences in disaster education. This suggests the necessity for extending disaster education.

• Atmosphere
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• v.31 no.4
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• pp.445-459
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• 2021

This study uses a cloud-resolving storm simulator (CReSS) to understand the individual effect of determinant meteorological factors on snowfall characteristics in the Yeongdong region based on the rawinsonde soundings for two snowfall cases that occurred on 23 February (Episode 1) and 13 December (Episode 2) 2016; one has a single-layered cloud and the other has two-layered cloud structure. The observed cloud and precipitation (snow crystal) features were well represented by a CReSS model. The first ideal experiment with a decrease in low-level temperature for Episode 1 indicates that total precipitation amount was decreased by 19% (26~27% in graupel and 53~67% in snow) compared with the control experiment. In the ideal experiment that the upper-level wind direction was changed from westerly to easterly, although total precipitation was decreased for Episode 1, precipitation was intensified over the southwestern side (specifically in terrain experiment) of the sounding point (128.855°E, 37.805°N). In contrast, the precipitation for Episode 2 was increased by 2.3 times greater than the control experiment under terrain condition. The experimental results imply that the low-level temperature and upper-level dynamics could change the location and characteristics of precipitation in the Yeongdong region. However, the difference in precipitation between the single-layered experiment and control (two-layered) experiment for Episode 2 was negligible to attribute it to the effect of upper-level cloud. The current results could be used for the development of guidance of snowfall forecast in this region.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.13-24
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• 2020

Abnormal weather conditions have lately been occurring frequently due to the rapid economic development and global warming. Natural disasters classified as storm and flood damages such as heavy rain, typhoon, strong wind, high seas and heavy snow arouse large-scale human and material damages. To minimize damages, it is important to estimate the scale of damage before disasters occur. This study is intended to develop a strong wind damage estimation function to prepare for strong wind damage among various storm and flood disasters. The developed function reflects weather factors and regional characteristics based on the strong wind damage history found in the Natural Disaster Yearbook. When the function is applied to a system that collects real-time weather information, it can estimate the scale of damage in a short time. In addition, this function can be used as the grounds for disaster control policies of the national and local governments to minimize damages from strong wind.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.2
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• pp.85-96
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• 2021

Irrigation return flow is defined as the excess of irrigation water that is not evapotranspirated by direct surface drainage, and which returns to an aquifer. It is important to quantitatively estimate the irrigation return flow of the water cycle in an agricultural watershed. However, the previous studies on irrigation return flow rates are limitations in quantifying the return flow rate by region. Therefore, simulating irrigation return flow by accounting for various water loss rates derived from agricultural practices is necessary while the hydrologic and hydraulic modeling of cultivated canal-irrigated watersheds. In this study, the irrigation return flow rate of agricultural water, especially for the entire agricultural watershed, was estimated using the SWMM (Storm Water Management Model) module from 2010 to 2019 for the Madun reservoir located in Anseong, Gyeonggi-do. The results of SWMM simulation and water balance analysis estimated irrigation return flow rate. The estimated average annual irrigation return flow ratio during the period from 2010 to 2019 was approximately 55.3% of the annual irrigation amounts of which 35.9% was rapid return flow and 19.4% was delayed return flow. Based on these results, the hydrologic and hydraulic modeling approach can provide a valuable approach for estimating the irrigation return flow under different hydrological and water management conditions.

• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.273-286
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• 2021

In recent years, as human casualties and property damage caused by hazardous waves have increased in the East Sea, precise wave prediction skills have become necessary. In this study, the Simulating WAves Nearshore (SWAN) third-generation numerical wave model was calibrated and optimized to enhance the accuracy of winter storm wave prediction in the East Sea. We used Source Term 6 (ST6) and physical observations from a large-scale experiment conducted in Australia and compared its results to Komen's formula, a default in SWAN. As input wind data, we used Korean Meteorological Agency's (KMA's) operational meteorological model called Regional Data Assimilation and Prediction System (RDAPS), the European Centre for Medium Range Weather Forecasts' newest 5th generation re-analysis data (ERA5), and Japanese Meteorological Agency's (JMA's) meso-scale forecasting data. We analyzed the accuracy of each model's results by comparing them to observation data. For quantitative analysis and assessment, the observed wave data for 6 locations from KMA and Korea Hydrographic and Oceanographic Agency (KHOA) were used, and statistical analysis was conducted to assess model accuracy. As a result, ST6 models had a smaller root mean square error and higher correlation coefficient than the default model in significant wave height prediction. However, for peak wave period simulation, the results were incoherent among each model and location. In simulations with different wind data, the simulation using ERA5 for input wind datashowed the most accurate results overall but underestimated the wave height in predicting high wave events compared to the simulation using RDAPS and JMA meso-scale model. In addition, it showed that the spatial resolution of wind plays a more significant role in predicting high wave events. Nevertheless, the numerical model optimized in this study highlighted some limitations in predicting high waves that rise rapidly in time caused by meteorological events. This suggests that further research is necessary to enhance the accuracy of wave prediction in various climate conditions, such as extreme weather.