• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.122-122
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• 2020

Urban flood simulation plays a vital role in national flood early warning, prevention and mitigation. In recent studies on 2-dimensional flood modeling, the integrated run-off inundation model is gaining grounds due to its ability to perform in greater computational efficiency. The adaptive quadtree shallow water numerical technique used in this model implements the adaptive mesh refinement (AMR) in this simulation, a procedure in which the grid resolution is refined automatically following the flood flow. The method discounts the necessity to create a whole domain mesh over a complex catchment area, which is one of the most time-consuming steps in flood simulation. This research applies the dynamic grid refinement method in simulating the recent extreme flood events in Metro Manila, Philippines. The rainfall events utilized were during Typhoon Ketsana 2009, and Southwest monsoon surges in 2012 and 2013. In order to much more visualize the urban flooding that incorporates the flow within buildings and high-elevation areas, Digital Surface Model (DSM) resolution of 5m was used in representing the ground elevation. Results were calibrated through the flood point validation data and compared to the present flood hazard maps used for policy making by the national government agency. The accuracy and efficiency of the method provides a strong front in making it commendable to use for early warning and flood inundation analysis for future similar flood events.

• Journal of Korea Water Resources Association
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• v.50 no.11
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• pp.735-743
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• 2017

With growing concerns about ever-increasing anthropogenic greenhouse gas emissions, it is crucial to enhance preparedness for unprecedented extreme weathers that can bring catastrophic consequences. In this study, we proposed a stochastic framework that considers uncertainty in weather forecasts for flood analyses. First, we calibrated a simple rainfall-runoff model against observed hourly hydrographs. Then, using probability density functions of rainfall depths conditioned by 6-hourly weather forecasts, we generated many stochastic rainfall depths for upcoming 48 hours. We disaggregated the stochastic 6-hour rainfalls into an hourly scale, and input them into the runoff model to quantify a probabilistic range of runoff during upcoming 48 hours. Under this framework, we assessed two rainfall events occurred in Bocheong River Basin, South Korea in 2017. It is indicated actual flood events could be greater than expectations from weather forecasts in some cases; however, the probabilistic runoff range could be intuitive information for managing flood risks before events. This study suggests combining deterministic and stochastic methods for forecast-based flood analyses to consider uncertainty in weather forecasts.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.155-169
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• 2016

In this study, we developed a multi-sensor blending short-term rainfall forecasting technique using radar and satellite data during extreme rainfall occurrences in Busan and Gyeongnam region in August 2014. The Tropical Z-R relationship ($Z=32R^{1.65}$) has applied as a optimal radar Z-R relation, which is confirmed that the accuracy is improved during 20mm/h heavy rainfall. In addition, the multi-sensor blending technique has applied using radar and COMS (Communication, Ocean and Meteorological Satellite) data for quantitative precipitation estimation. The very-short-term rainfall forecasting performance was improved in 60 mm/h or more of the strong heavy rainfall events by multi-sensor blending. AWS (Automatic Weather System) and MAPLE data were used for verification of rainfall prediction accuracy. The results have ensured about 50% or more in accuracy of heavy rainfall prediction for 1-hour before rainfall prediction, which are correlations of 10-minute lead time have 0.80 to 0.53, and root mean square errors have 3.99 mm/h to 6.43 mm/h. Through this study, utilizing of multi-sensor blending techniques using radar and satellite data are possible to provide that would be more reliable very-short-term rainfall forecasting data. Further we need ongoing case studies and prediction and estimation of quantitative precipitation by multi-sensor blending is required as well as improving the satellite rainfall estimation algorithm.

• Journal of Korea Water Resources Association
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• v.37 no.7
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• pp.553-567
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• 2004

This study proposes a modified standardized precipitation index (MSPI) which was developed to make up for the weakness of the SPI. Both MSPI and SPI are applied to the monthly rainfall at the Seoul station for the drought analysis. The MSPI proposed is nothing but the SPI for the normalized monthly rainfall, that is, an extra step for normalizing the monthly rainfall is included before driving the SPI. Thus, the MSPI has a structure to transfer the relative amount of rainfall to the next months, but the SPI the absolute amount of rainfall. The monthly rainfall data at the Seoul station used in this study are those collected from 1777 to 1996. The rainfall data collected before and after the long dry period around 1900 were also analyzed separately for the comparison. The results derived are as follows. (1) The MSPI was found to be more practical compared to the SPI. This was assured by comparing the analysis results of the data including and excluding the long dry period around 1900. (2) The MSPI is found to be less sensitive than the SPI to the extreme rainfall events. For the MSPI, the occurrence probabilities of moderate drought before and after the long dry period are similar, but those for the extreme drought becomes slightly decreased after the long dry period (from about 18 years of return period before the long dry period to the 16 years after the long dry period). However, the duration becomes longer after the long dry period (the duration for the extreme drought has been increased from 2 to 2.5 months after the long dry period). This results can also be compared with a rather unreasonable result derived by applying the SPI (for the extreme drought the return period has been decreased to be from 25 to 10 years after the long dry period, on the other hand the duration has been increased from 1.5 months to 3.5 months). So, we man conclude that the MSPI is more practical for the drought analysis that the SPI.

• Journal of Environmental Science International
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• v.24 no.4
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• pp.383-391
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• 2015

As occurrence of gradually increasing extreme temperature events in Jeju Island, a hybrid downscaling technique that simultaneously applies by dynamical method and statistical method has implemented on design rainfall in order to reduce flood damages from severe storms and typhoons.As a result of computation, Case 1 shows a strong tendency to excessively compute rainfall, which is continuously increasing. While Case 2 showed similar trend as Case 1, low design rainfall has computed by rainfall in A1B scenario. Based on the design rainfall computation method mainly used in Preventive Disaster System through Pre-disaster Effect Examination System and Basic Plan for River of Jeju Island which are considering climatic change for selecting 50-year and 100-year frequencies. Case 3 selecting for Jeju rain gage station and Case 1 for Seogwipo rain gage station. The results were different for each rain gage station because of difference in rainfall characteristics according to recent climatic change, and the risk of currently known design rainfall can be increased in near future.

• Journal of Wetlands Research
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• v.17 no.3
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• pp.251-263
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• 2015

This study suggested a novel approach of estimating the optimal probability density function (OPDF) of the annual maximum rainfall time series (AMRT) combining the L-moment ratio diagram and the geographical information system. This study also reported several interesting geographical characteristics of the AMRT in Korea. To achieve this purpose, this study determined the OPDF of the AMRT with the duration of 1-, 3-, 6-, 12-, and 24-hours using the method of L-moment ratio diagram for each of the 67 rain gages in Korea. Then, a map with the Thiessen polygons of the 67 rain gages colored differently according the different type of the OPDF, was produced to analyze the spatial trend of the OPDF. In addition, this study produced the color maps which show the fitness of a given probability density function to represent the AMRT. The study found that (1) both L-skewness and L-kurtosis of the AMRT have clear geographical trends, which means that the extreme rainfall events are highly influenced by geography; (2) the impact of the altitude on these two rainfall statistics is greater for the mountaneous region than for the non-mountaneous region. In the mountaneous region, the areas with higher altitude are more likely to experience the less-frequent and strong rainfall events than the areas with lower altitude; (3) The most representative OPDFs of Korea except for the Southern edge are Generalized Extreme Value distribution and the Generalized Logistic distribution. The AMRT of southern edge of Korea was best represented by the Generalized Pareto distribution.

• Atmosphere
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• v.29 no.4
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• pp.451-461
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• 2019

Medium-range forecast is highly dependent on ensemble forecast data. However, operational weather forecasters have not enough time to digest all of detailed features revealed in ensemble forecast data. To utilize the ensemble data effectively in medium-range forecasting, representative weather patterns in East Asia in this study are defined. The k-means clustering analysis is applied for the objectivity of weather patterns. Input data used daily Mean Sea Level Pressure (MSLP) anomaly of the ECMWF ReAnalysis-Interim (ERA-Interim) during 1981~2010 (30 years) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Using the Explained Variance (EV), the optimal study area is defined by 20~60°N, 100~150°E. The number of clusters defined by Explained Cluster Variance (ECV) is thirty (k = 30). 30 representative weather patterns with their frequencies are summarized. Weather pattern #1 occurred all seasons, but it was about 56% in summer (June~September). The relatively rare occurrence of weather pattern (#30) occurred mainly in winter. Additionally, we investigate the relationship between weather patterns and extreme weather events such as heat wave, cold wave, and heavy rainfall as well as snowfall. The weather patterns associated with heavy rainfall exceeding 110 mm day^-1 were #1, #4, and #9 with days (%) of more than 10%. Heavy snowfall events exceeding 24 cm day^-1 mainly occurred in weather pattern #28 (4%) and #29 (6%). High and low temperature events (> 34℃ and < -14℃) were associated with weather pattern #1~4 (14~18%) and #28~29 (27~29%), respectively. These results suggest that the classification of various weather patterns will be used as a reference for grouping all ensemble forecast data, which will be useful for the scenario-based medium-range ensemble forecast in the future.

• Journal of the Korean Geographical Society
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• v.43 no.5
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• pp.681-700
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• 2008

In this study, the spatial and temporal patterns of changes in means and extreme events of temperature and precipitation across the Republic of Korea over the last 35 years (1973-2007) are examined. Over the study period, meteorological winter (December-February) mean minimum (maximum) temperature has increased by $+0.54^{\circ}C$/decade ($+0.6^{\circ}C$/decade), while there have been no significant changes in meteorological summer (June-August) mean temperatures. According to analyses of upper or lower $10^{th}$ percentile-based extreme temperature indices, the annual frequency of cool nights (days) has decreased by -9.2 days/decade (-3.3 days/decade), while the annual frequency of warm nights (days) has increased by +4.9 days/decade (+6.8 days/decade). In contrast, the increase rates of summer warm nights (+8.0 days/$^{\circ}C$) and days (+6.6 days/$^{\circ}C$) relative to changes in summer means minimum and maximum temperatures means are greater than the decreasing rates of winter nights (-5.2 days/$^{\circ}C$) and days (-4.3 days/$^{\circ}C$) relative to changes in winter temperatures. These results demonstrate that seasonal and diurnal asymmetric changes in extreme temperature events have occurred. Moreover, annual total precipitation has increased by 85.5 mm/decade particularly in July and August, which led to the shift of a bimodal behavior of summer precipitation into a multi-modal structure. These changes have resulted from the intensification of heavy rainfall events above 40mm in recent decades, and spatially the statistically-significant increases in these heavy rainfall events are observed around the Taebaek mountain region.

• Journal of Soil and Groundwater Environment
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• v.17 no.2
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• pp.62-70
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• 2012

Rainfall, on Jeju Island varies regionally in relation to Mt. Halla with higher rainfall within southern area and lower in western area, and its variability is expected to expand according to the climate change scenario. Non-parametric trend analysis for rainfall, using both historic (1971-2010) and simulated (2011-2100) data assuming the A1B emissions scenario, shows regionally increasing trends with time. In perspective of agricultural land use, area for market garden including various crop types with high water demand is increasing over the Island, especially in the western area with lower rainfall compared to southern area. On the other hand, area for fruit including mandarin and kiwi with low water demand is widely distributed over southern and northern part having higher rainfall. These regional disparity of water demand/supply may be more affected by extreme events such as drought and heavy rainfall that has not yet been considered. Therefore, it is necessary to make policies for water resource management considering both demand and supply in different regions with climate change impacts over Jeju Island.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.2
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• pp.137-146
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• 2011

In this study, annual maximum storm events are evaluated by applying the bivariate extremal distribution. Rainfall quantiles of probabilistic storm event are calculated using OR case joint return period, AND case joint return period and interval conditional joint return period. The difference between each of three joint return periods was explained by the quadrant which shows probability calculation concept in the bivariate frequency analysis. Rainfall quantiles under AND case joint return periods are similar to rainfall depths in the univariate frequency analysis. The probabilistic storm events overcome the primary limitation of conventional univariate frequency analysis. The application of these storm event analysis provides a simple, statistically efficient means of characterizing frequency of extreme storm event.