• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.499-499
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• 2018

Hydro-meteorological extremes are trivial in these days. Therefore, it is important to identify extreme hydrological events in advance to mitigate the damage due to the extreme events. In this context, exploring temporal distribution of sub-daily extreme rainfall at multiple rain gauges would informative to identify different states to describe severity of the disaster. This study proposehidden Markov chain model (HMM) based rainfall analysis tool to understand the temporal sub-daily rainfall patterns over South Korea. Hourly and daily rainfall data between 1961 and 2017 for 92 stations were used for the study. HMM was applied to daily rainfall series to identify an observed hidden state associated with rainfall frequency and intensity, and further utilized the estimated hidden states to derive a temporal distribution of daily extreme rainfall. Transition between states over time was clearly identified, because HMM obviously identifies the temporal dependence in the daily rainfall states. The proposed HMM was very useful tool to derive the temporal attributes of the daily rainfall in South Korea. Further, daily rainfall series were disaggregated into sub-daily rainfall sequences based on the temporal distribution of hourly rainfall data.

• Magazine of the Korean Society of Agricultural Engineers
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• v.31 no.4
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• pp.72-80
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• 1989

Various analyses were made to investigate the stochastic structure of the daily rainfall in Korea. Records of daily rainfall amounts from 1951 to 1984 at Chinju Metesrological Station were used for this study. Obtained results are as follows : 1. Time series of the daily rainfall at Chinju were positively, serially correlated for the lag as large as one day. 2. Rainfall events, defined as a sequence of consecutive wet days separated by one or more dry days, showed a seasonal variation in the occurrence frequency. 3. The marginal distribution of event characteristics of each month showed significant dif- ferences each other. Events occurred in summer had longer duration and higher magnitude with higher intensity than those of events occurred in winter. 4. There were significant positive correlations among four event characteristics ; dura- tion, magnitude, average intensity, and maximum intensity. 5. Correlations among the daily rainfall amounts within an event were not significant in general. 6. There were no consistant significancy in identity or difference between the distribu- tions of daily rainfall amounts for different days within events. 7. Above mentioned characteristics of daily rainfall time series must be considered in building a stochastic model of daily rainfall.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.6
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• pp.73-80
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• 2017

The objective of this study is to investigate whether the daily rainfall depth derived from daily data represents the event rainfall depth derived from hourly data. For analysis, the 85th, 90th, and 95th percentile daily rainfall depths were first computed using daily rainfall data (1986~2015) collected at 63 weather stations. In addition, the storm event was separated by the interevent time definition (IETD) of 6, 12, 18, and 24 hr using hourly rainfall data. Based on the separated storm events, the 85th, 90th, and 95th percentile event rainfall depths were calculated and compared with the using hourly rainfall data with the 85th, 90th, and 95th percentile daily rainfall depths. The event rainfall depths computed using the IETD were greater than the daily rainfall depths. The difference between the event rainfall depth and the daily rainfall depth affects the design and size of the facility for controlling the stormwater. Therefore, the designer and policy decision-maker in designing the stormwater best management practices need to take into account the difference generated by the difference of the used rainfall data and the selected IETD.

• Journal of Korea Water Resources Association
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• v.52 no.2
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• pp.141-148
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• 2019

When simulating the daily rainfall amount by existing Markov Chain model, it is general to simulate the rainfall occurrence and to estimate the rainfall amount randomly from the distribution which is similar to the daily rainfall distribution characteristic using Monte Carlo simulation. At this time, there is a limitation that the characteristics of rainfall intensity and distribution by time according to the rainfall duration are not reflected in the results. In this study, 1-day, 2-day, 3-day, 4-day rainfall event are classified, and the rainfall amount is estimated by rainfall duration. In other words, the distributions of the total amount of rainfall event by the duration are set using the Kernel Density Estimation (KDE), the daily rainfall in each day are estimated from the distribution of each duration. Total rainfall amount determined for each event are divided into each daily rainfall considering the type of daily distribution of the rainfall event which has most similar rainfall amount of the observed rainfall using the k-Nearest Neighbor algorithm (KNN). This study is to develop the limitation of the existing rainfall estimation method, and it is expected that this results can use for the future rainfall estimation and as the primary data in water resource design.

• Magazine of the Korean Society of Agricultural Engineers
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• v.36 no.1
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• pp.63-72
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• 1994

The objective of the study is to develop weather generators for daily rainfall and small pan evaporation and to test the applicability with recorded data. Daily rainfall forecasting model(DRFM) was developed that uses a first order Markov chain to describe rainfall seque- nces and applies an incomplete Gamma function to predict the amount of precipitation. Daily evaporation forecasting model(DEFM) that adopts a normal distribution function to generate the evaporation for dry and wet days was also formulated. DRFM and DEFM were tested with twenty year weather data from eleven stations using Chi-square and Kolmogorov and Smirnov goodness of fit tests. The test results showed that the generated sequences of rainfall occurrence, amount of rainfall, and pan evaporation were statistically fit to recorded data from eleven, seven, and seven stations at the 5% level of significance. Generated rainfall data from DRFM were very close in frequency distri- bution patterns to records for stations all over the country. Pan evaporation for rainy days generated were less accurate than that for dry days. And the proposed models may be used as tools to provide many mathematical models with long-term daily rainfall and small pan evaporation data. An example is an irrigation scheduling model, which will be further detailed in the paper.

• Journal of Wetlands Research
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• v.24 no.3
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• pp.161-169
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• 2022

In this study to estimate suitable size of rain tank in Suwon district, monthly rainfall, daily rainfall duration curve and daily rainy days have been analyzed. Annual rainwater consumption and daily average amount of storage with respect to size of rain tank have been calculated by applying continuity equations that take account of daily rainfall, daily consumptive use, storage of rain tank, It has been shown that above 50 mm of rainfall in the ordinance related to reclamation water may be inappropriate regulation if annual amount of rainfall captured, efficiency of utilized rainwater, number of days for utilized rain tank, daily average amount of storage and daily consumptive use have been considered. Thus, it has been shown that suitable size of rain tank should be determined considering reasonable daily consumptive use with respect to district, constructed cost of rain tank and benefit of rain tank constructed.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.225-228
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• 2002

This research seeks to derive the design rainfalls through the L-moment with the test of homogeneity, independence and outlier of data on annual maximum daily rainfall in 38 Korean rainfall stations. To select the fit appropriate distribution of annual maximum daily rainfall data according to rainfall stations, applied were Generalized Extreme Value (GEV), Generalized Logistic (GLO) and Generalized Pareto (GPA) probability distributions were applied. and their aptness was judged Dusing an L-moment ratio diagram and the Kolmogorov-Smirnov (K-S) test, the aptitude was judged of applied distributions such as GEV, GLO and GPA. The GEV and GLO distributions were selected as the appropriate distributions. Their parameters were estimated Targetingfrom the observed and simulated annual maximum daily rainfalls and using Monte Carlo techniques, the parameters of GEV and GLO selected as suitable distributions were estimated and. dDesign rainfallss were then derived, using the L-moment. Appropriate design rainfalls were suggested by doing a comparative analysis of design rainfall from the GEV and GLO distributions according to rainfall stations.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.595-598
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• 2004

This research seeks to derive the design rainfalls through the L-moment with the test of homogeneity, independence and outlier of data on annual maximum daily rainfall at 38 rainfall stations in Korea. To select the appropriate distribution of annual maximum daily rainfall data by the rainfall stations, Generalized Extreme Value (GEV), Generalized Logistic (GLO), Generalized Pareto (GPA), Generalized Normal (GNO) and Pearson Type 3 (PT3) probability distributions were applied and their aptness were judged using an L-moment ratio diagram and the Kolmogorov-Smirnov (K-S) test. Parameters of appropriate distributions were estimated from the observed and simulated annual maximum daily rainfall using Monte Carlo techniques. Design rainfalls were finally derived by GEV distribution, which was proved to be more appropriate than the other distributions.

• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.3
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• pp.31-42
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• 2004

This research seeks to derive the design rainfalls through the L-moment with the test of homogeneity, independence and outlier of data on annual maximum daily rainfall at 38 rainfall stations in Korea. To select the appropriate distribution of annual maximum daily rainfall data by the rainfall stations, Generalized Extreme Value (GEV), Generalized Logistic (GLO), Generalized Pareto (GPA), Generalized Normal (GNO) and Pearson Type 3 (PT3) probability distributions were applied and their aptness were judged using an L-moment ratio diagram and the Kolmogorov-Smirnov (K-S) test. Parameters of appropriate distributions were estimated from the observed and simulated annual maximum daily rainfall using Monte Carlo techniques. Design rainfalls were finally derived by GEV distribution, which was proved to be more appropriate than the other distributions.

• The Journal of the Korea Contents Association
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• v.15 no.3
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• pp.406-414
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• 2015

Existing relative researches for traffic were studied under favorable weather or excluding impact of weather. This study present traffic volume variation according to rainfall intensity in national highway provincial road and rainfall-factor. Continuous traffic count section match AWS after selecting to analyze provincial road 256 section. Weekdays ADT(Average Daily Traffic) and rainfall-factor are influenced by rainfall a little because of business travel. But non-weekdays ADT and rainfall-factor are influenced much more than weekdays because of leisure travel. Estimated AADT(Annual Average Daily Traffic) by adjusting rainfall-factor is lower MAPE than non-adjusting rainfall factor. So, rainfall have to be considered when estimating AADT. ADT decrease according to rainfall intensity, continuous studies considered rainfall intensity are needed when road design and operation.