• Journal of Korean Society on Water Environment
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• v.24 no.4
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• pp.452-457
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• 2008

Nonpoint source (NPS) pollution is caused by rainfall moving over and through the ground. As the runoff moves, it picks up and carries away various pollutants from NPS. The discharge pattern of NPS pollutant loads is affected by the distribution of the rainfall during the year. This study analysed relationship between the rainfall event and the stream flow rate, and estimated the rainfall discharge ratio on the specific design flow which can be used as nonpoint discharge coefficient for the estimation of NPS pollution load. It is considered that nonpoint discharge coefficient can be effectively used for the calculation of NPS pollution load at the time of water quality modelling for the management of Total maximum daily load (TMDL).

• Journal of Wetlands Research
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• v.5 no.2
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• pp.1-13
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• 2003

In Korea, most of the rainfalls have been concentrated in the flood season and the flood study has received more attention than low flow analysis. One of the reasons that the analysis of low flows has less attention is the lacks of the required data like daily rainfall and so we have used the stochastic processes such as pulse noise, exponential distribution, and state model of Markov chain for the rainfall simulation in short term such as daily. Especially this study will pay attention to the state model of Markov chain. The previous study had performed the simulation study by the state model without considerations of the flood and non-flood periods and without consideration of the frequency of rainfall for the period of a state. Therefore this study considers afore mentioned two cases and compares the results with the known state model. As the results, the RMSEs of the suggested and known models represent the similar results. However, the PRE(relative percentage error) shows the suggested model is better results.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.150-150
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• 2022

This study investigates the roles of decomposition methods on high accuracy in daily rainfall prediction from light gradient boosting machine (LightGBM) algorithm. Here, empirical mode decomposition (EMD) and singular spectrum analysis (SSA) methods were considered to decompose and reconstruct input time series into trend terms, fluctuating terms, and noise components. The decomposed time series from EMD and SSA methods were used as input data for LightGBM algorithm in two hybrid models, including empirical mode-based light gradient boosting machine (EMDGBM) and singular spectrum analysis-based light gradient boosting machine (SSAGBM), respectively. A total of four parameters (i.e., temperature, humidity, wind speed, and rainfall) at a daily scale from 2003 to 2017 is used as input data for daily rainfall prediction. As results from statistical performance indicators, it indicates that the SSAGBM model shows a better performance than the EMDGBM model and the original LightGBM algorithm with no decomposition methods. It represents that the accuracy of LightGBM algorithm in rainfall prediction was improved with the SSA method when using multivariate dataset.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.1
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• pp.27-35
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• 2017

This research was to elucidate the generation method of daily precipitation data from monthly data. We applied a combined method of Markov chain and gamma distribution function using 4 specific parameters of ${\alpha}$, ${\beta}$, p(W/W) and p(W/D) for generation of daily rainfall data using daily precipitation data for the past 30 years which were collected from the country's 23 meteorological offices. Four parameters, applied to use for the combination method, were calculated by maximum likelihood method in location of 23 sites. There are high correlations of 0.99, 0.98 and 0.98 in rainfall days, rainfall probability and mean amount of daily rainfall between measured and simulated data in case of those parameters. In case of using parameters estimated from monthly precipitation, correlation coefficients in rainfall days, rainfall probability and mean amount of daily rainfall are 0.84, 0.83 and 0.96, respectively. We concluded that a combination method with parameter estimation from monthly precipitation data can be applied, in practical purpose such as assessment of climate change in agriculture and water resources, to get daily precipitation data in Korea.

• Journal of Korea Water Resources Association
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• v.44 no.3
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• pp.221-230
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• 2011

The climate change has been observed in Korea as well as in the entire world recently. The rainstorm has been gradually increased and then the damage has been grown. It is getting important to predict short-term rainfall. The Korea Meteorological Administration (KMA) generates numerical model outputs which are computed by Global Data Assimilation and Prediction System (GDAPS) and Regional Data Assimilation and Prediction System (RDAPS). The KMA predicts rainfall using RDAPS results. RDAPS model generates 48 hours data which is organized 3 hours data accumulated at 00UTC and 12UTC. RDAPS results which are organized 3 hours time scale are converted into daily rainfall to compare observed daily rainfall. In this study, 9 cases are applied to convert RDAPS results to daily rainfall data. The MAP (mean areal precipitation) in Geum river basin are computed by using KMA which are 2005 are used. Finally, the best case which gives the close value to the observed rainfall data is obtained using the average absolute relative error (AARE) especially for the Geum River basin.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1999.10c
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• pp.111-116
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• 1999

The daily irrigation water intakes from five reservoirs were measured and the water management characteristics analyzed . During the irrigation seasons in 1998 , the total water supply rates ranged from 534 to 864 mm, and thedelivery losses varied from5 to 17 pervent. Major factors affecting the water supply rates were rice transplanting and water management , and rainfall distributions during the growing seasons. The consumptive uses and effective rainfall from each researvoir were compared satisfactorily with the simulated results from the Daily Irrigation Reservoir Operation Model , DIROM.

• Journal of Korea Water Resources Association
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• v.49 no.1
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• pp.41-49
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• 2016

This study proposed a new drought index considering the accumulative pattern of daily rainfall, i.e., Rainfall Accumulation Drought Index (RADI). The RADI can be easily calculated at daily scale by comparing the long-term averaged cumulative rainfall to the observed cumulative rainfall for a specific duration. This study evaluated the availability of the RADI in the field of monitoring short-term and long-term droughts by investigating the spatial and temporal variability and the recurrence cycle of drought in South Korea. To present the short-term and long-term droughts, the various SPIs with different durations should be used in practice. However, the RADI can present and monitor both short-term and long-term droughts as a single index. By investigating the national average of the RADI, specific drought patterns of 20-year cycle were identified in this study. This study also proposed a five-level drought classification considering occurrence probability that would be a suitable alternative as a drought criterion for drought forecast/response.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.390-390
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• 2019

Application of Hidden Markov Model (HMM) to the hydrological time series would be an innovative way to identify extreme rainfall events in a series. Even though the optimum number of hidden states can be identify based on maximizing the log-likelihood or minimizing Bayesian information criterion. However, occasionally value for the log-likelihood keep increasing with the state which gives false identification of the optimum hidden state. Therefore, this study attempts to identify optimum number of hidden states for Colombo station, Sri Lanka as fundamental approach to identify frequency and percentage of extreme rainfall events for the station. Colombo station consisted of daily rainfall values between 1961 and 2015. The representative station is located at the wet zone of Sri Lanka where the major rainfall season falls on May to September. Therefore, HMM was ran for the season of May to September between 1961 and 2015. Results showed more or less similar log-likelihood which could be identified as maximum for states between 4 to 7. Therefore, measure of central tendency (i.e. mean, median, mode, standard deviation, variance and auto-correlation) for observed and simulated daily rainfall series was carried to each state to identify optimum state which could give statistically compatible results. Further, the method was applied for the second major rainfall season (i.e. October to February) for the same station as a comparison.

• Water for future
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• v.15 no.2
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• pp.23-32
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• 1982

This study is to clarify the relation between the maximum 24 hour rainfall and the daily rainfall of a designated time 10 A.M., using the 506 rainfall datum from 32 rain-guage stations on the Han river basin covering a period of 7 years and trying to estimate the ratio of two data in accordance with the amount of rainfall respectively. The Mononobe's formula, which is widely used in this country, has the value of 2/3 power in it. The "n" was considered instead of 2/3 and derivated for each guaging station. The results make it possible to establish the Ison-n value map, and show that the n value is affected mainly by the topographical conditions. The daily rainfall of a designated time can be modified by the results of this study and expressed as Y(%)=218.25/R$$. But in the case of exceeding 200mm/day, it is recommended to use the 110% for safety. On the problems of intensity-duration concerned with the planning of public works, the formula can be expressed as r$$=fRday/24.(24/t)$$, where "f" is Y(%) divided by 100. As this study was done with the datum within shor period, it is necessary to study more about the "n" and "f" value so as to get previse value in the future.o as to get previse value in the future.

• Journal of Korea Water Resources Association
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• v.45 no.3
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• pp.263-274
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• 2012

Recently, due to various climate variabilities, extreme rainfall events have been occurring all over the world. Extreme rainfall events in Korea mainly result from the summer typhoon storms and the localized convective storms. In order to estimate appropriate quantiles for extreme rainfall, this study considered the probability behavior of daily rainfall from the typhoons and the convective storms which compose the annual maximum rainfalls (AMRs). The conventional rainfall frequency analysis estimates rainfall quantiles based on the assumption that the AMRs are extracted from an identified single population, whereas this study employed a mixed distribution function to incorporate the different statistical characteristics of two types of rainfalls into the hydrologic frequency analysis. Selecting 15 rainfall gauge stations where contain comparatively large number of measurements of daily rainfall, for various return periods, quantiles of daily rainfalls were estimated and analyzed in this study. The results indicate that the mixed Gumbel distribution locally results in significant gains and losses in quantiles. This would provide useful information in designing flood protection systems.