• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.28-35
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• 2006

Global Flood Alert System (GFAS) is an attempt to make the best use of satellite rainfall data in flood forecasting. The project of GFAS is promoted both by Ministry of Land, Infrastructure and Transport-Japan (MLIT) and Japan Aerospace Exploration Agency (JAXA), under which Infrastructure Development Institute-Japan (IDI) has been working on the development of Internet-based information system and just launched trial run of GFAS in April 2006 on International Flood Network (IFNet) website. The function of GFAS is to connect space agencies and hydrological services/river authorities in charge of flood forecasting and warning by providing global rainfall information in maps, text data e-mails and so on which is produced from binary global rainfall data downloaded from National Aeronautics and Space Administration (NASA) website. Although the effectiveness of satellite rainfall data in flood forecasting and warning has yet to be verified, satellite rainfall is expected to play an important role to strengthen existing flood forecasting systems by diversifying hydrological data source.

• Journal of Environmental Science International
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• v.22 no.3
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• pp.303-308
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• 2013

This paper proposed a new method for estimating missing values in time series rainfall data. The proposed method integrated the two most widely used estimation methods, general linear model(GLM) and ordinary kriging(OK), by taking a weighted average of covariance matrices derived from each of the two methods. The proposed method was cross-validated using daily rainfall data at thirteen rain gauges in the Hyeong-san River basin. The goodness-of-fit of the proposed method was higher than those of GLM and OK, which can be attributed to the weighting algorithm that was designed to minimize errors caused by violations of assumptions of the two existing methods. This result suggests that the proposed method is more accurate in missing values in time series rainfall data, especially in a region where the assumptions of existing methods are not met, i.e., rainfall varies by season and topography is heterogeneous.

• Journal of Environmental Science International
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• v.27 no.10
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• pp.845-851
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• 2018

In this study, the runoff characteristics of the non-rainfall period were examined using daily rainfall data from 1977 to 2017 and the data of runoff into the dam. Results showed that, the mean runoff decreases with longer non-rainfall periods in the Andong dam basin. The correlation coefficient between non-rainfall days and average runoff reaches 0.85. The results of the analysis of the runoff characteristics during the non-rainfall period, based on the preceding rainfall of Andong dam are as follows. The runoff characteristics of the entire non-rainfall period, shows that, for a rainfall of 1.0 mm or less, the runoff height was larger than the rainfall size and the base runoff larger. The correlation between the antecedent rainfall and runoff height was reached as high as 0.9864 in the 30 ~ 50 mm interval of the antecedent rainfall period, and this is the interval where the linearity of rainfall and runoff was at its maximum in the Andong dam basin. The correlation between the antecedent rainfall and the runoff height reached 0.92 for rainfalls of 100.0 mm. However, for rainfalls of 100.0 mm greater, the correlation between the antecedent rainfall and runoff height during the rainfall period was 0.64, which is relatively small. In this study, we investigated the runoff characteristics of the rainfall period in the Andong dam watershed. As a result, it was confirmed that the mean runoff decreased with rainfall duration. The linearity was found to be weak for rainfall events greater than 100.0 mm. The results of this study can be used as data for water balance analysis and for formulating a water supply plan to establish water resource management of Andong dam.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2B
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• pp.137-147
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• 2010

In this study, Chukwooki and modern data were compared using annual maximum rainfall event series. Annual maximum series for specified rainfall duration in modern frequency analysis can not be constructed from Chukwooki data, so the concept of independent rainfall event is introduced to compare Chukwooki and modern data. Annual maximum rainfall event is determined by applying the bivariate exponential distribution and the parameters estimated annually are selected. The results using the annual parameter show that the hydrological meaning of the parameters is related to the variation of annual total rainfall amounts. For the whole independent rainfall events, the total rainfall and the rainfall intensity of Chukwooki data are greater than those of modern data, and rainfall duration of the two periods is similar. However modern annual maximum rainfall events show different characteristics that rainfall duration is much longer, rainfall intensity is similar and the total rainfall is greater than those of Chukwooki period. The increasing trend of rainfall duration and total rainfall of the modern annual rainfall events may be regarded as the one of components of the long-term cycle.

• Journal of Korea Multimedia Society
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• v.20 no.12
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• pp.1874-1889
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• 2017

The paper aims to analyze structure of I/Q data observed from radar and reliably estimate rainfall through quality control of I/Q data that can quantify uncertainty of I/Q data occurring due to resultant errors. Radar rainfall data have strong uncertainty due to various factors influencing quality. In order to reduce this uncertainty, previously enumerated errors in quality need to be eliminated. However, errors cannot be completely eliminated in some cases as seen in random errors, so uncertainty is necessarily involved in radar rainfall data. Multi-Lag Method, one of I/Q data quality control methods, was applied to estimate precipitation with regard to I/Q data of rainfall radar in Mt. Sobaek.

• Journal of Korean Society on Water Environment
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• v.32 no.3
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• pp.310-317
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• 2016

It has been a big problem to estimate rainfall for the studies of mud-debris flows because the estimated rainfall from the nearest AWS (Automatic Weather Station) can tend to be quite inaccurate at individual sites. This study attempts to improve this problem through accurate rainfall depth estimation by applying an artificial neural network with radar rainfall data. For this, three models were made according to utilizing methodologies of rainfall data. The first model uses the nearest rainfall, observing the site from an ungauged site. The second uses only radar rainfall data and the third model integrates the above two models using both radar and observed rainfall at the sites around the ungauged site. This methodology was applied to the metropolitan area in Korea. It appeared as though the third model improved rainfall estimations by the largest margin. Therefore, the proposed methodology can be applied to forecast mud-debris flows in ungageed sites.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.3
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• pp.29-39
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• 2017

In this study, the GPM (Global Precipitation Mission) IMERG (Integrated Multi-satellitE retrievals for GPM) rainfall data was verified and evaluated using ground AWS (Automated Weather Station) and radar in order to investigate the availability of GPM IMERG rainfall data. The SPI (Standardized Precipitation Index) was calculated based on the GPM IMERG data and also compared with the results obtained from the ground observation data for the Hoengseong Dam and Yongdam Dam areas. For the radar data, 1.5 km CAPPI rainfall data with a resolution of 10 km and 30 minutes was generated by applying the Z-R relationship ($Z=200R^{1.6}$) and used for accuracy verification. In order to calculate the SPI, PERSIANN_CDR and TRMM 3B42 were used for the period prior to the GPM IMERG data availability range. As a result of latency verification, it was confirmed that the performance is relatively higher than that of the early run mode in the late run mode. The GPM IMERG rainfall data has a high accuracy for 20 mm/h or more rainfall as a result of the comparison with the ground rainfall data. The analysis of the time scale of the SPI based on GPM IMERG and changes in normal annual precipitation adequately showed the effect of short term rainfall cases on local drought relief. In addition, the correlation coefficient and the determination coefficient were 0.83, 0.914, 0.689 and 0.835, respectively, between the SPI based GPM IMERG and the ground observation data. Therefore, it can be used as a predictive factor through the time series prediction model. We confirmed the hydrological utilization and the possibility of real time drought monitoring using SPI based on GPM IMERG rainfall, even though results presented in this study were limited to some rainfall cases.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.3
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• pp.37-48
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• 2010

The purpose of this paper is to evaluate how the rainfall field effect on a runoff simulation using grid radar rainfall data and ground gauge rainfall. The Gwangdeoksan radar and ground-gauge rainfall data were used to estimate a spatial rainfall field, and a hydrologic model was used to evaluate whether the rainfall fields created by each method reproduced a realistically valid spatial and temporal distribution. Pilot basin in this paper was the Naerin stream located in Inje-gun, Gangwondo, 250m grid scale digital elevation data, land cover maps, and soil maps were used to estimate geological parameters for the hydrologic model. For the rainfall input data, quantitative precipitation estimation(QPE), adjusted radar rainfall, and gauge rainfall was used, and then compared with the observed runoff by inputting it into a $Vflo^{TM}$ model. As a result of the simulation, the quantitative precipitation estimation and the ground rainfall were underestimated when compared to the observed runoff, while the adjusted radar rainfall showed a similar runoff simulation with the actual observed runoff. From these results, we suggested that when weather radars and ground rainfall data are combined, they have a greater hydrological usability as input data for a hydrological model than when just radar rainfall or ground rainfall is used separately.

• Journal of Korean Society of Water and Wastewater
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• v.22 no.2
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• pp.195-203
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• 2008

This study collected the latest 30-year (1976~2005) continuous rainfall data hourly recorded at 61 meterological observatories in Korea. The continuous rainfall data was divided into individual rainfall events. In addition, distribution charts of average intervent times between adjacent rainfall events were created to facilitate the application to the overflow risk-based design of storm-water infiltration basin. This study shows that the one-parameter exponential distribution is suitable for the frequency distribution of the average intervent times for the domestic rainfall data. Distribution charts of the average intervent times were created for 4 hour and 6 hour of storm separation time, respectively. The inland Gyeongsangbuk-do and Western coastal area had relatively longer average intervent times, whereas Southern coastal area and Jeju-do had relatively shorter average intervent times.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.152-152
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• 2022

Only employing historical data limits the estimation of the full distribution of probable Tropical Cyclone (TC) risk due to the insufficiency of samples. Addressing this limitation, this study introduces a semi-physical TC rainfall model that produces spatially and temporally resolved TC rainfall data to improve TC risk assessments. The model combines a statistical-based track model based on the Markov renewal process to produce synthetic TC tracks, with a physics-based model that considers the interaction between TC and the atmospheric environment to estimate TC rainfall. The simulated data from the combined model are then fitted to a probability distribution function to compute the spatially heterogeneous risk brought by landfalling TCs. The methodology is employed in South Korea as a case study to be able to implement a country-scale-based vulnerability inspection from damaging TC impacts. Results show that the proposed model can produce TC tracks that do not only follow the spatial distribution of past TCs but also reveal new paths that could be utilized to consider events outside of what has been historically observed. The model is also found to be suitable for properly estimating the total rainfall induced by landfalling TCs across various points of interest within the study area. The simulated TC rainfall data enable us to reliably estimate extreme rainfall from higher return periods that are often overlooked when only the historical data is employed. In addition, the model can properly describe the distribution of rainfall extremes that show a heterogeneous pattern throughout the study area and that vary per return period. Overall, results show that the proposed approach can be a valuable tool in providing sufficient TC rainfall samples that could be an aid in improving TC risk assessment.