• Journal of Wetlands Research
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• v.11 no.1
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• pp.49-64
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• 2009

Rainfall-runoff models are used for efficient management, distribution, planning, and design of water resources in accordance with the process of hydrologic cycle. The models simplify the transition of rainfall to runoff as rainfall through different processes including evaporation, transpiration, interception, and infiltration. As the models simplify complex physical processes, gaps between the models and actual rainfall events exist. For more accurate simulation, appropriate models that suit analysis goals are selected and reliable long-term hydrological data are collected. However, uncertainty is inherent in models. It is therefore necessary to evaluate reliability of simulation results from models. A number of studies have evaluated uncertainty ingrained in rainfall-runoff models. In this paper, Meta-Gaussian method proposed by Montanari and Brath(2004) was used to assess uncertainty of simulation outputs from rainfall-runoff models. The model, which estimates upper and lower bounds of the confidence interval from probabilistic distribution of a model's error, can quantify global uncertainty of hydrological models. In this paper, Meta-Gaussian method was applied to analyze uncertainty of simulated runoff outputs from $Vflo^{TM}$, a physically-based distribution model and HEC-HMS model, a conceptual lumped model.

• Atmosphere
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• v.24 no.3
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• pp.365-378
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• 2014

The limits of S-band dual-polarization radars in Korea are not reflected on the recent weather forecasts of Korea Meteorological Administration and furthermore, they are only utilized for rainfall estimations and hydrometeor classification researches. Therefore, this study applied four merging methods [SA (Simple Average), WA (Weighted Average), SSE (Sum of Squared Error), TV (Time-varying mergence)] to the QPE (Quantitative Precipitation Estimation) model [called RAR (Radar-AWS Rainfall) calculation system] using single-polarization radars and S-band dual-polarization radar in order to improve the accuracy of the rainfall estimation of the RAR calculation system. As a result, the merging results of the WA and SSE methods, which are assigned different weights due to the accuracy of the individual model, performed better than the popular merging method, the SA (Simple Average) method. In particular, the results of TVWA (Time-Varying WA) and TVSSE (Time-Varying SSE), which were weighted differently due to the time-varying model error and standard deviation, were superior to the WA and SSE. Among of all the merging methods, the accuracy of the TVWA merging results showed the best performance. Therefore, merging the rainfalls from the RAR calculation system and S-band dual-polarization radar using the merging method proposed by this study enables to improve the accuracy of the quantitative rainfall estimation of the RAR calculation system. Moreover, this study is worthy of the fundamental research on the active utilization of dual-polarization radar for weather forecasts.

• Journal of Korea Water Resources Association
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• v.40 no.11
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• pp.841-849
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• 2007

This study evaluated the effect of combined rainfall observation of using rain gauge and rain radar. The effect of combined observations is to be evaluated by considering the decrease of measurement error due to combined use of design orthogonal observation methods. As an example, this study evaluated the rain gauge network of the Keum river basin, and showed how the density of rain gauges could be decreased by combining the radar observation. This study applied the researches on sampling error by North and Nakamoto(1989), Yoo et al. (1996) and Yoo (1997), also the simple NFD model for representing the rainfall field. The model parameters were decided using the rainfall characteristics (correlation time and length) estimated using the data collected in the Keum River Basin by 28 rain gauges and the operation rule of radar was assumed arbitrarily. This study considered the rain gauge density criteria provided by WMO(1994) and the rain gauge density installed in the Keum river basin to decrease the rain gauge density under the condition of introducing the radar.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.743-754
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• 2019

The erosive potential of precipitation can be evaluated by the kinetic energy transferred to the soil by the impact of the rain drop. A kinetic energy rate of the rain drops was estimated by the disdrometer classifying impact signals. This equation in the form of power presented an adjustment measure between the rain rate and rainfall quantity of 97% and 95% for continental and maritime rains, respectively. The exponent of the power equation, initially, shows no dependence on the type of rainfall. However, the multiplicative factor presented variation, which can be adjusted according to rainfall events. This equation was validated by the coefficient of determination, the average absolute error and the confidence error. The kinetic energy of precipitation, associated to certain types of soil, will allow the determination of the potential of the erosion caused by the rains.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1053-1064
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• 2022

The Thiessen method, which is the current area average precipitation method, has serious structural limitations in accurately calculating the average precipitation in the watershed. In addition to the observation accuracy of the precipitation meter, errors may occur in the area average precipitation calculation depending on the arrangement of the precipitation meter and the direction of the heavy rain. When the watershed is small and the station density is sparse, in both simulation and observation history, the Thiessen method showed a peculiar tendency that the average precipitation in the watershed continues to increase and decrease rapidly for 10 minutes before and after the peak. And the average precipitation in the Thiessen basin was different from the rainfall radar at the peak time. In the case where the watershed is small but the station density is relatively high, overall, the Thiessen method did not show a trend of sawtooth-shaped over-peak, and the time-dependent fluctuations were similar. However, there was a continuous time lag of about 10 minutes between the rainfall radar observations and the ground precipitation meter observations and the average precipitation in the basin. As a result of examining the ground correction effect of the rainfall radar watershed average precipitation, the correlation between the area average precipitation after correction is rather low compared to the area average precipitation before correction, indicating that the correction effect of the current rainfall radar ground correction algorithm is not high.

• Journal of the Korean earth science society
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• v.34 no.1
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• pp.13-27
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• 2013

Quantitative precipitation estimation (QPE) is one of the most important elements in meteorological and hydrological applications. In this study, we adjusted the QPE from an S-band weather radar based on co-kriging method using the geostatistical structure function of error distribution of radar rainrate. In order to estimate the accurate quantitative precipitation, the error of radar rainrate which is a primary variable of co-kriging was determined by the difference of rain rates from rain gauge and radar. Also, the gauge rainfield, a secondary variable of co-kriging is derived from the ordinary kriging based on raingauge network. The error distribution of radar rain rate was produced by co-kriging with the derived theoretical variogram determined by experimental variogram. The error of radar rain rate was then applied to the radar estimated precipitation field. Locally heavy rainfall case during 6-7 July 2009 is chosen to verify this study. Correlation between adjusted one-hour radar rainfall accumulation and rain gauge rainfall accumulation improved from 0.55 to 0.84 when compared to prior adjustment of radar error with the adjustment of root mean square error from 7.45 to 3.93 mm.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.475-478
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• 2003

This study was conducted to compare the design rainfall derived by the at-site and regional frequency analysis based on the regionalization of the precipitation. The regional and at-site design rainfalls were calculated by Log-Pearson type III distribution using Indirect Methods of Moments(WRC). The regional and at-site analysis for the design rainfall were tested by Monte Carlo simulation. Relative root-mean-square error(RRMSE), Relative bias(RBIAS) and Relative reduction(RR) in RRMSE were computed and compared between design rainfalls resulted from observed and simulated data using the regional and at-site analysis. It was shown that the regional analysis procedure can substantially reduce the RRMSE, RBIAS in comparison with those of at-site analysis. Consequently, optimal design rainfalls following the regions and consecutive durations were derived by the regional frequency analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.318-322
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• 2004

This study was conducted to derive the regional design rainfall by the regional frequency analysis based on the regionalization of the precipitation. For the estimation of the regional design rain(all, classification of the climatologically and geographically homogeneous regions should be preceded preferentially The optimal regionalization of the precipitation data were classified by the above mentioned conditions for all over the regions except Jeju and Ulleung islands in Korea. Relative root mean square error(RRMSE), relative bias(RBIAS) and relative reduction(RR) in RRMSE for the design rainfall were computed and compared between the regional and at-site frequency analysis. Consequently, optimal design rainfalls following the classified regions and consecutive durations were derived by the regional frequency analysis using GEV distribution which was identified to be more optimal one than the other applied distributions.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.5
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• pp.751-757
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• 2011

The monitoring technique based on electrical conductivity (EC) can provide researchers with some advantages in maintenance management and is cost-effective as compared with existing CSOs monitoring. In this study, the flow rate estimation using EC data was executed in two sites where storm overflow chamber had installed. In the result of A-site, R2 of second order multinomial between dilution ratio of EC and observed flow rate was showed the range of 0.68 ~ 0.77. And $R^{2}$ of B-site was 0.62 ~ 0.81. On the other hand, cumulative frequency of A-site was 43.4 ~ 52.2% in the relative error level of under 20%. And B-site was 10.1 ~ 46.5%. The flow rate estimation formula was improved through consideration of some parameters including antecedent dry days and rainfall duration. And difference between estimated flow rate and observed flow rate in total rainfall event was very small.

• Journal of the Korean earth science society
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• v.37 no.7
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• pp.420-433
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• 2016

The purpose of this study is to improve the calibration matrixes of 2-D and 3-D convective rainfall rates (CRR) using the brightness temperature of the infrared $10.8{\mu}m$ channel (IR), the difference of brightness temperatures between infrared $10.8{\mu}m$ and vapor $6.7{\mu}m$ channels (IR-WV), and the normalized reflectance of the visible channel (VIS) from the COMS satellite and rainfall rate from the weather radar for the period of 75 rainy days from April 22, 2011 to October 22, 2011 in Korea. Especially, the rainfall rate data of the weather radar are used to validate the new 2-D and 3-DCRR calibration matrixes suitable for the Korean peninsula for the period of 24 rainy days in 2011. The 2D and 3D calibration matrixes provide the basic and maximum CRR values ($mm\;h^{-1}$) by multiplying the rain probability matrix, which is calculated by using the number of rainy and no-rainy pixels with associated 2-D (IR, IR-WV) and 3-D (IR, IR-WV, VIS) matrixes, by the mean and maximum rainfall rate matrixes, respectively, which is calculated by dividing the accumulated rainfall rate by the number of rainy pixels and by the product of the maximum rain rate for the calibration period by the number of rain occurrences. Finally, new 2-D and 3-D CRR calibration matrixes are obtained experimentally from the regression analysis of both basic and maximum rainfall rate matrixes. As a result, an area of rainfall rate more than 10 mm/h is magnified in the new ones as well as CRR is shown in lower class ranges in matrixes between IR brightness temperature and IR-WV brightness temperature difference than the existing ones. Accuracy and categorical statistics are computed for the data of CRR events occurred during the given period. The mean error (ME), mean absolute error (MAE), and root mean squire error (RMSE) in new 2-D and 3-D CRR calibrations led to smaller than in the existing ones, where false alarm ratio had decreased, probability of detection had increased a bit, and critical success index scores had improved. To take into account the strong rainfall rate in the weather events such as thunderstorms and typhoon, a moisture correction factor is corrected. This factor is defined as the product of the total precipitable waterby the relative humidity (PW RH), a mean value between surface and 500 hPa level, obtained from a numerical model or the COMS retrieval data. In this study, when the IR cloud top brightness temperature is lower than 210 K and the relative humidity is greater than 40%, the moisture correction factor is empirically scaled from 1.0 to 2.0 basing on PW RH values. Consequently, in applying to this factor in new 2D and 2D CRR calibrations, the ME, MAE, and RMSE are smaller than the new ones.