• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.275-275
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• 2021

A fixed Z-R relationship approach, such as the Marshall-Palmer relationship, for an entire year and for different seasons can be problematic in cases where the relationship varies spatially and temporally throughout a region. From this perspective, this study explores the use of long-term radar reflectivity for South Korea to obtain a nationwide calibrated Z-R relationship and the associated uncertainties within a Bayesian regression framework. This study also investigates seasonal differences in the Z-R relationship and their roles in reducing systematic error. Distinct differences in the Z-R parameters in space are identified, and more importantly, an inverse relationship between the parameters is clearly identified with distinct regimes based on the seasons. A spatially structured pattern in the parameters exists, particularly parameter α for the wet season and parameter β for the dry season. A pronounced region of high values during the wet and dry seasons may be partially associated with storm movements in that season. Finally, the radar rainfall estimates through the calibrated Z-R relationship are compared with the existing Z-R relationships for estimating stratiform rainfall and convective rainfall. Overall, the radar rainfall fields based on the proposed modeling procedure are similar to the observed rainfall fields, whereas the radar rainfall fields obtained from the existing Marshall-Palmer Z-R relationship show a systematic underestimation. The obtained Z-R relationships are validated by testing the predictions on unseen radar-gauge pairs in the year 2018, in the context of cross-validation. The cross-validation results are largely similar to those in the calibration process, suggesting that the derived Z-R relationships fit the radar-gauge pairs reasonably well.

• Journal of Korea Water Resources Association
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• v.53 no.7
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• pp.493-505
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• 2020

The rainfall-runoff model has been generally adopted to obtain a consistent runoff sequence with the use of the long-term ground-gauged based precipitation data. The Thiessen polygon is a commonly applied approach for estimating the mean areal rainfall from the ground-gauged precipitation by assigning weight based on the relative areas delineated by a polygon. However, spatial bias is likely to increase due to a sparse network of the rain gauge. This study aims to generate continuous runoff sequences with the mean areal rainfall obtained from radar rainfall estimates through a PRMS rainfall-runoff model. Here, the systematic error of radar rainfall is corrected by applying the G/R Ratio. The results showed that the estimated runoff using the corrected radar rainfall estimates are largely similar and comparable to that of the Thiessen. More importantly, one can expect that the mean areal rainfall obtained from the radar rainfall estimates are more desirable than that of the ground in terms of representing rainfall patterns in space, which in turn leads to significant improvement in the estimation of runoff.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.2161-2166
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• 2009

It is important for 0-6 hour nowcasting to provide for a high-quality initial condition in a meso-scale atmospheric model by a data assimilation of several observation data. The polarimetric radar data is expected to be assimilated into the forecast model, because the radar has a possibility of measurements of the types, the shapes, and the size distributions of hydrometeors. In this paper, an impact on rainfall prediction of the data assimilation of hydrometeor types (i.e. raindrop, graupel, snowflake, etc.) is evaluated. The observed information of hydrometeor types is estimated using the fuzzy logic algorism. As an implementation, the cloud-resolving nonhydrostatic atmospheric model, CReSS, which has detail microphysical processes, is employed as a forecast model. The local ensemble transform Kalman filter, LETKF, is used as a data assimilation method, which uses an ensemble of short-term forecasts to estimate the flowdependent background error covariance required in data assimilation. A heavy rainfall event occurred in Okinawa in 2008 is chosen as an application. As a result, the rainfall prediction accuracy in the assimilation case of both hydrometeor types and the Doppler velocity and the radar echo is improved by a comparison of the no assimilation case. The effects on rainfall prediction of the assimilation of hydrometeor types appear in longer prediction lead time compared with the effects of the assimilation of radar echo only.

• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.4
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• pp.25-36
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• 2004

This study was conducted to derive the design rainfall by the consecutive duration using the at-site frequency analysis. Using the errors, K-S tests and LH-moment ratios, Log Pearson type 3 (LP3) and Generalized Extreme Value (GEV) distributions of Gamma and Non-Gamma Family, respectively were identified as the optimal probability distributions among applied distributions. Parameters of GEV and LP3 distributions were estimated by the method of L and LH-moments and the Indirect method of moments respectively. Design rainfalls following the consecutive duration were derived by at-site frequency analysis using the observed and simulated data resulted from Monte Carlo techniques. Relative root-mean-square error (RRMSE) and relative efficiency (RE) in RRMSE for the design rainfall derived by at-site analysis in the observed and simulated data were computed and compared. It has shown that at-site frequency analysis by GEV distribution using L-moments is confirmed as more reliable than that of GEV and LP3 distributions using LH-moments and Indirect method of moments in view of relative efficiency.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.319-323
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• 2001

This study was conducted to derive the regional design rainfall by the regional frequency analysis based on the regionalization of the precipitation. Using the L-moment ratios and Kolmogorov-Smirnov test, the underlying regional probability distribution was identified to be the Generalized extreme value distribution among apt]lied distributions. regional and at-site parameters of the Generalized extreme value distribution were estimated by the method of L-moment. 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 with those resulting from at-site Monte Carlo simulation. All show that the regional analysis procedure can substantially reduce the RRMSE, RBIAS and RR in RRMSE in the prediction of design rainfall. Consequently, optimal design rainfalls following the regions and consecutive durations were derived by the regional frequency analysis.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.5
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• pp.70-82
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• 2001

This study was conducted to derive the regional design rainfall by the regional frequency analysis based on the regionalization of the precipitation suggested by the first report of this project. Using the L-moment ratios and Kolmogorov-Smirnov test, the underlying regional probability distribution was identified to be the Generalized extreme value distribution among applied distributions. Regional and at-site parameters of the generalized extreme value distribution were estimated by the linear combination of the probability weighted moments, L-moment. 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 with those resulting from at-site Monte Carlo simulation. All show that the regional analysis procedure can substantially reduce the RRMSE, RBIAS and RR in RRMSE in the prediction of design rainfall. Consequently, optimal design rainfalls following the legions and consecutive durations were derived by the regional frequency analysis.

• Journal of Korea Water Resources Association
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• v.54 no.1
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• pp.27-38
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• 2021

Recently, many studies have been conducted to use the radar rainfall in hydrology. However, in the case of weather radar, the beam is blocked due to the limitation of the observation such as mountain effect, which causes underestimation of the radar rainfall. In this study, the radar rainfall was estimated using the Hybrid Sacn Reflectivity (HSR) technique for hydrological use of weather radar and the runoff analysis was performed using the GRM model which is a distributed rainfall-runoff model. As a result of performing the radar rainfall correction and runoff simulation for 5 rainfall events, the accuracy of the dual-polarization radar rainfall using the HSR technique (Q_H_KDP) was the highest with an error within 15% of the ground rainfall. In addition, the result of runoff simulation using Q_H_KDP also showed an accuracy of R2 of 0.9 or more, NRMSE of 1.5 or less and NSE of 0.5 or more. From this study, we examined the application of the dual-polarization radar and this results can be useful for studies related to the hydrological application of dual-polarization radar rainfall in the future.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.737-748
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• 2022

Due to the lack of flood data, the water engineering practice calculates the design flood using rainfall frequency analysis and rainfall-runoff model. However, the rainfall frequency analysis for arbitrary duration does not reflect the regional characteristics of the duration and amount of storm event. This study proposed a practical method to calculate the design flood in a watershed considering the characteristics of storm event, based on the bivariate rainfall frequency analysis. After extracting independent storm events for the Pyeongchang River basin and the upper Namhangang River basin, we performed the bivariate rainfall frequency analysis to determine the design storm events of various return periods, and calculated the design floods using the HEC-1 model. We compared the design floods based on the bivariate rainfall frequency analysis (DF_BRFA) with those estimated by the flood frequency analysis (DF_FFA), and those estimated by the HEC-1 with the univariate rainfall frequency analysis (DF_URFA). In the case of the Pyeongchang River basin, except for the 100-year flood, the average error of the DF_BRFA was 11.6%, which was the closest to the DF_FFA. In the case of the Namhangang River basin, the average error of the DF_BRFA was about 10%, which was the most similar to the DF_FFA. As the return period increased, the DF_URFA was calculated to be much larger than the DF_FFA, whereas the BRFA produced smaller average error in the design flood than the URFA. When the proposed method is used to calculate design flood in an ungauged watershed, it is expected that the estimated design flood might be close to the actual DF_FFA. Thus, the design of the hydrological structures and water resource plans can be carried out economically and reasonably.

• Journal of Korea Water Resources Association
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• v.42 no.12
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• pp.1039-1052
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• 2009

Radar measurement with high temporal and spatial resolutions can be a valuable source of data, especially in the areas where rain gauge installment is not practical. However, this kind of data brings with it many errors. The objective of this paper is to propose a method to evaluate statistically the quantitative and qualitative accuracy at different radar ranges, temporal intervals and raingage densities and use a bias adjustment technique to improve the quality of radar rainfall for the purpose of hydrological application. The method is tested with the data of 2 storm events collected at Jindo (S band) and Kwanak (C band) radar stations. The obtained results show that the accuracy of radar rainfall estimation increases when time interval rises. Radar data at the shorter range seems to be more accurate than the further one, especially for C-band radar. Using the Monte Carlo simulation experiment, we find out that the sampling error of the bias between radar and gauge rainfall reduces nonlinearly with increasing raingage density. The accuracy can be improved considerably if the real-time bias adjustment is applied, making adjusted radar rainfall to be adequately good to apply for hydrological application.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.5
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• pp.465-475
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• 2020

I-D-F curves were induced by Box-Cox transformation using rainfall data from five major cities in Korea: Seoul, Busan, Daegu, Daejeon, and Gwangju, as well as from Sancheong (South Gyeongsang province) and Yeongcheon (North Gyeongsang province) stations. The practicality of the Box-Cox transformation is more scalable than the traditional method of frequency analysis in terms of applicability because it is available even if the analysis data are insufficient to perform general frequency analysis and do not produce an appropriate probability density function. For the case in which rainfall data for the entire period (10-1440 minutes) and short-term period (20, 30, 40, 50 minutes) at the foregoing 7 stations are omitted, there was a relative error of -23.0 % to 14.7 % at a duration of 10 to 60 minutes below the 100-year frequency. Accordingly, rainfall analysis requires inducing I-D-F curves, including for the short term (20, 30, 40, 50 minutes), and if rainfall data are omitted for the short term (20, 30, 40, 50 minutes), it is necessary to increase the existing margin rate depending on the point in order to ensure the safe design of small-scale hydraulic structures.