• Geotechnical Engineering
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• v.6 no.2
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• pp.55-66
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• 1990

Many landslides have been taken place during the wet season in Korea. Rainfall in one of the most significant factors relevant to the landsildes, which cause a great loss of lived and properties every year, However, forecast systems for landslides have not been sufficiently established in Korea. In order to minimize a disaster due to landslides, the relationship between landslides and rainfall was investigated based on meteorological records and landslides occrrence ranging from 1977 to 1987. According to rainfall patterns which cause landslides, such as the daily rainfall on failure day or the cumulative rainfalls before failure day, the area in which landslides were taken place, could be divided into three groups of Middl area, Young- Ho Nam area, and Young-Dong Area. And the frequency of landslides was also dependent on the hourly rainfall intensity. It shows from the analyses that prediction of landslides can be made based on both the cumulative rainfall and the hourly rainfall intensity.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.278-283
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• 2005

In this study, following works have been carried out : division of Nakdong River Basin into 25 sub basins, development of a technique to evaluate spatial distribution of rainfall and analysis of rainfall data of 169 stations, selection of control points, and selection of a hydrologic model(SSARR). Analysis results were found that total volume of discharges at every control points have errors in a reasonable range, and the correlations between observed and calculated daily runoff discharges at every control points were well coincided.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.6
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• pp.54-66
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• 1997

The objectives of this study is to introduce and apply neural network theory to real hydrologic systems for stochastic nonlinear predicting of daily runoff discharge in the river catchment. Back propagation algorithm of neural network model is applied for the estimation of daily stochastic runoff discharge using historical daily rainfall and observed runoff discharge. For the fitness and efficiency analysis of models, the statistical analysis is carried out between observed discharge and predicted discharge in the chosen runoff periods. As the result of statistical analysis, method 3 which has much processing elements of input layer is more prominent model than other models(method 1, method 2) in this study.Therefore, on the basis of this study, further research activities are needed for the development of neural network algorithm for the flood prediction including real-time forecasting and for the optimal operation system of dams and so forth.

• Magazine of the Korean Society of Agricultural Engineers
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• v.42 no.4
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• pp.76-86
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• 2000

The objective function of reservoir storage error was suggested to simulate daily reservoir inflow. DAWAST model, UMAX, LMAX, FC,CP, CE were calibrated. Daily reservoir inflow was imulated with calibrated parameters and reservoir storage was simulated on a daily basis. The simulated results were compared with the monthly results by Gajiyama equation and ten-day results by Tank rainfall-runoff model through equal value lines and hydrographs . DAWAST model showed the best results compared with Gajiymama equation and Tank model. Especially, DAWAST model showed a good agreement in dry periods. NEW concept using objective function of storage error was believed to be satisfactory and to be applied in estimating reservoir inflow.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1205-1214
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• 2021

Precipitation is one of a major causes of landslides by rising of pore water pressure, which leads to fluctuations of soil strength and stress. For this reason, precipitation is the most frequently used to determine the landslide thresholds. However, using only precipitation has limitations in predicting and estimating slope stability quantitatively for reducing false alarm events. On the other hand, Soil Moisture (SM) has been used for calculating slope stability in many studies since it is directly related to pore water pressure than precipitation. Therefore, this study attempted to evaluate the appropriateness of applying soil moisture in determining the landslide threshold. First, the reactivity of soil saturation level to precipitation was identified through time-series analysis. The precipitation threshold was calculated using daily precipitation (P_daily) and the Antecedent Precipitation Index (API), and the hydrological threshold was calculated using daily precipitation and soil saturation level. Using a contingency table, these two thresholds were assessed qualitatively. In results, compared to P_daily only threshold, Goesan showed an improvement of 75% (P_daily + API) and 42% (P_daily + SM) and Changsu showed an improvement of 33% (P_daily + API) and 44% (P_daily + SM), respectively. Both API and SM effectively enhanced the Critical Success Index (CSI) and reduced the False Alarm Rate (FAR). In the future, studies such as calculating rainfall intensity required to cause/trigger landslides through soil saturation level or estimating rainfall resistance according to the soil saturation level are expected to contribute to improving landslide prediction accuracy.

• Journal of Korea Water Resources Association
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• v.51 no.spc
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• pp.1149-1159
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• 2018

This study aims to predict the future flood damage cost of 113 middle range watersheds using 26 GCM outputs, hourly maximum rainfall, 10-min maximum rainfall, number of days of 80 mm/day, daily rainfall maximum, annual rainfall amount, DEM, urbanization ratio, population density, asset density, road improvement ratio, river improvement ratio, drainage system improvement ratio, pumping capacity, detention basin capacity and previous flood damage costs. A constrained multiple linear regression model was used to construct the relationships between the flood damage cost and other variables. Future flood damage costs were estimated for different RCP scenarios such as 4.5 and 8.5. Results demonstrated that rainfall related factors such as annual rainfall amount, rainfall extremes etc. widely increase. It causes nationwide future flood damage cost increase. Especially the flood damage cost for Eastern part watersheds of Kangwondo and Namgang dam area may mainly increase.

• Journal of Korea Water Resources Association
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• v.48 no.1
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• pp.57-67
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• 2015

One of main benefits of a dual polarization radar is improvement of quantitative rainfall estimation. In this paper, performance of two representative rainfall estimation methods for a dual polarization radar, JPOLE and CSU algorithms, have been compared by using data from a MOLIT S-band dual polarization radar. In addition, this paper presents evaluation of specific differential phase ($K_{dp}$) retrieval algorithm proposed by Lim et al. (2013). Current $K_{dp}$ retrieval methods are based on range filtering technique or regression analysis. However, these methods can result in underestimating peak $K_{dp}$ or negative values in convective regions, and fluctuated $K_{dp}$ in low rain rate regions. To resolve these problems, this study applied the $K_{dp}$ distribution method suggested by Lim et al. (2013) and evaluated by adopting new $K_{dp}$ to JPOLE and CSU algorithms. Data were obtained from the Mt. Biseul radar of MOLIT for two rainfall events in 2012. Results of evaluation showed improvement of the peak $K_{dp}$ and did not show fluctuation and negative $K_{dp}$ values. Also, in heavy rain (daily rainfall > 80 mm), accumulated daily rainfall using new $K_{dp}$ was closer to AWS observation data than that using legacy $K_{dp}$, but in light rain(daily rainfall < 80mm), improvement was insignificant, because $K_{dp}$ is used mostly in case of heavy rain rate of quantitative rainfall estimation algorithm.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.2
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• pp.1-15
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• 2020

In this study, the characteristics of crop damage area by flooding for 113 middle range watersheds during 2000-2016 were analyzed and future crop damage area by flooding were analyzed using 13 GCM outputs such as hourly maximum rainfall, 10-min maximum rainfall, number of days of 80 mm/day, daily rainfall maximum, annual rainfall amount associated with RCP 4.5 and RCP 8.5 scenarios and watershed characteristic data such as DEM, urbanization ratio, population density, asset density, road improvement ratio, river improvement ratio, drainage system improvement ratio, pumping capacity, detention basin capacity, and crop damage area by flooding. A constrained multiple linear regression model was used to construct the relationships between the crop damage area by flooding and other variables. Future flood index related to crop damage may mainly increase in the Mankyung watershed, Southwest part of Youngsan and Sumjin river basin and Southern part of Nackdong river basin. Results are useful to identify watersheds which need to establish strategies for responding to future flood damage.

• Journal of Astronomy and Space Sciences
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• v.27 no.4
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• pp.367-375
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• 2010

In this study, we compared the precipitable water vapor (PWV) data derived from the radiosonde observation data at Sokcho Observatory and the PWV data at Sokcho Global Positioning System (GPS) Observatory provided by Korea Astronomy and Space Science Institute, from 0000 UTC, June 1, 2007 to 1200 UTC, May 31, 2009, and analyzed the radiosonde bias between the day and the night. In the scatter diagram of the daytime and nighttime radiosonde PWV data and the GPS PWV data, dry bias was found in the daytime radiosonde observation as known in the previous study. In addition, for all the rainfall events, the tendency that the wet bias of the radiosonde PWV increased as the GPS PWV decreased and the dry bias of the radiosonde PWV increased as the GPS PWV increased was significantly less distinctive in nighttime than in daytime. The quantitative analysis of the bias and error of the radiosonde PWV data showed that the mean bias decreased in the second year, regardless of nighttime or daytime rainfall, and the non-rainfall root mean square error (RMSE) was similar to that of the previous studies, while the rainfall RMSE was larger to a certain extent.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.723-726
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• 2008

It is not always easy to estimate the parameters in hydrologic models due to insufficient hydrologic data when hydraulic structures are designed or water resources plan are established, uncertainty analysis, therefore, are inevitably needed to examine reliability for the estimated results. With regard to this point, this study applies a Bayesian Markov Chain Monte Carlo scheme to the NWS-PC rainfall-runoff model that has been widely used, and a case study is performed in Soyang Dam watershed in Korea. The NWS-PC model is calibrated against observed daily runoff, and thirteen parameters in the model are optimized as well as posterior distributions associated with each parameter are derived. The Bayesian Markov Chain Monte Carlo shows a improved result in terms of statistical performance measures and graphical examination. The patterns of runoff can be influenced by various factors and the Bayesian approaches are capable of translating the uncertainties into parameter uncertainties. One could provide against an expected runoff event by utilizing information driven by Bayesian methods. Therefore, the rainfall-runoff analysis coupled with the uncertainty analysis can give us an insight in evaluating flood risk and dam size in a reasonable way.