• 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.52 no.9
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• pp.615-626
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• 2019

Soil moisture and runoff have very close relationship. Especially the water retention capacity and drainage characteristics of the soil are determined by various factors of the soil. In this study, a total of 40 rainfall events were identified from the entire rainfall events of Sulma basin in 2016 and 2017. For each selected events, the constant-K method was used to separate direct runoff and baseflow from total flow and calculate the runoff coefficient which shows positive exponential curve with Antecedent Soil Moisture (ASM). In addition to that, the threshold of soil moisture was determined at the point where the runoff coefficient starts increasing dramatically. The threshold of soil moisture shows great correlation with runoff and depth to water table. It was founded that not only ASM but also various factors, such as Initial Soil Moisture (ISM), storage capacity of soil and precipitation, affect the results of runoff response. Furthermore, wet condition and dry condition are separated by ASM threshold and the start and peak response are analyzed. And the results show that the response under wet condition occurred more quickly than that of dry condition. In most events occurred in dry condition, factors reached peak in order of soil moisture, depth to water table and runoff. However, in wet condition, they reached peak in order of depth to water table, runoff and soil moisture. These results will help identify the interaction among factors which affect the runoff, and it will help establish the relationship between various soil conditions and runoff.

• Journal of Korea Water Resources Association
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• v.33 no.6
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• pp.701-709
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• 2000

Integrated storage function model (ISFM) is applied to some rainfall-runoff events of the selected basins in Korea to show validity of the proposed model. Comparing the numerical results of the model with the field measurements, the simulated hydrographs and peak flood discharges for the most part showed good agreements, except the occurrence time of the peak discharges which showed a bit discrepancy, and they showed it was very hard to have a sufficient lead-time to forecast the flood when the upstream inflow of the channel reach was more dominant than the inflow from the residual watershed of the channel.hannel.

• Journal of Korean Society of societal Security
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• v.4 no.1
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• pp.67-74
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• 2011

This paper was to examine the causes of steep slope failure during the season of heavy rainfall. For the purpose, the paper carefully analyzed the sites of steep slope failure, which happened in July 2009. The direct cause of steep slope failure was much related to heavy rainfall during summer. The paper continued to verify that additional causes include the malfunction of diverse waterways, the slope design without considering weathering soils and related characteristics, the lack of the waterway size, the intrusion of plant roots, the reinforced technique without considering slope conditions, etc.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.108-108
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• 2020

본 연구에서는 관측자료를 이용하여 제주도 한천 유역의 침투 특성을 파악하고자 하였다. 이를 위해 2012년부터 2019년까지 제주도 한천 유역의 주요 호우사상을 선정하고 한천 하류부의 위치한 제2동산교 지점의 유출량 자료를 이용하였다. 먼저 호우사상별 유출량 자료를 이용하여 유효우량(또는 유출고) 산정하고 초기손실을 결정하였다. 다음으로 유효우량과 초기손실을 NRCS CN 방법에 적용하여 최대잠재보유수량과 CN을 산정하였다. 산정된 호우사상별 CN 및 초기손실을 검토하기 위해 선행일수에 따른 선행강우량과 유출량과의 관계와 강우-유출 과정의 각 요소 간의 관계를 확인하였다. 마지막으로 초기손실, 최대잠재보유수량, CN 등 각 요소들의 분포와 이상치를 확인하여 제주도 한천 유역의 적절한 CN 및 초기손실의 규모를 검토하였으며, 그 결과, 제주도 한천 유역의 적절한 초기손실, 최대잠재보유수량, α, 지체시간의 규모는 각각 30 mm~40 mm, 170 mm~190 mm, 0.20~0.23, 50~60, 60 min~90 min으로 나타났다.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.3
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• pp.178-184
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• 2014

In this study, monitoring method which is more economic and easier in maintenance comparing to existing monitoring system was scrutinized for application to sewer intercepting chamber of 10.2 ha basin area by estimating CSOs (Combined Sewer Overflows) quantity and quality with 2 rainfall events using electrical conductivity data and civil research model. The result showed that determination coefficient of flow estimation by EC (Electrical Conductivity) dilution ratio and observed data was over 0.86 for all cases and the accuracy of estimation was improved from 0.5 to 0.8 for determination coefficient ($R^2$) and from 54.1% to 68.5% for accumulation frequency of relative error by considering antecedent dry days and rainfall duration. CSOs water quality estimation results by civil research model showed that determination coefficients were 0.64~0.97 for BOD and 0.70~0.95 for SS.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.26-41
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• 2016

Catchment Hydrologic Cycle Assess Tool (CAT) is a model for hydrologic cycle assessment based on physical parameters. In this study, CAT was applied for short-term runoff simulation and connected with model-independent parameter estimation (PEST) for auto-calibrating parameters. The model performance was compared with HEC-HMS, which is widely used for short-term runoff simulation. The study area is the Pangyo Watershed ($22.9km^2$), which includes the Unjung-Cheon and Geumto-Cheon tributaries of the Tan-Cheon stream. Simulation periods were selected from six rainfall events of a two-year period (2006-2007). For the runoff simulation, CAT was applied using three types of infiltration methods (excess rainfall, Green and Ampt and Horton). Sensitivity analysis was carried out to select the parameters and then CAT was optimized using PEST. The model performance of HEC-HMS and CAT-PEST for the rainfall events were within an acceptable limit with Nash Sutcliffe efficiencies (NSE) of 0.63-0.91 and 0.42-0.93, respectively. The simulation results of HEC-HMS have high accuracy in the case of rainfall events that have a sensitive relationship between initial soil moisture conditions and runoff characteristics. The results of CAT-PEST indicated the possibility of reflecting a real runoff system using various physical parameters.

• Journal of Korea Water Resources Association
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• v.44 no.3
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• pp.199-208
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• 2011

The objective of this study is to develop real-time river flow forecast model by linking continuous rainfall-runoff model with ensemble Kalman filter technique. Andong dam basin is selected as study area and the model performance is evaluated for two periods, 2006. 7.1~8.18 and 2007. 8.1~9.30. The model state variables for data assimilation are defined as soil water content, basin storage and channel storage. This model is designed so as to be updated the state variables using measured inflow data at Andong dam. The analysing result from the behavior of the state variables, predicted state variable as simulated discharge is updated 74% toward measured one. Under the condition of assuming that the forecasted rainfall is equal to the measured one, the model accuracy with and without data assimilation is analyzed. The model performance of the former is better than that of the latter as much as 49.6% and 33.1% for 1 h-lead time during the evaluation period, 2006 and 2007. The real-time river flow forecast model using rainfall-runoff model linking with data assimilation process can show better forecasting result than the existing methods using rainfall-runoff model only in view of the results so far achieved.

• Journal of Korea Water Resources Association
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• v.55 no.9
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• pp.679-686
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• 2022

As the occurrences of flash floods have increased due to climate change, faster and more accurate precipitation observation using X-band radar has become important. Therefore, the Ministry of Environment installed two dual-pol X-band radars at Samcheok and Uljin. The radar data used in this study were obtained from two different elevation angles and composed to reduce the shielding effect. To obtain quantitative rainfall, quality control (QC), K_DP retrieval, and Hybrid Surface Rainfall (HSR) methods were sequentially applied. To improve the accuracy of the quantitative precipitation estimation (QPE) of the X-band radar, we retrieved parameters for the relationship between rainfall rate and specific differential phase, which is commonly called the R-K_DP relationship; hence, an empirical approach was developed using multiple rain gauges for those two radars. The newly suggested relationship, R = 27.4K^0.81_DP, slightly increased the correlation coefficient by 1% more than the relationship suggested by the previous study. The root mean square error significantly decreased from 3.88 mm/hr to 3.68 mm/hr, and the bias of the estimated precipitation also decreased from -1.72 mm/hr to -0.92 mm/hr for overall cases, showing the improvement of the new method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4B
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• pp.389-398
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• 2006

The present study compositely applied the self-organizing map (SOM), which is a kind of artificial neural networks (ANNs), and the back propagation algorithm (BPA) for the rainfall-runoff prediction model taking account of the irregular variation of the spatiotemporal distribution of rainfall. To solve the problems from the previous studies on ANNs, such as the overestimation of low flow during the dry season, the underestimation of runoff during the flood season and the persistence phenomenon, in which the predicted values continuously represent the preceding runoffs, we introduced SOM theory for the preprocessing in the prediction model. The theory is known that it has the pattern classification ability. The method proposed in the present research initially includes the classification of the rainfall-runoff relationship using SOM and the construction of the respective models according to the classification by SOM. The individually constructed models used the data corresponding to the respectively classified patterns for the runoff prediction. Consequently, the method proposed in the present study resulted in the better prediction ability of runoff than that of the past research using the usual application of ANNs and, in addition, there were no such problems of the under/over-estimation of runoff and the persistence.