• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.350-350
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• 2021

한국건설기술연구원의 돌발홍수연구센터는 돌발홍수예측 시스템을 구축하여 2019년부터 전국에서의 돌발홍수정보를 제공하고 있다. 2019년에는 초단기 예측 모델인 Macgill Algorithm for Precipitation-nowcast by Lagrangian Extrapolation(MAPLE) 알고리즘으로부터 생산된 초단기 예측 강우를 활용하여 동(읍/면) 단위로 1시간 선행 예보를 제공하였다. 2020년에는 추가로 초단기 예측 강우와 수치예보 자료를 병합한 예측 병합 강우 자료를 생산하여 예측 선행시간을 1시간에서 3시간까지 확장하였다. 돌발홍수예측 시스템의 목표는 도시 및 산지소하천에서의 돌발홍수에 대응하기 위한 정보를 실시간으로 사용자에게 제공하여 수재해에 빠르게 대응하는 것이다. 이에 돌발홍수예측 시스템은 2019년부터 실시간으로 운영하여 홍수기에 보다 빠른 돌발홍수정보를 제공해왔다. 본 연구에서는 2020년 우기에 운영된 돌발홍수시스템에 대한 평가를 수행하였다. 이를 위해 부산(07.23), 대전(07.29), 서울(08.01), 경기-충북(08.02)에서 발생한 수재해 사례를 분석하였다.

• Journal of Korea Water Resources Association
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• v.49 no.4
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• pp.327-333
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• 2016

Due to climate changes accelerated by global warming, South Korea has experienced regional climate variations as well as increasing severities and frequencies of extreme weather. The precipitation in South Korea during the summer season in 2013 was concentrated mainly in the central region; the maximum number of rainy days were recorded in the central region while the southern region had the minimum number of rainy days. As a result, much attention has been paid to the importance of flood control due to damage caused by spatiotemporal intensive rainfalls. In this study, forecast rainfall data was used for rapid responses to prevent disasters during flood seasons. For this purpose, the applicability of numerical weather forecast data was analyzed using the ground observation rainfall and inflow rate. Correlation coefficient, maximum rainfall intensity percent error and total rainfall percent error were used for the quantitative comparison of ground observation rainfall data. In addition, correlation coefficient, Nash-Sutcliffe efficiency coefficient, and standardized RMSE were used for the quantitative comparison of inflow rate. As a result of the simulation, the correlation coefficient up to six hours was 0.7 or higher, indicating a high correlation. Furthermore, the Nash-Sutcliffe efficiency coefficient was positive until six hours, confirming the applicability of forecast rainfall.

• Journal of Multimedia Information System
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• v.5 no.4
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• pp.235-244
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• 2018

Potential maximum soil moisture retention (S) is a dominant parameter in the Soil Conservation Service (SCS; now called the USDA Natural Resources Conservation Service (NRCS)) runoff Curve Number (CN) method commonly used in hydrologic modeling for event-based flood forecasting (SCS, 1985). Physically, S represents the depth [L] soil could store water through infiltration. The depth of soil moisture retention will vary depending on infiltration from previous rainfall events; an adjustment is usually made using a factor for Antecedent Moisture Conditions (AMCs). Application of the method for continuous simulation of multiple storms has typically involved updating the AMC and S. However, these studies have focused on a time step where S is allowed to vary at daily or longer time scales. While useful for hydrologic events that span multiple days, this temporal resolution is too coarse for short-term applications such as flash flood events. In this study, an approach for deriving a time-variable potential maximum soil moisture retention curve (S-curve) at hourly time-scales is presented. The methodology is applied to the Napa River basin, California. Rainfall events from 2011 to 2012 are used for estimating the event-based S. As a result, we derive an S-curve which is classified into three sections depending on the recovery rate of S for soil moisture conditions ranging from 1) dry, 2) transitional from dry to wet, and 3) wet. The first section is described as gradually increasing recovering S (0.97 mm/hr or 23.28 mm/day), the second section is described as steeply recovering S (2.11 mm/hr or 50.64 mm/day) and the third section is described as gradually decreasing recovery (0.34 mm/hr or 8.16 mm/day). Using the S-curve, we can estimate the hourly change of soil moisture content according to the time duration after rainfall cessation, which is then used to estimate direct runoff for a continuous simulation for flood forecasting.

• Journal of Korea Water Resources Association
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• v.54 no.5
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• pp.347-354
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• 2021

In the past few years, various damages have occurred in the vicinity of rivers due to flooding. In order to alleviate such flood damage, structural and non-structural measures are being established, and one of the important non-structural measures is to establish a flood warning system. In general, in order to establish a flood warning system, the water level of the flood alarm reference point is set, the critical flow corresponding thereto is calculated, and the warning precipitation amount corresponding to the critical flow is calculated through the Geomorphological Instantaneous Unit Hydrograph (GIUH) rainfall-runoff model. In particular, when calculating the critical flow, various studies have calculated the critical flow through the Manning formula. To compare the adequacy of this, in this study, the critical flow was calculated through the HEC-RAS model and compared with the value obtained from Manning's equation. As a result of the comparison, it was confirmed that the critical flow calculated by the Manning equation adopted excessive alarm precipitation values and lead a very high flow compared to the existing design precipitation. In contrast, the critical flow of HEC-RAS presented an appropriate alarm precipitation value and was found to be appropriate to the annual average alarm standard. From the results of this study, it seems more appropriate to calculate the critical flow through HEC-RAS, rather than through the existing Manning equation, in a situation where various river projects have been conducted resulting that most of the rivers have been surveyed.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.140-140
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• 2021

Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.2
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• pp.36-52
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• 2020

This study performed 2-D(two-dimensional) hydrodynamic analysis using EFDC in the Nakdong River. For the simulation of the flood season and non-flood season, the measured data including water level, weir outflow and tributary inflow were used, and the accuracy and applicability of the model were verified by comparing the measured water level and computed one. In addition, statistical quantitative assessment of the model performance was performed by estimating PBIAS, RSR, and RMSE for the computed water level. Then, the average velocity for each section between weirs was calculated by applying constant discharge conditions, and it was compared and verified with the measured velocity by Hydrological Survey Center. In this study, a simple method for estimating the arrival time was proposed, and it is expected that it will be practically applicable in field practices such as flood forecasting and warning.

• Journal of Korea Water Resources Association
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• v.42 no.7
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• pp.513-523
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• 2009

The measuring point of the Hangang Bridge affected by tide has some special topographic characteristics due to Nodle Island. Furthermore the submerged weirs located on the upstream and downstream. Therefore flow is separated and joined by Nodle Island. Discharge measurement at the point of the Hangang Bridge is very important, because Hangang Bridge is key station in managing the discharge and flood forecasting. In the past, it was too difficult to measure discharge in tidal conditions. HRFCO(Han River Flood Control Office) installed automatic discharge measurement facilities for solving this problem. Measuring equipments operates and measures discharge every 10 minutes at 2 points(southern and northern section close to Nodle Island), and calculates flow discharge using Chiu's velocity law(Chiu, 1988). In order to verify the results of automatic discharge measurements, manual discharge measurements were carried out by ADCP. In addition, the monthly discharge were also compared.

• Journal of Korea Water Resources Association
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• v.30 no.5
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• pp.449-457
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• 1997

This study attempts to develop a stochastic-dynamic real-time flow forecasting model for an event-orient watershed storage function model (SFM), which has been used as an official flood computation model in Korea, and to evaluate its performance for real-time flow forecast. The study area is the 747.5$\textrm{km}^2$ Hwecheon basin with outlet at Gaejin and the 8 single flow events during 1983-1986 are selected for comparison and verification of model parameter and model performance. The used model parameters in this study are the same values on field work. It is shown that results from the existing model highly depend on the events, but those from the developed model are stable and well predict the flows for the selected flood events. The coefficient of model efficiency between observed and predicted flows for the events was above 0.90. It is concluded that the developed model that can consider model and observation uncertainties during a flood event is feasible and produces reliable real-time flow forecasts on the area.

• Journal of Korea Water Resources Association
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• v.39 no.9 s.170
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• pp.755-766
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• 2006

Since the accuracy of climate forecast information has improved from better understanding of the climatic system, particularly, from the better understanding of ENSO and the improvement in meteorological models, the forecasted climate information is becoming the important clue for streamflow prediction. This study investigated the available climate forecast information to improve the extended streamflow prediction in Korea, such as MIMI(Monthly Industrial Meteorological Information) and GDAPS(Global Data Assimilation and Prediction) and measured their accuracies. Both MIMI and the 10-day forecast of GDAPS were superior to a naive forecasts and peformed better for the flood season than for the dry season, thus it was proved that such climate forecasts would be valuable for the flood season. This study then forecasted the monthly inflows to Chungju Dam by using MIMI and GDAPS. For MIMI, we compared three cases: All, Intersection, Union. The accuracies of all three cases are better than the naive forecast and especially, Extended Streamflow Predictions(ESPs) with the Intersection and with Union scenarios were superior to that with the All scenarios for the flood season. For GDAPS, the 10-day ahead streamflow prediction also has the better accuracy for the flood season than for the dry season. Therefore, this study proved that using the climate information such as MIMI and GDAPS to reduce the meteorologic uncertainty can improve the accuracy of the extended streamflow prediction for the flood season.

• Journal of Korea Water Resources Association
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• v.46 no.9
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• pp.921-932
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• 2013

Runoff data availability is a substantial factor for precise flood control such as flood frequency or flood forecasting. However, runoff depths and/or peak discharges for small watersheds are rarely measured which are necessary components for hydrological analysis. To compensate for this discrepancy, a lumped concept such as a Storage Function Method (SFM) was applied for the partitioned Choongju Dam Watershed in Korea. This area was divided into 22 small watersheds for measuring the capability of spatial extension of runoff data. The chosen total number of flood events for searching parameters of SFM was 21 from 1991 to 2009. The parameters for 22 small watersheds consist of physical property based (storage coefficient: k, storage exponent: p, lag time: $T_l$) and flood event based parameters (primary runoff ratio: $f_1$, saturated rainfall: $R_{sa}$). Saturated rainfall and base flow from event based parameters were explored with respect to inflow at Choongju Dam while other parameters for each small watershed were fixed. When inflow of Choongju Dam was optimized, Youngchoon and Panwoon stations obtained average of Nash-Sutcliffe Efficiency (NSE) were 0.67 and 0.52, respectively, which are in the satisfaction condition (NSE > 0.5) for model evaluation. This result is showing the possibility of spatial data extension using a lumped concept model.