• Journal of Korea Water Resources Association
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• v.51 no.9
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• pp.813-826
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• 2018

Recently, weather radar system has been widely used for effectively monitoring near real-time weather conditions. The radar rainfall estimates are generally relies on the Z-R equation that is an indirect approximation of the empirical relationship. In this regards, the bias in the radar rainfall estimates can be affected by spatial-temporal variations in the radar profile. This study evaluates the uncertainty of the Z-R relationship while considering the rainfall types in the process of estimating the parameters of the Z-R equation in the context of stochastic approach. The radar rainfall estimates based on the Bayesian inference technique appears to be effective in terms of reduction in bias for a given season. The derived Z-R equation using Bayesian model enables us to better represent the hydrological process in the rainfall-runoff model and provide a more reliable forecast.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.15-15
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• 2017

하천의 수위와 유량 등의 수리량 자료는 치수, 이수, 환경 등에 관한 하천 계획과 관리의 기초자료로 활용된다. 따라서 체계적인 하천 관리를 위해서는 신뢰성 있는 하천 수리량 자료의 확보가 요구된다. 이에 따라 우리나라에서는 많은 하천에 수위 관측소를 설치하여 실시간 수위를 측정하고 있다. 하지만 유량의 측정은 국가하천 등 주요 대규모 하천에 국한되어 있는 실정이다. 반면에 우리나라 대부분의 홍수 피해는 중 소하천에서 발생하고 있지만, 중 소하천에 대한 수문조사는 상대적으로 매우 미진한 실정이다. 이러한 중 소하천에 대한 수리 정보 조사의 가장 큰 어려움은 대부분의 중 소하천이 지방자치단체에서 관리하고 있어 예산과 인력 지원이 어렵기 때문이다. 이에 본 연구에서는 중 소하천에 산재한 보와 같은 하천 횡단 구조물의 유량 측정 기능을 이용하고, 급성장하고 있는 영상 분석 기술과 IT를 하천공학 분야에 접목하여 중 소하천의 수리 정보를 저비용, 실시간으로 원격 측정하는 시스템을 개발하고자 하였다. 이를 위해 연구에서는 저가형 영상 장비를 이용하여 하천 횡단구조물 주변의 흐름 영상을 원격으로 확보하고, 해당 영상을 데이터베이스로 송신하는 장치인 WIA 시스템(wireless image acquisition system)을 개발하였다. 그리고 촬영된 영상을 분석하여 수위를 도출하는 영상 분석 프로그램, 수위-유량 관계 곡선(rating curve), 미계측 지점에 대한 하천 수리 정보를 계산하기 위한 HEC-RAS를 포함하는 River-HQ 시스템을 개발하였다. 개발된 WIA & River-HQ 시스템은 한국건설기술연구원 하천실험센터 내 직선 수로(실규모 하천) 구간에 대하여 정확성이 검증되었다. 이러한 WIA & River-HQ 시스템은 실제 하천에 대한 적용성과 안정성 검토를 통해 향후 중 소하천의 환경 관리와 홍수예경보 업무 등에 활용될 수 있을 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.94-94
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• 2023

유역에서의 홍수를 예측하기 위한 다양한 강우-유출 모형들이 개발되어 사용되고 있다. 개념적 강우-유출 모형들은 신뢰성과 적용성이 높아 실무에서 널리 활용되어왔으나, 강우-유출 과정을 단순화하여 고려하므로 유출예측의 정확도에 한계가 있다. 또한 모형의 매개변수에 여러 불확실성이 존재하므로 충분한 양의 관측자료를 사용한 보정 작업이 필요하다. 물리적 강우-유출 모형들은 유출예측 결과가 비교적 물리적으로 정확하다는 장점이 있지만, 높은 계산 비용 및 수치적 불안정성으로 인하여 실무에의 적용이 힘들다. 본 연구에서는 홍수 예측의 정확도와 효율성을 모두 확보할 수 있는 하이브리드 기법을 개발하였다. 본 연구에서 개발한 기법은 물리적 모형인 동역학파 모형과 개념적 모형인 순간단위도 모형, 그리고 딥러닝 모형을 결합하여 사용하는 기법이다. 유역의 조도계수 및 지형을 활용한 동역학파 시뮬레이션을 수행하였으며, 동역학파 시뮬레이션 결과 및 멱함수로 나타내어지는 비선형적 강우-유출 관계를 이용하여 유역의 순간단위도를 유도였다. 또한, 딥러닝 모형인 LSTM 모형을 활용하여 강우손실 매개변수를 추정하였으며, 이를 이용하여 강우손실을 계산한 후 유효강우주상도를 산정하였다. 그리고 유역 출구에서의 홍수수문곡선은 유효강우주상도와 순간단위도를 활용한 회선적분을 통해 예측되었다. 본 연구에서 개발한 기법을 시험유역 및 자연유역에서의 홍수 예측에 적용해보았으며, 예측 결과는 NSE=0.55-0.90, R²=0.67-0.95의 높은 정확도를 보였다. 본 연구에서 유도하는 순간단위도는 한 유역에서 유일하지 않으며, 유효 강우강도의 함수이므로 홍수 예측에 비선형적 강우-유출 관계를 고려할 수 있으며, 수많은 유효 강우강도에 대한 순간단위도들은 멱함수를 이용하여 순간적으로 유도될 수 있다. 또한, 유역의 강우 특성이나 지표면의 토양수분, 식생과 같은 특성을 딥러닝 모형을 통해 고려함으로써 강우 손실 산정의 불확실성을 줄일 수 있다. 또한, 순간단위도 유도를 위한 기초작업인 동역학파 시뮬레이션은 유역의 지형과 조도계수만을 필요로 하므로 미계측 유역에의 적용이 유리하다.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of Korea Water Resources Association
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• v.53 no.10
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• pp.903-915
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• 2020

Long-term high-resolution hydro-meteorological data has been recognized as an essential element in establishing the water resources plan. The increasing demand for spatial precipitation in various areas such as climate, hydrology, geography, ecology, and environment is apparent. However, potential limitations of the existing area-weighted and numerical interpolation methods for interpolating precipitation in high altitude areas remains less explored. The proposed PRISM (Precipitation-Elevation Regressions on Independent Slopes Model) model can produce gridded precipitation that can adequately consider topographic characteristics (e.g., slope and altitude), which are not substantially included in the existing interpolation techniques. In this study, the PRISM model was optimized with SCEM-UA (Shuffled Complex Evolution Metropolis-University of Arizona) to produce daily gridded precipitation. As a result, the minimum impact radius was calculated 9.10 km and the maximum 34.99 km. The altitude of coastal weighted was 681.03 m, the minimum and maximum distances from coastal were 9.85 km and 38.05 km. The distance weighting factor was calculated to be about 0.87, confirming that the PRISM result was very sensitive to distance. The results showed that the proposed PRISM model could reproduce the observed statistical properties reasonably well.

• Journal of Korea Water Resources Association
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• v.49 no.5
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• pp.361-372
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• 2016

Reliable long-term dam inflow prediction is necessary for efficient multi-purpose dam operation in changing climate. Since 2000s the teleconnection between global climate indices (e.g., ENSO) and local hydroclimate regimes have been widely recognized throughout the world. To date many hydrologists focus on predicting future hydrologic conditions using lag teleconnection between streamflow and climate indices. This study investigated the utility of teleconneciton for predicting dam inflow with 1-month lead time at Andong dam basin. To this end 40 global climate indices from NOAA were employed to identify potential predictors of dam inflow, areal averaged precipitation, temperature of Andong dam basin. This study compared three different approaches; 1) dam inflow prediction using SWAT model based on teleconneciton-based precipitation and temperature forecast (SWAT-Forecasted), 2) dam inflow prediction using teleconneciton between dam inflow and climate indices (CIR-Forecasted), and 3) dam inflow prediction based on the rank of current observation in the historical dam inflow (Rank-Observed). Our results demonstrated that CIR-Forecasted showed better predictability than the other approaches, except in December. This is because uncertainties attributed to temporal downscaling from monthly to daily for precipitation and temperature forecasts and hydrologic modeling using SWAT can be ignored from dam inflow forecast through CIR-Forecasted approach. This study indicates that 1-month lead dam inflow forecast based on teleconneciton could provide useful information on Andong dam operation.

• Journal of Korea Water Resources Association
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• v.48 no.5
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• pp.367-377
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• 2015

ADCPs have been highlighted so far for measuring steramflow discharge in terms of their high-order of accuracy, relatively low cost and less field operators driven by their easy in-situ operation. While ADCPs become increasingly dominant in hydrometric area, their actual measurement accuracy for velocity and bathymetry measurement has not been sufficiently validated due to the lack of reliable bench-mark data, and subsequently there are still many uncertain aspects for using ADCPs in the field. This research aimed at analyzing inter-comparison results between ADCP measurements with respect to the detailed ADV measurement in a specified field environment. Overall, 184 ADV points were collected for densely designed grids for the given cross-section that has 6 m of width, 1 m of depth, and 0.7 m/s of averaged mean flow velocity. Concurrently, ADCP fixed-points measurements were conducted for each 0.2m and 0.02m of horizontal and vertical spacing respectively. The inter-comparison results indicated that ADCP matched ADV velocity very accurately for 0.4~0.8 of relative depth (y/h), but noticeable deviation occurred between them in near surface and bottom region. For evaluating the capacity of measuring bathymetry of ADCPs, bottom tracking bathymetry based on oblique beams showed better performance than vertical beam approach, and similar results were shown for fixed and moving-boat method as well. Error analysis for velocity and bathymetry measurements of ADCP can be potentially able to be utilized for the more detailed uncertainty analysis of the ADCP discharge measurement.

• Journal of Korea Water Resources Association
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• v.57 no.5
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• pp.333-346
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• 2024

High-resolution medium-range streamflow prediction is crucial for sustainable water quality and aquatic ecosystem management. For reliable medium-range streamflow predictions, it is necessary to understand the characteristics of forcings and to effectively utilize weather forecast data with low spatio-temporal resolutions. In this study, we presented a comparative analysis of medium-range streamflow predictions using the distributed hydrological model, WRF-Hydro, and the numerical weather forecast Global Data Assimilation and Prediction System (GDAPS) in the Geumho River basin, Korea. Multiple forcings, ground observations (AWS&ASOS), numerical weather forecast (GDAPS), and Global Land Data Assimilation System (GLDAS), were ingested to investigate the performance of streamflow predictions with highresolution WRF-Hydro configuration. In terms of the mean areal accumulated rainfall, GDAPS was overestimated by 36% to 234%, and GLDAS reanalysis data were overestimated by 80% to 153% compared to AWS&ASOS. The performance of streamflow predictions using AWS&ASOS resulted in KGE and NSE values of 0.6 or higher at the Kangchang station. Meanwhile, GDAPS-based streamflow predictions showed high variability, with KGE values ranging from 0.871 to -0.131 depending on the rainfall events. Although the peak flow error of GDAPS was larger or similar to that of GLDAS, the peak flow timing error of GDAPS was smaller than that of GLDAS. The average timing errors of AWS&ASOS, GDAPS, and GLDAS were 3.7 hours, 8.4 hours, and 70.1 hours, respectively. Medium-range streamflow predictions using GDAPS and high-resolution WRF-Hydro may provide useful information for water resources management especially in terms of occurrence and timing of peak flow albeit high uncertainty in flood magnitude.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1433-1446
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• 2013

In this study, we proposed a methodology to develop Rating Curves for high water level using rainfall generation by the Monte Carlo Simulation (MCS) technique, optimized rainfall-runoff model, and flood routing model in an urban stream. The developed stage discharge Rating Curve based on observed data was contained flow measurement errors and uncertainties. The standard error ($S_e$) for observations was 0.056, and the random uncertainty ($2S_{mr}$) was analyzed by ${\pm}1.43%$ on average, and up to ${\pm}4.27%$. Moreover, it was found that the Rating Curve extensions by way of logarithmic and Stevens methods were overestimated to compare with the urban basin scale. Finally, we confirmed that the high water level extension by random generation of hydrological data using MCS can be reduced uncertainty of the high water level, and it will consider as a more reliable approach for high water level extension. In the near future, this results can be applied to real-time flood alert system for urban streams through construction of the high water level extension system using MCS procedures.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5B
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• pp.479-486
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• 2010

Reliable hydrologic data acquisition is the basic and essential requirement for efficient water management. Especially the acquisition of various stream data in a certain location is very important to construct on alarm system to response an urban flood which occurs frequently due to the effect of climate change. Although the frequency of stream inundation flood occurrence becomes low owing to the consistent stream improvement, the urban flood due to the drainage system problems such as deterioration and bad management occurs continuously. The consistent management and current status understanding of the urban drainage system is essential to reduce the urban flood. The purpose of this study is to develop the urban runoff network flow velocity monitoring system which has the capability of collecting stream data whenever, wherever and to whomever without expert knowledge using Code Division Multiple Access technique and Bluetooth near-distance wireless communication technique. The urban runoff network flow velocity monitoring system consists of three stages. In the first stage, the stream information obtained by using ubiquitous floater is transferred to the server computer. In the second stage, the current state of the urban drainage system is assessed through the server computer. In the last stage, the information is provided to the user through a GUI. As a result of applying, the developed urban runoff network flow velocity monitoring system to Woncheon-Stream in Suwon, the information necessary for urban drainage management can be managed in real time.