Flood Runoff Simulation Using Physical Based Distributed Model for Imjin-River Basin

Park, Jin-Hyeog; Hur, Young-Teck

doi:10.3741/jkwra.2009.42.1.51

Cited by 9 publications

(6 citation statements)

References 2 publications

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“…Motivated by the precedent studies, we utilize the multilayer network for representation learning of spatiotemporal variables. First, basing ourselves on results by prior knowledge and studies of the Han river (Shin & Yoon, 2005;Park & Baek, 2017), we partition the sites into 4 groups:…”

Section: Multilayer Network For Spatiotemporal Modelingmentioning

confidence: 99%

“…v 2 is the node corresponding to the Ganghwa bridge, B 5 . The Han river is a tidal river whose flow and level are affected by tides, and the Ganghwa bridge is located in the downstream area of the river (Park & Baek, 2017). Jung et al (2018) thought highly of the predicted tide level of B 5 as the most important covariate.…”

Section: Multilayer Network For Spatiotemporal Modelingmentioning

confidence: 99%

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Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms

Hong¹,

Choi²,

Jeon³

2023

Preprint

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Forecasting the water level of the Han river is essential to control traffic and avoid natural disasters. There are many variables related to the Han river, and they are intricately connected. In this work, we propose a novel transformer that exploits the causal relationship based on the prior knowledge among the variables and forecasts the water level at the Jamsu bridge in the Han river. Our proposed model considers spatial and temporal causation by formalizing the causal structure as a multilayer network and using masking methods. Due to this approach, we can have interpretability that consistent with prior knowledge. In real data analysis, we use the Han river dataset from 2016 to 2021 and compare the proposed model with deep learning models.

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Section: Multilayer Network For Spatiotemporal Modelingmentioning

confidence: 99%

Section: Multilayer Network For Spatiotemporal Modelingmentioning

confidence: 99%

Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms

Hong¹,

Choi²,

Jeon³

2023

Preprint

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“…넓은 지역에 대해 고해상도의 시공간 분해능을 갖는 레 이더 추정 강수는 산악지역에 대한 돌발홍수 예측이나 홍수 예보의 정확도 향상에 큰 가능성을 지닌 것으로 평가되고 있 으며 (Bae et al, 2012), 그동안 많은 관련 연구가 진행되어 왔 다 (Collier et al, 1986;Seo, 1998;Bringi et al, 2001;Borga, 2002;Thorndahl et al, 2014). 특히 국내에서는 단일 편파 레이더의 오차 및 면적평균환산의 오차, 지상 강우자료 의 합성 및 보정 방법, 레이더 반사도의 수직분포에 대한 보 정 등 레이더 추정 강수의 정확도를 평가하고 향상시키기 위 한 연구 (Bae et al, 2012;Kim et al, , 2009Kim et al, , 2010Kim et al, , 2011Kim and Yoo, 2014;Lee et al, 2014;Yoo et al, 2008Yoo et al, , 2009Yoo et al, , 2011aYoo and Yoon, 2010;Yoon et al, 2014b)와 레이더 추정 강우를 활용한 수문학적 홍수 모의 및 산사태 등 재해 예측 기법을 중심으로 많은 연구 (Ham and Hwang, 2014;Noh et al, 2013Noh et al, , 2014Park and Hur, 2009;Yoon et al, 2014a)가 진행되었다. 한편, 최근 수평편파만을 사용하는 기존의 단일 편파 레이 더에 비해 수직 편파를 추가로 사용하여 여러 수문 기상 변수 를 관측하는 이중 편파 기술의 도입에 의해 레이더의 강수 추 정 성능이 향상되고 있다 (Jeon et al, 2012;Lee, 2013;Lim et al, 2013b (Jeon et al, 2012;Lee, 2013).…”

Section: 서 론unclassified

Comparison of Quantitative Precipitation Estimation Algorithms using Dual Polarization Radar Measurements in Korea

Noh¹,

Lim²,

Choi³

et al. 2014

Journal of Korean Society of Hazard Mitigation

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Accurate estimation of precipitation is essential to predict and prevent natural disasters (e.g. flood, landslide, and heavy snowfall). Recently, weather radars have become a popular tool for quantitative precipitation estimation (QPE) with high spatio-temporal resolution. Especially, in the last decade, QPE performance has been improved by introduction of polarimetric technology to observe multiple hydrometeorological variables at various scales. By being able to measure variables such as the differential reflectivity, specific differential phase, and cross-correlation coefficient, the reliability of estimation has significantly improved as compared to the reflectivity-based method (Ryzhkov et al., 2005a). However, QPEs using dual polarization radar data are still subject to uncertainties resulting from rainfall conversion relationships, combination methods of different parameters, and sampling errors. In this study, we assessed the uncertainty and applicability of conventional QPE algorithms, such as JPOLE (Ryzhkov et al., 2005a) and CSU (Cifelli et al., 2011) algorithms, using the dual polarization radar at Mt. Biseul in the south-eastern part of the Korean Peninsula. Analysis results illustrated that the JPOLE algorithm outperformed the CSU algorithm slightly for daily and hourly rainfall analysis using various metrics. The higher accuracy was found in the stations located within less than 60 km from radar and 100 m in the elevation.

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“…The physically-based distributed hydrology model can be a proper tool for hydrological analysis over an ungauged basin. By using a digital elevation model, the land use and soil map to the distributed hydrologic model was resampled as 1 km, and the flood inflow was calculated based on the natural runoff condition in 2006 before the construction of the Hwanggang Dam [2]. To estimate the release from the Hwanggang Dam in North Korea, the NWS-PC model, a long-term runoff model, was used.…”

Section: Introductionmentioning

confidence: 99%

Flood Inflow Estimation in an Ungauged Simple Serial Cascade of Reservoir System Using Sentinel-2 Multi-Spectral Imageries: A Case Study of Imjin River, South Korea

Kim¹,

Kang

Kim

2022

Remote Sensing

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The Imjin River is a representative transboundary river in the Korean Peninsula, originating from North Korea and flowing into South Korea. Upstream of Imjin River, on the North Korean side, is the Hwanggang Dam, with a 350 million m3 of storage capacity, which is in operation for power generation and water supply. The sharing of the operation information of Hwanggang Dam has been limited due to political and military tension. South Korea has constructed the Gunnam Flood Control Reservoir downstream of the Imjin River to prevent potential flood damage due to the urgent and unilateral release from the Hwanggang Dam. However, it is difficult to manage the flood of the Imjin River basin under the situation of limited shared real-time information on the Hwanggang Dam operation. In this study, a hydrological analysis system was established to estimate the inflow and release of the Hwanggang Dam by building a lumped hydrological model and an Auto ROM (Reservoir Operation Method)-based reservoir operation algorithm. To estimate the inflow of the Gunnam Flood Control Reservoir, the water level of the Hwanggang Dam was calculated using the Sentinel-2 multi-spectral images, and the hydrological analysis system was calibrated. The evaluation index of the water level of the ungauged Hwanggang Dam derived from January 2017 to August 2020 using the hydrological analysis system was shown to have a coefficient of determination of 0.76 and an RMSE of 3.97 m. In the case of the flood event in August 2020, the coefficient of determination of the flood inflow in the Gunnam Flood Control Zone was calculated to be 0.86.

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Flood Runoff Simulation Using Physical Based Distributed Model for Imjin-River Basin

Cited by 9 publications

References 2 publications

Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms

Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms

Comparison of Quantitative Precipitation Estimation Algorithms using Dual Polarization Radar Measurements in Korea

Flood Inflow Estimation in an Ungauged Simple Serial Cascade of Reservoir System Using Sentinel-2 Multi-Spectral Imageries: A Case Study of Imjin River, South Korea

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