Application of the Urban Flooding Forecasting by the Flood Nomograph

Jo, Deok Jun

doi:10.9798/kosham.2014.14.6.421

Cited by 2 publications

(2 citation statements)

References 1 publication

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“…Seoul is one of the most highly urbanized cities in South Korea with several residential areas and industrial facilities located near flood plains that are at risk from damage caused by urban flooding. Several researchers, and even the South Korean government, have worked to develop flood forecasting systems and methodologies for urban flood disaster mitigation [22][23][24]. Moreover, many kinds of QPF data are issued for the Seoul metropolitan area by the KMA, allowing us to easily evaluate the hydrological applicability and accuracy of various QPFs.…”

Section: Study Areamentioning

confidence: 99%

“…We used the Storm Water Management Model (SWMM) and a 2-dimensional inundation analysis model to evaluate the performance of the various QPF products. The SWMM, which was developed with the support of the U.S. Environmental Protection Agency, is commonly applied to runoff analysis in urban areas [23,24,29]. It simulates flow movements during real storm events based on rainfall and drainage system characteristics to estimate quantitative discharge and water quality information.…”

Section: Hydrologic and Hydraulic Models For Urban Flood Forecasting mentioning

confidence: 99%

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Adaptive Blending Method of Radar-Based and Numerical Weather Prediction QPFs for Urban Flood Forecasting

Yoon

2019

Remote Sensing

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Preparing proper disaster prevention measures is important for decreasing the casualties and property losses resulting from floods. One of the most efficient measures in this regard is real-time flood forecasting using quantitative precipitation forecasts (QPFs) based on either short-term radar-based extrapolation or longer-term numerical weather prediction. As both methods have individual advantages and limitations, in this study we developed a new real-time blending technique to improve the accuracy of rainfall forecasts for hydrological applications. We tested the hydrological applicability of six QPFs used for urban flood forecasting in Seoul, South Korea: the McGill Algorithm for Prediction Nowcasting by Lagrangian Extrapolation (MAPLE), KOrea NOwcasting System (KONOS), Spatial-scale Decomposition method (SCDM), Unified Model Local Data Assimilation and Prediction System (UM LDAPS), and Advanced Storm-scale Analysis and Prediction System (ASAPS), as well as our proposed blended approach based on the assumption that the error of the previously predicted rainfall is similar to that of current predicted rainfall. We used the harmony search algorithm to optimize real-time weights that would minimize the root mean square error between predicted and observed rainfall for a 1 h lead time at 10 min intervals. We tested these models using the Storm Water Management Model (SWMM) and Grid-based Inundation Analysis Model (GIAM) to estimate urban flood discharge and inundation using rainfall from the QPFs as input. Although the blended QPF did not always have the highest correlation coefficient, its accuracy varied less than that of the other QPFs. In addition, its simulated water depth in pipe and spatial extent were most similar to observed inundated areas, demonstrating the value of this new approach for short-term flood forecasting.

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Section: Study Areamentioning

confidence: 99%

Section: Hydrologic and Hydraulic Models For Urban Flood Forecasting mentioning

confidence: 99%

Adaptive Blending Method of Radar-Based and Numerical Weather Prediction QPFs for Urban Flood Forecasting

Yoon

2019

Remote Sensing

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Establishment of a Flood Nomograph in Coastal Areas Considering Sea-Level Rise

Kim¹,

Choi²,

Song³

et al. 2021

J. Korean Soc. Hazard Mitig

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Climate change caused by global warming is raising the average sea level. The rise in sea level leads to an increase in river water levels within the affected range, which increases the possibility of flooding in water due to erosion of outfall to the coast and rivers. Therefore, it is necessary to recognize in advance the risk of occurrence of domestic flooding, which is aggravated by the effect of rising sea levels, and to construct new boundary conditions for predicting urban flooding accordingly. In this study, Flood Nomograph for two research areas was selected in consideration of the regional characteristics of coastal areas and the scenario of sea level rise. As a result of the analysis, as the sea level rose, the amount of flood critical rainfall decreased numerically. It is believed that this study can be used as a necessary basis for improving flood forecast and warning data considering sea level rise in coastal cities in the future.

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Application of the Urban Flooding Forecasting by the Flood Nomograph

Cited by 2 publications

References 1 publication

Adaptive Blending Method of Radar-Based and Numerical Weather Prediction QPFs for Urban Flood Forecasting

Adaptive Blending Method of Radar-Based and Numerical Weather Prediction QPFs for Urban Flood Forecasting

Establishment of a Flood Nomograph in Coastal Areas Considering Sea-Level Rise

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