Estimation of Real-Time Flood Risk on Roads Based on  Rainfall Calculated by the Revised Method of Missing Rainfall

Kim, Eun‐Mi; Ra, Ilkyeun; Rhee, Kyung Hyun; Kim, Chang Soo

doi:10.3390/su6096418

Cited by 10 publications

(8 citation statements)

References 13 publications

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“…Meteorological data analyses for the period from 1960 to 2009 in the SMA show that the air temperature and precipitation increase, relative humidity decreases, and heavy rainfall events with more than 20 mm h −1 also increase . Recently, the SMA has experienced blackouts of more than 1.6 million houses due to failure in electric power demand prediction after extremely hot weather in autumn, massive damage from shallow landslides due to heavy rainfall in 2011 (D. W. , several inundations by flash floods (Kim et al, 2014), building damage due to strong winds such as typhoons, traffic accidents as a result of road ice, and deaths due to annual heat/cold waves (Son et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

High-resolution urban observation network for user-specific meteorological information service in the Seoul Metropolitan Area, South Korea

Park

Chae

et al. 2017

Atmos. Meas. Tech.

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Abstract. To improve our knowledge of urban meteorology, including those processes applicable to high-resolution meteorological models in the Seoul Metropolitan Area (SMA), the Weather Information Service Engine (WISE) Urban Meteorological Observation System (UMS-Seoul) has been designed and installed. The UMS-Seoul incorporates 14 surface energy balance (EB) systems, 7 surface-based threedimensional (3-D) meteorological observation systems and applied meteorological (AP) observation systems, and the existing surface-based meteorological observation network. The EB system consists of a radiation balance system, sonic anemometers, infrared CO 2 /H 2 O gas analyzers, and many sensors measuring the wind speed and direction, temperature and humidity, precipitation, and air pressure. The EBproduced radiation, meteorological, and turbulence data will be used to quantify the surface EB according to land use and to improve the boundary-layer and surface processes in meteorological models. The 3-D system, composed of a wind lidar, microwave radiometer, aerosol lidar, or ceilometer, produces the cloud height, vertical profiles of backscatter by aerosols, wind speed and direction, temperature, humidity, and liquid water content. It will be used for highresolution reanalysis data based on observations and for the improvement of the boundary-layer, radiation, and microphysics processes in meteorological models. The AP system includes road weather information, mosquito activity, water quality, and agrometeorological observation instruments. The standardized metadata for networks and stations are documented and renewed periodically to provide a detailed observation environment. The UMS-Seoul data are designed to support real-time acquisition and display and automatically quality check within 10 min from observation. After the quality check, data can be distributed to relevant potential users such as researchers and policy makers. Finally, two case studies demonstrate that the observed data have a great potential to help to understand the boundary-layer structures more deeply, improve the performance of high-resolution meteorological models, and provide useful information customized based on the user demands in the SMA.

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Section: Introductionmentioning

confidence: 99%

High-resolution urban observation network for user-specific meteorological information service in the Seoul Metropolitan Area, South Korea

Park

Chae

et al. 2017

Atmos. Meas. Tech.

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“…On July 27, 2011 heavy rainfall led to extreme flooding in Seoul, South Korea. In Seoul, as a mega city with a high population concentration, 536 mm of rainfall over three days resulted in severe damage, 980 flood victims, 62 people reported as dead or missing and 19,215 inundated houses [36]. The conventional rainfall in 2011 was developed by convective systems which moved from the Yellow Sea to Seoul [37].…”

Section: Hydrological and Meteorological Datamentioning

confidence: 99%

Application of Artificial Neural Networks for Accuracy Enhancements of Real-Time Flood Forecasting in the Imjin Basin

Jabbari

Bae

2018

Water

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Hydrometeorological forecasts provide future flooding estimates to reduce damages. Despite the advances and progresses in Numerical Weather Prediction (NWP) models, they are still subject to many uncertainties, which cause significant errors forecasting precipitation. Statistical postprocessing techniques can improve forecast skills by reducing the systematic biases in NWP models. Artificial Neural Networks (ANNs) can model complex relationships between input and output data. The application of ANN in water-related research is widely studied; however, there is a lack of studies quantifying the improvement of coupled hydrometeorological model accuracy that use ANN for bias correction of real-time rainfall forecasts. The aim of this study is to evaluate the real-time bias correction of precipitation data, and from a hydrometeorological point of view, an assessment of hydrological model improvements in real-time flood forecasting for the Imjin River (South and North Korea) is performed. The comparison of the forecasted rainfall before and after the bias correction indicated a significant improvement in the statistical error measurement and a decrease in the underestimation of WRF model. The error was reduced remarkably over the Imjin catchment for the accumulated Mean Areal Precipitation (MAP). The performance of the real-time flood forecast improved using the ANN bias correction method. of these models is still a concern for hydrometeorological prediction studies [5]. NWP models are subject to many uncertainties, which cause significant errors in the forecasting of real-time precipitation. In coupled hydrometeorological studies, one critical issue before running a rainfall-runoff model is to reduce the input forcing errors that are produced by the meteorological model. Therefore, postprocessing of the model outputs of the real-time forecast data would result in a better match with the observation records. Statistical postprocessing methods improve real-time forecast accuracy by relating the model outputs to the observed values.Several studies have shown that statistical postprocessing improved forecast performance by reducing systematic biases [6,7]. The main purpose of statistical bias correction is to develop a relationship between the modeled and observed data. Commonly used statistical methods are the quantile-based mapping method [8] and regression approaches that include linear relationships [9] and nonlinear relationships [10]. In real-time flood forecasting, a comparison of different postprocessing methods such as Bayesian Model Averaging (BMA), classic poor man ensemble (PME) and Multimodel SuperEnsemble Dressing (MSD) indicated that the MSD approach provided better precipitation data for floods in Italy [11]. Six different bias correction methods (including linear scaling (LS), local intensity scaling (LOCI) scaling, Daily Translation (DT), daily bias correction (DBC), quantile mapping based on an empirical distribution (QME) and quantile mapping based on a gamma distribution (QMG)) were applied in ten No...

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“…Quality check algorithms for meteorological variables and flux data have previously been developed (Aubinet et al, 2012;Chae et al, 2014;M.-S. Park et al, , 2014aKim et al, 2015). Basic quality checks for meteorological variables include missing check, physical limit check, climate range check, and spike removal (Chae et al, 2014).…”

Section: Systemsmentioning

confidence: 99%

High-Resolution Urban Observation Network for a User-Specific Meteorological Information Service in the Seoul Metropolitan Area, Korea

Park¹,

Park²,

Choi³

et al. 2016

Preprint

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Abstract. To improve our knowledge of urban meteorology, including those processes applicable to high-resolution meteorological models in the Seoul Metropolitan Area, a Weather Information Service Engine urban meteorological observation system network (UMS-Seoul) has been designed and installed. The network incorporates 14 surface energy balance (EB) systems, 7 surface-based 3-dimensional meteorological observation (3D) systems, and applied meteorological observation (AP) systems, as well as the existing surface-based meteorological observation network. The EB system consists of a radiation balance system, sonic anemometers, infrared CO2/H2O gas analyzers, and many sensors to measure wind speed and direction, temperature and humidity, precipitation, and air pressure, etc. The EB-produced radiation, meteorological, and turbulence data will be used to quantify the surface energy balance according to land use, and improve the boundary layer and surface processes in meteorological models. The 3D system, composed of wind lidar, a microwave radiometer, an aerosol lidar, or a ceilometer, produces vertical profiles of backscatter by aerosols or water vapor, cloud height, wind speed and direction, temperature, humidity, and liquid water content. It will be used for high-resolution reanalysis data based on observations as well as for improvement of the boundary layer, radiation, and microphysics processes in meteorological models. The AP system includes road weather information, mosquito activity, and water quality observation instruments. The standardized metadata for networks and stations are documented and renewed periodically to provide a detailed observation environment. The UMS-Seoul data are designed to support real-time acquisition, as well as display and automatically quality check the data within 10 minutes of observation. After the quality check, data can be distributed to relevant potential users such as researchers and policy makers.

show abstract

Estimation of Real-Time Flood Risk on Roads Based on Rainfall Calculated by the Revised Method of Missing Rainfall

Cited by 10 publications

References 13 publications

High-resolution urban observation network for user-specific meteorological information service in the Seoul Metropolitan Area, South Korea

High-resolution urban observation network for user-specific meteorological information service in the Seoul Metropolitan Area, South Korea

Application of Artificial Neural Networks for Accuracy Enhancements of Real-Time Flood Forecasting in the Imjin Basin

High-Resolution Urban Observation Network for a User-Specific Meteorological Information Service in the Seoul Metropolitan Area, Korea

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