Estimation of Probable Maximum Precipitation in Korea using a Regional Climate Model

Lee, Jeong-Hoon; Choi, Jeonghyeon; Lee, Okjeong; Yoon, Jaeyoung; Kim, Sangdan

doi:10.3390/w9040240

Cited by 24 publications

(15 citation statements)

References 14 publications

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“…The most extreme events are selected for consideration for flood forecasting in the Imjin River basin. In 2002, Typhoon Rusa ripped through South Korea in the Gangneung area between 31 August and 1 September, affecting the eastern and southern parts of Korea with almost 1000 mm of rainfall in 30 h [33,34]. The typhoon caused the submergence of 9000 houses and killed 113 people.…”

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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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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“…특히, Ohara et al (2011) 및 Ishida et al (2015a, 2015b의 연구에서 물리기반 수치기상모형 의 동역학적 다운스케일링 기법을 이용하여 기상요소의 이동, 상대습도의 최대화, 기온의 증가 등을 조합하여 강우를 최대화 하는 방법이 개발되었다. 국내에서도 태풍 루사(RUSA)를 대 상으로 수치기상모델인 WRF (Weather Research and Forecast) 모형을 이용하여 기상요소의 변화를 통해 PMP를 산정하는 연구가 수행되었다 (Lee et al, 2017). 이와 같은 연구들은 모두 공통적으로 과거 발생한 강우사상을 선택하고 재현한 후 모형의 초기 및 경계 조건의 기상요소들을 변경하여 강우 를 최대화하는 방법을 적용하고 있다.…”

Section: 서 론unclassified

Effect of Sea Surface Temperature on Maximizing Typhoon Rainfall Depth

Choi¹,

Lee²,

Jang³

et al. 2018

J. Korean Soc. Hazard Mitig

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In this study, the WRF (Weather Research and Forecast) model was used to maximize typhoon rainfall depth by changing the sea surface temperature (SST) and relative humidity in the initial and boundary conditions. The effects of SST on maximizing typhoon rainfall depth were analyzed. Typhoon MAEMI's rainfall depth, which was numerically reproduced by pre-study, was maximized by increasing the amount of water vapor entering the typhoon. For this, the relative humidity of the initial and boundary conditions of the model was fixed at 100 %, and the typhoon rainfall was simulated by increasing SST from 0.0 °C to 5.0 °C. The simulation of typhoon rainfall under various SSTs indicates that the spatial distribution of the simulated total rainfall depth varies with increasing SST. In particular, it can be seen that the change in SST greatly affects the rain falling on the land. In addition, it is found that the total rainfall depth on land and changes in SST do not exhibit a linear relationship. Therefore, it is necessary to investigate the optimal increase in SST in accordance with the target typhoon by carrying out repeated numerical experiments using various SST conditions, rather than unconditionally increasing SST to maximizing typhoon rainfall depth.

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“…However, the projected future precipitation in the EAMC region is very difficult to attain credibility because it shows very large variability depending on what climate model is applied [26]. In fact, even when a model to reproduce typhoon-induced rainfall in the past is constructed, very different rainfall simulations are often obtained depending on how the physical options of the regional climate model are combined under the same initial conditions and boundary conditions [27,28]. Uncertainty of the climate model results weakens the reliability of future climate data (especially rainfall) composed of sub-daily resolution, and it is difficult to obtain the reliability of various future hydrological components derived by using a complex deterministic water balance model using such future uncertain climate data as input data [29].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Parsimonious Hydrologic Partitioning Model under East Asia Monsoon Climate and Its Application to Climate Change

Lee

Choi

Sim

et al. 2019

Water

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A conceptual hydrologic partitioning model suitable for the East Asia monsoon climate region is constructed parsimoniously, and the variability of Horton index, which is the ratio of water vaporization and wetting in the watershed, is investigated. Numerical simulations in the study area show that the inter-annual variability of Horton index is reduced to around 60% of the inter-annual variability of annual precipitation, and there is a strong inverse correlation between Horton index and annual precipitation. Using cumulant expansion theory, the probability distribution function of soil water with various hydro-meteorological variables and watershed characteristics is derived. Using the steady-state soil water probability distribution function, the sensitivity of Horton index to hydro-meteorological variables such as precipitation occurrence probability, average rainfall depth at rainy days, and evapotranspiration rate and hydro-geophysical characteristics such as surface runoff coefficients, threshold soil water value to control vaporization, and exponent value to control groundwater recharge is analyzed. Looking at the future Horton index of the study area using a variety of future climate information ensemble, it is projected that the water stress of vegetation in the watershed is likely to increase due to fluctuations in precipitation patterns and increase in potential evapotranspiration even if annual precipitation increases.

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Estimation of Probable Maximum Precipitation in Korea using a Regional Climate Model

Cited by 24 publications

References 14 publications

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

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

Effect of Sea Surface Temperature on Maximizing Typhoon Rainfall Depth

Stochastic Parsimonious Hydrologic Partitioning Model under East Asia Monsoon Climate and Its Application to Climate Change

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