Development of Continuous Rainfall-Runoff Model for Flood Forecasting on the Large-Scale Basin

Bae, Deg‐Hyo; Lee, Byong-Ju

doi:10.3741/jkwra.2011.44.1.51

Cited by 16 publications

(13 citation statements)

References 12 publications

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“…The SURR model was developed by the Water Resource and GIS Laboratory, Sejong University [42] and is based on the event-oriented storage function model (SFM) by [43]. The SURR considers the rainfall loss using a soil moisture accounting model for streamflow simulation and prediction.…”

Section: Sejong University Rainfall-runoff (Surr) Modelmentioning

confidence: 99%

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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: Sejong University Rainfall-runoff (Surr) Modelmentioning

confidence: 99%

“…The rainfall and evapotranspiration data have hourly temporal resolutions, which were spatially interpolated by the Thiessen polygons method using GIS. A detailed description of the SURR model is reported in [42]. The hydrologic models simulate the response of the basin to a given rainfall.…”

Section: Sejong University Rainfall-runoff (Surr) Modelmentioning

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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“…Q sur (t), Q lat (t) and Q gw (t) denote the runoff components of surface flow (mm), lateral flow (mm) and groundwater flow (mm), respectively. Additional detailed mathematical descriptions of the components were provided by Bae and Lee (2011). Bae and Lee (2011) showed that the SURR simulations are well fitted to observations, and Nash and Sutcliffe model efficiencies in the calibration and verification periods which are in the ranges of 0.81 to 0.95 and 0.70 to 0.94, respectively.…”

Section: Flash Flood Guidancementioning

confidence: 99%

“…Additional detailed mathematical descriptions of the components were provided by Bae and Lee (2011). Bae and Lee (2011) showed that the SURR simulations are well fitted to observations, and Nash and Sutcliffe model efficiencies in the calibration and verification periods which are in the ranges of 0.81 to 0.95 and 0.70 to 0.94, respectively. Additionally, the behavior of soil moisture depending on the rainfall and the annual loadings of simulated hydrologic components is rational.…”

Section: Flash Flood Guidancementioning

confidence: 99%

“…In this study, the SURR model was used to estimate soil moisture. This model was developed based on the storage function model (SFM) (Kimura, 1961) and improved hydrological components such as potential evapotranspiration, surface flow, lateral flow and groundwater flow based on the physical properties of these components (Bae and Lee, 2011). Moreover, this model uses estimates soil moisture continuously to determine time-dependent soil moisture conditions.…”

Section: Flash Flood Guidancementioning

confidence: 99%

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Development of a precipitation–area curve for warning criteria of short-duration flash flood

Bae

Lee

Moon

2018

Nat. Hazards Earth Syst. Sci.

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Abstract. This paper presents quantitative criteria for flash flood warning that can be used to rapidly assess flash flood occurrence based on only rainfall estimates. This study was conducted for 200 small mountainous sub-catchments of the Han River basin in South Korea because South Korea has recently suffered many flash flood events. The quantitative criteria are calculated based on flash flood guidance (FFG), which is defined as the depth of rainfall of a given duration required to cause frequent flooding (1-2-year return period) at the outlet of a small stream basin and is estimated using threshold runoff (TR) and antecedent soil moisture conditions in all sub-basins. The soil moisture conditions were estimated during the flooding season, i.e., July, August and September, over 7 years (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)) using the Sejong University Rainfall Runoff (SURR) model. A ROC (receiver operating characteristic) analysis was used to obtain optimum rainfall values and a generalized precipitation-area (P -A) curve was developed for flash flood warning thresholds. The threshold function was derived as a P -A curve because the precipitation threshold with a short duration is more closely related to basin area than any other variables. For a brief description of the P -A curve, generalized thresholds for flash flood warnings can be suggested for rainfall rates of 42, 32 and 20 mm h −1 in sub-basins with areas of 22-40, 40-100 and > 100 km 2 , respectively. The proposed P -A curve was validated based on observed flash flood events in different sub-basins. Flash flood occurrences were captured for 9 out of 12 events. This result can be used instead of FFG to identify brief flash flood (less than 1 h), and it can provide warning information to decision-makers or citizens that is relatively simple, clear and immediate.

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Classification of GIS-based models according to natural hazard types

Chang¹,

Park

2016

Spat. Inf. Res.

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Natural disaster and human-evoked disasters can't be clearly distinguished in these days. From monitoring, alerting, managing, responding and recovering, all the experts including scientists, policy makers, and software engineers have discussed what will happen, what should be done first, what impacts the disaster events produce on economy and societies. At this moment, experts in each domain would like to apply their scientific models to explain the phenomena of potential disaster events and to estimate the human casualty and economic and social losses. What is model? Do we need data to make a model? How in detail data are enough to build and verified models? This study aims to overcome the difference in understanding natural hazard-related models and to make research and development program on hazard estimation model which is applicable to real worlds with least mismatch or less gap in understanding the concept of model. Here we suggest classification of the level of applications based on the purpose of each model, regardless of disaster types. Classifications were applied to researches and programs on flood, drought, earthquake and volcanic disasters.

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Development of Continuous Rainfall-Runoff Model for Flood Forecasting on the Large-Scale Basin

Cited by 16 publications

References 12 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

Development of a precipitation–area curve for warning criteria of short-duration flash flood

Classification of GIS-based models according to natural hazard types

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