• The Journal of Engineering Geology
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• v.19 no.3
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• pp.365-371
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• 2009

This paper address the characteristics of loading pollutants caused by the unit agricultural area to establish an efficient management method in NPS (non-point source). The relationship between rainfall and runoff shows good coefficient with 0.92, when the event which shows relatively long antecedent dry days is excepted. The impact of runoff volume on the runoff coefficient can be described by the rainfall intensity strongly. The pollutant EMCs (event mean concentrations) in runoff increased by the increase of antecedent dry days due to dry soil conditions. As the similar pattern of pollutant's loads such as TSS, BOD, COD, TN and TP, it is cleared that other pollutants can be removed when TSS is removed. Therefore the system using only runoff coefficients is not sufficient for the prediction of pollutant loads. It is necessary to consider soil conditions such as rainfall, antecedent dry day, antecedent rainfall etc. for the prediction system.

• Journal of Korea Water Resources Association
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• v.44 no.1
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• pp.51-64
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• 2011

The objective of this study is to develop a continuous rainfall-runoff model for flood prediction on a large-scale basin. For this study, the hourly surface runoff estimation method based on the variable retention parameter and runoff curve number is developed. This model is composed that the soil moisture to continuous rainfall can be simulated with applying the hydrologic components to the continuous equation for soil moisture. The runoff can be simulated by linking the hydrologic components with the storage function model continuously. The runoff simulation to large basins can be performed by using channel storage function model. Nakdong river basin is selected as the study area. The model accuracy is evaluated at the 8 measurement sites during flood season in 2006 (calibration period) and 2007~2008 (verification period). The calibrated model simulations are well fitted to the observations. Nash and Sutcliffe model efficiencies in the calibration and verification periods exist in the range of 0.81 to 0.95 and 0.70 to 0.94, respectively. The behavior of soil moisture depending on the rainfall and the annual loadings of simulated hydrologic components are rational. From this results, continuous rainfall-runoff model developed in this study can be used to predict the discharge on large basins.

• Journal of the Korean Geotechnical Society
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• v.35 no.10
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• pp.17-31
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• 2019

This study describes a prediction method for rainfall-induced landslides and subsequently debris flows in a regional scale areas. Special attention is given to the calculation of the propagation of debris flows by considering rainfall infiltration into soil slopes and soil entrainments by debris flows. The proposed method was verified by comparing the analytical results and the measured ones reported by the previous research. As a result, predictions and observations were quite similar in terms of the front position, the velocity, volume and momentum of debris flows. Even when applied to natural mountain slope with complicated terrain, numerical results and observations were similar. At last, the combined analysis of landslides and debris flows were conducted. The landslides prediction showed a predictive rate of about 83%, and the result of the final volume of debris flow showed an error rate of 3%. As a result, the proposed combined method for landslides and debris flows overcomes the problem of separating the landslides analysis and the debris flows simulation. Especially, the proposed method can analyze the effects of rainfall on entrainments by debris flows as well as rainfall-induced landslides and the behavior of debris flows.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.16 no.8 s.99
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• pp.848-854
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• 2005

The attenuation due to rain has been recognized as one of the major causes of unavailability of radio communication systems operating above about 10 GHz. To design radio links for telecommunications and to evaluate attenuation due to rainfall, it is important to have a good prediction model for rain attenuation such as a model for drop-size distribution of rainfall(DSD), a theoretical model for specific rain attenuation, and an empirical model fur effective path length through rain. In this paper, the extended generalized gamma distribution for drop-size distribution, based on the measurements in Chnugnam National University, is proposed as a new DSD model, and predicted specific attenuation characteristics using proposed DSD model and rain attenuation values in the 44 GHz satellite path using ITU-R effective path length model, are analysed. The predicted attenuation levels are also compared. It is found that an accurate prediction method for DSD is very important to reduce the prediction error in the local satellite path.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.473-478
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• 2013

In this paper, we develop the Heavy Rain Advisory Decision Model based on intelligent neuro-fuzzy algorithm RBFNNs by using KLAPS(Korea Local Analysis and Prediction System) Reanalysis data. the prediction ability of existing heavy rainfall forecasting systems is usually affected by the processing techniques of meteorological data. In this study, we introduce the heavy rain forecast method using the pre-processing techniques of meteorological data are in order to improve these drawbacks of conventional system. The pre-processing techniques of meteorological data are designed by using point conversion, cumulative precipitation generation, time series data processing and heavy rain warning extraction methods based on KLAPS data. Finally, the proposed system forecasts cumulative rainfall for six hours after future t(t=1,2,3) hours and offers information to determine heavy rain advisory. The essential parameters of the proposed model such as polynomial order, the number of rules, and fuzzification coefficient are optimized by means of Differential Evolution.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.152-163
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• 2020

The present study introduces a procedure to prepare and manage a high-resolution rainfall and soil moisture (SM) database in the LAMP prediction system, especially for landslide researchers. The procedure also includes converting the data into spatial resolution suitable for their interest regions following proper map projection methods. The LAMP model precipitation and SM data are quantitatively and qualitatively evaluated to identify the model prediction characteristics using the ERA5 reanalysis precipitation and observed 10m depth SM data. A detailed process of converting LAMP Weather Research and Forecasting (WRF) output data for 10m horizontal resolution is described in a step-wise manner, providing technical convenience for users to easily convert NetCDF data from the WRF model into TIF data in ArcGIS. The converted data can be viewed and downloaded via the LAMP website (http://df.ncam.kr/lamp/index.do) of the National Center for AgroMeteorology. The constructed database will contribute to monitoring and prediction of landslide risk prior to landslide response steps and should be data quality controlled by more observation data.

• Atmosphere
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• v.31 no.2
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• pp.185-198
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• 2021

In this study, we examined a spatial downscaling method based on Gradient and Inverse Distance Squared (GIDS) weighting to produce high-resolution grid data from a numerical weather prediction model over Korean Peninsula with complex terrain. The GIDS is a simple and effective geostatistical downscaling method using horizontal distance gradients and an elevation. The predicted meteorological variables (e.g., temperature and 3-hr accumulated rainfall amount) from the Limited-area ENsemble prediction System (LENS; horizontal grid spacing of 3 km) are used for the GIDS to produce a higher horizontal resolution (1.5 km) data set. The obtained results were compared to those from the bilinear interpolation. The GIDS effectively produced high-resolution gridded data for temperature with the continuous spatial distribution and high dependence on topography. The results showed a better agreement with the observation by increasing a searching radius from 10 to 30 km. However, the GIDS showed relatively lower performance for the precipitation variable. Although the GIDS has a significant efficiency in producing a higher resolution gridded temperature data, it requires further study to be applied for rainfall events.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.135-142
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• 2022

Recently, with the development of data processing technology and the increase of computational power, methods to solving social problems using Artificial Intelligence (AI) are in the spotlight, and AI technologies are replacing and supplementing existing traditional methods in various fields. Meanwhile in Korea, heavy rain is one of the representative factors of natural disasters that cause enormous economic damage and casualties every year. Accurate prediction of heavy rainfall over the Korean peninsula is very difficult due to its geographical features, located between the Eurasian continent and the Pacific Ocean at mid-latitude, and the influence of the summer monsoon. In order to deal with such problems, the Korea Meteorological Administration operates various state-of-the-art observation equipment and a newly developed global atmospheric model system. Nevertheless, for precipitation nowcasting, the use of a separate system based on the extrapolation method is required due to the intrinsic characteristics associated with the operation of numerical weather prediction models. The predictability of existing precipitation nowcasting is reliable in the early stage of forecasting but decreases sharply as forecast lead time increases. At this point, AI technologies to deal with spatio-temporal features of data are expected to greatly contribute to overcoming the limitations of existing precipitation nowcasting systems. Thus, in this project the dataset required to develop, train, and verify deep learning-based precipitation nowcasting models has been constructed in a regularized form. The dataset not only provides various variables obtained from multiple sources, but also coincides with each other in spatio-temporal specifications.

• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.3
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• pp.57-69
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• 2016

In this study, we analyzed the extreme rainfall distribution scenarios based on probable rainfall calculation and applying various time distribution models over the landslide high risk zones in urban areas. We used observed rainfall data form total 71 ASOS (Automated Synoptic Observing System) station and AWS (Automatic Weather Station) in KMA (Korea Meteorological Administration), and we analyzed the linear trends for 1-hr and 24-hr annual maximum rainfall series using simple linear regression method, which are identified their increasing trends with slopes of 0.035 and 0.660 during 1961-2014, respectively. The Gumbel distribution was applied to obtain the return period and probability precipitation for each duration. The IDF (Intensity-Duration-Frequency) curves for landslide high risk zones were derived by applying integrated probability precipitation intensity equation. Results from IDF analysis indicate that the probability precipitation varies from 31.4~38.3 % for 1 hr duration, and 33.0~47.9 % for 24 hr duration. It also showed different results for each area. The $Huff-4^{th}$ Quartile method as well as Mononobe distribution were selected as the rainfall distribution scenarios of landslide high risk zones. The results of this study can be used to provide boundary conditions for slope collapse analysis, to analyze sediment disaster risk, and to use as input data for risk prediction of debris flow.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.101-110
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• 2010

The increased occurrence of flooding due to typhoons and local rainfall has necessitated damage prevention through the systematic construction of damage history and quantitative analysis of flood prediction data. In this study, we constructed a disaster information map for practical use by combining digital images and continuous cadastral maps of damaged areas using a geographic information system to provide basic data and attribute information. In addition, we predicted the areas at risk of flash floods by calculating the flood capacity of the study area for different rainfall frequencies through flood inundation simulation, which was used to obtain comprehensive disaster information. Further, we calculated the extent of the flooded area and the damage rate for different rainfall frequencies using cadastral information. Flood inundation simulation in the case of heavy rainfall was found to help improve the ability to react to a flood and enhance the efficiency of rescue work by supporting decision-making for disaster management.