• Journal of Korea Water Resources Association
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• v.54 no.6
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• pp.419-431
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• 2021

In Seoul, it has been confirmed that the duration of rainfall is shortened and the frequency and intensity of heavy rains are increasing with a changing climate. In addition, due to high population density and urbanization in most areas, floods frequently occur in flood-prone areas for the increase in impermeable areas. Furthermore, the Seoul City is pursuing various projects such as structural and non-structural measures to resolve flood-prone areas. A disaster prevention performance target was set in consideration of the climate change impact of future precipitation, and this study conducted to reduce the overall flood damage in Seoul for the long-term. In this study, 29 GCMs with RCP4.5 and RCP8.5 scenarios were used for spatial and temporal disaggregation, and we also considered for 3 research periods, which is short-term (2006-2040, P1), mid-term (2041-2070, P2), and long-term (2071-2100, P3), respectively. For spatial downscaling, daily data of GCM was processed through Quantile Mapping based on the rainfall of the Seoul station managed by the Korea Meteorological Administration and for temporal downscaling, daily data were downscaled to hourly data through k-nearest neighbor resampling and nonparametric temporal detailing techniques using genetic algorithms. Through temporal downscaling, 100 detailed scenarios were calculated for each GCM scenario, and the IDF curve was calculated based on a total of 2,900 detailed scenarios, and by averaging this, the change in the future extreme rainfall was calculated. As a result, it was confirmed that the probability of rainfall for a duration of 100 years and a duration of 1 hour increased by 8 to 16% in the RCP4.5 scenario, and increased by 7 to 26% in the RCP8.5 scenario. Based on the results of this study, the amount of rainfall designed to prepare for future climate change in Seoul was estimated and if can be used to establish purpose-wise water related disaster prevention policies.

• Korean Journal of Ecology and Environment
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• v.45 no.4
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• pp.459-473
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• 2012

This study evaluated the rainfall-runoff characteristics of Non-point Pollution Source (NPS) of the impervious area through on-site monitoring. In this study, trend analysis was performed by various runoff analysis method of non-point pollution source. The characteristics of rainfall at impervious area appeared to be influenced by rainfall strength. It is judged that the measure is required to be prepared against that now that concentration difference of non-point pollution source appeared to be big by precedent number of days of no rainfall. However, it appeared that Rainfall Sustaining Time (RST) has nothing to do with effluent concentration of non-point pollution source, however, the rising tendency that effluent concentration did not appear because the tendency that concentration of non-point pollution source reduces more than 50% within initial 60 min due to first flushing effects and rainfall sustaining time is long. If looking into the outflow tendency of non-point pollution source at the impervious area, it showed the tendency that the concentration lowers gradually as time goes by after initial concentration appeared very high. However, it could be recognized that the concentration of non-point pollution source appeared to be high as the pollutants integrated on the surface of the road during dry season. The Event Mean Concentrations (EMCs) in impervious area were ranged $9.2{\sim}199.3mg{\cdot}L^{-1}$ for TSS, $8.1{\sim}24.2mg{\cdot}L^{-1}$ for $COD_{Mn}$, $0.070{\sim}1.860mg{\cdot}L^{-1}$ for T-N. Based on such runoff characteristics of non-point pollution source, it is judged that it would be desirable to process initial rain efficiently as the measure against initial rain phenomenon at the impervious area.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.311-323
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• 2023

In order to reduce disaster damage by localized heavy rains, floods, and urban inundation, it is important to know in advance whether natural disasters occur. Currently, heavy rain watch and heavy rain warning by the criteria of the Korea Meteorological Administration are being issued in Korea. However, since this one criterion is applied to the whole country, we can not clearly recognize heavy rain damage for a specific region in advance. Therefore, in this paper, we tried to reset the current criteria for a special weather report which considers the regional characteristics and to predict the damage caused by rainfall after 1 hour. The study area was selected as Gyeonggi-province, where has more frequent heavy rain damage than other regions. Then, the rainfall inducing disaster or hazard-triggering rainfall was set by utilizing hourly rainfall and heavy rain damage data, considering the local characteristics. The heavy rain damage prediction model was developed by a decision tree model and a random forest model, which are machine learning technique and by rainfall inducing disaster and rainfall data. In addition, long short-term memory and deep neural network models were used for predicting rainfall after 1 hour. The predicted rainfall by a developed prediction model was applied to the trained classification model and we predicted whether the rain damage after 1 hour will be occurred or not and we called this as 1ST-Model. The 1ST-Model can be used for preventing and preparing heavy rain disaster and it is judged to be of great contribution in reducing damage caused by heavy rain.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.604-609
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• 2016

This study evaluated the long-term performance of RSR (Reinforced Subgrade for Railways) technology which increases the railway line capacity without the need for additional land. Its characteristics include the use of a short reinforcement with rigid wall, which make it possible to apply it in confined spaces. The 7m high and 40m long testbed employed to evaluate the long-term performance was designed and constructed near Jupo station on the Chang-hang line. This line, located close to a local bus route, had collapsed at the subgrade following heavy rainfall. The performance of the new type of subgrade was verified with long term measurements over a 2 year period including the surface and ground settlement, horizontal displacement of the wall, tensile strain of the reinforcement, and settlement of the rail top on the side track. Based on the results of the measurements made until now, we concluded that it had sufficient safety and serviceability for use as a railway subgrade. It is expected that RSR technology could be frequently used at sites which lack the necessary construction materials for an embankment and are located close to functional railway lines and boundaries, in order to settle civil complaints.

• Korean Journal of Ecology and Environment
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• v.43 no.2
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• pp.183-189
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• 2010

In this study long term rainfall-runoff model, developed based on SSARR, was applied to Geum river basin and its simulation results of major control points were compared with the corresponding observed channel discharges. The validities of the simulation results were examined with re-measured discharges of those control points. From the above procedure the points showing the unreliable results were found out and its principal causes are analyzed through hydrological inspection of runoff characteristics of their circumstances. Finally the simulation results were modified by the consideration of the effects by small-scale hydraulic structures which could directly affect the channel discharges. As a result the annual runoff simulations of two major points in Geum river basin, Yongdam and Daecheong dam sites, work well. However the low flow simulation of the point located between them, Sutong station, showed more or less the unreliable result. Its causes are considered by means of the hydraulic/hydrological inspection of the corresponding point.

• Journal of Korea Water Resources Association
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• v.30 no.4
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• pp.317-326
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• 1997

The Palmer Drought Severity Index has been ectensively used to quantitatively evaluate the drought severity at a location for both agricultural and water resources management purposes. In the present study the Palmer-type formula for drought index is drived for the whole country by analyzing the monthly rainfall and meteorological data at nine stations with a long period of records. The formula is then used to compute the monthly drought severity index at sixty-eight rainfall stations located throughout the country. For the past five significant drought periods the spatial variation of each drought is shown as a nationwide drought index map of a specified duration from which the relative severity of drought throughout the country is identifiable for a specific drought period. A comparative study is made to evaluate the relative severity of the significant droughts occurred in Korea since 1960's. It turned out that '94-'95 drought was one of the worst both in the areal extent and drought severity. It is found that the Palmer-type formula is a very useful tool in quantitatively evaluating the severity of drought over an area as well as at a point. When rainfall and meteorological forecast become feasible on a long-term basis the method could also be utilized as a tool for drought forecasting.

• Journal of Korea Water Resources Association
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• v.55 no.5
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• pp.333-343
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• 2022

For water resource management, the design flood is calculated using the flood frequency analysis technique and the rainfall runoff model. The method by design flood frequency analysis calculates the stochastic design flood by directly analyzing the actual discharge data and is theoretically evaluated as the most accurate method. Actual discharge data frequency analysis of the measured flow was limited due to data limitations in the existing flood flow analysis. In this study, design flood frequency analysis was performed using the measured flow data stably secured through the water level-discharge relationship curve formula. For the frequency analysis of design flood, the parameters were calculated by applying the bayesian inference, and the uncertainty of flood volume by frequency was quantified. It was confirmed that the result of calculating the design flood was close to that calculated by the rainfall-runoff model by applying long-term rainfall data. It is judged that hydrological analysis can be done from various perspectives by using long-term actual flow data through hydrological survey.

• Journal of Environmental Science International
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• v.23 no.4
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• pp.723-735
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• 2014

Sediment discharge by long-term runoff in the Nakdong River watershed should be predicted for the maintenance and management of the Nakdong River newly changed by the four major river restoration project. The data establishment by the analysis of runoff and sediment discharge using the long-term watershed model is necessary to predict possible problems by incoming sediments and to prepare countermeasures for the maintenance and management. Therefore, sediment discharges by long-term runoff in the main points of the Nakdong River were calculated using SWAT(soil and water assessment tool) model and the relations and features between rainfall, runoff, and sediment discharge were analyzed in this study. As a result of sediment discharge calculation in the main points of the Nakdong River and tributaries, the sediment discharge at the outlet of the Naesung Stream was greater than the Jindong Station in the Lower Nakdong River from 1999 to 2008 except the years with low precipitation. The sediment discharge at the Nakdong River Estuary Barrage (NREB) was corresponding to 20% of the Jindong Station which is located about 80 km upstream from NREB.

• Journal of Korean Society of Rural Planning
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• v.21 no.3
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• pp.171-179
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• 2015

The objectives of this research have been focussed on 1) developing prediction techniques for the flash flood and landslide based on rainfall prediction data in agricultural area and 2) developing an integrated forecasting system for the abrupt disasters using USN based real-time disaster sensing techniques. This study contains following steps to achieve the objective; 1) selecting rainfall prediction data, 2) constructing prediction techniques for flash flood and landslide, 3) developing USN and communication network protocol for detecting the abrupt disaster suitable for rural area, & 4) developing mobile application and SMS based early warning service system for local resident and tourist. Local prediction model (LDAPS, UM1.5km) supported by Korean meteorological administration was used for the rainfall prediction by considering spatial and temporal resolution. NRCS TR-20 and infinite slope stability analysis model were used to predict flash flood and landslide. There are limitations in terms of communication distance and cost using Zigbee and CDMA which have been used for existing disaster sensors. Rural suitable sensor-network module for water level and tilting gauge and gateway based on proprietary RF network were developed by consideration of low-cost, low-power, and long-distance for communication suitable for rural condition. SMS & mobile application forecasting & alarming system for local resident and tourist was set up for minimizing damage on the critical regions for abrupt disaster. The developed H/W & S/W for integrated abrupt disaster forecasting & alarming system was verified by field application.