• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.300-300
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• 2021

최근 기후변화와 기상이변으로 예측하지 못한 게릴라성의 국지성호우로 인해서 과거 장마와 같은 피해가 아닌 변화된 강우패턴으로 막대한 피해가 나타나고 있다. 또한, 이러한 게릴라성 호우는 예측 또한 어려운 경향을 나타낸다. 이러한 피해를 방지하기 위해 단기유출 예측을 위해 사용되는 다양한 모형들 가운데 GRM(Grid based Rainfall-runoff Model)을 사용하였으며, GRM모델은 단기유출해석에 사용되며 국내에서 개발된 물리적 기반 모형이다. 본 연구에서는 한강의 하류인 청미천 유역을 대상으로 강우-유출 분석을 진행하였으며, 환경부의 11개 기상관측소의 자료를 이용한 티센망도 기반의 면적강우량으로 산정하였고 이를 GRM에 적용하였다. 강우자료의 Event 선정기간은 2011년 6월 29일부터 2011년 7월 1일까지 86.83mm 강수가 내린 Event이다. 공간자료는 국토지리정보원의 90M DEM(Digital Elevation Model), 농촌진흥청의 정밀토양도와 토심, 환경부 환경공간서비스의 대분류 토지이용도를 이용하였다. 또한, 검정을 위해서 정형우도인 NSE, 비정형우도인 Log-normal 우도를 이용하여 분석하였으며, 각각의 결과값은 NSE 0.966, Log-normal은 -1214.97의 값을 나타냈다. 추후, 다양한 적합지표를 이용하여 GRM의 강우패턴별, 유역별대표매개수가 산정된다면 홍수방어를 위한 강우-유출 모형으로 매우 유용하게 활용될 것으로 판단된다.

• Journal of Korea Water Resources Association
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• v.43 no.7
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• pp.667-679
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• 2010

The distributed hydrologic model has been considerably improved due to rapid development of computer hardware technology as well as the increased accessibility and the applicability of hydro-geologic information using GIS. It has been acknowledged that physically-based distributed hydrologic model require significant amounts of data for their calibration, so its application at ungauged catchments is very limited. In this regard, this study was intended to develop a distributed hydrologic model (S-RAT) that is mainly based on conceptually grid-based water balance model. The proposed model shows advantages as a new distributed rainfall-runoff model in terms of their simplicity and model performance. Another advantage of the proposed model is to effectively assess spatio-temporal variation for the entire runoff process. In addition, S-RAT does not rely on any commercial GIS pre-processing tools because a built-in GIS pre-processing module was developed and included in the model. Through the application to the two pilot basins, it was found that S-RAT model has temporal and spatial transferability of parameters and also S-RAT model can be effectively used as a radar data-driven rainfall-runoff model.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.35-35
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• 2012

Most grid-based distributed hydrologic models are complex in terms of data requirements, parameter estimation and computational demand. To address these issues, a simple grid-based hydrologic model is developed in a geographic information system (GIS) environment using storage-release concept. The model is named GIS Storage Release Model (GIS-StoRM). The storage-release concept uses the travel time within each cell to compute howmuch water is stored or released to the watershed outlet at each time step. The travel time within each cell is computed by combining the kinematic wave equation with Manning's equation. The input to GIS-StoRM includes geospatial datasets such as radar rainfall data (NEXRAD), land use and digital elevation model (DEM). The structural framework for GIS-StoRM is developed by exploiting geographic features in GIS as hydrologic modeling objects, which store and process geospatial and temporal information for hydrologic modeling. Hydrologic modeling objects developed in this study handle time series, raster and vector data within GIS to: (i) exchange input-output between modeling objects, (ii) extract parameters from GIS data; and (iii) simulate hydrologic processes. Conceptual and structural framework of GIS StoRM including its application to Pleasant Creek watershed in Indiana will be presented.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1B
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• pp.29-39
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• 2012

Climate change has been obtained researchers' interest, especially in water resources engineering to adjust current conditions to the new circumstance influenced by climate change. In this study, WRF-ARW will be evaluated the capability to estimate distributed precipitation using global weather information instead of the data from rainfall observatory or radar. Cheongmi watershed is selected and adopted to generate a distributed rainfall-runoff model using ModClark. The results from the distributed model with precipitation data from WRF-ARW and the lumped model using observed precipitation data were compared to the observed discharge values. The final results showed that the distributed model, ModClark generated similar pattern of hydrograph to the observations in terms of the time and amount of peak discharge. In addition, the trend of hydrograph from the distributed model presented similar pattern to the observations.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.5
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• pp.143-148
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• 2010

In this study, the mixed model of the surface rainfall-runoff analysis using grid data and Illudas model was applied to the urban watershed of Bulgang river. After the surface rainfall-runoff was estimated with GIS data, the runoff hydrograph was calculated using network analysis at Jeungsan bridge, which is the final output of watershed. Estimated runoff hydrograph in this study was compared to the observed runoff hydrograph which is converted from the water stage at Jeungsan bridge. The relative errors of total runoff volume and peak discharge showed the range values of 11.70%~16.30% and 1.10%~6.96%, and then the difference of peak times had the values of less than 1 hour for 4 storms. Therefore, the mixed model in this study could be considered to estimate the runoff hydrograph for the prevention of disasters in urban watershed.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.6
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• pp.19-30
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• 2013

This study is to evaluate the use of dual-polarization radar data for storm runoff modeling in Namgang dam (2,293 $km^2$) watershed using KIMSTORM (Grid-based KIneMatic wave STOrm Runoff Model). The Bisl dual-polarization radar data for 3 typhoons (Khanun, Bolaven, Sanba) and 1 heavy rain event in 2012 were obtained from Han River Flood Control Office. Even the radar data were overall less than the ground data in areal average, the spatio-temporal pattern between the two data was good showing the coefficient of determination ($R^2$) and bias with 0.97 and 0.84 respectively. For the case of heavy rain, the radar data caught the rain passing through the ground stations. The KIMSTORM was set to $500{\times}500$ m resolution and a total of 21,372 cells (156 rows${\times}$137 columns) for the watershed. Using 28 ground rainfall data, the model was calibrated using discharge data at 5 stations with $R^2$, Nash and Sutcliffe Model Efficiency (ME) and Volume Conservation Index (VCI) with 0.85, 0.78 and 1.09 respectively. The calibration results by radar rainfall showed $R^2$, ME and VCI were 0.85, 0.79, and 1.04 respectively. The VCI by radar data was enhanced by 5 %.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.1
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• pp.33-45
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• 2019

Compound disaster is the type that increases the impact affected by two or more hazard events, and attention to compound disaster and multi-hazards risk is growing due to potential damages which are difficult to predict. The objective of this study is to analyze the possible impacts of post-fire landslide scenario quantitatively by using TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis), a physics-based landslide model. In the case of wildfire, soil organic material and density are altered, and saturated hydraulic conductivity decrease because of soil exposed to high temperature. We have included the change of soil saturated hydraulic conductivity into the TRIGRS model through literature review. For a case study, we selected the area of $8km^2$ in Pyeongchang County. The landslide modeling process was calibrated before simulate the post-wildfire impact based on landslide inventory data to reduce uncertainty. As a result, the mean of the total factor of safety values in the case of landslide was 2.641 when rainfall duration is 1 hour with rainfall intensity of 100mm per day, while the mean value for the case of post-wildfire landslide was lower to 2.579, showing potential landslide occurrence areas appear more quickly in the compound disaster scenario. This study can be used to prevent potential losses caused by the compound disaster such as post-wildfire debris flow or landslides.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.2
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• pp.1-11
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• 2019

Remote sensing products have long been used to monitor and forecast natural disasters. Satellite-derived rainfall products are becoming more accurate as space and time resolution improve, and are widely used in areas where measurement is difficult because of the periodic accumulation of images in large areas. In the case of North Korea, there is a limit to the estimation of precipitation for unmeasured areas due to the limited accessibility and quality of statistical data. CHIRPS (Climate Hazards Group InfraRed Precipitation with Stations) is global satellite-derived rainfall data of 0.05 degree grid resolution. It has been available since 1981 from USAID (U.S. Agency for International Development), NASA (National Aeronautics and Space Administration), NOAA (National Oceanic and Atmospheric Administration). This study evaluates the applicability of CHIRPS rainfall products for South Korea and North Korea by comparing CHIRPS data with ground observation data, and analyzing temporal and spatial drought trends using the Standardized Precipitation Index (SPI), a meteorological drought index available through CHIRPS. The results indicate that the data set performed well in assessing drought years (1994, 2000, 2015 and 2017). Overall, this study concludes that CHIRPS is a valuable tool for using data to estimate precipitation and drought monitoring in Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.136-136
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• 2021

In this study, a deep convolutional neural network (DCNN) model is proposed for short-term precipitation forecasting using weather radar-based images. The DCNN model is a combination of convolutional neural networks, autoencoder neural networks, and U-net architecture. The weather radar-based image data used here are retrieved from competition for rainfall forecasting in Korea (AI Contest for Rainfall Prediction of Hydroelectric Dam Using Public Data), organized by Dacon under the sponsorship of the Korean Water Resources Association in October 2020. This data is collected from rainy events during the rainy season (April - October) from 2010 to 2017. These images have undergone a preprocessing step to convert from weather radar data to grayscale image data before they are exploited for the competition. Accordingly, each of these gray images covers a spatial dimension of 120×120 pixels and has a corresponding temporal resolution of 10 minutes. Here, each pixel corresponds to a grid of size 4km×4km. The DCNN model is designed in this study to provide 10-minute predictive images in advance. Then, precipitation information can be obtained from these forecast images through empirical conversion formulas. Model performance is assessed by comparing the Score index, which is defined based on the ratio of MAE (mean absolute error) to CSI (critical success index) values. The competition results have demonstrated the impressive performance of the DCNN model, where the Score value is 0.530 compared to the best value from the competition of 0.500, ranking 16^th out of 463 participating teams. This study's findings exhibit the potential of applying the DCNN model to short-term rainfall prediction using weather radar-based images. As a result, this model can be applied to other areas with different spatiotemporal resolutions.

• Journal of Environmental Impact Assessment
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• v.27 no.5
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• pp.447-474
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• 2018

The purpose of this study is to build a new dataset of spatially interpolated climate data of South Korea by performing various geo-statistical interpolation techniques for comparison with the LDAPS grid data of KMA. Among 595 observation data in 2017, 80 % of the total points and remaining 117 points were used for spatial mapping and quantification,respectively. IDW, cokriging, and kriging were performed via the ArcGIS10.3.1 software and Python3.6.4, and each result was then divided into three clusters and four watersheds for statistical verification. As a result, cokriging produced the most suitable grid climate data for instantaneous temperature. For 1-hr accumulated precipitation, IDW was most suitable for expressing local rainfall effects.