• Journal of Korea Water Resources Association
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• v.41 no.9
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• pp.895-905
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• 2008

To analyze hydrologic processes in a watershed requires both various geographical data and hydrological time series data. Recently, not only geographical data such as DEM(Digital Elevation Model) and hydrologic thematic map but also hydrological time series from numerical weather prediction and rainfall radar have been provided as grid data, and there are studies on hydrologic analysis using these grid data. In this study, GRM(Grid based Rainfall-runoff Model) which is physically-based distributed rainfall-runoff model has been developed to simulate short term rainfall-runoff process effectively using these grid data. Kinematic wave equation is used to simulate overland flow and channel flow, and Green-Ampt model is used to simulate infiltration process. Governing equation is discretized by finite volume method. TDMA(TriDiagonal Matrix Algorithm) is applied to solve systems of linear equations, and Newton-Raphson iteration method is applied to solve non-linear term. Developed model was applied to simplified hypothetical watersheds to examine model reasonability with the results from $Vflo^{TM}$. It was applied to Wicheon watershed for verification, and the applicability to real site was examined, and simulation results showed good agreement with measured hydrographs.

• Journal of Korea Water Resources Association
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• v.47 no.3
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• pp.297-306
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• 2014

In this study, EFDC (Environmental Fluid Dynamics Code) model was used to simulate the salinity distribution for sea water inflow and rainfall runoff. The flowrate was given to the boundary conditions, which can be calculated by areal-specific flowrate method from the measured flowrate of the representative outfall. The boundary condition of the water elevation can be obtained from the hourly tidal elevation. The flowrate from the outfall can be calculated using the condition of the 245 mm raifall. The simulation results showed that at Sites 1~2 and the Mangsan island (Site 4) the salinity becomes 0 ppt after the rainfall. However, the salinity is 30 ppt when there is no rainfall. Time series of the salinity changes were compared with the measured data from January 1 to December 31, 2010 at the four sites (Site 2~5) of Yongwon channel. Lower salinities are shown at the inner sites of Yongwon channel (Site 1~4) and the sites of Songjeong river (Site 7~8). The intensive investigation near the Mangsan island showed that the changes of salinity were 21.9~28.8 ppt after the rainfall of 17 mm and those of the salinity were 2.33~8.05 ppt after the cumulative rainfall of 160.5 mm. This means that the sea water circulation is blocked in Yongwon channel, and the salinity becomes lower rapidly after the heavy rain.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.647-659
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• 2017

Garlic and onion are grown in major cultivation regions that depend on the crop condition and the meteorology of the production area. Therefore, when yields are to be predicted, it is reasonable to use a statistical model in which both the crop and the meteorological elements are considered. In this paper, using a multiple linear regression model, we predicted garlic and onion yields in major cultivation regions. We used the MODIS NDVI that reflects the crop conditions, and six meteorological elements for 7 major cultivation regions from 2006 to 2015. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, the MODIS NDVI in February was chosen the significant independent variable of the garlic and onion yield prediction model. In the case of meteorological elements, the garlic yield prediction model were the mean temperature (March), the rainfall (November, March), the relative humidity (April), and the duration time of sunshine (April, May). Also, the rainfall (November), the duration time of sunshine (January), the relative humidity (April), and the minimum temperature (June) were chosen among the variables as the significant meteorological elements of the onion yield prediction model. MODIS NDVI and meteorological elements in the model explain 84.4%, 75.9% of the garlic and onion with a root mean square error (RMSE) of 42.57 kg/10a, 340.29 kg/10a. These lead to the result that the characteristics of variations in garlic and onion growth according to MODIS NDVI and other meteorological elements were well reflected in the model.

• Journal of the Korean GEO-environmental Society
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• v.18 no.6
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• pp.21-30
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• 2017

This study analyzed the ground characteristics region by designating 3 research areas, Anrim-dong in Chungju City, Busa-dong in Daejeon Metropolitan City and Sinan-dong in Andong City out of the areas subject to concentrated management to prepare for sediment disaster in downtown areas. The correlation between ground characteristics were observed by using characteristics (crown density, root cohesion, rainfall characteristics, soil characteristics) and the risk areas were predicted through sediment disaster prediction modeling. Landslide MAPping (LSMAP), Stability Index MAPping (SINMAP) and Landslide Hazard MAP (LHMAP) were used for the comparative analysis of the hazard prediction model for sediment disaster. As a result of predicting the sediment disaster danger, in case of SINMAP which was generally used, excessive range was predicted as a hazardous area and in case of the Korea Forest Service's landslide hazard map (LHMAP), the smallest prediction area was assessed. LSMAP predicted a medium range of SINMAP and LHMAP as hazardous area. The difference of the prediction results is that the analysis parameters of LSMAP is more diverse and engineering than two comparative models, and it is found that more precise prediction is possible.

• Journal of Soil and Groundwater Environment
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• v.26 no.6
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• pp.106-117
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• 2021

The purpose of this study is prediction of landslide occurrence reflecting the subsurface flow characteristics within the soil layer in the future due to climate change in a large scale watershed. To do this, we considered the infinite slope stability theory to evaluate the landslide occurrence with predicted soil moisture content by SWAT model based on monitored data (rainfall-soil moisture-discharge). The correlation between the SWAT model and the monitoring data was performed using the coefficient of determination (R²) and the model's efficiency index (Nash and Sutcliffe model efficiency; NSE) and, an accuracy analysis of landslide prediction was performed using auROC (area under Receiver Operating Curve) analysis. In results comparing with the calculated discharge-soil moisture content by SWAT model vs. actual observation data, R² was 0.9 and NSE was 0.91 in discharge and, R² was 0.7 and NSE was 0.79 in soil moisture, respectively. As a result of performing infinite slope stability analysis in the area where landslides occurred in the past based on simulated data (SWAT analysis result of 0.7~0.8), AuROC showed 0.98, indicating that the suggested prediction method was resonable. Based on this, as a result of predicting the characteristics of landslide occurrence by 2050 using climate change scenario (RCP 8.5) data, it was calculated that four landslides could occur with a soil moisture content of more than 75% and rainfall over 250 mm/day during simulation. Although this study needs to be evaluated in various regions because of a case study, it was possible to determine the possibility of prediction through modeling of subsurface flow mechanism, one of the most important attributes in landslide occurrence.

• Atmosphere
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• v.15 no.1
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• pp.1-16
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• 2005

Land-surface and soil processes significantly affect mesoscale local weather systems as well as global/regional climate. In this study, characteristics of land-surface models (LSMs) and soil models (SMs) that are frequently coupled into mesoscale meteorological models are investigated. In addition, detailed analyses on three LSMs, employed by the PSU/NCAR MM5, are provided. Some impacts of LSMs on heavy rainfall prediction are also discussed.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.295-300
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• 2003

공간적으로 영향을 받는 위치에서의 상호 연관성을 고려한 예측모형 중에서 크리깅 (kriging) 방법은 관측된 데이터를 보간(interpolation)하고, 부드럽게 연결(smoothing)하며, 새로운 데이터를 예측(prediction)하는 통계적 모형으로서 많이 활용되고 있다. 크리깅 모형을 적용하기 위해서는 먼저 주어진 두 위치에서의 비연관성을 나타내는 세미베리오그램 (semivariogram)의 3가지 모수(nugget, sill, range)를 추정해야 한다. 본 연구에서는 전역 적 최적화 방법인 유전자 알고리즘(genetic algorithm)을 도입하여 세미베리오그램 모수들을 추정하였고, 이를 통해 강우량(rainfall)에 대한 크리깅 추정량을 산출하고 효과성을 판단하였다.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.7
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• pp.83-93
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• 2003

This research compared the observed and model predicted results that include; runoff, sediment yield, and nutrient losses from a 2.71 ha cattle grazing pasture field in North Alabama. Application of water quality computer simulation models can inexpensively and quickly assess the impact of pasture management practices on water quality. AGNPS single storm based model was applied to the three pasture species; Bermudagrass, fescue, and Ryegrass. While comparing model predicted results with observed data, it showed that model can reasonably predict the runoff, sediment yield and nutrient losses from the watershed. Over-prediction and under-prediction by the model occurred during very high and low rainfall events, respectively. The study concluded that AGNPS model can be reasonably applied to assess the impacts of pasture management practices and chicken litter application on water quality.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.202-206
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• 1994

Mathematical model of the pollution rate of substation insulators is constructed, taking the model parameters as wind speed, wind direction, typhoon conditions and rainfall in an hourly basis. The main feature of model construction is to distinguish the effect of each parameter by separately analyzing the positive and negative pollution causing factors. Model parameters for the insulators of Karatsu substation, Saga, Japan were estimated and model validation was done using the actual data, in which the pollution deposits on the insulators were measured using pilot insulator and 'salt meter'. The proposed model of the pollution rate [mg/cm$^{2}$/hr] enables the identification of the effective parameters and prediction of the pollution rate so that it helps for the automatic decision making for insulator cleaning or the model can be used as a tool for the substation engineers to make precautionary measures.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.51-55
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• 2005

This study aims to evaluate applicability of AVHRR NDVI for drought prediction. Drought, one of the typical natural disasters, has damaged almost every year in different places and at time varied. An AVHRR satellite image can be a comprehensive tool to measure the drought because it is easy to obtain the vegetation status over the nationwide. This study explored a possibility of NOAA AVHRR NDVI applicability to drought because NDVI can provide plant vitality. Using May AVHRR imageries in 1998, 1999, and 2000, NDVIs are derived and compared with the rainfall amount to pursue the relationship between NDVI and drought. From the result, AVHRR NDVI showed a possible applicability in using drought prediction.