• Proceedings of the Korea Water Resources Association Conference
• /
• 2006.05a
• /
• pp.701-705
• /
• 2006

강우자료는 수문 모델링 작업에서 가장 기초적인 수문학적 입력자료로 시간과 공간에 따른 변동성이 크므로 규명하기 복잡한 수문현상 중의 하나이다. 산악지역이 많은 우리나라의 지형학적 특성과 태풍, 장마 및 특히, 최근의 게릴라성 집중호우 등으로 인하여 이러한 변동성이 더욱 커지고 있는 실정이다. 장기간 실측된 수문기상 기초 자료가 부족한 우리나라의 실정상 홍수예보 및 수공구조물 설계를 위해 정확한 강우량 자료의 취득이 선행돼야 한다. 따라서 적절한 장소에 수문관측소 설치 및 관리를 통해 양호한 강우량 자료를 획득해야 하지만, 현장 여건상 등의 이유로 미계측 및 결측, 이상자료가 발생하고 있다. 따라서 이러한 미계측 혹은 결측지점의 우량을 추정할 수 있는 방법을 비교, 분석하여 적절한 보정과정을 수행할 필요가 있다. 그간의 연구에서는 미계측 지점 혹은 산악지역에서의 점 강우량 보정방법에 대한 연구가 진행되었지만, 본 연구에서는 '도시홍수재해관리기술연구사업단'에서 운영 중인 도시하천 유역 특히 소배수구역에서의 결측 자료에 대해 여러 추정 방법을 비교, 분석하여 적절한 방안을 찾고자 한다. 이를 위하여 중랑천 유역의 3개 소배수 구역(월계1 배수구역, 군자 배수구역, 어린이대공원 배수구역)에 설치된 3개 우량관측소와 건설교통부 관할 우량관측소 2개소의 우량자료를 사용하였다. 본 연구에서는 결측치 보간을 위하여 널리 이용되고 있는 산술평균법(Arithmetic Average method), 역거리법(Reciprocal Distance Squared method), 거리고도비율법(Ratio of Distance and Elevation method), 인근관측소와의 관계식 이용, 크리깅방법(Simple Kriging method)을 비교, 검토 적용하였다. 중랑천 유역의 소배수구역을 대상으로 연중 발생하는 큰 호우사상에 대해 임의의 강우관측소를 결측지점으로 가정하고 주변의 강우관측소로부터 각각의 방법을 이용해 가중치들을 산정하여 결측지점의 강우량 값을 보정하고자 하였다. 또한 각각의 방법을 이용하여 얻어진 결과에 대해 실측값과 보정값의 오차정도를 평균절대오차법(Mean Absolute Error)과 제곱평균제곱근오차법(Root Mean Squared Error)에 의해 산정하여 보정 방법간의 효율성을 검토하고자 하였다.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.60 no.6
• /
• pp.83-96
• /
• 2018

The objective of this study was to estimate the climate change impact on water quantity and quality to Saemanguem watershed using SWAT (Soil and water assessment tool) model. The SWAT model was calibrated and validated using observed data from 2008 to 2017 for the study watershed. The $R^2$ (Determination coefficient), RMSE (Root mean square error), and NSE (Nash-sutcliffe efficiency coefficient) were used to evaluate the model performance. RCP scenario data were produced from 10 GCM (General circulation model) and all relevant grid data including the major observation points (Gusan, Jeonju, Buan, Jeongeup) were extracted. The systematic error evaluation of the GCM model outputs was performed as well. They showed various variations based on analysis of future climate change effects. In future periods, the MIROC5 model showed the maximum values and the CMCC-CM model presented the minimum values in the climate data. Increasing rainfall amount was from 180mm to 250mm and increasing temperature value ranged from 1.7 to $5.9^{\circ}C$, respectively, compared with the baseline (2006~2017) in 10 GCM model outputs. The future 2030s and 2070s runoff showed increasing rate of 16~29% under future climate data. The future rate of change for T-N (Total nitrogen) and T-P (Total phosphorus) loads presented from -26 to +0.13% and from +5 to 47%, respectively. The hydrologic cycle and water quality from the Saemanguem headwater were very sensitive to projected climate change scenarios so that GCM model should be carefully selected for the purpose of use and the tendency analysis of GCM model are needed if necessary.

• Journal of Environmental Science International
• /
• v.24 no.3
• /
• pp.281-290
• /
• 2015

Recent climate change has led to fluctuations in agricultural production, and as a result national food supply has become an important strategic factor in economic policy. As such, in this study, panel data was collected to analyze the effects of seven meteorological elements on the production of five types of grain with error component panel data regression method following the test results of LM tests, Hausman test. The key factors affecting the production of rice were average temperature, average relative humidity and average ground surface temperature. The fluctuations in the other four grains types are not well explained by meterological elements. For other grains and beans, only average temperature and time (year) affect the production of other grains while average temperature, ground surface temperature, and time (year) influence the production of beans. For barley and millet, only average temperature positively affects the production of barley while ground surface temperature and time (year) negatively influence the production of millet. The implications of this study are as follow. First, it was confirmed that the meteorological elements have profound effects on the rice production. Second, when compared to existing studies, this study was not limited to rice but encompassed all five types of grains and went beyond other studies that were limited to temperature and rainfall to include various meteorological elements.

• Journal of Information Processing Systems
• /
• v.11 no.4
• /
• pp.556-572
• /
• 2015

This paper presents the applications of spatial interpolation and assimilation methods for satellite and ground meteorological data, including temperature, relative humidity, and precipitation in regions of Vietnam. In this work, Universal Kriging is used for spatially interpolating ground data and its interpolated results are assimilated with corresponding satellite data to anticipate better gridded data. The input meteorological data was collected from 98 ground weather stations located all over Vietnam; whereas, the satellite data consists of the MODIS Atmospheric Profiles product (MOD07), the ASTER Global Digital Elevation Map (ASTER DEM), and the Tropical Rainfall Measuring Mission (TRMM) in six years. The outputs are gridded fields of temperature, relative humidity, and precipitation. The empirical results were evaluated by using the Root mean square error (RMSE) and the mean percent error (MPE), which illustrate that Universal Kriging interpolation obtains higher accuracy than other forms of Kriging; whereas, the assimilation for precipitation gradually reduces RMSE and significantly MPE. It also reveals that the accuracy of temperature and humidity when employing assimilation that is not significantly improved because of low MODIS retrieval due to cloud contamination.

• Journal of the Korean Society of Hazard Mitigation
• /
• v.11 no.3
• /
• pp.157-163
• /
• 2011

In this study, after the target basin was divided into both overland and channel grids, the travel time from center of each grid cell to watershed's outlet was calculated based on the manning equation. Through this process, volumetric discharge was calculated according to the isochrones and finally, the direct runoff hydrograph was estimated considering watershed's hydrodynamic characteristics. Sanseong subwatershed located in main stream of Bocheong basin was selected as a target basin. The model parameters are only two: area threshold and channel velocity correction factor; the optimized values were estimated at 3,800 and 3.3, respectively. The developed model based on the tuned parameters led to well-matching results between observed and calculated hydrographs (mean of absolute error of peak discharge: 3.41%, mean of absolute error of peak time: 0.67 hr). Moreover, the analysis results regarding histogram of travel time-contribution area demonstrates that the proposed model characterizes relatively well hydrodynamic characteristics of the catchment due to effective rainfall.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.22 no.3
• /
• pp.37-50
• /
• 2019

In this study, we analyzed the soil salinity of Saemangeum agricultural land using GIS spatial interpolation method. Dominant soils series of experimental sites were Munpo (coarseloamy, mixed, non-acid, mesic, typically fluvaquents), which was based on the fluvio-marine deposit. Soil samples were periodically collected at 0~20cm and 20~40cm layer from each site. First, the distribution characteristics of EC, ESP, and SAR according to spatial interpolation were analyzed using 142 sample points. Through the error analysis of 143 validation points, the IDW method for EC and SAR, and the Kriging interpolation method for ESP were selected as the optimal interpolation method. Using the optimal interpolation method, the characteristics of EC, ESP, and SAR were analyzed for the change of soil salinity from 2014 to 2016. As a result, EC, ESP and SAR were decreased by 0.26mg/L, 5.97mg/L and 0.73mg/L respectively due to the dilution effect caused by rainfall.

• Journal of Korean Society on Water Environment
• /
• v.27 no.3
• /
• pp.264-272
• /
• 2011

The objective of this study was to develop a model for predicting long-term runoff in a basin using the ensemble streamflow prediction (ESP) technique and review its reliability. To achieve the objective, this study improved not only the ESP technique based on the ensemble scenario analysis of historical rainfall data but also conventional ESP techniques used in conjunction with qualitative climate forecasting information, and analyzed and assessed their improvement effects. The model was applied to the Geum River basin. To undertake runoff forecasting, this study tried three cases (case 1: Climate Outlook + ESP, case 2: ESP probability through monthly measured discharge, case 3: Season ESP probability of case 2) according to techniques used to calculate ESP probabilities. As a result, the mean absolute error of runoff forecasts for case 1 proposed by this study was calculated as 295.8 MCM. This suggests that case 1 showed higher reliability in runoff forecasting than case 2 (324 MCM) and case 3 (473.1 MCM). In a discrepancy-ratio accuracy analysis, the Climate Outlook + ESP technique displayed 50.0%. This suggests that runoff forecasting using the Climate Outlook +ESP technique with the lowest absolute error was more reliable than other two cases.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.130-130
• /
• 2022

기후변화로 인한 기상이변 현상으로 폭우와 홍수 등 수문학적 극치 사상의 출현 빈도가 잦아지고 있다. 따라서 이러한 기상이변 현상에 적응하기 위하여 보다 정확한 확률강우량 측정의 필요성이 증가하고 있다. 대장 지점의 미래 확률강우량 계산을 위해선 기후변화 시나리오의 비정상성을 고려해야 한다. 본 연구는 비정상적인 미래 기후에서 확률강우량이 어떻게 변화하는지 측정하는 것을 목표로 한다. Representative Concentration Pathway (RCP4.5)에 따른 우리나라의 확률강우량 계산에 인공신경망을 포함한 정상성, 비정상성 확률강우량 산정 모델들이 사용되었다. 지점빈도해석(AFA), 홍수지수법(IFM), 모분포홍수지수법(PIF), 인공신경망을 이용한 Quantile & Parameter regression technique(QRT & PRT)이 정상성 자료에 대해 확률강우량을 계산하는 모델로 사용되었으며, 비정상성 자료에 대해서는 비정상성 지점빈도해석(NS-AFA), 비정상성 홍수지수법(NS-IFM), 비정상성 모분포홍수지수법(NS-PIF), 인공신경망을 사용한 비정상성 Quantile & Parameter regression technique(NS-QRT & NS-PRT)이 사용되었다. Rescaled Akaike information criterion(rAIC)를 사용한 불확실성 분석과 적합도 검정을 통해서 generalized extreme value(GEV) 분포형 모델이 정상성 및 비정상성 확률강우량 산정에 가장 적합한 모델로 선정되었다. 이후, 관측자료가 GEV(0,0,0)을 따르고 시나리오 자료가 GEV(1,0,0)을 따르는 지점들을 선택하여 미래의 확률강우량 변화를 추정하였다. 각 빈도해석 모델들은 몬테카를로 시뮬레이션을 통해 bias, relative bias(Rbias), root mean square error(RMSE), relative root mean square error(RRMSE)를 바탕으로 측정하여 정확도를 계산하였으며 그 결과 QRT와 NS-QRT가 각각 정상성과 비정상성 자료로부터 가장 정확하게 확률강우량을 계산하였다. 본 연구를 통해 향후 기후변화의 영향으로 확률강우량이 증가할 것으로 예상되며, 비정상성을 고려한 빈도분석 또한 필요함을 제안하였다.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.25 no.1
• /
• pp.133-149
• /
• 2022

Existing domestic studies on estimating rice yield were mainly implemented at the level of cities and counties in the entire nation using MODIS satellite images with low spatial resolution. Unlike previous studies, this study tried to estimate rice yield at the level of eup-myon-dong in Gimje-si, Jeollabuk-do using Sentinel-2 satellite images with medium spatial resolution, rainfall and soil data, and then to evaluate its accuracy. Five vegetation indices such as NDVI, LAI, EVI2, MCARI1 and MCARI2 derived from Sentinel-2 images of August 1, 2018 for Gimje-si, Jeollabuk-do, rainfall and paddy soil-type data were aggregated by the level of eup-myon-dong and then rice yield was estimated with gamma generalized linear model, an expanded variant of multi-variate regression analysis to solve the non-normality problem of dependent variable. In the rice yield model finally developed, EVI2, rainfall days in September, and saline soils ratio were used as significant independent variables. The coefficient of determination representing the model fit was 0.68 and the RMSE for showing the model accuracy was 62.29kg/10a. This model estimated the total rice production in Gimje-si in 2018 to be 96,914.6M/T, which was very close to 94,470.3M/T the actual amount specified in the Statistical Yearbook with an error of 0.46%. Also, the rice production per unit area of Gimje-si was amounted to 552kg/10a, which was almost consistent with 550kg/10a of the statistical data. This result is similar to that of the previous studies and it demonstrated that the rice yield can be estimated using Sentinel-2 satellite images at the level of cities and counties or smaller districts in Korea.

• Journal of Korea Water Resources Association
• /
• v.46 no.7
• /
• pp.755-765
• /
• 2013

This study developed a real-time dam's hydrologic variables prediction model (DHVPM) and evaluated its performance for simulating historical dam inflow and outflow in the Chungju dam basin. The DHVPM consists of the Sejong University River Forecast (SURF) model for hydrologic modeling and an autoreservoir operation method (Auto ROM) for dam operation. SURF model is continuous rainfall-runoff model with data assimilation using an ensemble Kalman filter technique. The four extreme events including the maximum inflow of each year for 2006~2009 were selected to examine the performance of DHVPM. The statistical criteria, the relative error in peak flow, root mean square error, and model efficiency, demonstrated that DHVPM with data assimilation can simulate more close to observed inflow than those with no data assimilation at both 1-hour lead time, except the relative error in peak flow in 2007. Especially, DHVPM with data assimilation until 10-hour lead time reduced the biases of inflow forecast attributed to observed precipitation error. In conclusion, DHVPM with data assimilation can be useful to improve the accuracy of inflow forecast in the basin where real-time observed inflow are available.