• Journal of Korea Water Resources Association
• /
• v.42 no.10
• /
• pp.809-824
• /
• 2009

Hydrological system forecasting, which is the short term runoff historical data during the limited period in dam site, is a conditional precedent of hydrological persistence by stochastic analysis. We have forecasted the monthly hydrological system from Andong dam basin data that is the rainfall, evaporation, and runoff, using the seasonal ARIMA (autoregressive integrated moving average) model. Also we have conducted long term runoff simulations through the forecasted results of TANK model and ARIMA+TANK model. The results of analysis have been concurred to the observation data, and it has been considered for application to possibility on the stochastic model for dam inflow forecasting. Thus, the method presented in this study suggests a help to water resource mid- and long-term strategy establishment to application for runoff simulations through the forecasting variables of hydrological time series on the relatively short holding runoff data in an object basins.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2010.05a
• /
• pp.1645-1648
• /
• 2010

The goal of this research is to apply the neural networks model for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks model consists of recurrent neural networks model (RNNM). The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks model, it is composed of training and test performances, respectively. The training and test performances consist of the historic, the generated, and the mixed data, respectively. From this research, we evaluate the impact of RNNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2010.05a
• /
• pp.1346-1349
• /
• 2010

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training and test performances, respectively. The training and test performances consist of the historic, the generated, and the mixed data, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Water for future
• /
• v.9 no.2
• /
• pp.87-100
• /
• 1976

These studies are aimed at the analysis of systematic variation pattern of water resources in Korean river catchments and the development of their simulation models from the stochastic analysis of monthly and annual hydrologic data as main elements of water resources, i.e. rainfall and streamflow. In the analysis, monthly & annual rainfall records in Soul, Taegu, Pusan and Kwangju and streamflow records at the main gauging stations in Han, Nakdong and Geum river were used. Firstly, the systematic variation pattern of annual streamflow was found by the exponential function relationship between their standard deviations and mean values of log-annual runoff. Secondly, stochastic characteristics of annual rainfall & streamflow series were studied by the correlogram Monte Carlo method and a single season model of 1st-order Markov type were applied and compared in the simulation of annual hydrologic series. In the simulation, single season model of Markov type showed better results than LN-model and the simulated data were fit well with historical data. But it was noticed that LN-model gave quite better results in the simulation of annual rainfall. Thirdly, stochastic characteristics of monthly rainfall & streamflow series were also studied by the correlogram and spectrum analysis, and then the Model-C, which was developed and applied for the synthesis of monthly perennial streamflow by lst author and is a Markov type model with transformed skewed random number, was used in the simulation of monthly hydrologic series. In the simulation, it was proved that Model-C was fit well for extended area in Korea and also applicable for menthly rainfall as well as monthly streamflow.

• The Journal of Engineering Geology
• /
• v.30 no.3
• /
• pp.347-378
• /
• 2020

Based on monthly average groundwater recharge over a nearly 10 year period, results of fully integrated hydrologic modeling of SWAT-MODFLOW, land cover, land use, soil type and hydrologic response unit (HRU) was used to assess the dominant influencing factors of groundwater recharge spatial patterns in Jangseong district. As dominant factors, land cover was FRSE (forest-evergreen) and soil type was Samgag. Landsat-8 OLI imaging spectrometer data were acquired in the period 2003 to 2004 and seasonal bare soil lines (BSL) were estimated through NIR-RED plot. Extent of slope of BSL was from 1.092 to 1.343 and the intercept was from -0.004 to -0.015. To know correlation between spatial groundwater recharge and soil-vegetation indices (PVI, NDVI, NDTI, NDRI), this study employed frequency and regression analysis. On May, RED band increased up 3 to 4 times compared to other seasons and only one turning point appeared as recharge-index with upward parabola bell shape as results of existing research. Considering precipitation, if the various studies for relationship between groundwater recharge and soil-vegetation index just like NDVI are performed, it is possible to estimate groundwater recharge through analyzing remote sensing data.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.9 no.5
• /
• pp.1334-1343
• /
• 2008

Growing needs for efficient management of water resources urge the joint operation of dams and integrated management of whole basin. As one of the tools for supporting above tasks, this study aims to constitute a hydrologic model that can simulate the streamflow discharges at some control points located both upper and down stream of dams. One of the currently available models is being studied to be applied with a least effort in order to support the ongoing project of KWATER (Korea Water Resources Corporation), "Establishment of integrated operation scheme for the dams in Han River Basin". On this study, following works have been carried out : division of Han River Basin into 24 sub-basins, use of rainfall data of 151 stations to make spatial distribution of rainfall, selection of control points such as Soyanggang Dam, Chungju Dam, Chungju Release Control Dam, Heongseong Dam, Hwachun Dam, Chuncheon Dam, Uiam Dam, Cheongpyung Dam and Paldang Dam, selection of SSARR (Streamflow Synthesis and Reservoir Regulation) model as a hydrologic model, preparation of input data of SSARR model, sensitivity analysis of parameter using hydrologic data of 2002. The sensitivity analysis showed that soil moisture index versus runoff percent (SMI-ROP), baseflow infiltration index versus baseflow percent (BII-BFP) and surface-subsurface separation (S-SS) parameters are higher sensitive parameters to the simulation result.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.8 no.3
• /
• pp.61-71
• /
• 1988

The stage-discharge relation curve(rating curve) is the basic formula in hydrologic analysis. It plays an important role in converting to the discharge from available flood water level data including the daily mean stage. However, the river induces a cross section change at the gauging station because of the composed material of the river bed and three processes of the stream flow; i.e., erosion, transportation, and sedimentation. Rating curve has to be revised according to the temporal variation of the river bed due to the those factors. In this study, the basic rating curve is developed with respect to the current river bed to convert the existing rating curves and also to seize the hydraulic and geometric characteristics for the temporal variation of the river bed, relationships among the basic rating curve and the existing rating curves, water level, cross sectional area, and flow velocity are analyzed. Indogyo station, which is not only the key station of the Han river but also greatly changed the river bed after completion of the Han river development plan during the year 1983 to 1986, was chosen for the study. In this study, the river bed is assumed in a dynamic equilibrium condition. The basic rating curve is developed using hydrologic data of the physical year of 1987. For a given discharge, relationships for conversion of previous data, stage and velocity, the current one are formulated. To verify the usefulness of the relationships, stage-cross sectional area and stage velocity formula are also derived. Both hydrologic method using continuity equation and statistical method by the rating curve are compared and checked, then the validation of the both are positively shown.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.31 no.5B
• /
• pp.449-457
• /
• 2011

Recently, increasing heavy rainfalls due to climate change and/or variability result in hydro-climatic disasters being accelerated. To cope with the extreme rainfall events in the future, hydrologic frequency analysis is usually used to estimate design rainfalls in a design target year. The rainfall data series applied to the hydrologic frequency analysis is assumed to be stationary. However, recent observations indicate that the data series might not preserve the statistical properties of rainfall in the future. This study incorporated the residual analysis and the hydrologic frequency analysis to estimate design rainfalls in a design target year considering the non-stationarity of rainfall. The residual time series were generated using a linear regression line constructed from the observations. After finding the proper probability density function for the residuals, considering the increasing or decreasing trend, rainfalls quantiles were estimated corresponding to specific design return periods in a design target year. The results from applying the method to 14 gauging stations indicate that the proposed method provides appropriate design rainfalls and reduces the prediction errors compared with the conventional rainfall frequency analysis which assumes that the rainfall data are stationary.

• Journal of Korea Water Resources Association
• /
• v.48 no.12
• /
• pp.981-993
• /
• 2015

The analysis of hydrologic time series data is crucial for the effective management of water resources. Therefore, it has been widely used for the long-term forecasting of hydrologic variables. In tradition, time series analysis has been used to predict a time series without considering exogenous variables. However, many studies using decomposition have been widely carried out with the assumption that one data series could be mixed with several frequent factors. In this study, the empirical mode decomposition method was performed for decomposing a hydrologic time series data into several components, and each component was applied to the time series models, autoregressive moving average (ARMA). After constructing the time series models, the forecasting values are added to compare the results with traditional time series model. Finally, the forecasted estimates from ARMA model with empirical mode decomposition method showed better performance than sole traditional ARMA model indicated from comparing the root mean square errors of the two methods.

• Journal of Korea Water Resources Association
• /
• v.42 no.11
• /
• pp.921-931
• /
• 2009

For the assessment of climate change impacts for the Byeongseong stream, CGCM 3.1 T63 is selected as future climate information. The projections come from CGCM used to simulate the GHG emission scenario known as A2. Air temperature and precipitation information from the GCM simulations are converted to regional scale data using the statistical downscaling method known as MSPG. Downscaled climate data from GCM are then used as the input data for the SWAT model to generate regional runoff and water quality estimates in the Byeongseong stream. As a result of simple sensitivity analysis, the increase of CO2 concentration leads to increase water yield through reduction of evapotranspiration and increase of soil water. Hydrologic responses to climate change are in phase with precipitation change. Climate change is expected to reduce water yields in the period of 2021-2030. In the period of 2051-2060, stream flow is expected to be reduced in spring season and increased in summer season. While soil losses are also in phase with water yields, nutrient discharges (i.e., total nitrogen) are not always in phase with precipitation change. However, it should be noted that there are a lot of uncertainties in such multiple-step analysis used to convert climate information from GCM-based future climate projections into hydrologic information.