• Journal of Korea Water Resources Association
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• v.39 no.3 s.164
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• pp.215-226
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• 2006

In this study, we performed verification of VSRF (Very Short Range Forecast of precipitation) model and application of NWSPC (National Weather Service PC) rainfall-runoff model in Kyoungan-chun basin. We used two methods for verification of VSRF model. The first method is a meteorological verification that evaluates the special quality feature for rain amount between AWS and VSRF model over Kyoungan-chun basin, while second method is a hydrological verification that compares the calculated Mean Area Precipitation (MAP) between AWS and VSRF Quantitatively. This study examines the usefulness of VSRF precipitation forecasting model data in NWSPC hydrological model. As a result, correlation coefficient is over 0.6 within 3 hour lead time. It represents that the forecast results from VSRF are useful for water resources application.

• Journal of Korea Water Resources Association
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• v.42 no.12
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• pp.1039-1052
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• 2009

Radar measurement with high temporal and spatial resolutions can be a valuable source of data, especially in the areas where rain gauge installment is not practical. However, this kind of data brings with it many errors. The objective of this paper is to propose a method to evaluate statistically the quantitative and qualitative accuracy at different radar ranges, temporal intervals and raingage densities and use a bias adjustment technique to improve the quality of radar rainfall for the purpose of hydrological application. The method is tested with the data of 2 storm events collected at Jindo (S band) and Kwanak (C band) radar stations. The obtained results show that the accuracy of radar rainfall estimation increases when time interval rises. Radar data at the shorter range seems to be more accurate than the further one, especially for C-band radar. Using the Monte Carlo simulation experiment, we find out that the sampling error of the bias between radar and gauge rainfall reduces nonlinearly with increasing raingage density. The accuracy can be improved considerably if the real-time bias adjustment is applied, making adjusted radar rainfall to be adequately good to apply for hydrological application.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.721-727
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• 2002

Precipitation evapotranspiration and runoff are three key parameters of regional water balance. Problems exist in the traditional methods for calculating such factors , such as explaining of the geographic rationality of spatial interpolating methods and lacking of enough observation stations in many important area for bad natural conditions. With the development of modern spatial info-techniques, new efficient shifts arose for traditional studies. Guided by theories on energy flow and materials exchange within Soil-Atmosphere-Plant Continuant (SPAC), retrieval models of key hydrological parameters were established in the Yellow River basin using CMS-5 and FengYun-2 meteorological satellite data. Precipitation and evapotranspiration were then estimated: (1) Estimating tile amount of solar energy that is absorbed by the ground with surface reflectivity, which is measured in the visible wavelength band (VIS): (2) Assessing the partitioning of the absorbed energy between sensible and latent heat with the surface temperature, which was measured in the thermal infrared band (TIR), the latent heat representing the evapotranspiration of water; (3) Clouds are identified and cloud top levels are classified using both VIS and TIR data. Hereafter precipitation will be calculated pixel by pixel with retrieval model. Daily results are first obtained, which are then processed to decade, monthly and yearly products. Precipitation model has been has been and tested with ground truth data; meanwhile, the evapotranspiration result has been verified with Large Aperture Scintillometry (LAS) presented by Wageningen University of the Netherlands. Further studies may concentrate on the application of models, i.e., establish a hydrological model of the Yellow river basin to make the accurate estimation of river volume and even monitor the whole hydrological progress.

• Journal of Korean Society on Water Environment
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• v.27 no.6
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• pp.794-803
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• 2011

Self Organizing Map (SOM) was applied for pattern classification of hydrological and water quality data measured at Jangseong Reservoir on a monthly basis. The primary objective of the present study is to understand better data characteristics and relationship between the data. For the purpose, two SOMs were configured by a methodologically systematic approach with appropriate methods for data transformation, determination of map size and side lengths of the map. The SOMs constructed at the respective measurement stations for water quality data (JSD1 and JSD2) commonly classified the respective datasets into five clusters by Davies-Bouldin Index (DBI). The trained SOMs were fine-tuned by Ward's method of a hierarchical cluster analysis. On the one hand, the patterns with high values of standardized reference vectors for hydrological variables revealed the high possibility of eutrophication by TN or TP in the reservoir, in general. On the other hand, the clusters with low values of standardized reference vectors for hydrological variables showed the patterns with high COD concentration. In particular, Clsuter1 at JSD1 and Cluster5 at JSD2 represented the worst condition of water quality with high reference vectors for rainfall and storage in the reservoir. Consequently, SOM is applicable to identify the patterns of potential eutrophication in reservoirs according to the better understanding of data characteristics and their relationship.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.2
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• pp.144-159
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• 2006

Hydrological models are tools intended to realistically represent the basin's complex system in which hydrological characteristics result from a number of physical, vegetative, climatic, and anthropomorphic factors. Spatially distributed hydrological models were first developed in the 1960s, Remote sensing(RS) data and Geographical Information System(GIS) play a rapidly increasing role in the field of hydrology and water resources development. Although very few remotely sensed data can applied in hydrology, such information is of great. One of the greatest advantage of using RS data for hydrological modeling and monitoring is its ability to generate information in spatial and temporal domain, which is very crucial for successful model analysis, prediction and validation. In this paper, SLURP model is selected as semi-distributed hydrological model and MODIS Leaf Area Index(LAI), Normalized Difference Vegetation Index(NDVI) as Remote sensing input data to hydrological modeling of Kyung An-chen basin. The outlet of the Kyung An stage site was simulated, We evaluated two RS data, based on ability of SLURP model to simulate daily streamflows, and How the two RS data influence the sensitivity of simulated Evapotranspiration.

• Journal of the Korean earth science society
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• v.22 no.6
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• pp.491-499
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• 2001

Generally, several hydrological characteristics which can be obtained from geographic processing in GIS S/W would be utilized to actual hydrological applications such as landslide stability or drainage analysis. Furthermore, by using practical GIS extension programs, it can be possible that automatic extraction task of those information, which was time-consuming and complex processing, is performed as systematical and automatic processing. In this study, using ArcView GIS S/W and its extensions concerning hydrological processing, some waterflow and surface factors in the area of Yemi sheet scaled 1:50,000 were extracted. Then, assessment of landslide stability, determined by DEM-based variables were carried out in order to investigate practical applicability of extension programs. It is thought that several hydrological factors extracted by this study were significant information to predict field model or hydrological hazardous events. It also reveals that landslide potential is overall stable with stability index mapping and S-A plot, with theoretical basis. This methodology can be applicable to other areas related to hydrological characteristics with measurements in fields.

• Water Engineering Research
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• v.6 no.2
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• pp.91-99
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• 2005

One of the most important factors for estimating a flood runoff from streams is the lag time. It is well known that the lag time is affected by the morphometric properties of basin which can be expressed by catchment shape descriptors. In this paper, the notion of the geometric characteristics of an equivalent ellipse proposed by Moussa(2003) was applied for calculating the lag time of geomorphologic instantaneous unit hydrograph(GIUH) at a basin outlet. The lag time was obtained from the observed data of rainfall and runoff by using the method of moments and the procedure based on geomorphology was used for GIUH. The relationships between the basin morphometric properties and the hydrological response were discussed based on application to 3 catchments in Korea. Additionally, the shapes of equivalent ellipse were examined how they are transformed from upstream area to downstream one. As a result, the relationship between the lag time and descriptors was shown to be close, and the shape of ellipse was presented to approach a circle along the river downwards. These results may be expanded to the estimation of hydrological response of ungauged catchment.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.156-156
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• 2019

In this study, a gated recurrent unit (GRU) network is constructed based on a deep neural network (DNN) with the aim of restoring the missing daily flow data in river basins. Lai Chau hydrological station is located upstream of the Da river basin (Vietnam) is selected as the target station for this study. Input data of the model are data on observed daily flow for 24 years from 1961 to 1984 (before Hoa Binh dam was built) at 5 hydrological stations, in which 4 gauge stations in the basin downstream and restoring - target station (Lai Chau). The total available data is divided into sections for different purposes. The data set of 23 years (1961-1983) was employed for training and validation purposes, with corresponding rates of 80% for training and 20% for validation respectively. Another data set of one year (1984) was used for the testing purpose to objectively verify the performance and accuracy of the model. Though only a modest amount of input data is required and furthermore the Lai Chau hydrological station is located upstream of the Da River, the calculated results based on the suggested model are in satisfactory agreement with observed data, the Nash - Sutcliffe efficiency (NSE) is higher than 95%. The finding of this study illustrated the outstanding performance of the GRU network model in recovering the missing flow data at Lai Chau station. As a result, DNN models, as well as GRU network models, have great potential for application within the field of hydrology and hydraulics.

• Journal of Korean Society on Water Environment
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• v.27 no.5
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• pp.587-596
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• 2011

The interest in hydrological modeling has increased significantly recently due to the necessity of watershed management, specifically in regards to lumped models, which are being prosperously utilized because of their relatively uncomplicated algorithms which require less simulation time. However, lumped models require empirical coefficients for hydrological analyses, which do not take into consideration the heterogeneity of site-specific characteristics. To overcome such obstacles, a distributed model was offered as an alternative and the number of researches related to watershed management and distributed models has been steadily increasing in the recent years. Thus, in this study, the feasibility of a grid-based rainfall-runoff model was reviewed using the flood runoff process in the Han River basin, including the ChungjuDam, HoengseongDam and SoyangDam watersheds. Hydrological parameters based on GIS/RS were extracted from basic GIS data such as DEM, land cover, soil map and rainfall depth. The accuracy of the runoff analysis for the model application was evaluated using EFF, NRMSE and QER. The calculation results showed that there was a good agreement with the observed data. Besides the ungauged spatial characteristics in the SoyangDam watershed, EFF showed a good result of 0.859.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.183-183
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• 2021

Deep learning methods and their application have become an essential part of prediction and modeling in water-related research areas, including hydrological processes, climate change, etc. It is known that application of deep learning leads to high availability of data sources in hydrology, which shows its usefulness in analysis of precipitation, runoff, groundwater level, evapotranspiration, and so on. However, there is still a limitation on microclimate analysis and prediction with deep learning methods because of deficiency of gauge-based data and shortcomings of existing technologies. In this study, a real-time rainfall prediction model was developed from a sky image data set with convolutional neural networks (CNNs). These daily image data were collected at Chung-Ang University and Korea University. For high accuracy of the proposed model, it considers data classification, image processing, ratio adjustment of no-rain data. Rainfall prediction data were compared with minutely rainfall data at rain gauge stations close to image sensors. It indicates that the proposed model could offer an interpolation of current rainfall observation system and have large potential to fill an observation gap. Information from small-scaled areas leads to advance in accurate weather forecasting and hydrological modeling at a micro scale.