• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.326-326
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• 2018

The study will assess the seasonal effect of hydrological models on performance and parameters for streamflow simulation. TPHM, GR4J, CAT, and TANK-SM hydrological models will be applied for simulating streamflow in ten small and large watersheds located in South Korea. The readily available hydrometeorological data will be applied as an input to the four hydrological models and the potential evapotranspiration will be computed using the Penman-Monteith equation. The SCE-UA algorithm implemented in PEST will be used to calibrate the models considering similar objective functions bedside the calibration will be renewed to capture the seasonal effects on the model performance and parameters. The seasonal effects on the model performance and parameters will be presented after assessing the four hydrologic models results. The conventional approach and season-based results will be evaluated for each model in the tested watersheds and a conclusion will be made based on the finding of the results.

• 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 Korea Water Resources Association
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• v.41 no.10
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• pp.1059-1065
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• 2008

Rainfall data is the most basic input data to analyze the hydrological phenomena and can be missing due to various reasons. In this research, a neural network based model to estimate missing rainfall data as approximate values was developed for 12 rainfall stations in the Soyang river basin to improve existing methods. This approach using neural network has shown to be useful in many applications to deal with complicated natural phenomena and displayed better results compared to the popular offline estimating methods, such as RDS(Reciprocal Distance Squared) method and AMM(Arithmetic Mean Method). Additionally, we proposed automated data reconciliation systems composed of a neural network learning processer to be capable of real-time reconciliation to transmit reliable hydrological data online.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.261-264
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• 2002

Yi-dong experimental basin is operated for research on the rural basin characteristics and accumulation of a long term data by hydrological observation equipments. It is basin area 9,440ha, length 14.4km and slope 0.67%. Hydrological observation network is constructed of rainfall meter 4points, reservoir storage level 3points and river water level 2points.

• Journal of the Korean earth science society
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• v.42 no.4
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• pp.445-458
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• 2021

Gravity Recovery and Climate Experiment (GRACE) gravimeter satellites observed the Earth gravity field with unprecedented accuracy since 2002. After the termination of GRACE mission, GRACE Follow-on (GFO) satellites successively observe global gravity field, but there is missing period between GRACE and GFO about one year. Many previous studies estimated terrestrial water storage (TWS) changes using hydrological models, vertical displacements from global navigation satellite system observations, altimetry, and satellite laser ranging for a continuity of GRACE and GFO data. Recently, in order to predict TWS changes, various machine learning methods are developed such as artificial neural network and multi-linear regression. Previous studies used hydrological and climate data simultaneously as input data of the learning process. Further, they excluded linear trends in input data and GRACE/GFO data because the trend components obtained from GRACE/GFO data were assumed to be the same for other periods. However, hydrological models include high uncertainties, and observational period of GRACE/GFO is not long enough to estimate reliable TWS trends. In this study, we used convolutional neural networks (CNN) method incorporating only climate data set (temperature, evaporation, and precipitation) to predict TWS variations in the missing period of GRACE/GFO. We also make CNN model learn the linear trend of GRACE/GFO data. In most river basins considered in this study, our CNN model successfully predicts seasonal and long-term variations of TWS change.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.155-165
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• 2004

Hydrological model which is proper to watershed characteristics and analysis purpose must be used when we analyze water resources. But, although proper model is used, if objectivity and reasonability of data is low it is difficult to get good results from the model. So it is very important to decide the data which is used in selected model and estimate parameters by using the applied data. In this study, temporal and spatial data was constructed as standard data of test site and stored in HyGIS (Hydrological Geographic Information System) DB. A system which extracts temporal and spatial data required to run hydrological model from HyGIS DB by connecting TOPMODEL with HyGIS was developed. In this system, we can extract temporal and spatial data which is needed to run TOPMODEL from HyGIS DB and estimate model parameters by using genetic algorithm. We found that HyGIS and the system connected with TOPMODEL was effective to make temporal and spatial data used in TOPMODEL and estimate model parameters. From this study, we suggested the possibility that HyGIS could be applied properly to another hydrological model, too.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.412-418
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• 2000

The purpose of this study is estimation of daily runoff in the watershed with insufficient hydrological data using tank model. In order to estimate, twentysix watersheds were selected to calibrate tank model parameters that were defined by a trial and error method. Results were correlated with characteristics of watershed. Relationships between the parameters and the watershed characteristics were derived by a multiple regression analysis. The simulation results were in agreement with the observed data.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.3
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• pp.46-59
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• 1999

Remote sensing provides informations on the changes of the hydrological states and variables over with the temporal and spatial distribution to monitor hydrological conditions and changes for large area. Especially, it can extract a spatial distribution of hydrological parameters such as surface albedo, vegetation informations, and surface temperature to effectively manage water resources of the watershed. In this study, we analyzed the characteristic of temporal and spatial changes in surface hydrological parameters which is necessary to identify the spatial distribution of water resources. 5 Landsat TM data of 1995 which is collected for Bochong-chon watershed, located in the upper stream of Keum River, were used to estimate characteristics on the change of hydrological parameters and atmospheric correction was carried out using COST model. The study showed that the difference of the albedo by the land cover was very sensitive depending upon the change of sun elevation and the amount of water in the soil. The difference between the surface temperature analysis and the measured air temperature was from $2.5^{\circ}C$ to $3.86^{\circ}C$.

• Magazine of the Korean Society of Agricultural Engineers
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• v.40 no.3
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• pp.42-53
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• 1998

About 50 percent of irrigation and drainage facilities in our country are deteriorated as they were constructed over 40 years ago. Worsening the problems in function might be caused by these facilities' constant exposure to the elements. With these reason, efficient maintenance and management of irrigation and drainage facili- ties are required. A computerized system is tailored on the basis of the each characteristics'data of irrigation and drainage facilities. The unified management system to be introduced in this study is a package program consisting of three subprograms. Facility Management(FM) system, the first component, is a relational database system for image processing and registering the characteristics of irrigation and drainage facilities. The objective of this program is to manage the ledger of each facilities and to scan the characteristics of facilities. Telemeter(TM) system, the second component, monitors and processes the data from the sensors statistically. This system is preprogramed for the complete design of TC/TM system. Hydrological Data Management(HDM) system, the third component, executes the hydrological analysis using meteorological data. The unified management system can provide the latest information, such as image data, lists and items of facilities, and items of reforming and rebuilding etc., of the facilities to the manager. At the same time, this system can manage hydrological and meteorological data in realtime.

• Journal of the Korean association of regional geographers
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• v.17 no.2
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• pp.245-258
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• 2011

Best management practices (BMPs) are widely accepted and implemented as a mitigation method for soil erosion and non-point source problems. Estimating the amount of soil erosion and the effectiveness of BMPs using hydrological models help to understand the condition, identify the problems, and make plans for conservation practices in an area, typically a watershed. However, the accuracy and reliability of assessment of BMP impacts estimated by hydrological models can be often questionable due to the uncertainties from various sources including GIS(Geographic Information System) data, scale, and model. This study reviewed the development and the background of hydrological models, and the modeling issues such as the selection of models, scale, and uncertainties of data and models. This study also discussed the advantage of a small scale and spatially distributed model to estimate the impacts of BMPs.