• 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 Society of Agricultural Engineers
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• v.63 no.5
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• pp.83-93
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• 2021

Agricultural reservoirs are important facilities for storing or managing water for the purpose of securing agricultural water, creating and expanding agricultural production bases, and using them to increase agricultural production. In particular, the Korea Rural Community Corporation (KRC) manages agricultural reservoirs scattered across the country, and officially recognizes and distributes hydrological data to increase their public utilization and aims to improve the value of water resources. Data on the water level of agricultural reservoirs are important. However, errors such as missing values and outliners limit utilization of the data in various fields of research and industry. Therefore, water quality data measures should be devised to increase reliability. this study categorized different error types and looked at automatic correction methods to enhance the reliability of the vast hydrological data. In addition, the water level data corrected from errors were compared to the reference hydrologic data through expert judgment in accordance with the quality control procedure, and the most appropriate measures were verified. As KRC manages more agricultural reservoirs than any other institution, the proposed method of efficient and automatic water level data correction in this study is expected to increase the availability and reliability of the hydrological data.

• Environmental Engineering Research
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• v.14 no.1
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• pp.13-18
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• 2009

In this study, specific pollutant releases during the Asian monsoon season were estimated and the information was applied to the non-point pollutant sources management from two forested watersheds of the Soyang Lake. The two watersheds are part of the 2,703 km2 Soyang Lake watershed in the northern region of the Han River. The outlets of the two watersheds were respectively analyzed for continuous water quality concentration and for discharge during various single rainfall events. Statistical power function methods are utilized to compare stream discharge and pollutant flux release during the study period. Based on the monitoring data during the study period, the specific load flux method using simulated discharge was conducted and validated in the two watersheds. The model predictions corresponded well with the measured and calculated pollutant releases. The modeling approach taken in this study was found to be applicable for the two forested watersheds.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.03a
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• pp.181-188
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• 2002

Water flow rate into the tunnel usually determined by numerical analyses and mathematical formulas using water levels and permeability is obtained only a few limited districts of the whole tunneling site. However, underground is not a homogeneous but complicated mass. Therefore these methods can't reflect structural and geological aspects. In this study, assuming that the mountain stream in droughty season is to be the same as baseflow of its basin, hydrological method is applied to predict the constant water flow rate into the tunnel on construction field. Prediction of constant water inflow rate is performed on each section of tunnel construction field divided into 20 sections.

• Journal of Korean Society on Water Environment
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• v.36 no.4
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• pp.300-313
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• 2020

The Bayesian approach can be used to estimate hydrologic model parameters from the prior expert knowledge about the parameter values and the observed data. The purpose of this study was to compare the performance of the two Bayesian methods, the Metropolis-Hastings (MH) algorithm and the Generalized Likelihood Uncertainty Estimation (GLUE) method. These two methods were applied to the TANK model, a hydrological model comprising 13 parameters, to examine the uncertainty of the parameters of the model. The TANK model comprises a combination of multiple reservoir-type virtual vessels with orifice-type outlets and implements a common major hydrological process using the runoff calculations that convert the rainfall to the flow. As a result of the application to the Nam River A watershed, the two Bayesian methods yielded similar flow simulation results even though the parameter estimates obtained by the two methods were of somewhat different values. Both methods ensure the model's prediction accuracy even when the observed flow data available for parameter estimation is limited. However, the prediction accuracy of the model using the MH algorithm yielded slightly better results than that of the GLUE method. The flow duration curve calculated using the limited observed flow data showed that the marginal reliability is secured from the perspective of practical application.

• Journal of The Korean Society of Agricultural Engineers
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• v.47 no.3
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• pp.3-13
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• 2005

The conceptual rainfall-runoff models are used to predict complex hydrological effects of a basin. However, to obtain reliable results, there are some difficulties and problems in choosing optimum model, calibrating, and verifying the chosen model suitable for hydrological characteristics of the basin. In this study, Genetic Algorithm and SCE-UA method as global optimization methods were applied to compare the each optimization technique and to analyze the application for the rainfall-runoff models. Modified TANK model that is used to calculate outflow for watershed management and reservoir operation etc. was optimized as a long term rainfall-runoff model. And storage-function model that is used to predict real-time flood using historical data was optimized as a short term rainfall-runoff model. The optimized models were applied to simulate runoff on Pyeongchang-river watershed and Bocheong-stream watershed in 2001 and 2002. In the historical data study, the Genetic Algorithm and the SCE-UA method showed consistently good results considering statistical values compared with observed data.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.134-134
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• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.89-96
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• 2020

Water management agencies under the Ministry of Environment produce and accumulate qualified basic data for major rivers. However, the integrated management of the river water has been weak since the artificial water circulation process, such as the intaking and drainage of agricultural water, has not been examined in the basin, which includes many agricultural land. In this study, a study was conducted on how the power usage method (operating time method) based on the running time can be applied and improved among indirect flow rate measurement methods used to investigate flow rates collected by the riverside for agricultural water purposes, and thus the resultant data of high reliability can be obtained at low cost. The operation time method is suitable for small-scale water pumping stations where it is difficult to secure real-time power supply data. The reliability of the data was verified through the correlation analysis with the actual flow rate, and it was found that the flow rate calculated by the operation time method reflecting the level of the stream to which the inlet of the pumping station is connected can be reasonably matched with the actual flow rate. In addition, it was confirmed that the investment cost at the time of initial installation of the facility was highly efficient by generating qualified flow data at low cost through comparison with direct flow rate measurement methods. If flow data is secured by applying the operation time method to large and small water farms located along the riverside, it is expected that more quantitative and integrated stream water management will be possible.

• Journal of Korea Water Resources Association
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• v.52 no.1
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• pp.35-49
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• 2019

This study proposes a new method for categorizing the hydrological soil groups by considering the recent revision results of soil series. Also, the proposed method is evaluated by comparing the categorizing result with those based on existing three different methods. As an example, the proposed method is applied to Jeju Island to estimate the CN value, which is then compared with CN values estimated by applying the existing three different methods. Summaries of the results are as follow. (1) The revision result since 2007 shows that the soil texture has been changed in the 43 soil series, the drainage class in the 1 soil series, the permeability in the 15 soil series, and the impermeable layer in the 26 soil series. (2) The categorizing result of hydrological soil groups by applying the proposed method shows that the group B is the most dominant group covering up to 49.25%. On the other hand, one of the existing method of 1987 provides the group C as the most dominant group (46.43%). Method of 1995 defines the group B as the most dominant group (27.69%). The other method of 2007 distinguishes the group D (35.82%) to be the most dominant group. (3) Also, the CN value estimated by applying the proposed method to Jeju Island is found to be smaller than those based on existing three methods. This result indicates the possible overestimation of the CN value when applying the existing three methods.