• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.529-534
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• 2009

Climate models, both global and regional, have increased in sophistication and are being run at increasingly higher resolutions. The Land Surface Models (LSMs) coupled to these climate models have evolved from simple bucket models to sophisticated Soil-Vegetation-Atmosphere Transfer (SVAT) schemes needed to support complex linkages and processes. However, some underpinnings of terrestrial hydrologic parameterizations so crucial in the predictions of surface water and energy fluxes cause model errors that often manifest as non-linear drifts in the dynamic response of land surface processes. This requires the improved parameterizations of key processes for the terrestrial hydrologic scheme to improve the model predictability in surface water and energy fluxes. The Common Land Model (CLM), one of state-of-the-art LSMs, is the land component of the Community Climate System Model (CCSM). However, CLM also has energy and water biases resulting from deficiencies in some parameterizations related to hydrological processes. This research presents the implementation of a selected set of parameterizations and their effects on the runoff prediction. The modifications consist of new parameterizations for soil hydraulic conductivity, water table depth, frozen soil, soil water availability, and topographically controlled baseflow. The results from a set of offline simulations are compared with observed data to assess the performance of the new model. It is expected that the advanced terrestrial hydrologic scheme coupled to the current CLM can improve model predictability for better prediction of runoff that has a large impact on the surface water and energy balance crucial to climate variability and change studies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1139-1145
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• 2019

In order to efficiently use defined satellite network resources, it is a priority to understand the performance and usage of the network. In this paper, in order to analyze the operational efficiency and stability of the system in the satellite communication system operated by K-water flood forecast and alarm network, FTP and ping testing and network traffic analysis methods of measuring download and upload speed between central and observational countries were introduced. As a result of measuring the transmission speed by the introduced test method, the effects of TCP accelerators have been improved by 120% upon download from the observational station. Through the performance test and traffic analysis of the satellite hydrologic observation system introduced, environmental improvement and improvement points of the satellite communication system were derived so that the operational efficiency and stability of the communication network could be expected.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.325-325
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• 2020

Low impact development (LID) is a widely used technology that aims to reduce the peak flow volume and amount of pollutants in stormwater runoff while introducing physicochemical, biological or a combination of both mechanisms in order to improve water quality. This research aimed to determine the effect of hydrologic factors in removing the pollutants on stormwater runoff by an LID facility. Monitored storm events from 2010-2018 were analysed to evaluate the hydraulic and hydrological performance of a small constructed wetland (SCW). Standard methods for the examination water and wastewater were employed to assess the water quality of the collected samples (APHA et al, 1992). Primary hydrologic data were obtained from the Korea Meteorological Administration. The recorded average rainfall intensity and antecedent dry days (ADD) of SCW were 5.26 mm/hr and 7 days respectively. During the highest rainfall event (27 mm/hr), the removal efficiency of SCW for all the pollutants was ranging from 67% to 91%. While on the lowest rainfall event (0.7 mm/hr), the removal efficiency was ranging from -36% to 62%. Rainfall intensity has a significant effect to the removal efficiencies of each facility due to its dilution factor. In addition to that, there was no significant correlation of ADD to the mean concentrations of pollutants. Generally, stormwater runoff contains significant amount of pollutants that can cause harmful effects to the environment if not treated. Also, the component of this LID facility such as pre-treatment zone, media filters and vegetation contributed to the effectivity of the LID facilities in reducing the amounts of pollutants present in stormwater runof.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6B
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• pp.683-690
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• 2008

The objectives of this study are to provide an observation-based urban flood prediction model and to evaluate their performance on a restored Cheonggye stream. The study area, which has its own unique hydrologic and flooding conditions that can be characterized the standard of flood occurrence by watergate opening and walk lane inundation, measured stream discharges at the 5 sites and watergate opening and walk lane inundation through the main stream since 2006. This study derived the relationship between precipitation intensity and watergate opening and walk lane inundation time by using the observations of 2006 and verified their performance on 2007 flood events. The result showed that the coefficients of determination are ranged on 0.57-0.75, which would be acceptable if considering the complexity of the area and the proposed model simplicity. It also suggested the continuous observation of these properties is required for further improvement of the models.

• Journal of Korea Water Resources Association
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• v.47 no.10
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• pp.853-865
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• 2014

Hydrologic dam risk analysis depends on complex hydrologic analyses in that probabilistic relationship need to be established to quantify various uncertainties associated modeling process and inputs. However, the systematic approaches to uncertainty analysis for hydrologic risk analysis have not been addressed yet. In this paper, two major innovations are introduced to address this situation. The first is the use of a Hierarchical Bayesian model based regional frequency analysis to better convey uncertainties associated with the parameters of probability density function to the dam risk analysis. The second is the use of Bayesian model coupled HEC-1 rainfall-runoff model to estimate posterior distributions of the model parameters. A reservoir routing analysis with the existing operation rule was performed to convert the inflow scenarios into water surface level scenarios. Performance functions for dam risk model was finally employed to estimate hydrologic dam risk analysis. An application to the Dam in South Korea illustrates how the proposed approach can lead to potentially reliable estimates of dam safety, and an assessment of their sensitivity to the initial water surface level.

• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.437-450
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• 2020

This study presents a probabilistic distributed hydrological model for Ephemeral catchment, where zero flow often occurs due to the influence of distinct climate characteristics in South Korea. The gridded hydrological model is developed by combining the Sacramento Soil Moisture Accounting Model (SAC-SMA) runoff model with a routing model. In addition, an error model is employed to represent a probabilistic hydrologic model. To be specific, the hydrologic model is coupled with a censoring error model to properly represent the features of ephemeral catchments. The performance of the censoring error model is evaluated by comparing it with the Gaussian error model, which has been utilized in a probabilistic model. We first address the necessity to consider ephemeral catchments through a review of the extensive research conducted over the recent decade. Then, the Yongdam Dam catchment is selected for our study area to confirm the usefulness of the hydrologic model developed in this study. Our results indicate that the use of the censored error model provides more reliable results, although the two models considered in this study perform reliable results. In addition, the Gaussian model delivers many negative flow values, suggesting that it occasionally offers unrealistic estimations in hydrologic modeling. In an in-depth analysis, we find that the efficiency of the censored error model may increase as the frequency of zero flow increases. Finally, we discuss the importance of utilizing the censored error model when the hydrologic model is applied for ephemeral catchments in South Korea.

• Korean Journal of Hydrosciences
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• v.4
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• pp.1-9
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• 1993

It is a major objective for the management and operation of water resources system to forecast streamflows. The applicability of artificial neural network model to hydrologic system is analyzed and the performance is compared by statistical method with observed. Multi-layered perception was used to model rainfall-runoff process at Pyung Chang River Basin in Korea. The neural network model has the function of learning the process which can be trained with the error backpropagation (EBP) algorithm in two phases; (1) learning phase permits to find the best parameters(weight matrix) between input and output. (2) adaptive phase use the EBP algorithm in order to learn from the provided data. The generalization results have been obtained on forecasting the daily and hourly streamflows by assuming them with the structure of ARMA model. The results show validities in applying to hydrologic forecasting system.

• Journal of Korea Water Resources Association
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• v.45 no.5
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• pp.481-493
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• 2012

The Uncertainty of drought events can be regarded as supernatural phenomena so that the uncertainty of water supply system will be also uncontrollable. Decision making for water supply system operation must be dealt with in consideration of hydrologic uncertainty conditions. When ultimate small quantity of precipitation or streamflow lasts, water supply system might be impacted as well as stream pollution, aqua- ecosystem degradation, reservoir dry-up and river aesthetic waste etc. In case of being incapable of supplying water owing to continuation of severe drought, it can make the damage very serious beyond our prediction. This study analyzes comprehensively sustained drought impacts on the Han River Basin Water Supply System. Drought scenarios consisted of several sustained times and return periods for 5 sub-watersheds are generated using a stochastic hydrologic time series model. The developed drought scenarios are applied to assess water supply performance at the Paldang Dam. The results show that multi-year drought events reflecting spatial hydrologic diversity need to be examined in order to recognize variation of the unexpected drought impacts.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.26-41
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• 2016

Catchment Hydrologic Cycle Assess Tool (CAT) is a model for hydrologic cycle assessment based on physical parameters. In this study, CAT was applied for short-term runoff simulation and connected with model-independent parameter estimation (PEST) for auto-calibrating parameters. The model performance was compared with HEC-HMS, which is widely used for short-term runoff simulation. The study area is the Pangyo Watershed ($22.9km^2$), which includes the Unjung-Cheon and Geumto-Cheon tributaries of the Tan-Cheon stream. Simulation periods were selected from six rainfall events of a two-year period (2006-2007). For the runoff simulation, CAT was applied using three types of infiltration methods (excess rainfall, Green and Ampt and Horton). Sensitivity analysis was carried out to select the parameters and then CAT was optimized using PEST. The model performance of HEC-HMS and CAT-PEST for the rainfall events were within an acceptable limit with Nash Sutcliffe efficiencies (NSE) of 0.63-0.91 and 0.42-0.93, respectively. The simulation results of HEC-HMS have high accuracy in the case of rainfall events that have a sensitive relationship between initial soil moisture conditions and runoff characteristics. The results of CAT-PEST indicated the possibility of reflecting a real runoff system using various physical parameters.

• Journal of Wetlands Research
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• v.21 no.spc
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• pp.61-68
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• 2019

Recently, climate indices represented by quantifying atmospheric-ocean circulation patterns have been widely used to predict hydrologic variables for considering long-term climate variability. Hydrologic forecasting models based on artificial neural networks have been developed to provide accurate and stable forecasting performance. Forecasts of hydrologic variables considering climate variability can be effectively used for long-term management of water resources and environmental preservation. Therefore, identifying significant indicators for hydrologic variables and applying forecasting models still remains as a challenge. In this study, we selected representative climate indices that have significant relationships with dam inflow time series in the Han-River basin, South Korea for applying the dam inflow forecasting model. For this purpose, the ensemble empirical mode decomposition(EEMD) method was used to identify a significance between dam inflow and climate indices and an artificial neural network(ANN) ensemble model was applied to overcome the limitation of a single ANN model. As a result, the forecasting performances showed that the mean correlation coefficient of the five dams in the training period is 0.88, and the test period is 0.68. It can be expected to come out various applications using the relationship between hydrologic variables and climate variability in South Korea.