• Atmosphere
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• v.24 no.2
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• pp.173-188
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• 2014

The KMA has cooperated with the Oklahoma University in USA to develop a Polarimetric Radar Data (PRD) simulator to improve the microphysical processes in Korea Local Analysis and Prediction System (KLAPS), which is critical for the utilization of PRD into Numerical Weather Prediction (NWP) field. The simulator is like a tool to convert NWP data into PRD, so it enables us to compare NWP data with PRD directly. The simulator can simulate polarimetric radar variables such as reflectivity (Z), differential reflectivity ($Z_{DR}$), specific differential phase ($K_{DP}$), and cross-correlation coefficient (${\rho}_{hv}$) with input of the Drop Size Distribution (DSD) and scattering calculation of the hydrometeors. However, the simulator is being developed based on the foreign observation data, therefore the PRD simulator development reflecting rainfall characteristics of Korea is needed. This study analyzed a potential application of the 2-Dimension Video Disdrometer (2DVD) data by calculating the raindrop axis ratio according to the rain-types to reflect Korea's rainfall characteristics into scattering module in the simulator. The 2DVD instrument measures the precipitation DSD including the fall velocity and the shape of individual raindrops. We calculated raindrop axis ratio for stratiform, convective and mixed rainfall cases after checking the accuracy of 2DVD data, which usually represent the scattering characteristics of precipitation. The raindrop axis ratio obtained from 2DVD data are compared with those from foreign database in the simulator. The calculated the dual-polarimetric radar variables from the simulator using the obtained raindrop axis ratio are also compared with in situ dual-polarimetric observation data at Bislsan (BSL). 2DVD observation data show high accuracies in the range of 0.7~4.8% compared with in situ rain gauge data which represents 2DVD data are sufficient for the use to simulator. There are small differences of axis ratio in the diameter below 1~2 mm and above 4~5 mm, which are more obvious for bigger raindrops especially for a strong convective rainfall case. These differences of raindrop axis ratio between domestic and foreign rainfall data base suggest that the potential use of disdrometer observation can develop of a PRD simulated suitable to the Korea precipitation system.

• Journal of Korea Water Resources Association
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• v.39 no.1 s.162
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• pp.79-88
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• 2006

An accurate prediction of inflow water temperature is essentially required for real-time simulation and analysis of rainfall-induced turbidity 烈os in a reservoir. In this study, water temperature data were collected at every hour during the flood season of 2004 at the upstream of Daecheong Reservoir to justify its characteristics during rainfall event and model development. A significant drop of river water temperature by 5 to $10^{\circ}C$ was observed during rainfall events, and resulted in the development of density flow regimes in the reservoir by elevating the inflow density by 1.2 to 2.6 kg/$m^3$ Two types of statistical river water temperature models, a logistic model(DLG) and regression models(DMR-1, DMR-2, DMR-3) were developed using the field data. All models are shown to reasonably replicate the effect of rainfall events on the water temperature drop, but the regression models that include average daily air temperature, dew point temperature, and river flow as independent variables showed better predictive performance than DLG model that uses a logistic function to determine the air to water relation.

• International Journal of Contents
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• v.12 no.1
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• pp.49-53
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• 2016

"Hahoe Village" in Andong region is an UNESCO World Heritage Site. It should be protected against various disasters such as fire, flooding, earthquake, etc. Among these disasters, flooding has drastic impact on the lives and properties in a wide area. Since "Hahoe Village" is adjacent to Nakdong River, it is important to monitor the water level near the village. In this paper, we developed a hydrological modelling using multi-layer perceptron (MLP) to predict the water level of Nakdong River near "Hahoe Village". To develop the prediction model, error back-propagation (EBP) algorithm was used to train the MLP with water level data near the village and rainfall data at the upper reaches of the village. After training with data in 2012 and 2013, we verified the prediction performance of MLP with untrained data in 2014.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.2
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• pp.173-180
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• 2005

In this study, quantity and quality of collected rainwater by the ground collection system were investigated and the Rainwater Collection Prediction Model was developed to predict the amount of collected rainwater. The quantity of collected rainwater in the collection system was 9516 L(38.2%) and the quantity of infiltrated rainwater in the collection system was 9946 L(40.2%) through $25m^2$ area for the study period, respectively. Average turbidity of collected rainwater in collection system was 2.2 NTU, and average turbidity of infiltrated rainwater in collection system was 2.3 NTU for study period, respectively. The predicted amount by the model and the actual collected amount were 9842.4 L and 9516 L, which were very close showing that prediction was excellent. The optimal rainwater storage tank volume was simulated with a certain consumption condition for various cities with different rainfall patterns.

• Journal of Korea Water Resources Association
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• v.50 no.7
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• pp.489-502
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• 2017

The objective of this study is to propose and evaluate the BAYES-ESP, which is a dam inflow prediction method based on Ensemble Streamflow Prediction method (ESP) and Bayesian theory. ABCD rainfall-runoff model was used to predict monthly dam inflow. Monthly meteorological data collected from KMA, MOLIT and K-water and dam inflow data collected from K-water were used for the model calibration and verification. To estimate the performance of ABCD model, ESP and BAYES-ESP method, time series analysis and skill score (SS) during 1986~2015 were used. In time series analysis monthly ESP dam inflow prediction values were nearly similar for every years, particularly less accurate in wet and dry years. The proposed BAYES-ESP improved the performance of ESP, especially in wet year. The SS was used for quantitative analysis of monthly mean of observed dam inflows, predicted values from ESP and BAYES-ESP. The results indicated that the SS values of ESP were relatively high in January, February and March but negative values in the other months. It also showed that the BAYES-ESP improved ESP when the values from ESP and observation have a relatively apparent linear relationship. We concluded that the existing ESP method has a limitation to predict dam inflow in Korea due to the seasonality of precipitation pattern and the proposed BAYES-ESP is meaningful for improving dam inflow prediction accuracy of ESP.

• The Journal of Engineering Geology
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• v.17 no.3
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• pp.411-418
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• 2007

It is a very difficult thing to estimate an occurrence possibility location and hazard expectation area by landslide. The prediction difficulty of landslide occurrence has relativity in factor of various geological physical factors and contributions. However, estimation of landslide occurrence possibility and classification of hazard area became available correlation mechanism through analysis of landslide occurrence through landslide data analysis and statistical analysis. This study analyzed a damage possibility of a cultual heritage area due to landslide occurrence by a heavy rainfall. We make a landslide prediction map and tried to analysis of landslide occurrence possibility for the cultural heritage site. The study area chooses a temple of Silsang-Sa Baekjang-Am site and made a landslide prediction map. In landslide prediction map, landslide hazard possibility area expressed by occurrence probability and divided by each of probability degrees. This degree used to evaluate occurrence possibility for existence and nonexistence of landslide in the study site. For the prediction and evaluation of landslide hazard for the cultural heritage site, investigation and analysis technique which is introduced in this study may contribute an efficient management and investigation in the cultural heritage site, Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.149-153
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• 2009

Physics-based distributed rainfall-runoff models are now commonly used in a variety of hydrologic applications such as to estimate flooding, water pollutant transport, sedimentation yield and so on. Moreover, it is not surprising that GIS has become an integral part of hydrologic research since this technology offers abundant information about spatial heterogeneity for both model parameters and input data that control hydrological processes. This study presents the development of a distributed rainfall-runoff prediction system for the Guem river basin ($9,835km^2$) using an Object-oriented Hydrological Modeling System (OHyMoS). We developed three types of element modules: Slope Runoff Module (SRM), Channel Routing Module (CRM), and Dam Reservoir Module (DRM) and then incorporated them systemically into a catchment modeling system under the OHyMoS. The study basin delineated by the 250m DEM (resampled from SRTM90) was divided into 14 midsize catchments and 80 sub-catchments where correspond to the WAMIS digital map. Each sub-catchment was represented by rectangular slope and channel components; water flows among these components were simulated by both SRM and CRM. In addition, outflows of two multi-purpose dams: Yongdam and Daechung dams were calculated by DRM reflecting decision makers' opinions. Therefore, the Guem river basin rainfall-runoff modeling system can provide not only each sub-catchment outflow but also dam inand outflow at one hour (or less) time step such that users can obtain comprehensive hydrological information readily for the effective and efficient flood control during a flood season.

• Journal of Korean Society of Forest Science
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• v.103 no.2
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• pp.218-225
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• 2014

Landslides, one of earth's natural disasters, increase every year due to heavy rainfall, and cause damage to human life and assets. This study used the SHALSTAB to predict places at risk of landslides, in accordance with the intensity of rainfall. The parameter value of transmissivity was $19.58m^2/day$, the internal friction angle $36.3^{\circ}$, and the saturated unit weight $2.03t/m^3$. The slope stability status was classified into four categories, namely: unconditionally stable, stable, unstable and unconditionally unstable. In order to evaluate the applicability of the SHALSTAB, actual landslide areas were checked, with the unstable area under 263 mm rainfall. 85.1% of them were consistent. And so we can identify the distribution of places at risk of landslides, on the basis of the intensity of rainfall by means of SHALSTAB.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.2
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• pp.55-63
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• 2018

This study analyzed the applications of near real-time drought monitoring using satellite rainfall for the Korean Peninsula and un-gaged basins. We used AWS data of Yongdam-Dam, Hoengseong-Dam in Korea area, the meteorological station of Nakhon Rachasima, Pak chong for test-bed to evaluate the validation and the opportunity for un-gaged basins. In addition, we calculated EDI (Effective doought index) using the stations and co-located PERSIANN-CDR, TRMM (Tropical Rainfall Measurement Mission) TMPA (The TRMM Multisatellite Precipitation Analysis), GPM IMERG (the integrated Multi-satellitE Retrievals for GPM) rainfall data and compared the EDI-based station data with satellite data for applications of drought monitoring. The results showed that the correlation coefficient and the determination coefficient were 0.830 and 0.914 in Yongdam-dam, and 0.689 and 0.835 in Hoengseng-Dam respectively. Also, the correlation coefficient were 0.830, 0.914 from TRMM TMPA datasets and compasion with 0.660, 0.660 based on PERSIANN-CDR and TRMM data in nakhon and pakchong station. Our results were confirmed possibility of near real-time drought monitoring using EDI with daily satellite rainfall for un-gaged basins.

• Journal of Korea Water Resources Association
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• v.48 no.10
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• pp.817-830
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• 2015

This study performed prediction of extreme rainfall uncertainty and its frequency analysis based on climate change scenarios by Coupled Model Intercomparison Project Phase 5 (CMIP5) for the selected nine-General Circulation Models (GCMs) in the near future (2011-2040) over the Korean Peninsula (KP). We analysed uncertainty of scenarios by multiple model ensemble (MME) technique using non-parametric quantile mapping method and bias correction method in the basin scale of the KP. During the near future, the extreme rainfall shows a significant gradually increasing tendency with the annual variability and uncertainty of extreme ainfall in the RCP4.5, and RCP8.5 scenarios. In addition to the probability rainfall frequency (such as 50 and 100-year return periods) has increased by 4.2% to 10.9% during the near future in 2040. Therefore, in the longer-term water resources master plan, based on the various climate change scenarios (such as CMIP5 GCMs) and its uncertainty can be considered for utilizing of the support tool for decision-makers in water-related disasters management.