• Journal of Korea Water Resources Association
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• v.48 no.11
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• pp.905-913
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• 2015

This study deployed six rain gauges in a small area for a dense network observing rainfall and analyzed the spatial variability of rainfall. They were arranged in a $2{\times}3$ rectangular grid with equal space of 60 m. The rainfall measurements from five gauges were analyzed during the period of 50 days because one was seriously affected by alien substance. The maximum difference in cumulative rainfall from them is approximately 38.5 mm. The correlation coefficients from hourly rainfall time series differ from each other while daily rainfall coincide. The coefficient of variation in hourly rainfall varies up to 224% and that in daily rainfall up to 91%. The results from uncertainty analysis show that with only four rain gauges areal mean rainfall cannot be estimated over 95% accuracy. For reliable flood prediction and effective water management it is required to develop a new technique for the estimation of areal rainfall.

• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.448-451
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• 2005

This paper presents the methodology to analyze flow duration characteristics and performance prediction for small hydro power(SHP) plants and its application. The flow duration curvecan be decided by using monthly rainfall data at the most of the SHP sites with no useful hydrological data. It was proved that the monthly rainfall data can be characterized by using the cumulative density function of Weibull distribution and Thiessen method were adopted to decide flow duration curve at SHP plants. And, the performance prediction has been studied and development. One SHP plant was selected and performance characteristics was analyzed by using the developed technique. Primary design specfications such as design flowrate, plant capacity, operational rate and annual electricity production for the SHP plant were estimated. It was found that the methodology developed in this study can be a useful tool to predict the performance of SHP plants and candidate sites in Korea.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.117-121
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• 1999

Relations between GMS-5 infrared brightness temperature with SSM/I retrieved rain rate are determined by a probability matching method similar to Atlas et al. and Crosson et al. For this study, coincident data sets of the GMS-5 infrared measurements and SSM/I data during two summer seasons of 1997 and 1998 are constructed. The cumulative density functions (CDFs) of infrared brightness temperature and rain rate are matched at pairs of two variables which give the same percentile contribution. The method was applied for estimating rain rate on 31 July 1998, examining heavy rainfall estimation of a flash flood event over Mt. Jiri. Results were compared with surface gauge observations run by Korean Meteorological Administration. It was noted that the method produced reasonably good quality of rain estimate, however, there was large area giving false rain due to the anvil type clouds surrounding deep convective clouds. Extensive validation against surface rain observation is currently under investigation.

• Journal of Environmental Science International
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• v.20 no.3
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• pp.417-426
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• 2011

This study was conducted to investigate runoff characteristics of non-point pollutants source at the urban and rural zones in sangju area. The monitoring was conducted with seven events for ten months and Event mean Concentration(EMC) and First Flush Effect(FFE) of SS and BOD were calculated on the result of the water quality parameters. During rainfall event, the peak concentrations of SS and BOD were observed after 3~4 hours of rainfall in rural areas. Whereas, the peak concentrations occurred within 1~2 hours after rainfall and then the highest concentration of NPS pollutants sharply decreased, showing strong first flush effect in urban areas. The cumulative load curves for NPS pollutants showed above the $45^{\circ}$ straight line, indicating that fist flush effect occurred in urban areas. The mean SS EMC values of rural areas ranged from 0.9~3.3mg/L, it was higher value when compare to urban areas. While the mean BOD values of urban areas were shown the highest values.

• Journal of Korea Water Resources Association
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• v.51 no.1
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• pp.81-88
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• 2018

The rainfall-runoff model made of sewer networks in the urban area is vast and complex, making it unsuitable for real-time urban flood forecasting. Therefore, the rainfall-runoff model is constructed and simplified using the sewer network of Daerim baisn. The network simplification process was composed of 5 steps based on cumulative drainage area and all parameters of SWMM were calculated using weighted area. Also, in order to estimate the optimal simplification range of the sewage network, runoff and flood analysis was carried out by 5 simplification ranges. As a result, the number of nodes, conduits and the simulation time were constantly reduced to 50~90% according to the simplification ranges. The runoff results of simplified models show the same result before the simplification. In the 2D flood analysis, as the simplification range increases by cumulative drainage area, the number of overflow nodes significantly decreased and the positions were changed, but similar flooding pattern was appeared. However, in the case of more than 6 ha cumulative drainage area, some inundation areas could not be occurred because of deleted nodes from upstream. As a result of comparing flood area and flood depth, it was analyzed that the flood result based on simplification range of 1 ha cumulative drainage area is most similar to the analysis result before simplification. It is expected that this study can be used as reliable data suitable for real-time urban flood forecasting by simplifying sewer network considering SWMM parameters.

• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.261-272
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• 2023

In recent years, natural disasters such as heavy rainfall and typhoons have occurred more frequently, and their severity has increased due to climate change. The Korea Meteorological Administration (KMA) currently uses the same criteria for all regions in Korea for watch and warning based on the maximum cumulative rainfall with durations of 3-hour and 12-hour to reduce damage. However, KMA's criteria do not consider the regional characteristics of damages caused by heavy rainfall and typhoon events. In this regard, it is necessary to develop new criteria considering regional characteristics of damage and cumulative rainfalls in durations, establishing four stages: blue, yellow, orange, and red. A classification model, called DSCM (Disaster Severity Classification Model), for the four-stage disaster severity was developed using four machine learning models (Decision Tree, Support Vector Machine, Random Forest, and XGBoost). This study applied DSCM to local governments of Seoul, Incheon, and Gyeonggi Province province. To develop DSCM, we used data on rainfall, cumulative rainfall, maximum rainfalls for durations of 3-hour and 12-hour, and antecedent rainfall as independent variables, and a 4-class damage scale for heavy rain damage and typhoon damage for each local government as dependent variables. As a result, the Decision Tree model had the highest accuracy with an F1-Score of 0.56. We believe that this developed DSCM can help identify disaster risk at each stage and contribute to reducing damage through efficient disaster management for local governments based on specific events.

• The Journal of Engineering Geology
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• v.23 no.1
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• pp.19-27
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• 2013

Deformation of a slope at a coal waste depot and the natural slope under the depot was surveyed and investigated at Dogye village in Samcheock city, Gangwon Province. To investigate the behaviors of the slopes, wire sensors and a rain gauge were installed on the crest of the waste depot slope and inclinometers were installed in the natural slope. The results of deformation monitoring at the crest of the waste depot slope using wire sensors revealed increased deformation with increasing cumulative rainfall. The results of monitoring horizontal deformation of the natural slope revealed that maximum horizontal deformation was also affected by cumulative precipitation. However, the groundwater level at the natural slope showed no change with rainfall. These measurements confirm that deformation at coal mine waste depots is closely related to precipitation, indicating that self-loading at such depots increases with rainfall infiltration, thus causing deformation of the waste depot slope. In addition, increasing the self-load of the coal mine waste depot may cause deformation of the underlying natural slope.

• Journal of Korea Water Resources Association
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• v.48 no.6
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• pp.491-500
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• 2015

In order to improve the runoff estimation accuracy of the Natural Resources Conservation Service (NRCS) curve number (CN) model, this study incorporated rainfall and maximum potential retention as contributors for initial abstraction. The modification was proposed based on 658 rank-order data of rainfall and subsequent runoff from 15 watersheds. The NRCS-CN model (M1), one of its inspired modified model (M2), and the proposed model (M3) were analyzed employing different CN approaches. Using tabulated, calculated and least squares fitted CNs ($CN_T$, $CN_C$, $CN_{LSF}$, respectively), the models' performances were evaluated based on Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), and Percent Bias (PBIAS). Applications of model M1, M2, and M3, respectively exhibited watershed cumulative mean [RMSE (23.60, 18.12, 16.04), NSE (0.54, 0.73, 0.79), and PBIAS (36.54, 20.25, 12.00)]. Similarly, using CNC (for M1 and M2 model) and $CN_{LSF}$ (for M3 model), the performance of three models respectively were assessed based on watershed cumulative mean [RMSE (17.17, 15.88, 13.82), NSE (0.76, 0.80, 0.85), and PBIAS (3.06, 4.47, 0.11)]. The proposed model (M3) that linked all of the NRCS-CN variants showed more statistically significant agreement between the observed and estimated data.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.473-478
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• 2013

In this paper, we develop the Heavy Rain Advisory Decision Model based on intelligent neuro-fuzzy algorithm RBFNNs by using KLAPS(Korea Local Analysis and Prediction System) Reanalysis data. the prediction ability of existing heavy rainfall forecasting systems is usually affected by the processing techniques of meteorological data. In this study, we introduce the heavy rain forecast method using the pre-processing techniques of meteorological data are in order to improve these drawbacks of conventional system. The pre-processing techniques of meteorological data are designed by using point conversion, cumulative precipitation generation, time series data processing and heavy rain warning extraction methods based on KLAPS data. Finally, the proposed system forecasts cumulative rainfall for six hours after future t(t=1,2,3) hours and offers information to determine heavy rain advisory. The essential parameters of the proposed model such as polynomial order, the number of rules, and fuzzification coefficient are optimized by means of Differential Evolution.

• Journal of Wetlands Research
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• v.19 no.3
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• pp.287-295
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• 2017

In this study, rainfall runoff characteristics according to peak concentration were analyzed using the water quality and flow data in the Geumho river, and the direction of nonpoint source management such as monitoring and management period by pollution source was derived. Peak Water Quality Concentration is the concept that utilizes the extreme value as the concentration of non-point pollution control standard with the highest water quality in the rainwater runoff. Using this method, the evaluation factors such as cumulative precipitation(total precipitation), peak water quality concentration, cumulative precipitation up to peak water quality concentration, time to peak water quality concentration, and EMC to peak water quality concentration were examined and long- Rainfall runoff characteristics of nonpoint sources were analyzed. The results of the analysis suggested proper monitoring and management method to manage nonpoint source.