• Korean Journal of Soil Science and Fertilizer
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• v.44 no.3
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• pp.337-343
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• 2011

A particular empirical equation for rainfall kinetic energy is needed to compute rainfall erosivity, calculated by the annual sum of the product of total rainfall energy and maximum 30-min rainfall intensity. If rainfall kinetic energy equation was different, rainfall erosivity will be produced differently. However, the previous studies in Korea had little concern about rainfall kinetic energy equation and it was not clear which rainfall kinetic energy is suitable for Korea. The purpose of this study is to analyze and evaluate the difference of the rainfall erosivity based on different rainfall kinetic energy equations obtained from previous studies. This study introduced new rainfall erosivity factors based on rainfall kinetic energy equation of Noe and Kwon (1984) that is only regression model developed in Korea. Data of annual rainfall erosivity for 21 weather stations in 1980~1999 were used in this study. The result showed that rainfall erosivity factors by the previous equations had been about 10~20% overestimated than rainfall erosivity by Noe and Kwon (1984)'s equation in Korea.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.1
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• pp.3-13
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• 2007

In this study, spatial characteristics of rainfall in Imha basin were investigated by cross-correlation analysis among rainfall gaging stations and rainfall-runoff analysis used in HEC-HMS model for analysis of influence on observed rainfall. The Kriging technique was applied to rain(all analysis in Imha basin to reflect spatial characteristics of regional rainfall. Their results are compared to rainfall-runoff data with spatially distributed rainfall data as well as the classical thiessen method. The results by kriging technique approached by geostatistical method could reflect spatial characteristics of regional rainfall properly in Imha basin.

• Atmosphere
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• v.26 no.4
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• pp.495-508
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• 2016

In order to determine the prediction possibility of heavy rainfall, a variety of analyses was conducted by using three-dimensional data obtained from Korea Local Analysis and Prediction System (KLAPS) re-analysis data. Strong moisture convergence occurring around the time of the heavy rainfall is consistent with the results of previous studies on such continuous production. Heavy rainfall occurred in the cloud system with a thick convective clouds. The moisture convergence, temperature and potential temperature advection showed increase into the heavy rainfall occurrence area. The distribution of integrated liquid water content tended to decrease as rainfall increased and was characterized by accelerated convective instability along with increased buoyant energy. In addition, changes were noted in the various characteristics of instability indices such as K-index (KI), Showalter Stability Index (SSI), and lifted index (LI). The meteorological variables used in the analysis showed clear increases or decreases according to the changes in rainfall amount. These rapid changes as well as the meteorological variables changes are attributed to the surrounding and meteorological conditions. Thus, we verified that heavy rainfall can be predicted according to such increase, decrease, or changes. This study focused on quantitative values and change characteristics of diagnostic variables calculated by using numerical models rather than by focusing on synoptic analysis at the time of the heavy rainfall occurrence, thereby utilizing them as prognostic variables in the study of the predictability of heavy rainfall. These results can contribute to the identification of production and development mechanisms of heavy rainfall and can be used in applied research for prediction of such precipitation. In the analysis of various case studies of heavy rainfall in the future, our study result can be utilized to show the development of the prediction of severe weather.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1B
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• pp.1-9
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• 2008

The impact of snow accumulation and snowmelt in rainfall-runoff modelling was analyzed for the Soyanggang dam basin by comparing the measured and simulated discharges simulated by the NWS-PC model. Sugawara's conceptual model was used to simulate the snow accumulation and snowmelt phenomena and NWS-PC model was employed to simulate rainfall-runoff. Parameters in model calibration were estimated by the Multi-step Automated Calibration Scheme and optimized using SCE-UA algorithm in each step. The results of the model calibration and verification show that the model considering snowmelt process is better than the one without consideration of snowmelt under the performance criteria such as RMSE, PBIAS, NSE, and PME. The measured discharge time series has over 60 days of persistence. Correlograms for each simulation showed that the simulated discharge with snowmelt model reproduce the persistence closely to the measured discharge's while the one without snow accumulation and snowmelt model reproduce only 20 days of persistence. The study result indicates that the inclusion of snow accumulation and snowmelt model is important for the accurate simulation of rainfall-runoff phenomena in the Soyanggang dam basin.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.828-835
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• 2019

An artificial rainfall system is used widely as a research tool for generating model experiment data. Artificial rainfall devices have been used in many studies, but studies of the rainfall distribution are not considered as important issues. To simulate various rainfall characteristics, it should be possible to simulate from low to high intensity, and the homogeneity of the rainfall distribution should be ensured. In this study, the maximum rainfall intensity was set to 130mm/hr and controlled by 10mm/hr. In addition, the aim was to secure a uniform coefficient value of 80% or more. To this end, rainfall tests were performed according to the nozzle type, diameter, position, and pump pressure. The rainfall test showed that the circular nozzle was suitable, and the nozzle size was 1.9mm and 1.4mm. The optimal pump pressure was found to be 3~6kg/㎠. The rainfall intensity tended to increase linearly with increasing pump pressure. Based on the rainfall test results, a rainfall control manual was produced with variables, such as pump pressure, nozzle type, and number of nozzles. As a result of rainfall verification, rainfall intensity showed a 3.1% error with a uniformity coefficient of 86%.

• Water for future
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• v.19 no.4
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• pp.347-354
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• 1986

To predict flood runoff from rainfall and watershed Characteristics, Nash's parameters of N, K are needed to be determined. Also parameters of IUH N and K are derived by the moment method. Nash's model whose parameters are derived from rainfall characteristics is applied to the Wi-stream basin, which is a tributary located in the Nakdong river. For the derivation of IUH by applying linear conceptual model, the storage constant, K, with the rainfall characteristics was adopted as K=1.327 $$.$$$.$$$.$$$.$$$.$$ having a highly significant correlation coefficient, 0.970. Gamma function argumetn, N, derived with such rainfall characteristics was found to be N=0.032$$.$$$.$$$.$$$.$$$.$$ having a highly significant correlation coefficient, 0.970. From the tested results it is proved that Nash's IUH and consequently flood runoff can be predicted from rainfall characteristics.

• 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.

• Water for future
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• v.14 no.4
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• pp.27-34
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• 1981

The design flow of the urban strom drainage systems has been assessed largely on a basis of empirical relations between rainfall and runoff, and the rational formula has been widely used for the cities in our country. In order to estimate it more accurately, the urban runoff simulation model based on the RRl method has been developed and applied to the sample basin in this study. The rainfall hyetograph of the design stromfor the design flow has been obtained by the determination of the total rainfall and the temporal distributions of that rainfall. The total rainfall has been assessed from the empirical formula of rainfall intensity and the temporal distribution of that rainfall determined on the basis of Huff's method from the historical rainfall data of the basin. The virtual inflow hydrograph to each inlet of the basin has been constructed by computing the series of discharges in each time increment, using design strom hyetograph and time-area diagram. The actual runoff hydrograph at the basin outlet has been computed from the virtual inflow hydrographs by developing a relations between discharge and storage for the watershed. The discharge data for verification of the simulated runoff hydrograph are not available in the sample basin and so the sensitivity analysis of the simulation model has not been possible. The peak discharge for the design of drainage systems has been estimated from the computed runoff hydrograph at the basin outlet and compared to thatl obtained form the rational formula.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1147-1151
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• 2009

In this study, we address the problem of producing probability forecasts of summer seasonal rainfall, on the basis of Hindcast experiments from a ensemble of GCMs(cwb, gcps, gdaps, metri, msc_gem, msc_gm2, msc_gm3, msc_sef and ncep). An advanced Hierarchical Bayesian weighting scheme is developed and used to combine nine GCMs seasonal hindcast ensembles. Hindcast period is 23 years from 1981 to 2003. The simplest approach for combining GCM forecasts is to weight each model equally, and this approach is referred to as pooled ensemble. This study proposes a more complex approach which weights the models spatially and seasonally based on past model performance for rainfall. The Bayesian approach to multi-model combination of GCMs determines the relative weights of each GCM with climatology as the prior. The weights are chosen to maximize the likelihood score of the posterior probabilities. The individual GCM ensembles, simple poolings of three and six models, and the optimally combined multimodel ensemble are compared.

• Journal of Korea Water Resources Association
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• v.44 no.3
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• pp.221-230
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• 2011

The climate change has been observed in Korea as well as in the entire world recently. The rainstorm has been gradually increased and then the damage has been grown. It is getting important to predict short-term rainfall. The Korea Meteorological Administration (KMA) generates numerical model outputs which are computed by Global Data Assimilation and Prediction System (GDAPS) and Regional Data Assimilation and Prediction System (RDAPS). The KMA predicts rainfall using RDAPS results. RDAPS model generates 48 hours data which is organized 3 hours data accumulated at 00UTC and 12UTC. RDAPS results which are organized 3 hours time scale are converted into daily rainfall to compare observed daily rainfall. In this study, 9 cases are applied to convert RDAPS results to daily rainfall data. The MAP (mean areal precipitation) in Geum river basin are computed by using KMA which are 2005 are used. Finally, the best case which gives the close value to the observed rainfall data is obtained using the average absolute relative error (AARE) especially for the Geum River basin.