• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.164-164
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• 2016

Radar rainfall estimates have been widely used in calculating rainfall amount approximately and predicting flood risks. The radar rainfall estimates have a number of error sources such as beam blockage and ground clutter hinder their applications to hydrological flood forecasting. Moreover, it has been reported in paper that those errors are inter-correlated spatially and temporally. Therefore, in the current study, we tested influence about spatio-temporal errors in radar rainfall estimates. Spatio-temporal errors were simulated through a stochastic simulation model, called Multivariate Autoregressive (MAR). For runoff simulation, the Nam River basin in South Korea was used with the distributed rainfall-runoff model, Vflo. The results indicated that spatio-temporal dependent errors caused much higher variations in peak discharge than spatial dependent errors. To further investigate the effect of the magnitude of time correlation among radar errors, different magnitudes of temporal correlations were employed during the rainfall-runoff simulation. The results indicated that strong correlation caused a higher variation in peak discharge. This concluded that the effects on reducing temporal and spatial correlation must be taken in addition to correcting the biases in radar rainfall estimates. Acknowledgements This research was supported by a grant from a Strategic Research Project (Development of Flood Warning and Snowfall Estimation Platform Using Hydrological Radars), which was funded by the Korea Institute of Construction Technology.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.95-95
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• 2020

Urban flooding occurs in the form of internal-water inundation on roads and lowlands due to heavy rainfall. Unlike in the case of rivers, inundation in urban areas there is lacking in research on predicting and warning through measurement data. In order to analyze urban flood patterns and prevent damage, it is necessary to analyze flooding measurement data for various rainfalls. In this study, the pattern of urban flooding caused by rainfall was analyzed by utilizing the urban flooding measuring sensor, which is being test-run in the flood prone zone for urban flooding management. For analysis, 2019 rainfall data, surface water depth data, and water level data of a street inlet (storm water pipeline) were used. The analysis showed that the amount of rainfall that causes flooding in the target area was identified, and the timing of inundation varies depending on the rainfall pattern. The results of the analysis can be used as verification data for the urban inundation limit rainfall under development. In addition, by using rainfall intensity and rainfall patterns that affect the flooding, it can be used as data for establishing rainfall criteria of urban flooding and predicting that may occur in the future.

• Journal of the Korean Society of Hazard Mitigation
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• v.4 no.1 s.12
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• pp.25-31
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• 2004

Infiltration of rainfall causes railway embankment to be unstable and may result in failure. Basic relationship between the rainfall and stability of railway embankment is defined to analyze the stability of embankment by rainfall. An experimental study for defining of infiltration rate of rainfall into slope is conducted in the lab. The results of Rainfall Infiltration show that rainfall Infiltration is not equal to infiltration as like reservoir because rate of rainfall infiltration is controlled by slope angle. Based on these results, boundary condition of rainfall is altered and various numerical analysis are performed. The variation of shear strength, the degree of saturation and pore-water pressure for railway slope during rainfall can be predicted and the safety factor of railway slope can be expressed as the function of rainfall amount, namely rainfall index. Therefore, it is judged that this rainfall index can be a good tool for the rail-transport operation control.

• Magazine of the Korean Society of Agricultural Engineers
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• v.24 no.1
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• pp.63-72
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• 1982

To maintain an optimum condition for the plant growth on upland soil, the irrigation planning after the natural rainfall should be given enormous considerations on the rainfall effectiveness. This study has been intended to develop the computer model for estimating the effec- tiveness of the rainfall. The computer model should also estimated the infiltration due to the rainfall and the soil moisture deficiency at the root zone of the plant. For this purpose, the experiments of infiltration using rainfall simulator and the observations of the change of soil moisture content before and after rainfall were carried out. Needed input data for the developed model include final infiltration capacity and field capacity of the soil, porosity of the top soil, root depth of the plant, rainfall intensity and duration, and the Horton's decay coefficient. Among the needed input data for the developed model, final infiltration capacity and Horton's decay coefficient were determined by the experiments of infiltration. And from the result of the experiments, it is found that there is a great correlation between initial infiltration capacity and initial moisture content. And it is also found that the infiltration due to rainfall can be estimated with the Horton's equation. The developed model was tested by the experimental data with two rainfall intensities. Tests were conducted on the different root depths at each rainfall. Observed and estimated effective rainfalls were found to have great correlation. The result of the experiments showed that the effectiveness of the rainfall were 100%, so the comparisons were conducted by the comsumption rates of infiltration at each depth. The developed model can be also used for estimating the deficiency of rainfall, if the rainfall is not sufficient to the needed soil moisture. But, test was not carried out.

• Water for future
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• v.29 no.3
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• pp.187-195
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• 1996

In this study, a methodology for the hourly prediction of rainfall surfaces was applied to the Pyungchang river basin at the upstream of South Han river with meteorological radar and ground rainfall data. The methods for the exclusion of abnormal echoes, and suppression of ground clutter, and the augmentation of attenuation effects associated with rainfall phenomena were reviewed, and the relationship between radar reflectivity (Z) and rainfall rate (R) was analyzed. The transformation of augmented radar reflectivities into the rdar rainfall surfaces was carried out, and afterward they were synthesized with the ground rainfall data generating the hourly rainfall surfaces. For the prediction of hourly rainfall surface, the moving factors of rainfall field estimated by the cross correlation coefficient method and the temporal variation of radar rainfall intensities were considered. The synthesized hourly rainfall surfaces were used to predict the hourly rainfall surfaces up to 3 hours in advance and subsequently the results were compared with the measured and the synthesized. It seems that the prediction method need to be verified with more data and be complemented further to consider the physical characteristics of rainfall field and the topography of the basin.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.16-16
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• 2011

Statistics of extreme rainfall play a vital role in engineering practice from the perspective of mitigation and protection of infrastructure and human life from flooding. While flood frequency assessments, based on river flood flow data are preferred, the analysis of rainfall data is often more convenient due to the finer spatial nature of rainfall recording networks, often with longer records, and potentially more easily transferable from site to site. The rainfall frequency analysis as a design tool has developed over the years in New Zealand from Seelye's daily rainfall frequency maps in 1947 to Thompson's web based tool in 2010. This paper will present a history of the development of New Zealand rainfall frequency analysis methods, and the details of the latest method, so that comparisons may in future be made with the development of Korean methods. One of the main findings in the development of methods was new knowledge on the distribution of New Zealand rainfall extremes. The High Intensity Rainfall Design System (HIRDS.V3) method (Thompson, 2011) is based upon a regional rainfall frequency analysis with the following assumptions: $\bullet$ An "index flood" rainfall regional frequency method, using the median annual maximum rainfall as the indexing variable. $\bullet$ A regional dimensionless growth curve based on the Generalised Extreme Value (GEV), and using goodness of fit test for the GEV, Gumbel (EV1), and Generalised Logistic (GLO) distributions. $\bullet$ Mapping of median annual maximum rainfall and parameters of the regional growth curves, using thin-plate smoothing splines, a $2km\times2km$ grid, L moments statistics, 10 durations from 10 minutes to 72 hours, and a maximum Average Recurrence Interval of 100 years.

• Journal of Korea Water Resources Association
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• v.52 no.2
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• pp.125-132
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• 2019

In this study, we developed a heavy rainfall risk impact matrix, which can be used to evaluate the influence of heavy rainfall risk, and propose a method to evaluate the impact of heavy rainfall risk. We evaluated the heavy rainfall risk impact for each receptor (Residential, Transport, Utility) on Sadang-dong using the heavy rainfall event on July 27, 2011. For this purpose, the potential risk impact was calculated by combining the impact level and the rainfall depth based on the grid. Heavy Rainfall Risk Impact was calculated by combining with Likelihood to predict the heavy rainfall impact, and the degree of heavy rainfall in the Sadang-dong area was analyzed and presented based on grid.

• Journal of Wetlands Research
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• v.12 no.1
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• pp.51-61
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• 2010

Changes in climate have largely increased concentrated heavy rainfall, which in turn is causing enormous damages to humans and properties. Therefore, it is important to understand the spatial-temporal features of rainfall. In this study, RADAR rainfall was used to calculate gridded areal rainfall which reflects the spatial-temporal variability. In addition, Kalman-filter method, a stochastical technique, was used to combine ground rainfall network with RADAR rainfall network to calculate areal rainfall. Thiessen polygon method, Inverse distance weighting method, and Kriging method were used for calculating areal rainfall, and the calculated data was compared with adjusted areal RADAR rainfall measured using the Kalman-filter method. The result showed that RADAR rainfall adjusted with Kalman-filter method well-reproduced the distribution of raw RADAR rainfall which has a similar spatial distribution as the actual rainfall distribution. The adjusted RADAR rainfall also showed a similar rainfall volume as the volume shown in rain gauge data. Anseong-Cheon basin was used as a study area and the RADAR rainfall adjusted with Kalman-filter method was applied in $Vflo^{TM}$ model, a physical-based distributed model, and ModClark model, a semi-distributed model. As a result, $Vflo^{TM}$ model simulated peak time and peak value similar to that of observed hydrograph. ModClark model showed good results for total runoff volume. However, for verifying the parameter, $Vflo^{TM}$ model showed better reproduction of observed hydrograph than ModClark model. These results confirmed that flood runoff simulation is applicable in domestic settings(in South Korea) if highly accurate areal rainfall is calculated by combining gauge rainfall and RADAR rainfall data and the simulation is performed in link to the distributed hydrological model.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.260-260
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• 2016

Soil erosion is a very serious problem from agricultural as well as environmental point of view. Various computer models have been used to estimate soil erosion and assess erosion control practice. Universal Soil loss equation (USLE) is a popular model which has been used in many countries around the world. Erosivity (USLE R-factor) is one of the USLE input parameters to reflect impacts of rainfall in computing soil loss. Value of R factor depends upon Energy (E) and maximum rainfall intensity of specific period ($I30_{max}$) of that rainfall event and thus can be calculated using higher temporal resolution rainfall data such as 10 minute interval. But 10 minute interval rainfall data may not be available in every part of the world. In that case we can use hourly rainfall data to compute this R factor. Maximum 60 minute rainfall ($I60_{max}$) can be used instead of maximum 30 minute rainfall ($I30_{max}$) as suggested by USLE manual. But the value of Average annual R factor computed using hourly rainfall data needs some correction factor so that it can be used in USLE model. The objective of our study are to derive relation between averages annual R factor values using 10 minute interval and hourly rainfall data and to determine correction coefficient for R factor using hourly Rainfall data.75 weather stations of Korea were selected for our study. Ten minute interval rainfall data for these stations were obtained from Korea Meteorological Administration (KMA) and these data were changed to hourly rainfall data. R factor and $I60_{max}$ obtained from hourly rainfall data were compared with R factor and $I30_{max}$ obtained from 10 minute interval data. Linear relation between Average annual R factor obtained from 10 minute interval rainfall and from hourly data was derived with $R^2=0.69$. Correction coefficient was developed for the R factor calculated using hourly rainfall data.. Similarly, the relation was obtained between event wise $I30_{max}$ and $I60_{max}$ with higher $R^2$ value of 0.91. Thus $I30_{max}$ can be estimated from I60max with higher accuracy and thus the hourly rainfall data can be used to determine R factor more precisely by multiplying Energy of each rainfall event with this corrected $I60_{max}$.