• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.747-757
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• 2021

Quantification of extreme rainfall is very important in establishing a flood protection plan, and a general measure of extreme rainfall is expressed as an T-year return level. In this study, a method was proposed for quantifying spatial distribution and uncertainty of daily rainfall depths with various return periods using a hierarchical Bayesian model combined with climate and geographical information, and was applied to the Seoul-Incheon-Gyeonggi region. The annual maximum daily rainfall depth of six automated synoptic observing system weather stations of the Korea Meteorological Administration in the study area was fitted to the generalized extreme value distribution. The applicability and reliability of the proposed method were investigated by comparing daily rainfall quantiles for various return levels derived from the at-site frequency analysis and the regional frequency analysis based on the index flood method. The uncertainty of the regional frequency analysis based on the index flood method was found to be the greatest at all stations and all return levels, and it was confirmed that the reliability of the regional frequency analysis based on the hierarchical Bayesian model was the highest. The proposed method can be used to generate the rainfall quantile maps for various return levels in the Seoul-Incheon-Gyeonggi region and other regions with similar spatial sizes.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.2
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• pp.69-77
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• 2001

An artificial neural network model was developed to analyze and forecast daily steamflow flow a small watershed. Error Back propagation neural networks (EBPN) of daily rainfall and runoff data were found to have a high performance in simulating stremflow. The model adopts a gradient descent method where the momentum and adaptive learning rate concepts were employed to minimize local minima value problems and speed up the convergence of EBP method. The number of hidden nodes was optimized using Bayesian information criterion. The resulting optimal EBPN model for forecasting daily streamflow consists of three rainfall and four runoff data (Model34), and the best number of the hidden nodes were found to be 13. The proposed model simulates the daily streamflow satisfactorily by comparison compared to the observed data at the HS#3 watershed of the Baran watershed project, which is 391.8 ha and has relatively steep topography and complex land use.

• Journal of Korea Water Resources Association
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• v.32 no.6
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• pp.627-636
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• 1999

In 60 years when the double $CO_2$concentration is anticipated the average annual rainfall depth is expected to be increased by 5 10% due to global warming. However, in the water resources area the frequency change of meteorological extremes such as droughts and floods attracts more interests than the increase of annual rainfall amount. Even though recent frequent occurrences of this kind of meteorological extremes are assumed as an indirect proof of global warming, the prediction of its overall tendency has not yet been made. Thus, in this research we propose a possible methodology to be used for its prediction. The methodology proposed is based on the frequency distribution of daily rainfall be Todorovie and Woolhiser(1975), and Katz(1977), where the input parameters are modified to consider the change of monthly or annual rainfall depth and, thus, to result in the change of frequency distribution. We adopt two values(10mm, 50mm) as thresholds and investigate the change of occurrence probability due to the change monthly and annual rainfall depth. these changes do not directly indicate the changes of occurrence probability of floods and droughts, but it may still be a very useful information for their prediction. Finally, the changes of occurrence probability were found to be greater when considering the monthly rainfall rather than the annual rainfall, and those in rainy season than those in dry season.

• Journal of Korean Society on Water Environment
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• v.27 no.3
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• pp.293-299
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• 2011

The quantity of a discharge load can change with changes in rainfall in the area with a combined sewer system (CSS). To evaluate the implementation appropriately in the management of total maximum daily loads (TMDLs), the effects of rainfall changes should be considered in the estimation of the discharge load. The rainfall condition for the estimation of the discharge load in a certain year should be standardized to the same rainfall condition as that of the reference year. However, the calculation process is very complicated with its potential limitations. This study investigated and developed relatively simple methods for estimating the discharge load. Load conversion method (LCM) is designed to convert the discharge load under the current rainfall condition into that of the reference rainfall conditions. Simple rainfall data method (SRDM) is to simplify the estimation process of the discharge load by the simple conversion of rainfall data. These methods were applied to calculate the discharge load and examine the estimation results. From the results of this study the application of these methods may be useful for estimating the discharge load in the TMDL process.

• Magazine of the Korean Society of Agricultural Engineers
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• v.37 no.5
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• pp.81-89
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• 1995

One of the most difficult problem to estimate the flood inflow is how to understand the effective rainfall. The effective rainfall is absolutely influenced by the condition of soil moisture in the watershed just before the storm event. DAWAST model developed to simulate the daily streamflow considering the meteologic and geographic characteristics in the Korean watersheds was applied to understand the soil moisture and estimate the effective rainfall rather accurately through the daily water balance in the watershed. From this soil moisture and effective rainfall, concentration time, dimensionless hydrograph, and addition of baseflow, the rainfall-runoff model for flood flow was developed by converting the concept of long-term runoff into short-term runoff. And, real-time flood forecasting model was also developed to forecast the flood-inflow hydrograph to the river and reservoir, and called RETFLO model. According to the model verification, RETFLO model can be practically applied to the medium and small river and reservoir to forecast the flood hydrograph with peak discharge, peak time, and volume. Consequently, flood forecasting and warning system in the river and the reservoir can be greatly improved by using personal computer.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.228-232
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• 2017

Extreme hydrological events can cause serious threats to the society. Hence, the selection of probability distributions for extreme rainfall is a fundamental issue. For this reason, this study was focused on understanding possible distributional changes in annual daily maximum rainfalls (AMRs) over time in Sri Lanka using quantile regression. A simplified nine-category distributional-change scheme based on comparing empirical probability density function of two years (i.e. the first year and the last year), was used to determine the distributional changes in AMRs. Daily rainfall series of 13 station over Sri Lanka were analyzed for the period of 1960-2015. 4 distributional change categories were identified for the AMRs. 5 stations showed an upward trend in all the quantiles (i.e. 9 quantiles: from 0.05 to 0.95 with an increment of 0.01 for the AMR) which could give high probability of extreme rainfall. On the other hand, 8 stations showed a downward trend in all the quantiles which could lead to high probability of the low rainfall. Further, we identified a considerable spatial diversity in distributional changes of AMRs over Sri Lanka.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.49-60
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• 2021

BCSA (Bias-Correction and Stochastic Analog) is a statistical downscaling technique designed to effectively correct the systematic errors of GCM (General Circulation Model) output and reproduce basic statistics and spatial variability of the observed precipitation filed. In this study, the applicability of BCSA was evaluated using the ASOS observation data over South Korea, which belongs to the monsoon climatic zone with large spatial variability of rainfall and different rainfall characteristics. The results presented the reproducibility of temporal and spatial variability of daily precipitation in various manners. As a result of comparing the spatial correlation with the observation data, it was found that the reproducibility of various climate indices including the average spatial correlation (variability) of rainfall events in South Korea was superior to the raw GCM output. In addition, the needs of future related studies to improve BCSA, such as supplementing algorithms to reduce calculation time, enhancing reproducibility of temporal rainfall patterns, and evaluating applicability to other meteorological factors, were pointed out. The results of this study can be used as the logical background for applying BCSA for reproducing spatial details of the rainfall characteristic over the Korean Peninsula.

• Journal of Korea Water Resources Association
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• v.49 no.4
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• pp.283-291
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• 2016

As the infrastructures and populations are the condensed in the mega city, urban flood management becomes very important due to the severe loss of lives and properties. For the more accurate calculation of runoff from the urban catchment, hourly or even minute rainfall data have been utilized. However, the time steps of the measured or forecasted data under climate change scenarios are longer than hourly, which causes the difficulty on the application. In this study, daily rainfall data was disaggregated into hourly using the stochastic method. Based on the historical hourly precipitation data, Gram Schmidt orthonormalization process and K-Nearest Neighbor Resampling (KNNR) method were applied to disaggregate daily precipitation into hourly. This method was originally developed to disaggregate yearly runoff data into monthly. Precipitation data has smaller probability density than runoff data, therefore, rainfall patterns considering the previous and next days were proposed as 7 different types. Disaggregated rainfall was resampled from the only same rainfall patterns to improve applicability. The proposed method was applied rainfall data observed at Seoul weather station where has 52 years hourly rainfall data and the disaggregated hourly data were compared to the measured data. The proposed method might be applied to disaggregate the climate change scenarios.

• Journal of Korean Society on Water Environment
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• v.27 no.5
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• pp.572-579
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• 2011

We have examined the runoff characteristics of nonpoint source (NPS) in fields. Two monitoring sites were equipped with an automatic velocity meter and water sampler. Monitoring was conducted at fields 1 and field 2 during the rainfall event. Ten rainfall-runoff events were monitored and analyzed during the study period. The results show that runoff occurred if daily rainfall and intensity were higher than 40 mm and 1.6 mm/hr except a few extreme rainfall events with very high intensity. Runoff of field 1 was approximately twice of that of field 2. Event mean concentrations (EMC) and pollution load of analyzed water quality indices were also higher in field 2 than in field 1. Especially, TN load from field 2 was $75.4 mg/m^2$ and was about 5 times higher than that from field 1. Analysis of Pearson correlation coefficient of water quality parameter indicates that besides of TN all items in fields 1 have tight relationship respectively (p < 0.01). But those of fields 2 have a significant (p < 0.05). Estimating units loading of NPS, we suggested that variable such as soil texture, rainfall amount and intensity and slope were needed to be considered from agricultural landuses. The results of this study can be used as a basic data in the development and implementation of total maximum daily loads (TMDL) in Korea.

• Journal of the Korean Society of Tobacco Science
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• v.26 no.2 s.52
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• pp.85-92
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• 2004

This study was conducted to investigate the relationship between weather factors during the growing season and chemical components of burley tobacco. Chemical components used in this study was from 'Farm Leaf Tobacco Test' conducted at KT&G Central Research Institute from 1987 through 2002. Data of weather factors during growing season(April to July) were collected in 6 districts measured at Korea Meteorological Adminstration(KMA). Total nitrogen content was increased from 1987 through 2002. Year to year variation of rainfall was the largest, followed by that of sunshine hour. Month to month variation of rainfall also was the largest, followed by that of mean daily air temperature. A negative correlation was found between rainfall and sunshine hour. Relative humidity(R.H.) was correlated positively with rainfall, whereas negatively with sunshine hour. The negative correlations were found between nicotine content and rainfall(in June, May$\~$June, June$\~$July, May$\~$July and average), and R.H.(in June, May$\~$June, June$\~$July, May$\~$July and average), respectively. The negative correlations were found between crude ash content and rainfall(in June and May$\~$June), and R.H.(in June, May$\~$June, June$\~$July and May$\~$July), respectively. Ether extraction content was correlated positively with mean daily air temperature(in July, June $\~$July and May$\~$July) and with sunshine hour(in July, June$\~$July and May$\~$July), but negatively with rainfall(average) and with R.H.(in April, July, June$\~$July, April$\~$June, May­July and average), respectively. Chloride content was correlated positively with sunshine hour(in May, April$\~$May, May$\~$June, April$\~$June, May$\~$July and average), but negatively with rainfall(in June, May$\~$June, June$\~$July, April$\~$June, May$\~$July and average).