• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5B
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• pp.441-452
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• 2009

This paper describes the evaluation technique for climate change effect on daily precipitation frequency using daily precipitation generator that can use outputs of the climate model offered by IPCC DDC. Seoul station of KMA was selected as a study site. This study developed daily precipitation generation model based on two-state markov chain model which have transition probability, scale parameter, and shape parameter of Gamma-2 distribution. Each parameters were estimated from regression analysis between mentioned parameters and monthly total precipitation. Then the regression equations were applied for computing 4 parameters equal to monthly total precipitation downscaled by K-NN to generate daily precipitation considering climate change. A2 scenario of the BCM2 model was projected based on 20c3m(20th Century climate) scenario and difference of daily rainfall frequency was added to the observed rainfall frequency. Gumbel distribution function was used as a probability density function and parameters were estimated using probability weighted moments method for frequency analysis. As a result, there is a small decrease in 2020s and rainfall frequencies of 2050s, 2080s are little bit increased.

• Atmosphere
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• v.24 no.4
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• pp.481-489
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• 2014

The Chugugi and Wootaek data of Gyeongsang-do (Dagu, Jinju, Goseong) were restored from "Gaksadeungnok", the governmental documents reported by the local government to the central during the Joseon Dynasty, and analyzed. The duration of the restored data represents 6 years for Daegu (1863, 1872, 1890, 1897, 1898, and 1902), 3 years for Jinju (1897, 1898, and 1900), and 2 years for Goseong (1871 and 1873). Total number of the restored data was 134, including 83 in Daegu, 25 in Jinju, and 26 in Goseong with the period ranging from March to September. The summer data from June to August accounts for approximately 50% (73 data), while the April data also shows relatively high number of 22, followed by September and March. Most data was collected from March to October, while this time winter data was not found even in October. The rainfall patterns using Chugugi data were investigated. First, the number of days with rainfall by annual mean showed 41 days in Daegu, 39 in Jinju, 33 in Goseong, respectively. In terms of the time series distribution of daily rainfall, the ratio between the number of occurrences with over 40 mm of heavy rainfall and the number of rainy days showed 14 times (8%) in Daegu, 24 (39%) in Jinju, and 4 (6%) in Goseong, respectively. The maximum daily rainfall during the period was recorded with 80mm in Jinju on August 24, 1900. The result of analyzing monthly amount of rainfall clearly indicated more precipitation in summer (June, July and August) with the relatively high records of 284 mm and 422 mm in April, 1872 and July, 1902, respectively, in Daegu, while Jinju recorded the highest value of 506 mm in June, 1898. When comparing the data with those observed by Chugugi in Seoul during the same period from "Seungjeongwonilgi", the monthly rainfall patterns in Daegu and Seoul were quite similar except for the year of 1890 and 1897 in which many data were missing. In particular, in June 1898 the rainfall amount of Jinju recorded as much as 506 mm, almost 4 times of that of Seoul (134 mm). Based on this, it is possible to presume that there was a large amount of the precipitation in the southern region during 1898. According to the calculated result of Wootaek data based on Chugugi observations, the unit of 1 'Ri' and 1 'Seo' in Daegu can be interpreted into 18.6 mm and 7.8 mm. When taking into consideration with the previous result found in Gyeonggi-do (Cho et al., 2013), 1 'Ri' and 1 'Seo' may be close to 20.5 mm and 8.1 mm, however, more future investigations and studies will be essential to verify the exact values.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1317-1328
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• 2021

This study aims to provide a predictive model based on climate models for simulating continuous daily rainfall sequences by combining bias-correction and spatio-temporal downscaling approaches. For these purposes, this study proposes a combined modeling system by applying conditional Copula and Multisite Non-stationary Hidden Markov Model (MNHMM). The GloSea5 system releases the monthly rainfall prediction on the same day every week, however, there are noticeable differences in the updated prediction. It was confirmed that the monthly rainfall forecasts are effectively updated with the use of the Copula-based bias-correction approach. More specifically, the proposed bias-correction approach was validated for the period from 1991 to 2010 under the LOOCV scheme. Several rainfall statistics, such as rainfall amounts, consecutive rainfall frequency, consecutive zero rainfall frequency, and wet days, are well reproduced, which is expected to be highly effective as input data of the hydrological model. The difference in spatial coherence between the observed and simulated rainfall sequences over the entire weather stations was estimated in the range of -0.02~0.10, and the interdependence between rainfall stations in the watershed was effectively reproduced. Therefore, it is expected that the hydrological response of the watershed will be more realistically simulated when used as input data for the hydrological model.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.09a
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• pp.113-114
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• 2004

In order to classify the groundwater recharge characteristics in an urban area, a time series analysis of groundwater level data was performed. For this study, the daily groundwater level data from 35 monitoring wells were collected for 3 years (Fig. 1). The use of the cross-correlation function (CCF), one of the time series analysis, showed both the close relationship between rainfall and groundwater level change and the lag time (delay time) of groundwater level fluctuation after a rainfall event. Based on the result of CCF, monitored wells were classified into two major groups. Group I wells (n=10) showed a fast response of groundwater level change to rainfall event, with a delay time of maximum correlation between rainfall and groundwater level near 1 to 7 days. On the other hand, the delay time of 17-68 days was observed from Group II wells (n=25) (Fig. 1). The fast response in Group I wells is possibly caused by the change of hydraulic pressure of bedrock aquifer due to the rainfall recharge, rather than the direct response to rainfall recharge.

• Journal of The Korean Society of Agricultural Engineers
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• v.50 no.6
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• pp.3-12
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• 2008

It is important to estimate accurate effective rainfall to analyse flood flow and long-term runoff for the rational planning, design, and management of water resource. The initial abstraction is also important to estimate effective rainfall. The Soil Conservation Service (SCS) has developed a procedure and it has been most commonly applied to estimate effective rainfall. But the SCS method still has weak points, because of unnatural assumptions such as antecedent moisture conditions and initial abstraction. The coefficient of initial abstraction(K) is depending on the soil moisture condition and antecedent rainfall. The maximum storage capacity of Umax which is calibrated by stream flow data in the proposed watershed was derived from the DAWAST(DAily WAtershed STreamflow) model. The values of K obtained from 69 storm events at the five watersheds are ranging from 0.133 to 0.365 and its mean value is 0.207. Effective rainfall could be estimated more reasonably by introducing new concept of initial abstraction. The equation of $K=0.076Sa^{0.255}$ was recommended instead of 0.2 and it could be applicable to the small-medium rural watersheds.

• Atmosphere
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• v.30 no.2
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• pp.125-139
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• 2020

On 6 August 2018, heavy rainfall of daily precipitation of more than 200 mm occurred in the Yeong-dong coastal area, and especially, 1-hour precipitation of 93 mm (0251~0351 LST (local standard time) 6 August) at Gangneung station, ranked second in the history of meteorological survey of the station. In this study, this heavy rainfall case over the Gangneung area would be studied to investigate the process in which the heavy rainfall occurred. A developed ridge moved toward the Yeong-dong coastal area from the Maritime Province in Russia. The approaching of the ridge led to the northeasterly cold wind over the coastal region, causing the collision between the incoming northeasterly cold wind, and the humid and warm (convectively unstable) air located over the Yeong-dong area. This collision led to a strong convergence (maximum -206 × 10^-5 s^-1) at 925 hPa level over the vicinity of Gangneung at 0300 LST 6 August, resulting updraft of up to about 4.4 m s^-1 at 700 hPa level over the area. This strong updraft forced to lift rapidly the convectively unstable, warm and humid air layer, located over the vicinity of Gangneung, leading to the heavy rainfall (1-hour precipitation of 93 mm) over the area.

• 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.

• Journal of Korea Water Resources Association
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• v.35 no.5
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• pp.575-581
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• 2002

This study evaluates the variation of estimation error of area-average rainfall due to rainfall seasonality. Both the cases considering and not considering the spatial correlation are compared to derive the characteristics of estimation error. Similar cases with different accumulation time without considering the rainfall seasonality are also investigated. This study was applied to the Geum-river basin with total 28 rain gauge measurements haying more than 30 years of daily rainfall measurements. As results of the study we found that: (1) The absolute estimation error of monthly area-average rainfall show strong seasonality like the total rainfall amount. However, the relative estimation error normalized by its mean was estimated to have similar values about 5 to 8% except January and December. (2) The relative estimation error of annual area-average rainfall estimated was found to have the estimation error about 3% of its annual mean. (3) However, the relative estimation error normalized by the standard deviation remains almost the same for both monthly and annual rainfall amounts, which was estimated about 11% of its standard deviation. (4) Finally, the estimation error without considering the spatial correlation was found to become almost twice the estimation error with considering the spatial correlation.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.188-188
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• 2017

Rainfall is an important input to hydrological models. The accuracy of hydrological studies for water resources and floods management depend primarily on the estimation of rainfall. Thailand is among the countries that have regularly affected by floods. Flood forecasting and warning are necessary to prevent or mitigate loss and damage. Merging near real time satellite-based precipitation estimation with relatively high spatial and temporal resolutions to ground gauged precipitation data could contribute to reducing uncertainty and increasing efficiency for flood forecasting application. This study tested the applicability of satellite-based rainfall for water resources management and flood forecasting. The objectives of the study are to assess uncertainty associated with satellite-based rainfall estimation, to perform bias correction for satellite-based rainfall products, and to evaluate the performance of the bias-corrected rainfall data for the prediction of flood events. This study was conducted using a case study of Thai catchments including the Chao Phraya, northeastern (Chi and Mun catchments), and the eastern catchments for the period of 2006-2015. Data used in the study included daily rainfall from ground gauges, telegauges, and near real time satellite-based rainfall products from TRMM, GSMaP and PERSIANN CCS. Uncertainty in satellite-based precipitation estimation was assessed using a set of indicators describing the capability to detect rainfall event and efficiency to capture rainfall pattern and amount. The results suggested that TRMM, GSMaP and PERSIANN CCS are potentially able to improve flood forecast especially after the process of bias correction. Recommendations for further study include extending the scope of the study from regional to national level, testing the model at finer spatial and temporal resolutions and assessing other bias correction methods.

• Magazine of the Korean Society of Agricultural Engineers
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• v.30 no.1
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• pp.50-62
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• 1988

This study refers to the development of a hydrologic model simulating daily inflow and release rates for irrigation reservoirs. A daily - based model is needed for adequate operation of an irrigation reservoir sufficing the water demand for paddy fields which is closely related to meteorological conditions. Inflow rates to a reservoir need to be accurately described, which may be simulated using a hydrologic model from daily rainfall data. And the objective of this paper is to develop, test, and apply a hydrologic model for daily runoff simmulation. A well - known tank model was selected and modified to simulate daily inflow rates. The model parameters were calibrated using observed runoff data from twelve watersheds, Relationships between the parameters and the watershed characteristics were derived by a multiple regression analysis. The simulation results were in agreement with the data. The inflow model was found to simulate low flow conditions more accurately than high flow conditions, which may be adequate for water resources utilization.