• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.417-425
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• 2020

In order to predict and prevent hydrological disasters such as flood, it is necessary to accurately estimate rainfall. In this paper, an areal average rainfall estimation method using multiple elevation observation data of an electromagnetic wave rain gauge is presented. The small electromagnetic rain gauge system is a very small precipitation radar that operates at K-band with dual-polarization technology for very short distance observation. The areal average rainfall estimation method is based on the assumption that the variation in rainfall over the observation range is small because the observation distance and time are very short. The proposed method has been evaluated by comparing with ground instruments such as tipping-bucket rain gauges and a Parsivel. The evaluation results show that the methodology works fairly well for the rainfall events which are shown here.

• Water Engineering Research
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• v.1 no.2
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• pp.147-158
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• 2000

In this study, we characterized the seasonal variation of rainrate fields in the Han river basin using the WGR multi-dimensional precipitation model (Waymire, Gupta, and Rodriguez-Iturbe, 1984) by estimating and comparing the parameters derived for each month and for the plain area, the mountain area and overall basin, respectively. The first-and second-order statistics derived from observed point gauge data were used to estimate the model parameters based on the Davidon-Fletcher-Powell algorithm of optimization. As a result of the study, we can find that the higher rainfall amount during summer is mainly due to the arrival rate of rain bands, mean number of cells per cluster potential center, and raincell intensity. However, other parameters controlling the mean number of rain cells per cluster, the cellular birth rate, and the mean cell age are found invariant to the rainfall amounts. In the application to the downstream plain area and upstream mountain area of the Han river basin, we found that the number of storms in the mountain area was estimated a little higher than that in the plain area, but the cell intensity in the mountain area a little lower than that in the plain area. Thus, in the mountain area more frequent but less intense storms can be expected due to the orographic effect, but the total amount of rainfall in a given period seems to remain the same.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.445-454
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• 2017

The performance of spatial downscaling models depends on the quality of input coarse scale products. Thus, the impact of intrinsic errors contained in coarse scale satellite products on predictive performance should be properly assessed in parallel with the development of advanced downscaling models. Such an assessment is the main objective of this paper. Based on a synthetic satellite precipitation product at a coarse scale generated from rain gauge data, two synthetic precipitation products with different amounts of error were generated and used as inputs for spatial downscaling. Geographically weighted regression, which typically has very high explanatory power, was selected as the trend component estimation model, and area-to-point kriging was applied for residual correction in the spatial downscaling experiment. When errors in the coarse scale product were greater, the trend component estimates were much more susceptible to errors. But residual correction could reduce the impact of the erroneous trend component estimates, which improved the predictive performance. However, residual correction could not improve predictive performance significantly when substantial errors were contained in the input coarse scale data. Therefore, the development of advanced spatial downscaling models should be focused on correction of intrinsic errors in the coarse scale satellite product if a priori error information could be available, rather than on the application of advanced regression models with high explanatory power.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.65-71
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• 2018

The Long-Term Hydrologic Impact Assessment (L-THIA) model is a quick and straightforward analysis tool to estimate direct runoff and nonpoint source pollution. L-THIA was originally implemented as a spreadsheet application. GIS-based versions of L-THIA have been developed in ArcView 3 and upgraded to ArcGIS 9. However, a major upgrade was required for L-THIA to operate in the current version of ArcGIS and to provide more options in runoff and NPS estimation. An updated L-THIA interfaced with ArcGIS 10.0 and 10.1 has been developed in the study as an ArcGIS Desktop Tool. The model provides a user-friendly interface, easy access to the model parameters, and an automated watershed delineation process. The model allows use of precipitation data from multiple gauge locations for the watershed when a watershed is large enough to have more than one precipitation gauge station. The model estimated annual direct runoff well for our study area compared to separated direct runoff in the calibration and validation periods of ten and nine years. The ArcL-THIA, with a user-friendly interface and enhanced functions, is expected to be a decision support model requiring less effort for GIS processes or to be a useful educational hydrology model.

• Magazine of the Korean Society of Agricultural Engineers
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• v.26 no.4
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• pp.37-51
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• 1984

The purpose of this study is to find out the determination method of designed duty of water in the rice fields through the comparison of the net unit duty of water at the late reduction division to heading stage with that at the planting stage. The data used for analysing this problem are the data of precipitation and gauge evaporation observed by Cheong-ju Meterological Center, the coefficient of evapotranspiration by College of Agriculture, Chung Buk University and the data of transplanting progressing in Boun area. The results obtained from this analysis are summarized as follows. 1.The occurring year of 1/10 probability value for available precipitation, gauge evaporation and mean maximum daily evapotranspiration during growing season is the year of 1977. 2.The 1/10 probability values of mean maximum evapotranspiration per day under the production rate of 1, 400kg/l0a and 1, 500kg/10a based on the weight of dry matters are 9. 2mm/day and 9. 6mm/day, respectively. 3.The net unit duty of water required in the fields that the maximum planting rate exists is more than the one in the fields that the planting rate is uniform in the planting stage. 4.The determination of net unit duty of water in the late reduction division to heading stage or the planting stage depends upon the daily evapotranspiration and percolation rate in the late reduction division to heading stage or the water depth required for planting and daily consumptive use of water after planting at the planting stage. Therefore the use of figure 5-(1) to figure 5-(6) can easily make the determination of the designed net unit duty of water out of above two kinds of net unit duty of water.

• Journal of Korea Water Resources Association
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• v.42 no.11
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• pp.1017-1028
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• 2009

NSRPM (Neyman-Scott Rectangular Pulse Model) is one of the common model for generating future precipitation time series in stochastical hydrology. There are 5 parameters to compose the NSRPM model for generating precipitation time series. Generally parameter estimation using moment has some problems related with increased objective functions and shows different results in accordance with random variable generating models. In this study, direct parameter estimation method was proposed to cover with disadvantages of parameter estimation using moment. To apply the direct parameter estimation, generating stochastical data variance in accordance with numbers of precipitation events of NSRPM was done. Both kinds of methods were applied at the Cheongju gauge station data. Precipitation time series were generated using 4 different random variable generator, and compared with observed time series to check the accuracies. As a results, direct method showed more stable and better results.

• Journal of Korea Water Resources Association
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• v.46 no.11
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• pp.1129-1140
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• 2013

Precipitation is one of the important factors in the hydrological cycle. It needs to understand accurate of spatial precipitation field because it has large spatio-temporal variability. Precipitation data obtained through the Tropical Rainfall Monitoring Mission (TRMM) 3B43 product is inaccurate because it has 25 km space scale. Downscaling of TRMM 3B43 product can increase the accuracy of spatial precipitation field from 25 km to 1 km scale. The relationship between precipitation and the normalized difference vegetation index(NDVI) (1 km space scale) which is obtained from the Moderate Resolution Imaging Spectroradiometers (MODIS) sensor loaded in Terra satellite is variable at different scales. Therefore regression equations were established and these equations apply to downscaling. Two renormalization strategies, Geographical Difference Analysis (GDA) and Geographical Ratio Analysis (GRA) are implemented for correcting the differences between remote sensing-derived and rain gauge data. As for considering the GDA method results, biases, the root mean-squared error (RMSE), MAE and Index of agreement (IOA) is equal to 4.26 mm, 172.16 mm, 141.95 mm, 0.64 in 2009 and 17.21 mm, 253.43 mm, 310.56 mm, 0.62 in 2011. In this study, we can see the 1km spatial precipitation field map over Korea. It will be possible to get more accurate spatial analysis of the precipitation field through using the additional rain gauges or radar data.

• 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.47 no.12
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• pp.1227-1237
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• 2014

Dual-polarization can distinguish precipitation type and dual-polarization is provide not only meteorological phenomena in the atmosphere but also non-precipitation echoes. Therefore dual-polarization radar can improve radar estimates of rainfall. However polarimetric measurements by transmitting vertically vibration waves and horizontally vibrating waves simultaneously is contain systematic bias of the radar itself. Thus the calibration bias is necessary to improve quantitative precipitation estimation. In this study, the calibration bias of reflectivity (Z) and differential reflectivity ($Z_{DR}$) from the Bislsan dual-polarization radar is calculated using the 2-Dimensional Video Disdrometer (2DVD) data. And an improvement in rainfall estimation is investigated by applying derived calibration bias. A total of 33 rainfall cases occurring in Daegu from 2011 to 2012 were selected. As a results, the calibration bias of Z is about -0.3 to 5.5 dB, and $Z_{DR}$ is about -0.1 dB to 0.6 dB. In most cases, the Bislsan radar generally observes Z and $Z_{DR}$ variables lower than the simulated variables. Before and after calibration bias, compared estimated rainfall from the dual-polarization radar with AWS rain gauge in Daegu found that the mean bias has fallen by 1.69 to 1.54 mm/hr, and the RMSE has decreased by 2.54 to 1.73 mm/hr. And estimated rainfall comparing to the surface rain gauge as ground truth, rainfall estimation is improved about 7-61%.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.151-151
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• 2019

Because drought is a complex and stochastic phenomenon in nature, statistical approaches for drought assessment receive great attention for water resource planning and management. Generally drought characteristics such as severity, duration and intensity are modelled separately. This study aims to develop a relationship between drought characteristics using a bivariate copula model. To achieve the objective, we calculated the Standardized Precipitation Index (SPI) using rainfall data at 6 rain gauge stations for the period of 1961-1999 in Jehlum River Basin, Pakistan, and investigated the drought characteristics. Since there is a significant correlation between drought severity and duration, they are usually modeled using different marginal distributions and joint distribution function. Using exponential distribution for drought severity and log-logistic distribution for drought duration, the Galambos copula was recognized as best copula to model joint distribution of drought severity and duration based on the KS-statistic. Various return periods of drought were calculated to identify time interval of repeated drought events. The result of this study can provide useful information for effective water resource management and shows superiority against univariate drought analysis.