• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.943-947
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• 2005

This study was intended to interpose an objection about the analysis of rainfall spatial distribution without a proper standard, and offer the improved approach using 1,he geostatistical analysis method to analyze it. For this, spatially distributed daily rainfall data sets were collected for 41 weather stations in study area, and variogram and correlation analysis were conducted. In the results of correlation analysis, it was found that the longer distance between the stations reduces the correlation of the rainfall data, and maltes the characteristics of the rainfall spatial distribution. The variogram analysis shows that correlation range was less than 50 km for the 17 daily rainfall data sets of total 91 sets. It says that it involves some rike, to determine the application method for rainfall spatial distribution without some qualifications, hence the Application standards of the Rainfall Spatial Distribution Analysis Technique, were essential and that was contingent on characteristics of rainfall and landscape.

• Journal of Wetlands Research
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• v.16 no.1
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• pp.103-112
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• 2014

Area average rainfall estimation is important to determine the exact amount of the available water resources and the essential input data for rainfall-runoff analysis. Like that, the necessary criterion for accurate area average rainfall estimate is the uniform spatial distribution of raingauge network. In this study, we suggest the spatial distribution evaluation methodology of raingauge network to estimate better area average rainfall and after the suggested method is applied to Han River and Geum River basin. The spatial distribution of rainfall network can be quantified by the nearest neighbor index. In order to evaluate the effects of the spatial distribution of rainfall network by each basin, area average rainfall was estimated by arithmetic mean method, the Thiessen's weighting method and estimation theory for 2013's rainfall event, and evaluated the involved errors by each cases. As a result, it can be found that the estimation error at the best basin of spatial distribution was lower than the worst basin of spatial distribution.

• Journal of Wetlands Research
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• v.12 no.1
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• pp.63-73
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• 2010

Rainfall is one of the most important input data of hydrologic models. Rain gage is used to estimate areal rainfall for hydrologic models using several interpolation method such as Thiessen polygon, Inverse Distance Squared(IDS) and Kriging. However, it is still difficult to derive actual spatial distribution of the rainfall using the aforementioned approaches. On the other hand, radar can offer a significant analytic improvement for rainfall analysis by providing directly more representative of the true spatial distribution of rainfall. In this study, In this study, spatial distributions of rainfall derived form rain gages using IDS and Kriging and rainfall from radar are compared. As results, it is found that using radar can provide actual spatial distribution than rain gages.

• Environmental Engineering Research
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• v.21 no.3
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• pp.226-232
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• 2016

Rainfall is one of crucial factors that impact on our environment. Rainfall data is important in water resources management, flood forecasting, and designing hydraulic structures. However, it is not available in some rural watersheds without rain gauges. Thus, effective ways of interpolating the available records are needed. Despite many widely used spatial interpolation methods, few studies have investigated rainfall center characteristics. Based on the theory that the spatial distribution of convective rainfall event has a definite center with maximum rainfall, we present a mathematical interpolation method to estimate convective rainfall distribution and indicate the rainfall center location and the center rainfall volume. We apply the method to estimate three convective rainfall events in Santa Catalina Island where reliable hydrological data is available. A cross-validation technique is used to evaluate the method. The result shows that the method will suffer from high relative error in two situations: 1) when estimating the minimum rainfall and 2) when estimating an external site. For all other situations, the method's performance is reasonable and acceptable. Since the method is based on a continuous function, it can provide distributed rainfall data for distributed hydrological model sand indicate statistical characteristics of given areas via mathematical calculation.

• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.757-767
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• 2010

This study employs the KED method using the correlations between probability rainfall and topographical factors as single auxiliary variable for assessing the effectiveness of external variables to improve the reliability in the estimation of spatial distribution of probability rainfall. As a result, the KED method gives similar results compared with deterministic spatial interpolation methods and kriging methods in the estimation of rainfall spatial distribution and mean areal rainfall, and as a result of the cross-validations of KED and kriging methods, the KED method using terrain elevation as auxiliary variable gives the best results, which are not significantly different in comparisons with other methods.

• Atmosphere
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• v.25 no.2
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• pp.283-294
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• 2015

For urban flash flood simulation, we need the higher resolution radar rainfall than radar rainfall of KMA, which has 10 min time and 1km spatial resolution, because the area of subbasins is almost below $1km^2$. Moreover, we have to secure the high quantitative accuracy for considering the urban hydrological model that is sensitive to rainfall input. In this study, we developed the quantitative precipitation estimation (QPE), which has 250 m spatial resolution and high accuracy using KMA AWS and SK Planet stations with Mt. Gwangdeok radar data in Seoul area. As the results, the rainfall field using KMA AWS (QPE1) is showed high smoothing effect and the rainfall field using Mt. Gwangdeok radar is lower estimated than other rainfall fields. The rainfall field using KMA AWS and SK Planet (QPE2) and conditional merged rainfall field (QPE4) has high quantitative accuracy. In addition, they have small smoothed area and well displayed the spatial variation of rainfall distribution. In particular, the quantitative accuracy of QPE4 is slightly less than QPE2, but it has been simulated well the non-homogeneity of the spatial distribution of rainfall.

• Journal of Korea Water Resources Association
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• v.43 no.11
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• pp.933-944
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• 2010

This study compares the CEM and SGS methods which are geostatistical stochastic simulation methods for assessing the uncertainty by spatial variability in the estimation of the spatial distribution of probability rainfall. In the stochastic simulations using CEM and SGS, two methods show almost similar results for the reproduction of spatial correlation structure, the statistics (standard deviation, coefficient of variation, interquartile range, and range) of realizations as uncertainty measures, and the uncertainty distribution of basin mean rainfall. However, the CEM is superior to SGS in aspect of simulation efficiency.

• Journal of Wetlands Research
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• v.10 no.2
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• pp.143-153
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• 2008

The purpose of this paper is to evaluate impacts of ENSO on frequency and spatial distribution of rainfall in South Korea. In this paper, First, rainfall data in 60 climate stations were categorized into Warm(El Nino), Cold(La Nina), Normal episodes based on the Cold & Warm Episodes by Season, then 100 years of daily rainfall data were generated for each episodic events(El Nino, La Nina, Normal) using Markov Chain model. Finally, Estimating frequency based flood and comparison for each episodes were conducted. From the results, it shows that there are significant changes in the rainfall frequency and the spatial distribution of rainfall among Warm(EL Nino), Cold(La Nina) and Normal episodes.

• Journal of Wetlands Research
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• v.17 no.4
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• pp.370-379
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• 2015

In recent, the natural disaster like localized heavy rainfall due to the climate change is increasing. Therefore, it is important issue that the precise observation of rainfall and accurate spatial distribution of the rainfall for fast recovery of damaged region. Thus, researches on the use of the radar rainfall data have been performed. But there is a limitation in the estimation of spatial distribution of rainfall using rain gauge. Accordingly, this study uses the Kriging method which is a spatial interpolation method, to measure the rainfall field in Namgang river dam basin. The purpose of this study is to apply KED(Kriging with External Drift) with OK(Ordinary Kriging) and CK(Co-Kriging), generally used in Korea, to estimate rainfall field and compare each method for evaluate the applicability of each method. As a result of the quantitative assessment, the OK method using the raingauge only has 0.978 of correlation coefficient, 0.915 of slope best-fit line, and 0.957 of $R^2$ and shows an excellent result that MAE, RMSE, MSSE, and MRE are the closest to zero. Then KED and CK are in order of their good results. But the quantitative assessment alone has limitations in the evaluation of the methods for the precise estimation of the spatial distribution of rainfall. Thus, it is considered that there is a need to application of more sophisticated methods which can quantify the spatial distribution and this can be used to compare the similarity of rainfall field.

• Journal of Korea Water Resources Association
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• v.40 no.12
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• pp.943-956
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• 2007

Among various statistics, the spatial correlation function, that is "correlogram", is frequently used to evaluate or design the rain gauge network and to model the rainfall field. The spatial correlation structure of rainfall has the significant variation due to many factors. Thus, the variation of spatial correlation structure of rainfall causes serious problems when deciding the spatial correlation function of rainfall within the basin. In this study, the spatial rainfall structure was modeled using bivariate mixed distributions to derive monthly spatial correlograms, based on Gaussian and lognormal distributions. This study derived the correlograms using hourly data of 28 rain gauge stations in the Keum river basin. From the results, we concluded as following; (1) Among three cases (Case A, Case B, Case C) considered, the Case A(+,+) seems to be the most relevant as it is not distorted much by zero measurements. (2) The spatial correlograms based on the lognormal distribution, which is theoretically as well as practically adequate, is better than that based on the Gaussian distribution. (3) The spatial correlation in July exponentially decrease more obviously than those in other months. (4) The spatial correlograms should be derived considering the temporal resolution(hourly, daily, etc) of interest.