• Journal of Korea Water Resources Association
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• v.33 no.6
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• pp.783-791
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• 2000

A multidimensional representation for precipitation, given In the theory proposed by E. Waymire et al. (1984), is used for simulating rainfall in space and time. The model produces moving storms with realistic meso-scale meteorological features in time and space. The first- and second-order statistics derived from observed JX)int gauge data were used to estimate the model parameters based on the Nelder-Mead algorithm of optimization. Then twelve-year traces of rainfall intensities at fixed gage stations were generated at intervals of 1 hours. First- and second-order statistics are evaluated from the above series, which are used for estimating the parameters of one dimensional model of temporal rainfall at a point. As a result from the comparisons of one dimensional model parameters used observed and generated data from multidimensional model, we found that the multidimensional rainfall model generated visually realistic spatial patterns of rainfall as well as realistic temporal hyetographs of rainfall at a point. point.

• Journal of Korea Water Resources Association
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• v.38 no.5 s.154
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• pp.415-425
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• 2005

This study estimated the optimal rain gauge density and sub-basin size for the application of a daily rainfall-runoff analysis model called SWAT (Soil and Water Assessment Tool). Simulated rainfall data using a WGR multi-dimensional precipitation model (Waymire et al., 1984) were applied to SWAT for runoff estimation, and then the runoff error was analyzed with respect to various rain gauge density and sub-basin size. As results of the study, we could find that the optimal sub-basin size and the representative area of one rain gauge are similar to be about $80km^2$ for the Yong-Dam dam basin.

• Journal of Korea Water Resources Association
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• v.30 no.5
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• pp.441-447
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• 1997

Rainfall observation using rain gage network or satellites includes the sampling error depending on the observation methods or plans. For example, the sampling using rain gages is continuous in time but discontinuous in space, which is nothing but the source of the sampling error. The sampling using satellites is the reverse case that continuous in space and discontinuous in time. The sampling error may be quantified by use of the temporal-spatial characteristics of rainfall and the sampling design. One of recent works on this problem was done by North and Nakamoto (1989), who derived a formulation for estimating the sampling error based on the temporal-spatial rainfall spectrum and the design scheme. The formula enables us to design an optimal rain gage network or a satellite operation plan providing the statistical characteristics of rainfall. In this paper the formula is reviewed and applied for the sampling error problems using several multi-dimensional precipitation models. The results show the limitation of the formulation, which cannot distinguish the model difference in case the model parameters can reproduce similar second order statistics of rainfall. The limitation can be improved by developing a new way to consider the higher order statistics, and eventually the probability density function (PDF) of rainfall.