• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.3
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• pp.65-73
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• 2005

Because the geological features of Imha basin are composed of clay and shale layer, much soil particle flows into reservoir in shape of muddy water when it rains a lot. Therefore, turbidity data can be indirect-index to estimate the soil erosion of Imha basin. This study evaluated annual soil erosion using GIS-based soil erosion model and applied rainfall weighting value method by time-series rainfall data to estimate monthly soil erosion. In view of 2003 turbidity data, monthly soil erosion with rainfall weighting value is more efficient than monthly soil erosion with rainfall data.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.70-74
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• 2005

Soil particles from rainfall flow into reservoir and give lots of influence In water quality because the geological conditions and landcover characteristics of imha basin have a weakness against soil loss. Especially, much soil particles induced to reservoir in shape of muddy water when it rains a lot because the geological characteristics of imha reservoir are composed of clay and shale layer. Therefore, field turbidity data can be Indirect-standards to estimate the soil erosion of imha basin. This study evaluated annual soil erosion using GIS-based RUSLE (Revised Universal Soil Loss Equation) and developed rainfall weighting value method using time-series rainfall data to estimate monthly soil erosion. In view of field turbidity data(2003 yr), we can find out monthly soil erosion with rainfall weighting value is more efficient than that with monthly rainfall data.

• Magazine of the Korean Society of Agricultural Engineers
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• v.26 no.3
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• pp.35-45
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• 1984

Generally speaking, agriculture exist in a climatic environment of uncertainty. Namely, normal rainfall value, as given by the mean values, does not exist. Thought on exists, itl does not affect like extreme Precipitation value on the part of agriculture and of others. Therefore, it is important that we measure the duration and severity index of drought caused by extreme precipitation deficit. In this purpose, this study was dealt with the calculation of drought duration and severity indexs by the method of monthly weighting coefficient. There is no quantitive definition of drought that is universally acceptable. Most of the criteria was used to identify drought have been arbitrary because a drought is a 'non-event' as opposed to a distinct event such as a flood. Therefore, confusion arises when an attempt is made to define the drought phenomenon, the calculation of duration, drought index is based on the following four fundamental question, and this study was dealt with the answers of these four questions as they related to this analytical method, as follows. First, the primary interest in this study is to be the lack of precipitation as it relates to agricultural effective rainfall. Second, the time interval was used to be month in this analysis. Third, Drought event, distinguished analytically from other event, is noted by monthly weighting coefficient method based on monthly rainfall data. Fin-ally, the seven regions used in this study have continually affected by drought on account of their rainfall deficit. The result from this method was very similar to the previous papers studied by many workers. Therefore, I think that this method is very available in Korea to identify the duration of drought, the deficit of precipitation and severity index of drought, But according to the climate of Korea exist the Asia Monsoon zone. The monthly weighting coefficient is modify a little, Because get out of 0.1-0.4 occasionally.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.485-493
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• 2006

This study is to combine precipitation data with different spatial-temporal characteristics using an optimal weighting method. This optimal weighting method is designed for combination of AWS rain gage data and S-band RADAR-estimated rain data with weighting function in inverse proportion to own mean square error for the previous time step. To decide the optimal weight coefficient for optimized precipitation according to different training time, the method has been performed on Changma case with a long spell of rainy hour for the training time from 1 hour to 10 hours. Horizontal field of optimized precipitation tends to be smoothed after 2 hours training time, and then optimized precipitation has a good agreement with synoptic station rainfall assumed as true value. This result suggests that this optimal weighting method can be used for production of high-resolution quantitative precipitation rate using various data sets.

• Journal of Wetlands Research
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• v.12 no.1
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• pp.51-61
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• 2010

Changes in climate have largely increased concentrated heavy rainfall, which in turn is causing enormous damages to humans and properties. Therefore, it is important to understand the spatial-temporal features of rainfall. In this study, RADAR rainfall was used to calculate gridded areal rainfall which reflects the spatial-temporal variability. In addition, Kalman-filter method, a stochastical technique, was used to combine ground rainfall network with RADAR rainfall network to calculate areal rainfall. Thiessen polygon method, Inverse distance weighting method, and Kriging method were used for calculating areal rainfall, and the calculated data was compared with adjusted areal RADAR rainfall measured using the Kalman-filter method. The result showed that RADAR rainfall adjusted with Kalman-filter method well-reproduced the distribution of raw RADAR rainfall which has a similar spatial distribution as the actual rainfall distribution. The adjusted RADAR rainfall also showed a similar rainfall volume as the volume shown in rain gauge data. Anseong-Cheon basin was used as a study area and the RADAR rainfall adjusted with Kalman-filter method was applied in $Vflo^{TM}$ model, a physical-based distributed model, and ModClark model, a semi-distributed model. As a result, $Vflo^{TM}$ model simulated peak time and peak value similar to that of observed hydrograph. ModClark model showed good results for total runoff volume. However, for verifying the parameter, $Vflo^{TM}$ model showed better reproduction of observed hydrograph than ModClark model. These results confirmed that flood runoff simulation is applicable in domestic settings(in South Korea) if highly accurate areal rainfall is calculated by combining gauge rainfall and RADAR rainfall data and the simulation is performed in link to the distributed hydrological model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.1 no.1
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• pp.12-19
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• 1999

CERES-rice, a rice growth simulation model, was used in conjunction with daily weather data to figure out the spatial variation of the phenology and yields of paddy rice at 168 rice cultivation zone units(CZU) of Kyunggi Province in 1997. Two sets of cultivar specific coefficients, which represent early and mid-season maturing varieties, were derived from field experiments conducted at two crop experiment stations. The minimum data set to run the model for each CZU (daily maximum and minimum temperature, solar irradiance, and rainfall) was obtained by spatial averaging of existing 'Digital Map of Korean Climate'(Shin et al., 1999). Soil characteristics and management information at each CZU were available from the Rural Development Administration. According to a preliminary test using 5 to 9 years field data, trends of the phasic development(heading and physiological maturity), which were obtained from the model adjusted for these coefficients, were in good agreement with the observed data. However, the simulated inter-annual variation was somewhat greater than the reported variation. Rough rice yields of the early maturing cultivar calculated by the model were comparable with the reported data in terms of both absolute value and inter -annual variation. But those of the mid season cultivar showed overestimation. After running the simulation model runs with 1997 weather data for 168 CZU's, rough rice yields of the 168 CZU's calculated by the model were aggregated into corresponding 33 counties by acreage-weighting to facilitate direct comparison with the reported statistics from the Ministry of Agriculture and Forestry. The simulation results were good at 22 out of the 26 counties with reportedly increasing yield trend with respect to the past 9 years average.