• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.4
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• pp.127-135
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• 2012

This paper introduced the flow forecast modeling system that a water management agency in west central Florida, Tampa Bay Water has been operated to forecast monthly rainfall and streamflow in the Tampa Bay region, Florida. We evaluated current 1-year monthly rainfall forecasts and flow forecasts and actual observations to investigate the benefits of incorporating rainfall forecasts into monthly flow forecast. Results for rainfall forecasts showed that the observed annual cycle of monthly rainfall was accurately reproduced by the $50^{th}$ percentile of forecasts. While observed monthly rainfall was within the $25^{th}$ and $75^{th}$ percentile of forecasts for most months, several outliers were found during the dry months especially in the dry year of 2007. The flow forecast results for the three streamflow stations (HRD, MB, and BS) indicated that while the 90 % confidence interval mostly covers the observed monthly streamflow, the $50^{th}$ percentile forecast generally overestimated observed streamflow. Especially for HRD station, observed streamflow was reproduced within $5^{th}$ and $25^{th}$ percentile of forecasts while monthly rainfall observations closely followed the $50^{th}$ percentile of rainfall forecasts. This was due to the historical variability at the station was significantly high and it resulted in a wide range of forecasts. Additionally, it was found that the forecasts for each station tend to converge after several months as the influence of the initial condition diminished. The forecast period to converge to simulation bounds was estimated by comparing the forecast results for 2006 and 2007. We found that initial conditions have influence on forecasts during the first 4-6 months, indicating that FMS forecasts should be updated at least every 4-6 months. That is, knowledge of initial condition (i.e., monthly flow observation in the last-recent month) provided no foreknowledge of the flows after 4-6 months of simulation. Based on the experimental flow forecasts using the observed rainfall data, we found that the 90 % confidence interval band for flow predictions was significantly reduced for all stations. This result evidently shows that accurate short-term rainfall forecasts could reduce the range of streamflow forecasts and improve forecast skill compared to employing the stochastic rainfall forecasts. We expect that the framework employed in this study using available observations could be used to investigate the applicability of existing hydrological and water management modeling system for use of stateof-the-art climate forecasts.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1967-1971
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• 2007

In general, outflow is larger with rainfall but it is various in the initial moisture condition of basin and condition of rainfall distribution in both time and space. In this study, changes of outflow with time varied rainfall data were analyzed in the basin in which the moisture distribution is constant. Outflow differences with rainfall intensive of first period, middle period, and last period of month are 6.1% in January, 7.8% in February, 9.8% in March, 22.6% in April, 15.7% in May, 19.1% in June, 22.6% in July, 22.4% in August, and 16.8% in september respectably. The results show that 10 days outflow differences are ranged from 6.1% to 22.6% under the constant moisture condition, Outflow differences in the flood seasons are larger than them in the drought seasons.

• Water Engineering Research
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• v.2 no.1
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• pp.63-72
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• 2001

The variation of sampling errors was characterized using the Waymire-Gupta-Rodriguez-Iturbe multi-dimensional rainfall model(WGR model). The parameters used for this study are those derived by Jung et al. (2000) for the Han River Basin using a genetic algorithm technique. The sampling error problems considered are those for using raingauge network, satellite observation and also for both combined. The characterization of sampling errors was done for each month and also for the downstream plain area and the upstream mountain area, separately. As results of the study we conclude: (1) The pattern of sampling errors estimated are obviously different from the seasonal pattern of monthly rainfall amounts. This result may be understood from the fact that the sampling error is estimated not simply by considering the rainfall amounts, but by considering all the mechanisms controlling the rainfall propagation along with its generation and decay. As the major mechanism of moisture source to the Korean Peninsula is obviously different each month, it seems rather normal to provide different pattern of sampling errors from that of monthly rainfall amounts. (2) The sampling errors estimated for the upstream mountain area is about twice higher than those for the down stream plain area. It is believed to be because of the higher variability of rainfall in the upstream mountain arean than in the down stream plain area.

• Environmental Engineering Research
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• v.22 no.1
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• pp.87-94
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• 2017

This study was conducted to determine the effect of the climate change to the level of groundwater recharging. This research was conducted on the watershed of Bangga by using the Soil Water Balance of MockWyn-UB model. Input data compose of evapotranspiration, monthly rainfall, watershed area, canopy interception, heavy rain factor and the influence of climate change factors (rainfall and temperature). The conclusion of this study indicates that there is a decreasing trend in annual groundwater recharge observed from 1995 to 2011. The amount of groundwater recharge varied linearly with monthly rainfall and between 3% to 25% of the rainfall. This result implies that rain contributed more than groundwater recharge to runoff and evaporation and the groundwater recharge and Bangga River discharge depends largely on the rainfall. In order to increase the groundwater recharge in the study area, reforestation programmes should be intensified.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.1B
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• pp.1-11
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• 2010

In this study, To analyzed the monthly long-term change characteristics of Chukwooki rainfall data set (CWK) and modern rain gage rainfall data set (MRG), tests of trend or variation were performed of each data sets using five statistical trend or variation test method. furthermore, changing characteristics of rainfall was analyzed through the accomplishment of the 2-dimensional LOWESS regression (or smoothing) which can consider both annual time-variation and inter-monthly time-variation. From the trend test, it is difficult to confirm that given data sets have significant trends. From the 2-dimensional LOWESS analysis for four rainfall characteristics, after near A.D. 1980, inter-monthly variation width in addition to quantative increment of rainfall are increased rapidly and persistently.

• Journal of Korea Water Resources Association
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• v.32 no.6
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• pp.627-636
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• 1999

In 60 years when the double $CO_2$concentration is anticipated the average annual rainfall depth is expected to be increased by 5 10% due to global warming. However, in the water resources area the frequency change of meteorological extremes such as droughts and floods attracts more interests than the increase of annual rainfall amount. Even though recent frequent occurrences of this kind of meteorological extremes are assumed as an indirect proof of global warming, the prediction of its overall tendency has not yet been made. Thus, in this research we propose a possible methodology to be used for its prediction. The methodology proposed is based on the frequency distribution of daily rainfall be Todorovie and Woolhiser(1975), and Katz(1977), where the input parameters are modified to consider the change of monthly or annual rainfall depth and, thus, to result in the change of frequency distribution. We adopt two values(10mm, 50mm) as thresholds and investigate the change of occurrence probability due to the change monthly and annual rainfall depth. these changes do not directly indicate the changes of occurrence probability of floods and droughts, but it may still be a very useful information for their prediction. Finally, the changes of occurrence probability were found to be greater when considering the monthly rainfall rather than the annual rainfall, and those in rainy season than those in dry season.

• 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.

• Water for future
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• v.17 no.4
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• pp.281-291
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• 1984

In our Koreans river basins there are many of monthly rainfall data, but unfortrnately streamflow data needed are rare. Analysing monthly rainfall data of Somjin river basin, the stochastic theory model for calculation of monthly streamflow series of that region is determined. The model is composed of Box & Jenkins stansfer function plus ARIMA residual models. This linear stochastic differenced time series equation models can adapt themselves to the structure and variety of rainfall, streamflow data on the assumption of the stationary covarience. The fiexibility of Box-Jenkins method consists mainly in the iterative technique of building an AIRMA model from observations and by the use of autocorrelation functions. The best models for Somjin river basin belong to the general calss: $Y_t=($\omega$o-$\omega$_1B) C_iX_t+$\varepsilon$t$ $Y_t$ monthly streamflow, $X_t$ : monthly rainfall, $C_i$ :monthly run-off, $$\omega$o-$\omega$_1$ : transfer parameter, $$\varepsilon$_t$ : residual The streamflow series resulted from the proposed model is satisfactory comparing with the exsting streamflow data of Somjin gauging station site.

• Journal of Korea Water Resources Association
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• v.42 no.11
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• pp.933-951
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• 2009

Climate change of global warming may affect the water circulation in Korea. Rainfall is occurred with complex of multiple climatic indices. Therefore, the rainfall is one of the most significant index due to climate change in the process of water circulation. In this research, multiple time series data of rainfall events were extracted to represent the rainfall characteristics. In addition, the occurrence of rainfall time series analyzed by annual, seasonal and monthly data. Analysis method used change analysis of mean and standard deviation and trend analysis. Also, changes in rainfall characteristics and the relative error was calculated during the last 10 years for comparison with past data. At the results, significant statistical results weren't showed by randomness of rainfall data. However, amount of rainfall generally increased last 10 years, and number of raining days had trend of decrease. In addition, seasonal and monthly changes in the rainfall characteristics can be found to appear differently.