• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.1
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• pp.27-35
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• 2017

This research was to elucidate the generation method of daily precipitation data from monthly data. We applied a combined method of Markov chain and gamma distribution function using 4 specific parameters of ${\alpha}$, ${\beta}$, p(W/W) and p(W/D) for generation of daily rainfall data using daily precipitation data for the past 30 years which were collected from the country's 23 meteorological offices. Four parameters, applied to use for the combination method, were calculated by maximum likelihood method in location of 23 sites. There are high correlations of 0.99, 0.98 and 0.98 in rainfall days, rainfall probability and mean amount of daily rainfall between measured and simulated data in case of those parameters. In case of using parameters estimated from monthly precipitation, correlation coefficients in rainfall days, rainfall probability and mean amount of daily rainfall are 0.84, 0.83 and 0.96, respectively. We concluded that a combination method with parameter estimation from monthly precipitation data can be applied, in practical purpose such as assessment of climate change in agriculture and water resources, to get daily precipitation data in Korea.

• Journal of Environmental Science International
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• v.2 no.3
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• pp.179-191
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• 1993

This paper aims to know the characteristics of occurrence of the anomaly level and variability of the monthly precipitation in Kyeongnam, Korea. For this study, it was investigated 주e distribution of the annual and cont비y mean precipitation, the precipitation variability and its annual change, and the characteristics of occurrence of the anomaly level in Kyeongnam area the results were summarized as follows : 1) she mean of annual total precipitation averaged over Kyeongnam area is 1433.3mm. I'he spatial distribution of the annual total precipitation shows that in Kyeongnam area, the high rainfall area locates in the southwest area and south coast and the low rainfall area in an inland area. 2) Monthly mean precipitation in llyeongnam area was the highest in July(266.4mm) 각lowed by August(238.0mm), June(210.2mm) in descending order. In summer season, rainfall was concentrated and accounted for 49.9 percent of the annual total precipitation. Because convergence of the warm and humid southwest current which was influenced by Changma and typhoon took place well in this area. 3) The patterns of annual change of precipitaion variability can be divided into two types; One is a coast type and the other an inland type. The variability of precipitation generally appears low in spring and summer season and high in autumn and winter season. This is in accord with the large and small of precipitation. 4) The high frequency of anomaly level was N( Normal)-level and the next was LN( Low Informal) -level and 25(Extremely Subnormal)-level was not appeared in all stations. The occurrence frequency of N level was high in high rainfall area and distinguish성 in spring and summer season but the low rainfall area was not. hey Words : anomaly level, variability, precipitation, coast type, inland type.

• Journal of Climate Change Research
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• v.7 no.4
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• pp.507-512
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• 2016

This study was conducted to classify agroclimatic zones in South Korea. To classify the agroclimatic zones, such climatic factors as amount of rainfall from April to May, amount of rainfall in October, monthly average air temperature in January, monthly average air temperature from April to May, monthly average air temperature from April to September, monthly average air temperature from December to March, monthly minimum air temperature in January, monthly minimum air temperature from April to May, Warmth Index were considered as major influencing factors on the crop growth. Climatic factors were computed from monthly air temperature and precipitation of climatological normal year (1981~2010) at 1 km grid cell estimated from a geospatial climate interpolation method. The agroclimatic zones using k-means cluster analysis method were classified into 6 zones.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1317-1328
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• 2021

This study aims to provide a predictive model based on climate models for simulating continuous daily rainfall sequences by combining bias-correction and spatio-temporal downscaling approaches. For these purposes, this study proposes a combined modeling system by applying conditional Copula and Multisite Non-stationary Hidden Markov Model (MNHMM). The GloSea5 system releases the monthly rainfall prediction on the same day every week, however, there are noticeable differences in the updated prediction. It was confirmed that the monthly rainfall forecasts are effectively updated with the use of the Copula-based bias-correction approach. More specifically, the proposed bias-correction approach was validated for the period from 1991 to 2010 under the LOOCV scheme. Several rainfall statistics, such as rainfall amounts, consecutive rainfall frequency, consecutive zero rainfall frequency, and wet days, are well reproduced, which is expected to be highly effective as input data of the hydrological model. The difference in spatial coherence between the observed and simulated rainfall sequences over the entire weather stations was estimated in the range of -0.02~0.10, and the interdependence between rainfall stations in the watershed was effectively reproduced. Therefore, it is expected that the hydrological response of the watershed will be more realistically simulated when used as input data for the hydrological model.

• Solar Energy
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• v.9 no.3
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• pp.81-91
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• 1989

This study represents the method to predict the flow duration curve and primary design specifications of small hydropower plant at hydropower site through analyzing the monthly rainfall data. Weibull distribution was selected to characterize the rainfall data and Thiessen method was used to calculate monthly average flowrate at site. Application of these results, primary design specifications such as design flowrate, annual average load factor and utility factor, annual average hydropower density and annual electric energy production were estimated and discussed for surveyed site located in Daigi-ri, Kangwon province. And performance characteristic model of small hydro-power plant was applied to estimate these specifications.

• Magazine of the Korean Society of Agricultural Engineers
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• v.26 no.2
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• pp.106-124
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• 1984

Monthly streanflow of watersheds is one of the most important elements for the planning, design, and management of water resources development projects, e.g., determination of storage requirement of reservoirs and control of release-water in lowflow rivers. Modeling of longterm runoff is theoretically based on water-balance analysis for a certain time interval. The effect of the casual factors of rainfall, evaporation, and soil-moisture storage on streamflow might be explained by multiple regression analysis. Using the basic concepts of water-balance and regression analysis, it was possible to develop a generalized model called the Regionalized Regression Model for Monthly Streamflow in Korean Watersheds. Based on model verification, it is felt that the model can be reliably applied to any proposed station in Korean watersheds to estimate monthly streamflow for the planning, design, and management of water resources development projects, especially those involving irrigation. Modeling processes and properties are summarized as follows; 1. From a simplified equation of water-balance on a watershed a regression model for monthly streamflow using the variables of rainfall, pan evaporation, and previous-month streamflow was formulated. 2. The hydrologic response of a watershed was represented lumpedly, qualitatively, and deductively using the regression coefficients of the water-balance regression model. 3. Regionalization was carried out to classify 33 watersheds on the basis of similarity through cluster analysis and resulted in 4 regional groups. 4. Prediction equations for the regional coefficients were derived from the stepwise regression analysis of watershed characteristics. It was also possible to explain geographic influences on streamflow through those prediction equations. 5. A model requiring the simple input of the data for rainfall, pan evaporation, and geographic factors was developed to estimate monthly streamflow at ungaged stations. The results of evaluating the performance of the model generally satisfactory.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.3
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• pp.795-804
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• 2014

In this study, Andong Reservoir monthly and ten days inflows from July 2011 to September 2011 are predicted using SWAT model and ensemble technique. The weight method using monthly and ten days rainfall forecasts from Korea Meteorological Administration is applied for accurate analysis. If the rainfall prediction announced by Korea Meteorological Administration is close to the actual rainfall, the PDF-Ratio Method shows the best result. If the past high rainfall occurrence is close to the actual rainfall, the modified PDF-Ratio method shows the best result. This method can improve the prediction accuracy even though the Korea Meteorological Administration forecast is not accurate. On the contrary, if Korea Meteorological Administration forecast is different from the actual rainfall and the past rainfall occurrence statistics of lower section, the uniform method shows the best result.

• Journal of Industrial Technology
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• v.21 no.B
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• pp.241-248
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• 2001

Normally at a flood season the operation of the dam depends on a short range weather forecast that makes many difficulties of the management at a dry season. It is needed to study the pattern of the long period rainfall. The concept of PMP(Probable Maximum Precipitation) was used for designing dam. From the concept, this study is applied the concept of monthly probable maximum precipitation for operating dam. It can be possible to let us know the appropriateness of a limiting water level at a rainy season. For the operation of dam at a dry season this study can predict roughly the flood season's pattern of precipitation by month or period, therfore the prediction of precipitation can rise efficient operation of a dam.

• Journal of Korea Water Resources Association
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• v.33 no.2
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• pp.159-167
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• 2000

The purpose of this study is to estimate the stochastic monthly runoff model for the Kunwi south station of Wi-stream basin in Nakdong river system. This model was based on the theory of Box-Jenkins multiplicative ARlMA and the state-space model to simulate changes of monthly runoff. The forecasting monthly runoff from the pair of estimated effective rainfall and observed value of runoff in the uniform interval was given less standard error then the analysis only by runoff, so this study was more rational forecasting by the use of effective rainfall and runoff. This paper analyzed the records of monthly runoff and effective rainfall, and applied the multiplicative ARlMA model and state-space model. For the P value of V AR(P) model to establish state-space theory, it used Ale value by lag time and VARMA model were established that it was findings to the constituent unit of state-space model using canonical correction coefficients. Therefore this paper confirms that state space model is very significant related with optimization factors of VARMA model.

• Atmosphere
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• v.22 no.1
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• pp.129-135
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• 2012

This study restores rainfall measurements taken with the Chugugi (rain gauge) at Wonju, Hamheung, and Haeju from the Deungnok (government records from the Joseon Dynasty). We restored rainfall data corresponding to a total of 9, 13, and 18 years for Wonju, Hamheung, and Haeju, respectively. Based on the restored data, we reconstructed monthly rainfall data. Restoration was most successful for the rainy season months of June, July and August. The restored rainfall data were compared with the summer rainfall data for Seoul as recorded by the Seungjeongwon (Royal Secretariat). In June, the variation in the restored rainfall data was similar to that of the Seungjeongwon data for Seoul. In July and August, however, the variations in the reconstructed data were markedly different from those in the Seoul data (Seungjeongwon). In the case of the worst drought in the summer of 1888, a substantial shortage of rainfall was found in both the Seungjeongwon data for Seoul and the restored data for the three regional locations.