• 한국수자원학회논문집
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• 제43권5호
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• pp.471-482
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• 2010

수문학적 물순환과정에서 강우는 여러 기상학적 인자들과 밀접한 관련을 갖으며 발생한다. 따라서 본 연구에서는 대표적인 수문기상인자인 해수면온도와 한반도에 발생하는 월강우량 사이의 관계에 대하여 분석하였다. 우리나라의 61개 지점의 월평균 강우량과 위도 및 경도 자료를 이용하여 군집분석을 수행하였다. 군집 분석 결과에서 우리나라의 월강우자료를 이용하여 크게 4개의 군집으로 구분할 수 있었다. 군집별로 구분된 강우관측소의 월강우량 자료들을 주성분을 추출하였다. 추출된 주성분과 해수면온도와의 상관성 분석을 수행하였다. 상관성 분석 결과에서 양(+)의 상관관계가 음(-)의 상관관계보다 더 크게 나타났다. 또한, 상관관계가 가장 큰 지점의 해수면온도를 이용하여 3개월의 월강우량을 지역가중다항식을 통해 예측하였다. 지역가중다항식을 통한 예측 결과는 군집에 따라 정확성에 차이는 있으나, 정량적인 예측이 가능한 것으로 판단되었다. 따라서 해수면온도와 같은 수문기상인자를 통한 강우량의 예측에 대한 지속적인 연구가 필요하다.

• 한국농공학회지
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• 제18권4호
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• pp.4232-4241
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• 1976

This study was carried out to clarify the stochastic characteristics of monthly rainfalls and to select a proper model for generating the sequential monthly rainfall amounts. The results abtained are as follows: 1. Log-Normal distribution function is the best fit theoretical distribution function to the empirical distribution of monthly rainfall amounts. 2. Seasonal and random components are found to exist in the time series of monthly rainfall amounts and non-stationarity is shown from the correlograms. 3. The Monte Carlo model shows a tendency to underestimate the mean values and standard deviations of monthly rainfall amounts. 4. The 1st order Markov model reproduces means, standard deviations, and coefficient of skewness with an error of ten percent or less. 5. A correlogram derived from the data generated by 1st order Markov model shows the charaterstics of historical data exactly. 6. It is concluded that the 1st order Markov model is superior to the Monte Carlo model in their reproducing ability of stochastic properties of monthly rainfall amounts.

• Environmental Engineering Research
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• 제22권2호
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• pp.162-168
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• 2017

Rainfall is one of the most important phenomena of the natural system. In Bangladesh, agriculture largely depends on the intensity and variability of rainfall. Therefore, an early indication of possible rainfall can help to solve several problems related to agriculture, climate change and natural hazards like flood and drought. Rainfall forecasting could play a significant role in the planning and management of water resource systems also. In this study, univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to forecast monthly rainfall for twelve months lead-time for thirty rainfall stations of Bangladesh. The best SARIMA model was chosen based on the RMSE and normalized BIC criteria. A validation check for each station was performed on residual series. Residuals were found white noise at almost all stations. Besides, lack of fit test and normalized BIC confirms all the models were fitted satisfactorily. The predicted results from the selected models were compared with the observed data to determine prediction precision. We found that selected models predicted monthly rainfall with a reasonable accuracy. Therefore, year-long rainfall can be forecasted using these models.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2004년도 학술발표회
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• pp.1103-1106
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• 2004

The objective of this study is to induce the design drought rainfall by the methodology of L-moment including testing homogeneity, independence and outlier of the data of annual minimum monthly rainfall in 57 rainfall stations in Korea in terms of consecutive duration for 1, 2, 4, 6, 9 and 12 months. To select appropriate distribution of the data for annual minimum monthy rainfall by rainfall station, the distribution of generalized extreme value (GEV), generalized logistic (GLO) as well as that of generalized pareto (GPA) are applied and the appropriateness of the applied GEV, GLO, and GPA distribution is judged by L-moment ratio diagram and Kolmogorov-Smirnov (K-S) test. As for the annual minimum monthly rainfall measured by rainfall station and that stimulated by Monte Carlo techniques, the parameters of the appropriately selected GEV and GPA distributions are calculated by the methodology of L-moment and the design drought rainfall is induced. Through the comparative analysis of design drought rainfall induced by GEV and GPA distribution by rainfall station, the optimal design drought rainfall by rainfall station is provided.

• 한국방재학회:학술대회논문집
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• 한국방재학회 2010년도 정기 학술발표대회
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• pp.51.2-51.2
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• 2010

A novel approach of Poisson cluster stochastic rainfall generator was validated in its ability to reproduce important rainfall and watershed response characteristics at 104 locations of the United States. The suggested novel approach - The Hybrid Model(THM), as compared to the traditional ones, has an additional function to account for the year-to-year variability of rainfall statistics. The two-sample Kolmogorov-Smirnov test was used to see how well THM and traditional approach of Poisson cluster rainfall model reproduce the distribution of the following hydrologic variables: monthly maximum rainfall depths with 1, 3, 6, 12, and 24 hour duration, monthly maximum flow peaks at the virtual watersheds with Curve Number of 50, 60, 70, 80 and 90; and monthly runoff depths at the same virtual watersheds. In all of the testing variables, THM significantly outperformed the traditional approach. This result indicates that the year-to-year variability of rainfall statistics contains important information about the characteristics of rainfall processes that were not considered by the conventional approach of Poisson cluster rainfall modeling and that further considering it in rainfall simulation will enhance the performance of the rainfall models.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.

• 한국농공학회지
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• 제24권4호
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• pp.99-108
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• 1982

This thesis aims to estimate the rainfall runoff from paddy field in a small watershed during irrigation period. When the data observed at the proposed site are not available, the Monthly Runoff Equation of Korean Rivers which was derived from data observed under the following assumptions is used to study the water balance. a. Monthly base flow was assumed as 10. 2mm even if these is no mouthly rainmfall. b. Monthly comsumption of rainfall was ranged from 100 to 2OOmm without relation to the rainfall depth. However, the small watershed which consists mainly of paddy fields encounters severe droughts and accordingly the baseflow is negligible. Under the circumstances the author has developed the following equation called "Flood Irrigation Method for Rainfall Runoff "taking account of the evapotranspiration, precipitation, seepage, less of transportation, etc. R= __ A 7000(1 +F) -5n(n+1)+ (n+1)(Pr-S-Et)] where: R: runoff (ha-m) A: catchment area (ha) F: coefficient of loss (o.o-0. 20) Pr: rainfall (mm) S: seepage Er: evapotranspiration (mm) To verify the above equation, the annual runoff ratio for 28 years was estimated using the Monthly Runoff Equation of Korean Rivers the Flood Irrigation Method and the Complex Hydrograph Method based on meteorological data observed in the Dae Eyeog project area, and comparison was made with data observed in the Han River basin. Consequently, the auther has concluded that the Flood Irrigation Method is more consi- stent with the Complex Hydrograph Method and data observed than the Monthly Runoff Equation of Korean Rivers.

• 한국수자원학회논문집
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• 제44권8호
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• pp.619-628
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• 2011

본 연구의 목적은 월강우량을 이용하여 강우침식인자를 추정하는 기존의 방법인, Fournier 지수, modified Fournier 지수, IAS (Institute of Agricultural Sciences) 지수 등의 적용성을 확인하고 더 합리적인 월강우량 기반의 강우침식인자 추정모델을 제시하기 위한 것이다. 본 연구에서는 월강우량 기반의 수정 IAS 지수를 새롭게 제안하였다. 이것은 연중가장 비가 많이 내린 두 달의 강우량의 합으로써 강우침식인자를 추정하는 기존의 IAS 지수의 개념을 확장한 것이다. 본 연구에서는 25년 이상의 21개 지점에 대한 월강우량 및 연 강우침식인자를 토대로 각 추정방법에 대한 상관분석 및 회귀분석을 실시하였다. 그 결과 수정IAS 지수가 기존의 연강수량 및 월강우량을 이용한 추정방법 보다 우리나라 중서부 및 남서부 지역의 강우침식인자의 변동을 잘 나타내는 합리적인 지표임을 알 수 있었다.

• 한국방재학회 논문집
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• 제9권4호
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• pp.29-35
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• 2009

본 연구에서는 기본 통계치 비교, K-S 검정 및 상자그림과 같은 통계적 기법을 이용하여 측우기 강우량 관측계열(CWK)과 근대우량계 강우량 관측계열(MRG)에 대해 정량적 동질성 분석을 실시하였다. 측우기 관측계열과 근대우량계 관측계열간의 월별 동질성을 분석하기 위해 월강우량, 월강우량에 대한 해당월 최대 일강우량의 비, 월강우일수, 일강우강도의 4개 강우특성자료 계열을 산정하였고, 이표본 K-S 검정을 통한 분포에 대한 동질성 검정과 상자그림을 이용한 정량적 비교를 수행하였다. 분석 결과 각 분석과정에서 M00은 전체적으로 CWK와 MRG의 월강우일수간 차이에 명확한 통계적 유의성을 보이고 있어 CWK와 MRG 간의 관측정밀도에 차이가 있다고 판단된다. 그러나, CWK와 MRG의 강우량은 상대적으로 유의성이 크지 않은 미소한 차이를 보이고 있는 것으로 나타났다.

• Journal of the Korean Data and Information Science Society
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• 제24권3호
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• pp.603-623
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• 2013

최근 들어 예측하기 힘든 기후의 변동성이 심해지고 한국의 산업이 고도화됨에 따라 날씨의 변화에 능동적으로 대처하기 위해 날씨보험이나 날씨 파생상품을 활용할 수 있으나 현재 실제로 이러한 금융상품을 이용하여 날씨위험을 관리하는 데에는 많은 어려움과 한계가 있다. 본 논문에서는 다양한 강수보험의 활성화에 필요한 강수횟수와 강수량의 확률적 모델링을 통하여 여러 가지 강수 보험을 제안하고 추정된 결합분포를 통하여 보험료를 산출하려 한다. 이를 위하여 최근 30년 동안 한국 9개 지역의 7월-9월의 월 강수량과 월 강수 횟수를 확률분포에 적합하고 두 확률변수의 상관성을 코퓰라를 이용하여 분석한다. 그리고 개별분포와 추정된 코퓰라를 이용하여 시뮬레이션을 통하여 여러 가지 강수 보험의 가격을 결정하는 방법을 제안한다.