• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1447-1456
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• 2017

Accurate inference of parameters of a stochastic rainfall generation model is essential to improve the applicability of the rainfall generation model which modeled the rainfall process and the structure of rainfall events. In this study, the model parameters of a stochastic rainfall generation model, NSRPM (Neyman-Scott rectangular pulse model), were estimated using DFP (Davidon-Fletcher-Powell), GA (genetic algorithm), Nelder-Mead, and DE (differential evolution) methods. Summer season hourly rainfall data of 20 rainfall observation sites within the Nakdong river basin from 1973 to 2017 were used to estimate parameters and the regional applicability of inference methods were analyzed. Overall results demonstrated that DE and Nelder-Mead methods generate better results than that of DFP and GA methods.

• 한국방재학회:학술대회논문집
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• 2010.02a
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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.

• Magazine of the Korean Society of Agricultural Engineers
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• v.18 no.4
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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.

• Magazine of the Korean Society of Agricultural Engineers
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• v.31 no.4
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• pp.72-80
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• 1989

Various analyses were made to investigate the stochastic structure of the daily rainfall in Korea. Records of daily rainfall amounts from 1951 to 1984 at Chinju Metesrological Station were used for this study. Obtained results are as follows : 1. Time series of the daily rainfall at Chinju were positively, serially correlated for the lag as large as one day. 2. Rainfall events, defined as a sequence of consecutive wet days separated by one or more dry days, showed a seasonal variation in the occurrence frequency. 3. The marginal distribution of event characteristics of each month showed significant dif- ferences each other. Events occurred in summer had longer duration and higher magnitude with higher intensity than those of events occurred in winter. 4. There were significant positive correlations among four event characteristics ; dura- tion, magnitude, average intensity, and maximum intensity. 5. Correlations among the daily rainfall amounts within an event were not significant in general. 6. There were no consistant significancy in identity or difference between the distribu- tions of daily rainfall amounts for different days within events. 7. Above mentioned characteristics of daily rainfall time series must be considered in building a stochastic model of daily rainfall.

• Journal of Korea Water Resources Association
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• v.47 no.8
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• pp.693-701
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• 2014

In this study a stochastic rainfall generation model is used to analyze the structural variability of rainfall events since it has limitations in the traditional approach of measuring rainfall variability according to different durations. The NSRPM(Neyman-Scott Rectangular Pulse Model) is a stochastic rainfall generation model using a point process with 5 model parameters which is widely used in hydrologic fields. The five model parameters have physical meaning associated with rainfall events. The model parameters were estimated using hourly rainfall data from 1973 to 2011 at Seoul stations. The variability of model parameter estimates was analyzed and compared with results of traditional analysis.

• Journal of Korea Water Resources Association
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• v.50 no.11
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• pp.735-743
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• 2017

With growing concerns about ever-increasing anthropogenic greenhouse gas emissions, it is crucial to enhance preparedness for unprecedented extreme weathers that can bring catastrophic consequences. In this study, we proposed a stochastic framework that considers uncertainty in weather forecasts for flood analyses. First, we calibrated a simple rainfall-runoff model against observed hourly hydrographs. Then, using probability density functions of rainfall depths conditioned by 6-hourly weather forecasts, we generated many stochastic rainfall depths for upcoming 48 hours. We disaggregated the stochastic 6-hour rainfalls into an hourly scale, and input them into the runoff model to quantify a probabilistic range of runoff during upcoming 48 hours. Under this framework, we assessed two rainfall events occurred in Bocheong River Basin, South Korea in 2017. It is indicated actual flood events could be greater than expectations from weather forecasts in some cases; however, the probabilistic runoff range could be intuitive information for managing flood risks before events. This study suggests combining deterministic and stochastic methods for forecast-based flood analyses to consider uncertainty in weather forecasts.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.3
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• pp.821-831
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• 2014

Stochastic rainfall generators or stochastic simulation have been widely employed to generate synthetic rainfall sequences which can be used in hydrologic models as inputs. The calibration of Poisson cluster stochastic rainfall generator (e.g. Modified Bartlett-Lewis Rectangular Pulse, MBLRP) is seriously affected by local minima that is usually estimated from the local optimization algorithm. In this regard, global optimization techniques such as particle swarm optimization and shuffled complex evolution algorithm have been proposed to better estimate the parameters. Although the global search algorithm is designed to avoid the local minima, reliable parameter estimation of MBLRP model is not always feasible especially in a limited parameter space. In addition, uncertainty associated with parameters in the MBLRP rainfall generator has not been properly addressed yet. In this sense, this study aims to develop and test a Bayesian model based parameter estimation method for the MBLRP rainfall generator that allow us to derive the posterior distribution of the model parameters. It was found that the HBM based MBLRP model showed better performance in terms of reproducing rainfall statistic and underlying distribution of hourly rainfall series.

• Journal of Korean Society on Water Environment
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• v.24 no.1
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• pp.99-106
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• 2008

With globally increasing interests in climate-soil-vegetation system, a new stochastic model of soil water and plant water stress is derived for better understanding of the soil water and plant water stress dynamics and their role in water-controlled ecosystem. The steady-state assumption is used for simplifying the equations. The derived model is simple yet realistic that it can account for the essential features of the system. The model represents the general characteristics of rainfall, soil, and vegetation; i.e. the soil moisture constitutes the decrease form of the steady-state and the plant water stress becomes increasing with the steady state when the rainfall is decreased. With this model, further deep study for the effects of soil water and plant water stress on the system will be accomplished.

• Journal of Korea Water Resources Association
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• v.35 no.2
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• pp.187-194
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• 2002

To improve the accuracy of the areal rainfall estimates over a river basin, the optimal design method of rainfall network was studied using the stochastic characteristics of measured rainfall data. The objective function was constructed with the estimation error of areal rainfall and observation cost of point rainfall and the observation sites with minimum objective function value were selected as the optimal network. As a stochastic variance estimator, kriging model was selected to minimize the error terms. The annual operation cost including the installation cost was considered as the cost terms and an accuracy equivalent parameter was used to combine the error and cost terms. The optimal design method of rainfall network was studied in the Yongdam dam basin whose raingauge numbers need to be enlarged for the optimal rainfall networks of the basin.

• Journal of Wetlands Research
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• v.20 no.4
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• pp.345-352
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• 2018

In this study, rainfall data with various temporal scales (3-, 6-, 12-, 24-hr) are disaggregated into 1-hourly rainfall data to evaluate the performance of rainfall disaggregation technique. The rainfall disaggregation technique is based on a database generated by the stochastic point rainfall model, the Neyman-Scott Rectangular Pulse Model (NSRPM). Performance evaluation is carried out using July rainfall data of Ulsan, Changwon, Busan and Milyang weather stations in Korea. As a result, the rainfall disaggregation technique showed excellent performance that can consider not only the major statistics of rainfall but also the spatial correlation. It also indirectly shows the uncertainty of future climate change scenarios with daily temporal scale. The rainfall disaggregation technique is expected to disaggregate the future climate change scenarios, and to be effective in the future watershed management.