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

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.189-189
• /
• 2015

본 연구는 기후변화의 영향을 고려한 포아송 강우생성모형의 일종인 MBLRP(Modified Bartlett-Lewis Rectangular Pulse)를 개발하고, 대한민국 주요 도시에 대해 향후 100년간 강우의 변화를 살펴보았다. 기존 MBLRP 모형에서 기후변화에 따른 강우량 변화를 고려할 수 있도록 GCM 모형의 강우 자료를 활용하였고, GCM 모형으로부터 발생하는 불확실성을 고려하기 위해 IPCC의 RCP(Representative Concentration Pathways) 시나리오를 모의한 16개의 GCM 모형을 사용하였다. 2007년부터 2099년까지의 미래기간을 3개의 시 구간으로 구분하고, 16개 GCM 앙상블을 사용하여 미래기간 동안 대한민국 16개 도시에 대해 1000개의 샘플을 BWA 방법을 이용하여 생성하였다. 제어기간(1973-2005) 대비 미래기간(2007-2099)의 변화율을 나타내는 FOC(factor of change)와 온도의 연별 변화율을 나타내는 SF(scaling factor)의 개념을 결합하여 미래기간에 대한 CF(correction factor)를 산정하였다. 이때 CF는 16개 도시의 연 단위 강우량 변화 비율을 월별로 나타내며, 제어기간의 월 강우 관측치와 CF를 몬테카를로 모의를 실시하여 미래기간의 강우 시나리오를 산정한다. 이를 통해 월 평균 강우량 통계치를 연 단위로 얻을 수 있으며, 월 평균 강우량이 월 평균 분산, 무강우확률, 자기상관계수와 가지는 선형 관계를 통해 강우 통계치를 산출한다. 이와 같은 강우 통계치는 가상강우생성모형인 MBLRP 모형에 입력 자료로 활용되어 월 강우량을 시 단위의 강우 시계열 자료로 생성해낸다. 최종적으로 MBLRP 모형으로 산정된 시 단위 강우 시계열은 기후변화 영향을 고려한 GCMs 앙상블로 생성된 강우 시나리오를 기반으로 산출되기 때문에 향후 수자원 분석에 활용 가능할 것이라 기대된다.

• Journal of Korea Water Resources Association
• /
• v.46 no.5
• /
• pp.439-447
• /
• 2013

The applicability of the parameter map of the Modified Bartlett-Lewis Rectangular Pulse (MBLRP) model for the Korean Peninsula was assessed from the perspective of flood prediction. The design rainfalls estimated from the MBLRP model were smaller than those from observed values by 5% to 40%, and the degree of underestimation of design rainfall increases with the increase of the recurrence interval of the design rainfall. The design floods at a virtual watershed estimated using the simulated rainfall time series based on MBLRP model were also smaller than those derived from the observed rainfall time series by 20% to 45%. The degree of underestimation of design flood increases with the increase of the recurrence interval of the design flood.

• Journal of Korea Water Resources Association
• /
• v.48 no.9
• /
• pp.729-741
• /
• 2015

This study examined the applicability of MBLRP (Modified Bartlett-Lewis Rectangular Pulse) rainfall generation model for an urban flood simulation which is a type of Poisson cluster rainfall generation model. This study constructed XP-SWMM model for Namgajwa area of Hongjecheon basin, which is a two-dimensional pipe network-surface flood simulation program and computed a flood discharge and a flooded area with input data of synthetic rainfall time series of 200 years that were generated by the MBLRP model. This study compared the data of flood with synthetic rainfall and flood with corresponding values which were based on design rainfall. The results showed that the flooded area computed with MBLRP model was somewhat smaller than the corresponding values on the basis of the design. A degree of underestimation was from 8% (5 year) to 34% (200 year) and the degree of underestimation increased as a return period increased. This study is meaningful in that it proposes methodology that enables quantifiability of uncertain variables which are related to a flooding through Monte Carlo analysis of urban flooding simulation and applicability and limitations thereof.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.11-11
• /
• 2018

본 연구에서는 1시간부터 1년 단위의 강우 특성들을 잘 모의하는 혼합 추계 강우 생성 모형을 개발하였다. 본 모형의 가상 강우 생성 과정은 4단계로 이루어진다. 첫 단계에서 Seasonal ARIMA 모형을 통하여 시계열 특성을 반영한 월 강우를 생성한다. 두 번째 단계는 생성된 월 강우에 해당하는 일 단위 이하의 강우 통계치 세트를 생성하는 것이며, 통계치간 상관관계를 통해 평균, 표준편차, 자기상관 계수, 무강우 확률을 생성한다. 생성된 통계치 세트는 세 번째 단계에서 Modified Bartlett-Lewis Rectangular Pulse (MBLRP) 모형의 6개의 매개변수를 보정하는데 사용되며, 마지막으로 MBLRP 매개변수 세트를 통해 가상 강우 시계열을 생성한다. 위 모형을 통해 미국 동부 지역 29개 강우 관측소에 대하여 200년 길이의 가상 강우를 생성하였으며, 그 결과 시 단위부터 연 단위까지 강우의 1차, 2차 통계치 및 무강우 확률을 성공적으로 재현하였다. 또한 기존 MBLRP 모형에 비하여 극한 강우 사상을 재현하는 능력이 향상되었다. 빈도분석 결과를 통하여 MBLRP 모형이 재현기간에 따라 10%에서부터 40%까지 극한 사상을 과소 추정한 반면, 본 모형에서는 20% 이내의 값을 나타내었다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.3
• /
• pp.773-784
• /
• 2014

This study evaluated the applicability of the Modified Bartlett-Lewis Rectangular Pulse (MBLRP) rainfall generation model for modeling extreme rainfalls and floods in Korean Peninsula. Firstly, using the ISPSO (Isolated Species Particle Swarm Optimization) method, the parameters of the MBLRP model were estimated at the 61 ASOS (Automatic Surface Observation System) rain gauges located across Korean Peninsula. Then, the synthetic rainfall time series with the length of 100 years were generated using the MBLRP model for each of the rain gauges. Finally, design rainfalls and design floods with various recurrence intervals were estimated based on the generated synthetic rainfall time series, which were compared to the values based on the observed rainfall time series. The results of the comparison indicate that the design rainfalls based on the synthetic rainfall time series were smaller than the ones based on the observation by 20% to 42%. The amount of underestimation increased with the increase of return period. In case of the design floods, the degree of underestimation was 31% to 50%, which increases along with the return period of flood and the curve number of basin.

• Journal of Korea Water Resources Association
• /
• v.49 no.4
• /
• pp.335-346
• /
• 2016

This study produced the parameter maps of the Modified Bartlett-Lewis Rectangular Pulse (MBLRP) stochastic rainfall generation model across South Korea and developed and validated the web application that automates the process of rainfall generation based on the produced parameter maps. To achieve this purpose, three deferent sets of parameters of the MBLRP model were estimated at 62 ground gage locations in South Korea depending on the distinct purpose of the synthetic rainfall time series to be used in hydrologic modeling (i.e. flood modeling, runoff modeling, and general purpose). The estimated parameters were spatially interpolated using the Ordinary Kriging method to produce the parameter maps across South Korea. Then, a web application has been developed to automate the process of synthetic rainfall generation based on the parameter maps. For validation, the synthetic rainfall time series has been created using the web application and then various rainfall statistics including mean, variance, autocorrelation, probability of zero rainfall, extreme rainfall, extreme flood, and runoff depth were calculated, then these values were compared to the ones based on the observed rainfall time series. The mean, variance, autocorrelation, and probability of zero rainfall of the synthetic rainfall were similar to the ones of the observed rainfall while the extreme rainfall and extreme flood value were smaller than the ones derived from the observed rainfall by the degree of 16%-40%. Lastly, the web application developed in this study automates the entire process of synthetic rainfall generation, so we expect the application to be used in a variety of hydrologic analysis needing rainfall data.

• Journal of Korea Water Resources Association
• /
• v.41 no.4
• /
• pp.379-394
• /
• 2008

Recently, extreme precipitation events beyond design capacity of hydraulic system have been occurred and this is the causes of failure of hydraulic structure for flood prevention and of severe flood damage. Therefore it is very important to understand temporal and spatial characteristics of extreme precipitation events as well as expected changes in extreme precipitation events and distributional characteristics during design period under future climate change. In this paper, climate change scenarios were used to assess the impacts of future climate change on extreme precipitation. Furthermore, analysis of future extreme precipitation characteristics and I-D-F analysis were carried out. This study used SRES B2 greenhouse gas scenario and YONU CGCM to simulate climatic conditions from 2031 to 2050 and statistical downscaling method was applied to establish weather data from each of observation sites operated by the Korean Meteorological Administration. Then quantile mapping of bias correction methods was carried out by comparing the simulated data with observations for bias correction. In addition Modified Bartlett Lewis Rectangular Pulse(MBLRP) model (Onof and Wheater, 1993; Onof 2000) and adjust method were applied to transform daily precipitation time series data into hourly time series data. Finally, rainfall intensity, duration, and frequency were calculated to draw I-D-F curve. Although there are 66 observation sites in Korea, we consider here the results from only Seoul, Daegu, Jeonju, and Gwangju sites in this paper. From the results we found that the rainfall intensity will be increased and the bigger intensity will be occurred for longer rainfall duration when we compare the climate conditions of 2030s with present conditions.