Changes in the extreme daily rainfall in South Korea

Park, Jeong‐Soo; Kang, Hyun‐Suk; Lee, Youngsaeng; Kim, Maeng-Ki

doi:10.1002/joc.2236

Cited by 77 publications

(57 citation statements)

References 28 publications

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“…The general form of the cumulative distribution function of the GEV distribution and detailed information on different types of GEV (i.e. Gumbel, Frechet and Weibull distributions) can be seen in Park et al (2011).…”

Section: Stationary and Non-stationary Generalized Extreme Value Distmentioning

confidence: 99%

Extreme Rainfall Nonstationarity Investigation and Intensity–Frequency–Duration Relationship

2014

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Non-stationary behaviour of recent climate increases concerns amongst hydrologists about the currently used design rainfall estimates. Therefore, it is necessary to perform analysis to confirm stationarity or detect non-stationarity of extreme rainfall data in order to derive accurate design rainfall estimates for infrastructure projects and flood mitigation works.Extreme rainfall non-stationarity analysis of the storm durations from 6 min to 72 hours was conducted in this study using data from the Melbourne Regional Office station in Melbourne (Australia) for the period of 1925-2010. Stationary Generalized Extreme Value (GEV) models were constructed to obtain Intensity-Frequency-Duration relationships for the above storm durations using data of two time periods : 1925-1966 and 1967-2010 after identifying the year 1967 as the change point year. Design rainfall estimates of the stationary models for the two periods were compared to identify the possible changes. Non-stationary GEV models, which were developed for storm durations that showed statistically significant extreme rainfall trends, did not show advantage over stationary GEV models. There was no evidence of non-stationarity according to stationarity tests, despite the presence of statistically significant extreme rainfall trends. The developed methodology consisting of trend and nonstationarity tests, change point analysis, and stationary and non-stationary GEV models was demonstrated successfully using the data of the selected station.

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Section: Stationary and Non-stationary Generalized Extreme Value Distmentioning

confidence: 99%

Extreme Rainfall Nonstationarity Investigation and Intensity–Frequency–Duration Relationship

2014

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“…They considered nonstationarity in frequency analysis through introducing time dependency to the parameters of generalised Pareto distribution (GPD), which is one of the widely used distributions in frequency analysis of extreme values. Park et al (2011) developed non-stationary generalised extreme value (GEV) distribution (another commonly used extreme value distribution) models for frequency analysis of extreme rainfalls in Korea considering non-stationarity similar to Sugahara et al (2009). Tramblay et al (2013) performed non-stationary heavy rainfall (it should be noted that "heavy" rainfall used here as same as "extreme" rainfall in Sugahara et al, 2009) analysis using daily rainfall data of the period in France.…”

Section: A G Yilmaz Et Al: Climate Change and Variability On Rainfmentioning

confidence: 99%

Effect of climate change and variability on extreme rainfall intensity–frequency–duration relationships: a case study of Melbourne

Yilmaz

Hossain

Perera

2014

Hydrol. Earth Syst. Sci.

115

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Abstract. The increased frequency and magnitude of extreme rainfall events due to anthropogenic climate change, and decadal and multi-decadal climate variability question the stationary climate assumption. The possible violation of stationarity in climate can cause erroneous estimation of design rainfalls derived from extreme rainfall frequency analysis. This may result in significant consequences for infrastructure and flood protection projects since design rainfalls are essential input for design of these projects. Therefore, there is a need to conduct frequency analysis of extreme rainfall events in the context of non-stationarity, when nonstationarity is present in extreme rainfall events. A methodology consisting of threshold selection, extreme rainfall data (peaks over threshold data) construction, trend and nonstationarity analysis, and stationary and non-stationary generalised Pareto distribution (GPD) models was developed in this paper to investigate trends and non-stationarity in extreme rainfall events, and potential impacts of climate change and variability on intensity-frequency-duration (IFD) relationships. The methodology developed was successfully implemented using rainfall data from an observation station in Melbourne (Australia) for storm durations ranging from 6 min to 72 h. Although statistically significant trends were detected in extreme rainfall data for storm durations of 30 min, 3 h and 48 h, statistical non-stationarity tests and non-stationary GPD models did not indicate non-stationarity for these storm durations and other storm durations. It was also found that the stationary GPD models were capable of fitting extreme rainfall data for all storm durations. Furthermore, the IFD analysis showed that urban flash flood producing hourly rainfall intensities have increased over time.

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“…The longest drought (#1) lasted for 13 months from April 2008-April 2009 at Jecheon rainfall station with a drought severity of 16.04. This is because of the non-stationary precipitation pattern at Jecheon station [38]. The most severe drought (#2) occurred at Yeongju station and lasted for 12 months from March 1982-February 1983 with a drought severity of 22.30.…”

Section: Comparison Of Return Periods Using Identified Drought Eventsmentioning

confidence: 99%

Analysis of Changes in Spatio-Temporal Patterns of Drought across South Korea

Maeng

Azam

Kim³

et al. 2017

Water

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Abstract:Since the climatic features of South Korea are highly complex and time variable, spatio-temporal-based drought frequency analysis is a prerequisite for drought risk management. The spatial extent of drought risk analysis in a bivariate framework has scarcely been applied in South Korea before. In this study, the spatio-temporal changes in drought events are investigated at 55 stations across South Korea during 1980-2015. A variety of probability distributions and copulas are applied, and the best fitted is selected on the basis the goodness of fit. The spatial distributions of primary and secondary return periods showed a high risk of drought due to the unusual precipitation pattern in the western coast areas and at Uljin station and a relative low risk of drought in the northwestern portion and surrounding areas of Yeongju, Uiseong, Boeun and Daejeon stations. Overall, the spatial distribution patterns of primary and secondary (Kendall) return periods are similar. However, their applicability changes according to the type of drought risk to be considered. The spatio-temporal quantification of the return period can be used for establishing the proper water demand and supply system and helps to overcome the challenges faced in the hydrometeorological regulations of reservoirs in the southwest coast.

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Changes in the extreme daily rainfall in South Korea

Cited by 77 publications

References 28 publications

Extreme Rainfall Nonstationarity Investigation and Intensity–Frequency–Duration Relationship

Extreme Rainfall Nonstationarity Investigation and Intensity–Frequency–Duration Relationship

Effect of climate change and variability on extreme rainfall intensity–frequency–duration relationships: a case study of Melbourne

Analysis of Changes in Spatio-Temporal Patterns of Drought across South Korea

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