Regional frequency analysis of extreme rainfall events in Jakarta

2014 ◽  
Vol 75 (2) ◽  
pp. 1075-1104 ◽  
Author(s):  
Jiandong Liu ◽  
Chi Dung Doan ◽  
Shie-Yui Liong ◽  
Richard Sanders ◽  
Anh Tuan Dao ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Extreme Rainfall ◽  
Regional Frequency Analysis ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Regional Frequency

scholarly journals Regional frequency analysis of extreme rainfall in Belgium based on radar estimates

2017 ◽  
Vol 21 (10) ◽  
pp. 5385-5399 ◽  
Author(s):  
Edouard Goudenhoofdt ◽  
Laurent Delobbe ◽  
Patrick Willems
Keyword(s):  
Confidence Interval ◽  
Frequency Analysis ◽  
Extreme Rainfall ◽  
Extreme Value ◽  
Regional Frequency Analysis ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Temporal Sampling ◽  
Rainfall Extremes ◽  
Regional Frequency

Abstract. In Belgium, only rain gauge time series have been used so far to study extreme rainfall at a given location. In this paper, the potential of a 12-year quantitative precipitation estimation (QPE) from a single weather radar is evaluated. For the period 2005–2016, 1 and 24 h rainfall extremes from automatic rain gauges and collocated radar estimates are compared. The peak intensities are fitted to the exponential distribution using regression in Q-Q plots with a threshold rank which minimises the mean squared error. A basic radar product used as reference exhibits unrealistic high extremes and is not suitable for extreme value analysis. For 24 h rainfall extremes, which occur partly in winter, the radar-based QPE needs a bias correction. A few missing events are caused by the wind drift associated with convective cells and strong radar signal attenuation. Differences between radar and gauge rainfall values are caused by spatial and temporal sampling, gauge underestimations and radar errors. Nonetheless the fit to the QPE data is within the confidence interval of the gauge fit, which remains large due to the short study period. A regional frequency analysis for 1 h duration is performed at the locations of four gauges with 1965–2008 records using the spatially independent QPE data in a circle of 20 km. The confidence interval of the radar fit, which is small due to the sample size, contains the gauge fit for the two closest stations from the radar. In Brussels, the radar extremes are significantly higher than the gauge rainfall extremes, but similar to those observed by an automatic gauge during the same period. The extreme statistics exhibit slight variations related to topography. The radar-based extreme value analysis can be extended to other durations.

Extreme events Analysis of Short-Duration Rainfall and Intensity-Duration-Frequency curve using Scaling Model in North Korea

2020 ◽  
Author(s):  
Younghun Jung ◽  
Kyungwon Joo ◽  
JoonHak Lee ◽  
Jun-Haeng Heo
Keyword(s):  
Climate Change ◽  
Short Duration ◽  
Frequency Analysis ◽  
21St Century ◽  
North Korea ◽  
Flood Control ◽  
Extreme Rainfall ◽  
Regional Frequency Analysis ◽  
Index Flood ◽  
Regional Frequency

<p>Climate change has emerged as one of the defining issues of the early 21st century. Recent research confirms that the imprint of human induced climate change can be recognized in current accident events. There is a high probability of observed trends, such as increases in heat waves and heavy extreme rainfall events, intensifying over the 21st century. Extreme weather and climate events are anticipated to generate significant risks to societies and ecosystem. This paper focuses on estimation rainfall quantile using sclaling model for short duration IDF curve in North Korea. It is very important to manage the flood control facilities because of increasing the frequency and magnitude of severe rain storms. For managing flood control facilities in possibly hazardous regions, data sets such as elevation, gradient, channel, land use and soil data should be filed up. Using this information, the disaster situations can be simulated to secure evacuation routes for various rainfall scenarios. The aim of this study is to investigate and determine extreme rainfall quantile estimates in North Korea Cities using index flood method with L-moments parameter estimation. Regional frequency analysis trades space for time by using annual maximum rainfall data from nearby or similar sites to determine estimates for any given site in a homogeneous region. Regional frequency analysis based on pooled data is recommended for estimation of rainfall quantiles at sites with record lengths less than 5T, where T is return period of interest. Many variables relevant to precipitation can be used for grouping a region in regional frequency analysis. For regionalization of Han River basin, the k-means method is applied for grouping regions using variables of meteorology and geomorphology. The results from the k-means method are compared for each region using various probability distributions. In the final step of the regionalization analysis, goodness-of-fit measure is used to evaluate the accuracy of a set of candidate distributions. And rainfall quantiles by index flood method are obtained based on the appropriate distribution(GEV and GLO). Therefore, it could be possible to estimate rainfall quantiles using scale invariance and frequency analysis for Wonsan, Jangjeon, and Pyeonggang rainfall stations in North Korea. And then, rainfall quantiles based on various scenarios are used as input data for disaster simulations.</p><p>Acknowledgements</p><p>This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2019R1A2C2010854).</p><p> </p>

scholarly journals Estimating probability of extreme rainfall over Japan using Extended Regional Frequency Analysis

2017 ◽  
Vol 11 (1) ◽  
pp. 19-23 ◽  
Author(s):  
Masato Sugi ◽  
Yukiko Imada ◽  
Toshiyuki Nakaegawa ◽  
Kenji Kamiguchi
Keyword(s):  
Frequency Analysis ◽  
Extreme Rainfall ◽  
Regional Frequency Analysis ◽  
Regional Frequency

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

2014 ◽  
Vol 18 (10) ◽  
pp. 4065-4076 ◽  
Author(s):  
A. G. Yilmaz ◽  
I. Hossain ◽  
B. J. C. Perera
Keyword(s):  
Climate Change ◽  
Frequency Analysis ◽  
Flash Flood ◽  
Extreme Rainfall ◽  
Rainfall Data ◽  
Decadal Climate Variability ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Climate Change And Variability ◽  
Hourly Rainfall

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 non-stationarity is present in extreme rainfall events. A methodology consisting of threshold selection, extreme rainfall data (peaks over threshold data) construction, trend and non-stationarity 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.

scholarly journals Design storm estimation in Tuscany (Italy) through regional frequency analysis and generalized additive modelling

2020 ◽  
Author(s):  
Matteo Pampaloni ◽  
Virginia Vannacci ◽  
Enrica Caporali ◽  
Chiara Bocci ◽  
Valentina Chiarello ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Central Italy ◽  
Additive Model ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Extreme Value ◽  
Regional Frequency Analysis ◽  
Annual Maximum ◽  
Design Storm ◽  
Regional Frequency

<p>In the field of extreme hydrological events, design storm identification represents key element due to the links with flood risk as well as water resources availability and management.</p><p>In order to obtain a regional frequency analysis for studying and understanding the annual maximum of daily rainfall, two different statistic methods are proposed here on Tuscany Region (Central Italy). The first method concerns with the hierarchical approach on three levels: the studied area is divided into homogeneous regions and sub-regions, then the statistical homogeneity within the regions is verified through several homogeneity tests. Furthermore, the Two-Component Extreme Value (TCEV) probability distribution of the extreme rainfall is considered identical within each homogeneous region unless a scale factor, i.e. the index rainfall, estimated through a multivariate model based on climatic and geomorphological characteristics.</p><p>A Generalized Additive Model (GAM) for extremes is also implemented on the studied area assuming that the observations follow a Generalized Extreme Value - GEV distribution whose locations are spatially dependent. The research has been carried out starting from a general set of 922 rain gauges (Regional Hydrological Service of Tuscany – SIR), on time series of annual maximum of daily rainfall recorded from 1916 to 2017. The application of the two methods is discussed based on the comparison between the maps of the design storm for daily duration and 2, 20, 50, 100 e 200 years return periods.</p>

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