scholarly journals Introduction of Confidence Interval Based on Probability Limit Method Test into Non-Stationary Hydrological Frequency Analysis

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2727
Author(s):  
Keita Shimizu ◽  
Tadashi Yamada ◽  
Tomohito J. Yamada
Keyword(s):  
Confidence Interval ◽  
Frequency Analysis ◽  
Extreme Value Distribution ◽  
Extreme Rainfall ◽  
Extreme Value ◽  
Value Distribution ◽  
Distribution Model ◽  
Probability Limit ◽  
Time Variations ◽  
Hydrological Frequency Analysis

Nonstationarity in hydrological variables has been identified throughout Japan in recent years. As a result, the reliability of designs derived from using method based on the assumption of stationary might deteriorate. Non-stationary hydrological frequency analysis is among the measures to counter this possibility. Using this method, time variations in the probable hydrological quantity can be estimated using a non-stationary extreme value distribution model with time as an explanatory variable. In this study, we build a new method for constructing the confidence interval regarding the non-stationary extreme value distribution by applying a theory of probability limit method test. Furthermore, by introducing a confidence interval based on probability limit method test into the non-stationary hydrological frequency analysis, uncertainty in design rainfall because of lack of observation information was quantified, and it is shown that assessment pertaining to both the occurrence risk of extremely heavy rainfall and changes in the trend of extreme rainfall accompanied with climate change is possible.

Performance of L-moments and nonparametric flood frequency analysis

1994 ◽  
Vol 21 (5) ◽  
pp. 856-862 ◽  
Author(s):  
Denis Gingras ◽  
Kaz Adamowski
Keyword(s):  
Frequency Analysis ◽  
Extreme Value Distribution ◽  
Nonparametric Methods ◽  
Extreme Value ◽  
Flood Frequency ◽  
Value Distribution ◽  
Generalized Extreme Value Distribution ◽  
Flood Frequency Analysis ◽  
Generalized Extreme Value ◽  
L Moments

A simulation study was undertaken to compare parametric L-moments and nonparametric approaches in flood frequency analysis. Data of various sample lengths were generated from a given generalized extreme value distribution and the quantiles estimated using the fixed-kernel nonparametric method and from a generalized extreme value distribution fitted by L-moments. From the resulting root-mean-square errors for various quantiles, it was concluded for unimodal distributions that nonparametric methods are preferable for large return period floods estimated from short (<30 years) samples while parametric methods are preferable in other circumstances. It was also pointed out that nonparametric methods are more suitable for mixed distributions. Key words: frequency analysis, L-moments, nonparametric methods, simulation.

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.

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