applied hydrology regional frequency analysis - example
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Applied Hydrology Regional Frequency Analysis - Example. Prof. Ke-Sheng Cheng Department of Bioenvironmental Systems Engineering National Taiwan University. Estimating the return period of region-wide catastrophic rainfalls. Ke-Sheng Cheng, Tsong-Hsiun Lien, Guan-Ming Su - PowerPoint PPT PresentationTRANSCRIPT
Applied Hydrology
Regional Frequency Analysis - Example
Prof. Ke-Sheng ChengDepartment of Bioenvironmental Systems Engineering
National Taiwan University
Estimating the return period of region-wide catastrophic rainfalls
Ke-Sheng Cheng, Tsong-Hsiun Lien, Guan-Ming SuNational Taiwan University
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Introduction• Occurrences of extraordinary rainfalls can
complicate the work of hydrological frequency analysis.– Examples in Taiwan (Typhoon Morakot, 2009)
• Jia-Sien – 1040mm/24hr, 1601mm/48hr, 1856mm/72hr• Weiliaoshan – 1415mm/24hr, 2216mm/48hr,
2564mm/72hr
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• Frequency analysis of 24-hr annual maximum rainfalls (AMR) at Jia-Sien station using 50 years of historical data– 1040mm/24 hours (by Morakot) excluding Morakot
– 901 years return period – Return period inclusive of Morakot – 171 years
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The same amount (1040mm/24 hours) was found to be associated with a return period of more than 2000 years by another study which used 25 years of annual maximum rainfalls.
• Extraordinary rainfalls are extreme outliers. • Whether outliers should be included/excluded
in frequency is arguable. • Random characteristics of extraordinary
rainfalls– Occurrences of extraordinary rainfalls are very rare.– Within a not-too-long period, the probability of having repeated
occurrences of extraordinary rainfalls at one station is very low. However, extraordinary rainfalls can occur at different locations.
• Regional frequency analysis (RFA) is adopted to deal with presence of extraordinary rainfalls in frequency analysis.
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• Previous studies have suggested that RFA performs better than the site-specific frequency analysis. However, how much confidence do we have?
• The main objectives of this study– To estimate the return period of catastrophic
rainfalls using regional frequency analysis– To demonstrate the superior performance of RFA
using stochastic simulation.
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General procedures ofregional frequency analysis
1. Data screening– Correctness check– Data should be stationary over time.
2. Identifying homogeneous regions– A set of characteristic variables are used for
delineation of homogeneous regions. – Homogeneous regions are often determined by
cluster analysis.
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3. Choice of an appropriate regional frequency distribution (GOF test)
– GOF test using rescaled samples from different sites within the same homogeneous region.
– The chosen distribution not only should fit the data well but also yield quantile estimates that are robust to physically plausible deviations of the true frequency distribution from the chosen frequency distribution.
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4. Parameter estimation of the regional frequency distribution
– Estimating parameters of the site-specific frequency distribution.
– Estimating parameters of the regional frequency distribution using record-length weighted average.
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Study area and rainfall stations
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28 rainfall stations in southern Taiwan. (1951 – 2010)Not all stations have the same record length.Annual maximum rainfalls (AMR) of various durations (1, 2, 6, 12, 18, 24, 48, 72 hours)
• Homogeneous regions identification using Cluster analysis – Characteristic variables: Mean, standard deviation
and coeff. of skewness of annual maximum rainfalls.– Cluster analysis was conducted for AMR of various
durations.– Two homogeneous regions with 21 satations (region
I) and 7 stations (region II), respectively, were identified.
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(Mean, std dev, skewness) space of the gamma density
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A 3-parameter distribution
Regional frequency analysis• Delineating homogeneous regions
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Hot spots for occurrences of extreme rainfalls
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1992 – 2010Number of extreme typhoon events
Choice of an appropriate regional frequency distribution (GOF test)
• Site-specific rescaled annual max rainfalls– Rescaled with respect to site-specific mean and
standard deviation• Rescaled AMR is equivalent to the frequency factor, K.• Rescaled AMR can be considered as an index variable
with zero expectation and unity standard deviation.– Other studies also used as the index
variable.
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/)( X
)/( X
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• Region I – Extreme value type I (EV1) distribution
• Region II – Log Pearson type III (LPT3) distribution– Considering the results of GOF tests for AMR of
various durations– AIC, BIC and HQIC values were calculated for best-fit
model selection.
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Regional frequency analysisparameter estimation
• Method of L-moments for site-specific parameter estimation
• Regional parameter estimation• Establishing regional growth curves for
individual homogeneous regions– Region 1: Extreme Value type I– Region 2: Log Pearson type III
(Model selection was based on the criterion of loss of information using AIC, BIC and HQIC.)
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RFA results index variable
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/)( X
RFA resultsindex variable
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/X
• 24-hr, 100-year rainfall at the Jia-Sien station– 1018 mm (using (X-)/ as the index variable)
• The 24-hr rainfall of Morakot (1040 mm) is associated with a return period of 115 years.
– 1648 mm (using (X/) as the index variable)
• Site-specific frequency analysis
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Which index variable performs better?
Does RFA really perform better than the site-specific freq analysis? Or, just by chance?
Stochastic simulation• Simulating n years (same as the record length
of the historical data) of annual maximum rainfalls at each individual station, using site-specific distribution parameters. Such simulated data set is called a block of simulated samples.
• Generating 1000 blocks of simulated samples.• Conducting site-specific frequency analysis and
RFA for each block of simulated samples. • Calculating 24-hr rainfalls of 5, 20, 50, 100, 200
years return period for each block of simulated samples.06/25/2013 2013 AOGS Conference 24
• RMSE comparison
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RFA Site-specific RFA Site-specific
using (X/) as the index variable using (X-)/ as the index variable
Probability for RFA (using (X-)/ as the index variable) being superior = 0.77.
Further study• Modeling dependence of extraordinary rainfall
occurrences at different stations.
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Conclusions• Regional frequency analysis using (X-)/ as
the index variable is recommended to deal with extraordinary rainfalls (extreme outliers).
• It has been demonstrated through stochastic simulation that there is a high probability (0.77 in our study) that RFA performs better than site-specific frequency analysis.
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Thanks for listening.Your comments and suggestions
are most welcome.
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