Probable Maximum Precipitation Estimation Using the Revised Km-Value Method in Hong Kong

Lan, Ping; Lin, Bingzhang; Zhang, Shaodong; Chen, Hong

doi:10.1061/(asce)he.1943-5584.0001517

Cited by 20 publications

(10 citation statements)

References 8 publications

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“…And based on scaling concepts determined the PMP for sub-daily (hourly) durations. Lan et al (2017) indicated that using a standardized factor denoted by Φ is more appropriate for estimating PMP than using Km for China, although other studies (e.g. Lin and Vogel, 1993) indicated the opposite.…”

Section: Traditional Statistical Methodsmentioning

confidence: 99%

PMP and Climate Variability and Change: A Review

Salas

Anderson²,

Papalexiou

et al. 2020

J. Hydrol. Eng.

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A state-of-the-art review on the Probable Maximum Precipitation (PMP) as it relates to climate variability and change is presented. The review consists of an examination of the current practice and the various developments published in literature. The focus is on relevant research where the effect of climate dynamics on the PMP are discussed as well as statistical methods developed for estimating very large extreme precipitation including the PMP. Often confusion arises on the interpretation of extreme events without considering the effect of low frequency components of the climate system, their probabilistic nature that may be described by heavy-tail models, and the effect of the uncertainty of several factors determining them, such as atmospheric moisture, its transport into storms, wind, and their future changes. The review examines these issues as well as the underlying historical and proxy data. In addition, we summarize the procedures and guidelines established by some countries (e.g. USA, Australia, Canada, UK, EU, and others), states (e.g. California, Quebec), and the current manual of the World Meteorological Organization for estimating the PMP. In doing so, we paid attention whether the current guidelines and research published literature take into consideration the effects of the variability and change of climatic processes and the underlying uncertainties.

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Section: Traditional Statistical Methodsmentioning

confidence: 99%

PMP and Climate Variability and Change: A Review

Salas

Anderson²,

Papalexiou

et al. 2020

J. Hydrol. Eng.

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“…384.0 mm (956.0 mm) recorded at Lantau (Tai Mo Mountain) in Hong Kong but is smaller than the world record. Besides, they are smaller compared to the PMPs estimated by a revised Km-value method in Hong Kong (i.e., 558.5 mm for 4-h and 1,753.0 mm for 24-h) [49,50]. However, the PMP estimates obtained here so far are actually just embryonic PMPs, so more comparisons should be made after conducting some necessary adjustments such as moisture maximization.…”

Section: Rainfall Categorymentioning

confidence: 81%

A New Look at Storm Separation Technique in Estimation of Probable Maximum Precipitation in Mountainous Areas

Yi-fan

Lin

Chen

et al. 2020

Water

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Storm separation is a key step when carrying out storm transposition analysis for Probable Maximum Precipitation (PMP) estimation in mountainous areas. The World Meteorological Organization (WMO) has recommended the step-duration-orographic-intensification-factor (SDOIF) method since 2009 as an effective storm separation technique to identify the amounts of precipitation caused by topography from those caused by atmospheric dynamics. The orographic intensification factors (OIFs) are usually developed based on annual maximum rainfall series under such assumption that the mechanism of annual maximum rainfalls is close to that of the PMP-level rainfall. In this paper, an alternative storm separation technique using rainfall quantiles, instead of annual maximum rainfalls, with rare return periods estimated via Regional L-moments Analysis (RLMA) to calculate the OIFs is proposed. Based on Taiwan’s historical 4- and 24-h precipitation data, comparisons of the OIFs obtained from annual maximum rainfalls with that from extreme rainfall quantiles at different return periods, as well as the PMP estimates of Hong Kong from transposing the different corresponding separated nonorographic rainfalls, were conducted. The results show that the OIFs obtained from rainfall quantiles with certain rare probabilities are more stable and reasonable in terms of stability and spatial distribution pattern.

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“…For example, Koutsoyiannis & Papalexiou 14 proposed nomographs for estimating the k m value. Lan et al 9 used a standardized variable, defined as the maximum deviation from the mean of a sample scaled by the standard deviation of the sample to replace the k m factor and found it to be more reasonable., Koutsoyiannis 25 fitted a generalized extreme value (GEV) to the frequency factor values for the same data set as used by Hershfield and found that the k m value of 15 corresponded to a 60,000 year return period PMP. This shows that there can be a large variation in the value of PMP resulting from the frequency factor value.…”

Section: Methods For Computing Pmpmentioning

confidence: 99%

Computation of probable maximum precipitation and its uncertainty

Singh

2018

IJH

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Probable Maximum Precipitation (PMP) is used for estimating Probable Maximum Flood (PMF) which, in turn, is used for design of major hydraulic structures, such as dams and spillways, flood protection works, and nuclear power plants. One of the commonly used methods for estimating PMP is the statistical method, also called Hershfield method that entails computation of frequency factor, adjustment of the frequency factor, construction of an enveloping curve of the frequency factor, estimation of PMP, choosing a probability distribution of PMP, and determination of the return period of PMP. There are, however, uncertainties associated with the PMP values estimated using the statistical method. This study determined the PMP values for different durations using the statistical method with data from the Brazos River basin, Texas. It was found that significant uncertainty in the PMP estimates can occur with the use of enveloping curve of the frequency factor and the number of stations involved in its construction. Hershfield's curve yielded higher frequency factor values by 16% for 1 hour duration, by 17.9% for 6 hour duration, and by 22.1% for 24 hour duration. In comparison with basin-specific values, the PMP values from the Hershfield enveloping curve were 16.8% higher for 1-hour duration, 18.5% for 6-hour duration, and 23.4% for 24-hour duration. For most of the Brazos River basin the return period of the PMP values was in the range of 1000 to 3000 years which was less than the range of 103 to 106 years reported in HMR 51, showing the degree of risk associated with the PMP values. Therefore, a basin specific-enveloping curve is suggested. From 24 commonly used statistical distributions and 5 goodness of fit tests, the Burr Type XII distribution was found to be the best frequency distribution for describing PMP. It was observed that the return period obtained from the Burr type XII frequency distribution was not significantly higher than that obtained from the hydreometeorological reports (HMRs) of National Weather Service and other studies. .

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Probable Maximum Precipitation Estimation Using the Revised Km-Value Method in Hong Kong

Cited by 20 publications

References 8 publications

PMP and Climate Variability and Change: A Review

PMP and Climate Variability and Change: A Review

A New Look at Storm Separation Technique in Estimation of Probable Maximum Precipitation in Mountainous Areas

Computation of probable maximum precipitation and its uncertainty

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