Estimating precipitation extremes using the log‐histospline

Huang, Whitney K.; Nychka, Douglas; Zhang, Hao

doi:10.1002/env.2543

Cited by 20 publications

(15 citation statements)

References 69 publications

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“…This analysis is conducted on the full history of the Houston Hobby Airport station, and the threshold used for the GPD fit is our chosen 25.4 mm (1 inch). The selection of Hobby for this example is due to its long period of record (about 87 years), its central location in Houston, and its use in other literature on the Houston area (Huang et al 2019). The results indicate that the GPD provides higher estimates of return period rainfall than either the GEV annual or quarterly max (Table 1).…”

Section: Comparison Of Methodsmentioning

confidence: 98%

“…For example, Naveau et al (2016) propose an extended GPD model which allows the user to not have to select a threshold. Another uses a log-histospline approach to look at the full range of data as opposed to just the upper tail (Huang et al 2019). We find these models do not fit our data as well or their methods are too complicated for the goals of this analysis.…”

Section: Introductionmentioning

confidence: 89%

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Characterizing spatiotemporal trends in extreme precipitation in Southeast Texas

et al. 2020

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Rainfall extreme value analysis provides information that has been crucial in characterizing risk, designing successful infrastructure systems, and ultimately protecting people and property from the threat of rainfall-induced flooding. However, in the Houston region recent events (such as the unprecedented rainfall wrought by Hurricane Harvey) have highlighted the inability of existing analyses to accurately characterize current climate conditions. Specifically, there has been little research investigating how spatial patterns of extreme precipitation have shifted through time in the Texas Gulf Coast region, which has led to mis-characterization of existing intensity–duration–frequency curves. This study investigates spatiotemporal trends in extreme precipitation in southeast Texas using a statistical approach for peaks-over-threshold modeling that employs a generalized Pareto distribution. Precipitation data from over 600 rain gauges across the region are analyzed in 40-year time windows to evaluate shifts in distribution parameters and extreme rainfall levels through time. Spatial analysis of these trends focuses on highlighting regions with increasing, stationary, and decreasing extreme rainfall through time. Results demonstrate heterogeneity in spatiotemporal trends across the entire study region, but significant increases in extreme rainfall over the Houston urban area. Spatial analysis of these trends focuses on how extreme rainfall has changed within different watersheds. Return level estimates of extreme rainfall values are also compared to the current standards for Harris County. Results from this study identify areas that have experienced significant shifts in extreme rainfall, and can help inform where design standards may be inaccurate or outdated.

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Section: Comparison Of Methodsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 89%

Characterizing spatiotemporal trends in extreme precipitation in Southeast Texas

et al. 2020

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“…It can be explained by the contemporary climate change scenarios that predict a significant increase in the frequency of high intensity rainfall events, primarily in the dry areas. Moreover, precipitation can induce shallow landslides [52,53] and debris flows [54].…”

Section: Comparison Of Gg-based Statistical Test and Peaks Over Thresmentioning

confidence: 99%

Probability Models and Statistical Tests for Extreme Precipitation Based on Generalized Negative Binomial Distributions

Korolev

Gorshenin

2020

Mathematics

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Mathematical models are proposed for statistical regularities of maximum daily precipitation within a wet period and total precipitation volume per wet period. The proposed models are based on the generalized negative binomial (GNB) distribution of the duration of a wet period. The GNB distribution is a mixed Poisson distribution, the mixing distribution being generalized gamma (GG). The GNB distribution demonstrates excellent fit with real data of durations of wet periods measured in days. By means of limit theorems for statistics constructed from samples with random sizes having the GNB distribution, asymptotic approximations are proposed for the distributions of maximum daily precipitation volume within a wet period and total precipitation volume for a wet period. It is shown that the exponent power parameter in the mixing GG distribution matches slow global climate trends. The bounds for the accuracy of the proposed approximations are presented. Several tests for daily precipitation, total precipitation volume and precipitation intensities to be abnormally extremal are proposed and compared to the traditional PoT-method. The results of the application of this test to real data are presented.

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“…Using nonparametric methods for estimating precipitation extremes appears to be particularly challenging. The estimator (9) in which is estimated using spline methods seems to be particularly promising for this aim [19].…”

Section: Applications To Precipitation Estimationmentioning

confidence: 99%