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Extreme Value Analysis of Short-Duration Rainfall and Intensity–Duration–Frequency Models

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 26928

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Special Issue Editor

Dr. Hans Van de Vyver

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Guest Editor

Royal Meteorological Institute of Belgium, Ringlaan 3, Uccle, Brussels B1180, Belgium
Interests: extreme value analysis; weather and climate extremes; IDF-models; spatial precipitation extremes; drought; past and future climate change

Special Issue Information

Dear Colleagues,

Extreme rainfall events have a large impact on society and can lead to loss of life and property, for example, by causing landslides or flooding due to dike breach or dam failures. For planning, design, and operation of water resources projects, the estimation of flood risks often relies on the statistics of extreme precipitation.

The main aim is to develop methodologies and applications for the assessment of past and future characteristics of (short-duration) rainfall extremes. In particular, we welcome research findings in the form of intensity–duration–frequency (IDF) models. The research is not only relevant at the local scale, but also at the catchment or the global scales.

The research activities include a wide range of expertise, and may focus on (i) analysis of temporal or spatial trends in extreme rainfall intensities, (ii) the estimation of the impact of climate change on future climate IDF relationships, with associated uncertainties, (iii) the estimation of IDF curves at ungauged sites by means of spatial extreme value models, scale invariance properties, or any other methodology or framework, (iv) the conversion of IDF characteristics at the local scale to catchment-average rainfall intensity, (v) the use of alternative rainfall datasets, i.e., other than rain gauge measurements, such as remote sensing rainfall records, and (vi) any other advanced statistical methodology such as multivariate extreme value theory to estimate joint probabilities between extreme rainfall intensities and other meteorological conditions.

Dr. Hans Van de Vyver
Guest Editor

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Keywords

intensity–duration–frequency curves
design storms
subdaily precipitation extremes
scale invariance
extreme value theory
past and future precipitation extremes
spatial extremes
downscaling
uncertainty analysis

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Published Papers (6 papers)

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Research

20 pages, 30118 KiB

Open AccessArticle

Cloud Physical and Climatological Factors for the Determination of Rain Intensity

by Bengt Dahlström

Water 2021, 13(16), 2292; https://doi.org/10.3390/w13162292 - 21 Aug 2021

Cited by 3 | Viewed by 2875

Abstract

The focus of this research is to develop a general method for estimation of rain intensity for application in various geographical regions. In a world with a changing climate, a high importance is attributed to the potential threats caused by increased temperature and rainfall intensity levels. The rainfall intensity climate is here interpreted by a combination of cloud physical factors affecting rain intensity and further developed by the use of climate data and rain intensity statistics. A formula was developed that estimates extreme rainfall and the frequency of these extremes with durations in the intervals of 5 min to 24 h. The obtained estimates are compared in this article with results from statistical methods for the extreme value analysis of measurements. The comparison shows about 90% of the explained variance. The coefficients in the formula are connected with climatological predictors based on the climatological norms of temperature and rainfall. Rain intensity maps over Sweden were produced using the developed formula. Examples of the function of the formula are also given for six European countries. The application of the formula in connection with the probable maximum precipitation (PMP) is presented, where the return period of extreme rainfall is a key factor. The formula is tested with an assumed increased warming of the atmosphere of 1 to 5 °C, and the result indicates an increase of 5.9% of the rainfall amount per each warming degree in intense rainfall. Full article

(This article belongs to the Special Issue Extreme Value Analysis of Short-Duration Rainfall and Intensity–Duration–Frequency Models)

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20 pages, 3136 KiB

Open AccessArticle

Evaluating the Performance of a Max-Stable Process for Estimating Intensity-Duration-Frequency Curves

by Oscar E. Jurado, Jana Ulrich, Marc Scheibel and Henning W. Rust

Water 2020, 12(12), 3314; https://doi.org/10.3390/w12123314 - 25 Nov 2020

Cited by 10 | Viewed by 3071

Abstract

To explicitly account for asymptotic dependence between rainfall intensity maxima of different accumulation duration, a recent development for estimating Intensity-Duration-Frequency (IDF) curves involves the use of a max-stable process. In our study, we aimed to estimate the impact on the performance of the return levels resulting from an IDF model that accounts for such asymptotical dependence. To investigate this impact, we compared the performance of the return level estimates of two IDF models using the quantile skill index (QSI). One IDF model is based on a max-stable process assuming asymptotic dependence; the other is a simplified (or reduced) duration-dependent GEV model assuming asymptotic independence. The resulting QSI shows that the overall performance of the two models is very similar, with the max-stable model slightly outperforming the other model for short durations (

d \leq 10 h

). From a simulation study, we conclude that max-stable processes are worth considering for IDF curve estimation when focusing on short durations if the model’s asymptotic dependence can be assumed to be properly captured. Full article

(This article belongs to the Special Issue Extreme Value Analysis of Short-Duration Rainfall and Intensity–Duration–Frequency Models)

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21 pages, 1781 KiB

Open AccessArticle

Estimating IDF Curves Consistently over Durations with Spatial Covariates

by Jana Ulrich, Oscar E. Jurado, Madlen Peter, Marc Scheibel and Henning W. Rust

Water 2020, 12(11), 3119; https://doi.org/10.3390/w12113119 - 6 Nov 2020

Cited by 22 | Viewed by 4980

Abstract

Given that long time series for temporally highly resolved precipitation observations are rarely available, it is necessary to pool information to obtain reliable estimates of the distribution of extreme precipitation, especially for short durations. In this study, we use a duration-dependent generalized extreme value distribution (d-GEV) with orthogonal polynomials of longitude and latitude as spatial covariates, allowing us to pool information between durations and stations. We determine the polynomial orders with step-wise forward regression and cross-validated likelihood as a model selection criterion. The Wupper River catchment in the West of Germany serves as a case study area. It allows us to estimate return level maps for arbitrary durations, as well as intensity-duration-frequency curves at any location—also ungauged—in the research area. The main focus of the study is evaluating the model performance in detail using the Quantile Skill Index, a measure derived from the popular Quantile Skill Score. We find that the d-GEV with spatial covariates is an improvement for the modeling of rare events. However, the model shows limitations concerning the modeling of short durations

d \leq 30

min. For ungauged sites, the model performs on average as good as a generalized extreme value distribution with parameters estimated individually at the gauged stations with observation time series of 30–35 years available. Full article

(This article belongs to the Special Issue Extreme Value Analysis of Short-Duration Rainfall and Intensity–Duration–Frequency Models)

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16 pages, 16100 KiB

Open AccessArticle

A Rainfall Intensity Data Rescue Initiative for Central Chile Utilizing a Pluviograph Strip Charts Reader (PSCR)

by Roberto Pizarro-Tapia, Fernando González-Leiva, Rodrigo Valdés-Pineda, Ben Ingram, Claudia Sangüesa and Carlos Vallejos

Water 2020, 12(7), 1887; https://doi.org/10.3390/w12071887 - 1 Jul 2020

Cited by 2 | Viewed by 4192

Abstract

To develop intensity-duration-frequency (IDF) curves, it is necessary to calculate annual maximum rainfall intensities for different durations. Traditionally, these intensities have been calculated from the analysis of traces recorded by rain gauges on pluviograph strip charts (PSCs). For many years, these charts have been recorded and analyzed by the personnel who operate and maintain the pluviograph gauges, thus the reliability of the observational analysis depends exclusively on the professional experience of the person performing the analysis. Traditionally, the analyzed PSCs are physically stored in data repository centers. After storing rainfall data on aging paper for many years, the risk of losing rainfall records is very high. Therefore, the conversion of PSC records to digital format is crucial to preserve and improve the historical instrumental data base of these records. We conducted the first “Data Rescue Initiative” (DRI) for central Chile using a pluviograph strip charts reader (PSCR), a tool that uses a scanner-type device combined with digital image processing techniques to estimate maximum rainfall intensities for different durations for each paper band (>80,000 paper bands). On the paper bands, common irregularities associated with excess ink, annotations, or blemishes can affect the scanning process; this system was designed with a semi-automatic module that allows users to edit the detected trace to improve the recognition of the data from each PSC. The PSCR’s semi-automatic characteristics were designed to read many PSCs in a short period of time. The tool also allows for the calculation of rainfall intensities in durations ranging between 15 min to 1 h. This capability improves the value of the data for water infrastructure design, since intense storms of shorter duration often have greater impacts than longer but less intense storms. In this study, the validation of the PSCR against records obtained from observational analysis showed no significant differences between maximum rainfall intensities for durations of 1 h, 6 h, and 24 h. Full article

(This article belongs to the Special Issue Extreme Value Analysis of Short-Duration Rainfall and Intensity–Duration–Frequency Models)

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17 pages, 1861 KiB

Open AccessArticle

Estimating Rainfall Design Values for the City of Oslo, Norway—Comparison of Methods and Quantification of Uncertainty

by Julia Lutz, Lars Grinde and Anita Verpe Dyrrdal

Water 2020, 12(6), 1735; https://doi.org/10.3390/w12061735 - 17 Jun 2020

Cited by 14 | Viewed by 4063

Abstract

Due to its location, its old sewage system, and the channelling of rivers, Oslo is highly exposed to urban flooding. Thus, it is crucial to provide relevant and reliable information on extreme precipitation in the planning and design of infrastructure. Intensity-Duration-Frequency (IDF) curves are a frequently used tool for that purpose. However, the computational method for IDF curves in Norway was established over 45 years ago, and has not been further developed since. In our study, we show that the current method of fitting a Gumbel distribution to the highest precipitation events is not able to reflect the return values for the long return periods. Instead, we introduce the fitting of a Generalised Extreme Value (GEV) distribution for annual maximum precipitation in two different ways, using (a) a modified Maximum Likelihood estimation and (b) Bayesian inference. The comparison of the two methods for 14 stations in and around Oslo reveals that the estimated median return values are very similar, but the Bayesian method provides upper credible interval boundaries that are considerably higher. Two different goodness-of-fit tests favour the Bayesian method; thus, we suggest using the Bayesian inference for estimating IDF curves for the Oslo area. Full article

(This article belongs to the Special Issue Extreme Value Analysis of Short-Duration Rainfall and Intensity–Duration–Frequency Models)

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31 pages, 8094 KiB

Open AccessEditor’s ChoiceArticle

Web-Based Tool for the Development of Intensity Duration Frequency Curves under Changing Climate at Gauged and Ungauged Locations

by Andre Schardong, Slobodan P. Simonovic, Abhishek Gaur and Dan Sandink

Water 2020, 12(5), 1243; https://doi.org/10.3390/w12051243 - 27 Apr 2020

Cited by 26 | Viewed by 6293

Abstract

Rainfall Intensity–Duration–Frequency (IDF) curves are among the most essential datasets used in water resources management across the globe. Traditionally, they are derived from observations of historical rainfall, under the assumption of stationarity. Change of climatic conditions makes use of historical data for development of IDFs for the future unreliable, and in some cases, may lead to underestimated infrastructure designs. The IDF_CC tool is designed to assist water professionals and engineers in producing IDF estimates under changing climatic conditions. The latest version of the tool (Version 4) provides updated IDF curve estimates for gauged locations (rainfall monitoring stations) and ungauged sites using a new gridded dataset of IDF curves for the land mass of Canada. The tool has been developed using web-based technologies and takes the form of a decision support system (DSS). The main modifications and improvements between version 1 and the latest version of the IDF_CC tool include: (i) introduction of the Generalized Extreme value (GEV) distribution; (ii) updated equidistant matching algorithm (QM); (iii) gridded IDF curves dataset for ungauged location and (iv) updated Climate Models. Full article

(This article belongs to the Special Issue Extreme Value Analysis of Short-Duration Rainfall and Intensity–Duration–Frequency Models)

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