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Open AccessArticle

Estimating IDF Curves Consistently over Durations with Spatial Covariates

1
Institute of Meteorology, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6-12, 12165 Berlin, Germany
2
Wupperverband, Untere Lichtenplatzer Str. 100, 42289 Wuppertal, Germany
*
Author to whom correspondence should be addressed.
Water 2020, 12(11), 3119; https://doi.org/10.3390/w12113119
Received: 26 September 2020 / Revised: 3 November 2020 / Accepted: 5 November 2020 / Published: 6 November 2020
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 d30min. 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. View Full-Text
Keywords: extreme value statistics; extreme precipitation; subdaily precipitation extremes; intensity-duration-frequency curve; duration-dependent GEV; vector generalized linear model; spatial covariates extreme value statistics; extreme precipitation; subdaily precipitation extremes; intensity-duration-frequency curve; duration-dependent GEV; vector generalized linear model; spatial covariates
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MDPI and ACS Style

Ulrich, J.; Jurado, O.E.; Peter, M.; Scheibel, M.; Rust, H.W. Estimating IDF Curves Consistently over Durations with Spatial Covariates. Water 2020, 12, 3119. https://doi.org/10.3390/w12113119

AMA Style

Ulrich J, Jurado OE, Peter M, Scheibel M, Rust HW. Estimating IDF Curves Consistently over Durations with Spatial Covariates. Water. 2020; 12(11):3119. https://doi.org/10.3390/w12113119

Chicago/Turabian Style

Ulrich, Jana; Jurado, Oscar E.; Peter, Madlen; Scheibel, Marc; Rust, Henning W. 2020. "Estimating IDF Curves Consistently over Durations with Spatial Covariates" Water 12, no. 11: 3119. https://doi.org/10.3390/w12113119

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