Precipitation extremes derived from temporally aggregated time series and the efficiency of their correction

Hnilica, Jan; Slámová, Romana; Šípek, Václav; Tesař, Miroslav

doi:10.1080/02626667.2021.1988087

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…Thus, the obtained results do not coincide with the commonly used approach of adopting one fixed probability distribution for all meteorological stations in a given region-typically this is the Gumbel distribution [4,5,73,74]. The assumption of GEV distribution found in some studies [6,7,42,75] seems to be more reasonable.…”

Section: Discussionmentioning

confidence: 64%

“…The initial formulas were semi-empirical [2,3], while, with the development of statistical modeling of hydrological time series, the general formula for the IDF relationship was introduced by Koutsoyiannis et al [4], in accordance with the probabilistic theory of hydrological maxima analysis. The basic DDF relation and the underlying structure of the DDF curves have been widely applied and discussed in the literature with respect to maximum precipitation [5][6][7], as well as maximum flows [8][9][10]. One of the modifications to the traditional DDF method is multi-scaling based on the multifractal properties of temporal rainfall [6,11,12].…”

Section: Introductionmentioning

confidence: 99%

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Depth–Duration–Frequency Relationship Model of Extreme Precipitation in Flood Risk Assessment in the Upper Vistula Basin

Markiewicz

2021

Water

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The Upper Vistula Basin is a flood-prone region in the summer season (May–October) due to intensive rainfall. From the point of view of water management, it is particularly important to assess the variability in this main factor of flood risk, as well as to establish the depth–duration–frequency (DDF) relationship for maximum precipitation, this having not yet been derived for the region. The analysis of a 68-year (1951–2018) data series of summer maximum precipitation collected by 11 meteorological stations showed the series’ stationarity, which supports the conclusion that there is no increase in the risk of rainfall floods due to the intensification of extreme precipitation. A new approach is proposed for the determination of the DDF relationship, where the best-fitted distribution for each station is selected from among the set of candidate distributions, instead of adopting one fixed distribution for all stations. This approach increases the accuracy of the DDF relationships for individual stations as compared to the commonly used approach. In particular, the traditionally used Gumbel distribution turns out to be not well fitted to the investigated data series, and the advantage of the recently popular GEV distribution is not significant.

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Section: Discussionmentioning

confidence: 64%

Section: Introductionmentioning

confidence: 99%

Depth–Duration–Frequency Relationship Model of Extreme Precipitation in Flood Risk Assessment in the Upper Vistula Basin

Markiewicz

2021

Water

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“…Several issues can compromise the correct rainfall estimation, such as the relocation of gauge stations, the use of different rain gauge types, and the change of gauge station surroundings, determining relevant effects on many related investigations including the evolution of extreme rainfall trends and the determination of rainfall depth-durationfrequency curves [19,20].…”

Section: Introductionmentioning

confidence: 99%

Impacts of Rainfall Data Aggregation Time on Pluvial Flood Hazard in Urban Watersheds

Lompi

Tamagnone

Pacetti

et al. 2022

Water

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Pluvial floods occur when heavy rainstorms cause the surcharge of the sewer network drainage, representing one of the most impacting natural hazard in urban watersheds. Pluvial flood hazard is usually assessed considering the effect of annual maxima rainfall of short duration, comparable with the typical concentration times of small urban watersheds. However, short duration annual maxima can be affected by an error of underestimation due to the time resolution as well as the aggregation time of the rainfall time series. This study aims at determining the impact of rainfall data aggregation on pluvial flood hazard assessment. Tuscany region (Central Italy) is selected as a case study to perform the assessment of the annual maxima rainfall underestimation error, since the entire region has the same temporal aggregation of rainfall data. Pluvial flood hazard is then evaluated for an urban watershed in the city of Florence (Tuscany) comparing the results obtained using observed (uncorrected) and corrected annual maxima rainfall as meteorological forcing. The results show how the design of rainfall events with a duration of 30 min or shorter is significantly affected by the temporal aggregation, highlighting the importance of correcting annual maxima rainfall for a proper pluvial flood hazard evaluation.

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Variability of sampling adjustment factors for extreme rainfall in Germany

Pöschmann

Kronenberg

Bernhofer

2023

Theor Appl Climatol

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The sampling adjustment factor (SAF) can correct the underestimation of fixed time interval maxima (F-Maxima) relative to maxima of moving time windows (M-Maxima) as a direct consequence of temporal discretization of time series. Radar data can help to understand the significance of using moving window aggregation rather than fixed window. Here, we investigate SAFs for two gridded radar quantitative precipitation estimates from the German Meteorological Service (RADKLIM-RW and RADKLIM-YW, DWD 2018) with different temporal resolutions (5 min and hourly) and a spatial resolution of 1 km $$\times $$ × 1 km from 2001 to 2016. For each grid cell’s time series within Germany, the overall maximum intensities per chosen duration were derived with (1) moving and (2) fixed window aggregation. Correction factors (SAF) from fixed to moving window were retrieved. The findings of this study partly match previous findings, more for the 5-min product than for the hourly product. No clear dependency on topography or season could be derived from the analysis. It showed that high SAF are usually related to high(er) actual rainfall values. The study emphasises the probabilistic nature of the rainfall maxima correction and shows that it is important to not only consider average SAFs, but also take an in-depth look at the distribution when correcting maxima. As a consequence, design precipitation as used, e.g., in context of flash or urban floods could profit from spatially adjusted or uniform scaling, depending on the characteristics found in the spatial distribution of the SAFs.

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Precipitation extremes derived from temporally aggregated time series and the efficiency of their correction

Cited by 4 publications

References 22 publications

Depth–Duration–Frequency Relationship Model of Extreme Precipitation in Flood Risk Assessment in the Upper Vistula Basin

Depth–Duration–Frequency Relationship Model of Extreme Precipitation in Flood Risk Assessment in the Upper Vistula Basin

Impacts of Rainfall Data Aggregation Time on Pluvial Flood Hazard in Urban Watersheds

Variability of sampling adjustment factors for extreme rainfall in Germany

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