Reliable estimation of high floods: A method to select the most suitable ordinary distribution in the Metastatistical extreme value framework

Mushtaq, Sumra; Miniussi, Arianna; Merz, Ralf; Basso, Stefano

doi:10.1016/j.advwatres.2022.104127

Cited by 19 publications

(38 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, non-asymptotic methods suggest that extremes are realizations of the underlying ordinary events (Marani and Ignaccolo, 2015;Lombardo et al, 2019), which can thus be used to assess rare events. These methods have significantly improved the estimation of extreme values with lower uncertainty (Marra et al, 2018;Miniussi and Marani, 2020;Mushtaq et al, 2022;Hu et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

“…This prior identification of tail behavior is crucial to avoid potential underestimation of flood extremes. (Miniussi et al, 2020;Mushtaq et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…However, obtaining reliable estimates from these methods requires long data records , which can be challenging globally (Lins, 2008) and may cause bias when comparing data across sites with different record lengths (Cunderlik and Burn, 2002;Wietzke et al, 2020). To reduce uncertainty, especially in estimating extremes, it is recommended to analyze ordinary dynamics instead of focusing solely on maximum values (Marani and Ignaccolo, 2015;Mushtaq et al, 2022), and to investigate the underlying factors that contribute to extreme events (Wilson and Toumi, 2005;Tarasova et al, 2020;.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Inferring heavy tails of flood distributions from common discharge dynamics

Wang

Merz

Yang

et al. 2023

Preprint

View full text Add to dashboard Cite

Abstract. Floods are often disastrous due to underestimation of the magnitude of rare events. Underestimation commonly happens when the occurrence of floods follow a heavy-tailed distribution, but this behavior is not recognized and thus neglected for flood hazard assessment. In fact, identifying heavy-tailed flood behavior is challenging because of limited data records and the lack of physical support for currently used indices. We address these issues by deriving a new index of heavy-tailed flood behavior from a physically-based description of streamflow dynamics. The proposed index, which is embodied by the hydrograph recession exponent, enables inferring heavy-tailed flood behavior from daily flow records, even of short length. We test the index in a large set of case studies across Germany encompassing a variety of climatic and physiographic settings. Our findings demonstrate that the new index enables reliable identification of cases with either heavy or nonheavy tailed flood behavior from daily flow records. Additionally, the index suitably estimates the severity of tail heaviness and ranks it across cases, achieving robust results even with short data records. The new index addresses the main limitations of currently used metrics, which lack physical support and require long data records to correctly identify tail behaviors, and provides valuable information on the tail behavior of flood distributions and the related flood hazard in river basins using commonly available discharge data.

show abstract

Section: Discussionmentioning

confidence: 99%

“…This prior identification of tail behavior is crucial to avoid potential underestimation of flood extremes. (Miniussi et al, 2020;Mushtaq et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Inferring heavy tails of flood distributions from common discharge dynamics

Wang

Merz

Yang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…2). The identification follows the procedure recommended by the guidelines of the US Water Resources Council, which has been widely adopted in many studies 53,54 . First, all local peaks with a minimum distance T = 5 days + log(A) between neighboring peaks were selected, where A is the basin area in square miles and T is rounded to an integer.…”

Section: Preprocessing Of Training Samplesmentioning

confidence: 99%

The importance of compounding drivers for large river floods

Jiang

Tarasova

et al. 2023

Preprint

View full text Add to dashboard Cite

Estimating river flood risks under global warming is challenging, largely because of the compounding nature of various drivers1,2. Yet to date, the interplay of multiple drivers and how they affect river floods are not well understood. Here we use explainable machine learning to disentangle the interactions between flood drivers and identify the compounding drivers of river floods in thousands of catchments around the world. We find that the majority of river floods worldwide over the past 40 years were attributable to compounding drivers, which often amplified river flood magnitude. Furthermore, the role of compounding drivers becomes more important with increasing flood magnitude in nearly all of the studied catchments, with the strength of this relationship generally depending on the catchment physio-climatic conditions. Based on these findings, we demonstrate that traditional statistical methods using flood frequency analysis underestimate the magnitude of extreme floods because compounding drivers are not properly taken into account3. Overall, our results highlight the need for careful incorporation of compounding drivers in flood risk assessment to improve estimates of extreme floods, in particular in the face of climate change4,5.

show abstract

“…Merz et al, 2015Merz et al, , 2021, although the scientific literature repeatedly signalled the pervasiveness of these behaviors terming them in various ways. In fact, heavy-tailed distributions of floods (Farquharson et al, 1992;Bernardara et al, 2008;Villarini & Smith, 2010), inversions of concavity and step changes in flood magnitude-frequency curves (Rogger et al, 2012;Guo et al, 2014;Basso et al, 2016) and large values of the ratios between the maximum flood of record and the sample flood with a specified recurrence time (Smith et al, 2018) and between empirical high flow percentiles (Mushtaq et al, 2022) are all manifestations of a marked increase of the magnitude of the rarer floods highlighted by means of different approaches.…”

Section: Introductionmentioning

confidence: 99%

Identifying discontinuities of flood frequency curves

Miniussi

Merz

Kaule

et al. 2022

Preprint

View full text Add to dashboard Cite

Discontinuities in flood frequency curves, here referred to as flood divides, hinder the estimation of rare floods. In this paper we develop an automated methodology for the detection of flood divides from observations and models, and apply it to a large set of case studies in the USA and Germany. We then assess the reliability of the PHysically-based Extreme Value (PHEV) distribution of river flows to identify catchments that might experience a flood divide, validating its results against observations. This tool is suitable for the identification of flood divides, with a high correct detection rate especially in the autumn and summer seasons. It instead tends to indicate the emergence of flood divides not visible in the observations in spring and winter. We examine possible reasons of this behavior, finding them in the typical streamflow dynamics of the concerned case studies. By means of a controlled experiment we also re-evaluate detection capabilities of observations and PHEV after discarding the highest maxima for all cases where both empirical and theoretical estimates display flood divides. PHEV mostly confirms its capability to detect a flood divide as observed in the original flood frequency curve, even if the shortened one does not show it. These findings prove its reliability for the identification of flood divides and set the premises for a deeper investigation of physiographic and hydroclimatic attributes controlling the emergence of discontinuities in flood frequency curves.

show abstract

Reliable estimation of high floods: A method to select the most suitable ordinary distribution in the Metastatistical extreme value framework

Cited by 19 publications

References 57 publications

Inferring heavy tails of flood distributions from common discharge dynamics

Inferring heavy tails of flood distributions from common discharge dynamics

The importance of compounding drivers for large river floods

Identifying discontinuities of flood frequency curves

Contact Info

Product

Resources

About