A seasonal mixed-POT model to estimate high flood quantiles from different event types and seasons

Fischer, Svenja

doi:10.1080/02664763.2018.1441385

Cited by 38 publications

(25 citation statements)

References 23 publications

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Section: Causative Classifications Of Instrumental Series Of River Flmentioning

confidence: 99%

“…For practical reasons, using shorter, classified flood series might be less beneficial than using long unclassified series sampled in similar fashion, although more research is needed to exactly identify the value of flood event classification in flood frequency estimation. It is, however, advisable to use POT flood sampling to assure that flood samples of every type have a sufficient size (Brunner et al, 2017;Fischer, 2018).…”

Section: Application Of Causative Classifications Of River Flood Eventsmentioning

confidence: 99%

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Causative classification of river flood events

Tarasova

Merz

Kiss³

et al. 2019

WIREs Water

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124

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A wide variety of processes controls the time of occurrence, duration, extent, and severity of river floods. Classifying flood events by their causative processes may assist in enhancing the accuracy of local and regional flood frequency estimates and support the detection and interpretation of any changes in flood occurrence and magnitudes. This paper provides a critical review of existing causative classifications of instrumental and preinstrumental series of flood events, discusses their validity and applications, and identifies opportunities for moving toward more comprehensive approaches. So far no unified definition of causative mechanisms of flood events exists. Existing frameworks for classification of instrumental and preinstrumental series of flood events adopt different perspectives: hydroclimatic (large‐scale circulation patterns and atmospheric state at the time of the event), hydrological (catchment scale precipitation patterns and antecedent catchment state), and hydrograph‐based (indirectly considering generating mechanisms through their effects on hydrograph characteristics). All of these approaches intend to capture the flood generating mechanisms and are useful for characterizing the flood processes at various spatial and temporal scales. However, uncertainty analyses with respect to indicators, classification methods, and data to assess the robustness of the classification are rarely performed which limits the transferability across different geographic regions. It is argued that more rigorous testing is needed. There are opportunities for extending classification methods to include indicators of space–time dynamics of rainfall, antecedent wetness, and routing effects, which will make the classification schemes even more useful for understanding and estimating floods. This article is categorized under: Science of Water > Water Extremes Science of Water > Hydrological Processes Science of Water > Methods

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Section: Causative Classifications Of Instrumental Series Of River Flmentioning

confidence: 99%

Section: Application Of Causative Classifications Of River Flood Eventsmentioning

confidence: 99%

Causative classification of river flood events

Tarasova

Merz

Kiss³

et al. 2019

WIREs Water

Self Cite

124

View full text Add to dashboard Cite

show abstract

“…Also, as we focused only on large annual floods (annual maxima), we did not represent the flood seasonality in our analysis. Yet, some recent works emphasise the need to include such information on the flood type (Brunner et al, 2017) or on flood seasonality (Brunner et al, 2018b) into bivariate analysis of floods, or to represent a mixture of both flood type and flood seasonality in flood frequency analysis (Fischer et al, 2016;Fischer, 2018). Thus, the proposed selection methods could potentially be extended to account for different flood types during representative parameter selection, using e.g.…”

Section: Limitations and Perspectivesmentioning

confidence: 99%

Downsizing parameter ensembles for simulations of extreme floods

Sikorska‐Senoner¹,

Schaefli²,

Seibert³

2020

Preprint

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Abstract. For extreme flood estimation, simulation-based approaches represent an interesting alternative to purely statistical approaches, particularly if hydrograph shapes are required. Such simulation-based methods are adapted within continuous simulation frameworks that rely on statistical analyses of continuous streamflow time series derived from a hydrologic model fed with long precipitation time series. These frameworks are, however, affected by high computational demands, particularly if floods with return periods > 1000 years are of interest or if modelling uncertainty due to different sources (meteorological input or hydrologic model) is to be quantified. Here, we propose three methods for reducing the computational requirements for the hydrological simulations for extreme flood estimation, so that long streamflow time series can be analysed at a reduced computational cost. These methods rely on simulation of annual maxima and on analyzing their simulated range to downsize the hydrological parameter ensemble to a small number suitable for continuous simulation frameworks. The methods are tested in a Swiss catchment with 10 000 years of synthetic streamflow data simulated with a weather generator. Our results demonstrate the reliability of the proposed downsizing methods for robust simulations of extreme floods with uncertainty. The methods are readily transferable to other situations where ensemble simulations are needed.

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“…As input for the conditional probability in Equation 1, quantiles for given return periods and flood types are estimated for each tributary and the main river with the type-based statistics proposed by Fischer (2018). More precisely, for each flood type, the distribution can be derived with a peak-over-threshold approach, such that the annual distribution function describing the probability in terms of one year is given by…”

Section: Multivariate Flood Type-specific Return Periods Considering Confluencesmentioning

confidence: 99%

Multivariate Flood Frequency Analysis in Large River Basins Considering Tributary Impacts and Flood Types

Fischer

Schumann

2021

Water Resources Research

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Flood events are characterized by the nature of runoff-generating precipitation events and distributions of runoff volumes among tributaries • A statistical simulation generates peak-volume pairs based on flood types at confluences, consistently for upstream and downstream gauges • Vine copulas are used to estimate most probable combinations of flood types and superposition for downstream events of given return periods

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A seasonal mixed-POT model to estimate high flood quantiles from different event types and seasons

Cited by 38 publications

References 23 publications

Causative classification of river flood events

Causative classification of river flood events

Downsizing parameter ensembles for simulations of extreme floods

Multivariate Flood Frequency Analysis in Large River Basins Considering Tributary Impacts and Flood Types

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