Using the probability of storm transposition for estimating the frequency of rare floods

Alexander, Gavin

doi:10.1016/0022-1694(63)90032-5

Cited by 41 publications

(15 citation statements)

References 6 publications

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“…It can be understood as a bootstrap technique using regional storm observations. Originally introduced by Alexander (), SST has subsequently been explored by England et al (), Fontaine and Potter (), Foufoula‐Georgiou (), Franchini et al (), and Gupta (). Wright et al (, ) coupled SST with high‐resolution gage‐adjusted radar rainfall and highlighted some of its capabilities.…”

Section: Methodsmentioning

confidence: 99%

The Impact of Rainfall Space‐Time Structure in Flood Frequency Analysis

Zhu

Wright

2018

Water Resources Research

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Flood hydrologic response is influenced by rainfall structure (i.e., variability in space and time). How this structure shapes flood frequency is unknown, and flood frequency analyses typically neglect or simplify potentially important aspects of rainfall variability. This study seeks to understand how rainfall structure impacts flood frequency. We use stochastic storm transposition combined with a 15‐year record of hourly, 4‐km2 radar rainfall to generate 10,000 synthetic extreme rain events. These events are resampled into four “scenarios” with differing spatial and temporal resolutions, which are used as input to a distributed hydrologic model. Analysis of variance is used to identify the proportions of total flood peak variability attributable to spatial and to temporal rainfall variability under two antecedent soil moisture conditions. We simulate peak discharges for recurrence intervals of 2 to 500 years for 1,343 subwatersheds ranging in size from 16 to 4,400 km2 in Turkey River in the Midwestern United States, which is situated in a typically humid continental climactic region. Antecedent soil moisture modulates the role of rainfall structure in simulated flood response, particularly for more frequent events and large watershed scales. Rainfall spatial structure is more important than temporal structure for drainage areas larger than approximately 2,000 km2 (approximately 200 km2) for wet (dry) initial soil conditions, especially when soils are dry, while the reverse is true for smaller subwatersheds. The results appear to be related to the differing propensities for surface and subsurface runoff production as a function of basin scale, event magnitude, and soil saturation. Our results suggest that hydrologic model‐based flood frequency analyses, and particularly efforts attempting to spanning a range of scales, must carefully consider rainfall structure.

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Section: Methodsmentioning

confidence: 99%

The Impact of Rainfall Space‐Time Structure in Flood Frequency Analysis

Zhu

Wright

2018

Water Resources Research

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“…"Regionalization,"-the pooling of hazard information over a larger area in order to inform analyses at particular locations (see, e.g. Alexander, 1963 for an early discussion of rainfall regionalization and Stedinger et al, 1993 for a review)-has helped with IDF estimation using short records in areas where rain gage densities are moderate or high. These techniques offer little help, however, in parts of the world where rain gages are few or nonexistent, and do not offer a framework for incorporating rainfall space-time properties into hazard estimation.…”

Section: Introductionmentioning

confidence: 99%

“…It accomplishes this by generating large numbers of extreme rainfall "scenarios," each of which has realistic rainfall structure based directly on observations. Alexander (1963), Foufoula-Georgiou (1989), and Fontaine and Potter (1989) describe the general SST framework, while Wilson and Foufoula-Georgiou (1990) use the method for rainfall frequency analysis and Gupta (1972) and Franchini et al (1996) use it for flood Wright et al Page 3 Environ Model Softw. Author manuscript; available in PMC 2018 April 12. frequency analysis.…”

Section: Introductionmentioning

confidence: 99%

A remote sensing-based tool for assessing rainfall-driven hazards

Wright

Mantilla

Peters‐Lidard

2017

Environmental Modelling & Software

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RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, RainyDay can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, RainyDay can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. RainyDay can be useful for hazard modeling under nonstationary conditions.

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“…0043-1397/89/88WR-04331 $05.00 viewed in the recent National Research Council Report (NRC) [1988], have several methodological similarities but also distinct procedural dissimilarities. For example, Gupta [1972] and Fontaine and Potter [1989] transpose historical storms, while Alexander [1963] and YAEC [1984] transpose conceptualized single-center storms with isohyetal patterns derived from the maximum depth-area-duration (DAD) curves. Alexander and YAEC have made several arbitrary simplifying assumptions [see, NRC, 1988, chapter 4] in accounting for the storm/catchment interactions.…”

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confidence: 99%

A probabilistic storm transposition approach for estimating exceedance probabilities of extreme precipitation depths

Foufoula‐Georgiou¹

1989

Water Resources Research

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A storm transposition approach is investigated as a possible tool of assessing the frequency of extreme precipitation depths, that is, depths of return period much greater than 100 years. This paper focuses on estimation of the annual exceedance probability of extreme average precipitation depths over a catchment. The probabilistic storm transposition methodology is presented, and the several conceptual and methodological difficulties arising in this approach are identified. The method is implemented and is partially evaluated by means of a semihypothetical example involving extreme midwestern storms and two hypothetical catchments (of 100 and 1000 mi 2 (=260 and 2600 km2)) located in central Iowa. The results point out the need for further research to fully explore the potential of this approach as a tool for assessing the probabilities of rare storms, and eventually floods, a necessary element of risk-based analysis and design of large hydraulic structures.

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Using the probability of storm transposition for estimating the frequency of rare floods

Cited by 41 publications

References 6 publications

The Impact of Rainfall Space‐Time Structure in Flood Frequency Analysis

The Impact of Rainfall Space‐Time Structure in Flood Frequency Analysis

A remote sensing-based tool for assessing rainfall-driven hazards

A probabilistic storm transposition approach for estimating exceedance probabilities of extreme precipitation depths

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