Regional Rainfall Frequency Analysis via Stochastic Storm Transposition

Wilson, Larry L.; Foufoula‐Georgiou, Efi

doi:10.1061/(asce)0733-9429(1990)116:7(859)

Cited by 28 publications

(23 citation statements)

References 9 publications

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“…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%

“…frequent, low-intensity) events if m is small (e.g. Foufoula-Georgiou, 1989;Franchini et al, 1996;Wilson and Foufoula-Georgiou, 1990; see Wright et al, 2013 for a discussion). The sensitivity of SST results to the choice of m and A′ is explored in detail in Section 4.3, but m ≈ 10n generally minimizes the low bias for frequent events, and would likely be a good starting point for new analyses.…”

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confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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

Wright

Mantilla

Peters‐Lidard

2017

Environmental Modelling & Software

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“…Storm transposition has long been a critical component of the estimation of probable maximum floods which are used to design emergency spillways for high-hazard dams. More recently, stochastic storm transposition has been used to estimate the probability distribution rainfall at a point for various durations (Foufoula-Georgiou, 1989;Fontaine and Potter, 1989;Wilson and Foufoula-Georgiou, 1990;Franchini et al, 1996;Wright et al, 2013Wright et al, , 2014.…”

Section: Climate Change and Storm Transpositionmentioning

confidence: 99%

Evaluating Infiltration Requirements for New Development Using Extreme Storm Transposition: A Case Study from Dane County, WI

Hayden

Potter

Liebl

2016

J American Water Resour Assoc

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Changes in land use and extreme rainfall trends can lead to increased flood vulnerability in many parts of the world, especially for urbanized watersheds. This study investigates the performance of existing stormwater management strategies for the Upper Yahara watershed in Dane County, WI to determine whether they are adequate to protect urban and suburban development from an extreme rainfall. Using extreme storm transposition, we model the performance of the stormwater infiltration practices required for new development under current county ordinances. We find during extreme rainfall the volume of post‐development runoff from impervious surfaces from a typical site would increase by over 55% over pre‐development conditions. We recommend the ordinance be strengthened to reduce vulnerability to flooding from future urban expansion and the likely increase in the magnitude and frequency of extreme storms.

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“…The storm spatial distribution is a simple, parsimonious model based on DAD data. It is assumed that the storm is single-centered, and isohyets are geometrically similar in the form of an ellipse (Hansen et al, 1982;Wilson and Foufoula-Georgiou, 1990), where (x s , y s ) is the storm center and a and b are major and minor axes, respectively (Fig. 2).…”

Section: Extreme Stormsmentioning

confidence: 99%