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Spatial variability of extreme rainfall at radar subpixel scale

Spatial variability of extreme rainfall at radar subpixel scale
Spatial variability of extreme rainfall at radar subpixel scale
Extreme rainfall is quantified in engineering practice using Intensity–Duration–Frequency curves (IDF) that are traditionally derived from rain-gauges and more recently also from remote sensing instruments, such as weather radars. These instruments measure rainfall at different spatial scales: rain-gauge samples rainfall at the point scale while weather radar averages precipitation on a relatively large area, generally around 1 km2. As such, a radar derived IDF curve is representative of the mean areal rainfall over a given radar pixel and neglects the within-pixel rainfall variability. In this study, we quantify subpixel variability of extreme rainfall by using a novel space-time rainfall generator (STREAP model) that downscales in space the rainfall within a given radar pixel. The study was conducted using a unique radar data record (23 years) and a very dense rain-gauge network in the Eastern Mediterranean area (northern Israel). Radar-IDF curves, together with an ensemble of point-based IDF curves representing the radar subpixel extreme rainfall variability, were developed fitting Generalized Extreme Value (GEV) distributions to annual rainfall maxima. It was found that the mean areal extreme rainfall derived from the radar underestimate most of the extreme values computed for point locations within the radar pixel (on average, ?70%). The subpixel variability of rainfall extreme was found to increase with longer return periods and shorter durations (e.g. from a maximum variability of 10% for a return period of 2 years and a duration of 4 h to 30% for 50 years return period and 20 min duration). For the longer return periods, a considerable enhancement of extreme rainfall variability was found when stochastic (natural) climate variability was taken into account. Bounding the range of the subpixel extreme rainfall derived from radar-IDF can be of a major importance for different applications that require very local estimates of rainfall extremes.
0022-1694
1-58
Peleg, Nadav
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Marra, Francesco
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Fatichi, Simone
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Paschalis, Athanasios
e7626e9f-172b-4da2-882c-bddb219f3fb6
Molnar, Peter
99f2d15c-5348-4c80-bb35-fb54c133862d
Burlando, Paolo
5484fcec-b4d3-45e9-a72c-206ccbb5265f
Peleg, Nadav
eb6cb5dc-ea19-4764-8943-acbfaf29a5c6
Marra, Francesco
14cbf6ef-b8aa-48fa-9550-f4e78f45e429
Fatichi, Simone
2a12468d-8094-495b-922d-4d00aa0afb11
Paschalis, Athanasios
e7626e9f-172b-4da2-882c-bddb219f3fb6
Molnar, Peter
99f2d15c-5348-4c80-bb35-fb54c133862d
Burlando, Paolo
5484fcec-b4d3-45e9-a72c-206ccbb5265f

Peleg, Nadav, Marra, Francesco, Fatichi, Simone, Paschalis, Athanasios, Molnar, Peter and Burlando, Paolo (2016) Spatial variability of extreme rainfall at radar subpixel scale. Journal of Hydrology, 1-58. (doi:10.1016/j.jhydrol.2016.05.033).

Record type: Article

Abstract

Extreme rainfall is quantified in engineering practice using Intensity–Duration–Frequency curves (IDF) that are traditionally derived from rain-gauges and more recently also from remote sensing instruments, such as weather radars. These instruments measure rainfall at different spatial scales: rain-gauge samples rainfall at the point scale while weather radar averages precipitation on a relatively large area, generally around 1 km2. As such, a radar derived IDF curve is representative of the mean areal rainfall over a given radar pixel and neglects the within-pixel rainfall variability. In this study, we quantify subpixel variability of extreme rainfall by using a novel space-time rainfall generator (STREAP model) that downscales in space the rainfall within a given radar pixel. The study was conducted using a unique radar data record (23 years) and a very dense rain-gauge network in the Eastern Mediterranean area (northern Israel). Radar-IDF curves, together with an ensemble of point-based IDF curves representing the radar subpixel extreme rainfall variability, were developed fitting Generalized Extreme Value (GEV) distributions to annual rainfall maxima. It was found that the mean areal extreme rainfall derived from the radar underestimate most of the extreme values computed for point locations within the radar pixel (on average, ?70%). The subpixel variability of rainfall extreme was found to increase with longer return periods and shorter durations (e.g. from a maximum variability of 10% for a return period of 2 years and a duration of 4 h to 30% for 50 years return period and 20 min duration). For the longer return periods, a considerable enhancement of extreme rainfall variability was found when stochastic (natural) climate variability was taken into account. Bounding the range of the subpixel extreme rainfall derived from radar-IDF can be of a major importance for different applications that require very local estimates of rainfall extremes.

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HYDROL21415R3_ACCEPTED_toeprints.pdf - Accepted Manuscript
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Accepted/In Press date: 24 May 2016
e-pub ahead of print date: 24 May 2016
Organisations: Water & Environmental Engineering Group

Identifiers

Local EPrints ID: 395248
URI: https://eprints.soton.ac.uk/id/eprint/395248
ISSN: 0022-1694
PURE UUID: f8af437a-2f18-49ef-8e24-d51a5b264c5f
ORCID for Athanasios Paschalis: ORCID iD orcid.org/0000-0003-4833-9962

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Date deposited: 26 May 2016 09:12
Last modified: 10 Dec 2019 06:32

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Contributors

Author: Nadav Peleg
Author: Francesco Marra
Author: Simone Fatichi
Author: Athanasios Paschalis ORCID iD
Author: Peter Molnar
Author: Paolo Burlando

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