Abstract

With increasing concerns on the impacts of climate change, there is wide interest in understanding whether hydrometric and environmental series display any sort of trend. Many studies however, focus on the analysis of highly variable individual series at each measuring location. We propose a novel and straightforward approach to trend detection, modelling the test statistic for trend at each location via an areal model in which the information across measuring locations is pooled together. We exemplify the method with a detailed study of change in high flows in Great Britain. Using areal models, we detect a statistically relevant signal for a positive trend across Great Britain in the recent decades. This evidence is also found when different temporal subsets of the records are analysed. Further, the model identifies areas where the increase has been higher or lower than average, thus providing a way to prioritise intervention.
Original languageEnglish
JournalGeophysical Research Letters
Early online date9 Nov 2019
DOIs
Publication statusE-pub ahead of print - 9 Nov 2019

Cite this

@article{aaacded7de2147a58a59e8cba0cdbaf1,
title = "Areal models for spatially coherent trend detection: the case of British peak river flows",
abstract = "With increasing concerns on the impacts of climate change, there is wide interest in understanding whether hydrometric and environmental series display any sort of trend. Many studies however, focus on the analysis of highly variable individual series at each measuring location. We propose a novel and straightforward approach to trend detection, modelling the test statistic for trend at each location via an areal model in which the information across measuring locations is pooled together. We exemplify the method with a detailed study of change in high flows in Great Britain. Using areal models, we detect a statistically relevant signal for a positive trend across Great Britain in the recent decades. This evidence is also found when different temporal subsets of the records are analysed. Further, the model identifies areas where the increase has been higher or lower than average, thus providing a way to prioritise intervention.",
author = "Ilaria Prosdocimi and Emiko Dupont and Nicole Augustin and Thomas Kjeldsen and Dan Simpson and Theresa Smith",
year = "2019",
month = "11",
day = "9",
doi = "10.1029/2019GL085142",
language = "English",
journal = "Geophysical Research Letters",
issn = "0094-8276",
publisher = "Wiley-Blackwell",

}

TY - JOUR

T1 - Areal models for spatially coherent trend detection: the case of British peak river flows

AU - Prosdocimi, Ilaria

AU - Dupont, Emiko

AU - Augustin, Nicole

AU - Kjeldsen, Thomas

AU - Simpson, Dan

AU - Smith, Theresa

PY - 2019/11/9

Y1 - 2019/11/9

N2 - With increasing concerns on the impacts of climate change, there is wide interest in understanding whether hydrometric and environmental series display any sort of trend. Many studies however, focus on the analysis of highly variable individual series at each measuring location. We propose a novel and straightforward approach to trend detection, modelling the test statistic for trend at each location via an areal model in which the information across measuring locations is pooled together. We exemplify the method with a detailed study of change in high flows in Great Britain. Using areal models, we detect a statistically relevant signal for a positive trend across Great Britain in the recent decades. This evidence is also found when different temporal subsets of the records are analysed. Further, the model identifies areas where the increase has been higher or lower than average, thus providing a way to prioritise intervention.

AB - With increasing concerns on the impacts of climate change, there is wide interest in understanding whether hydrometric and environmental series display any sort of trend. Many studies however, focus on the analysis of highly variable individual series at each measuring location. We propose a novel and straightforward approach to trend detection, modelling the test statistic for trend at each location via an areal model in which the information across measuring locations is pooled together. We exemplify the method with a detailed study of change in high flows in Great Britain. Using areal models, we detect a statistically relevant signal for a positive trend across Great Britain in the recent decades. This evidence is also found when different temporal subsets of the records are analysed. Further, the model identifies areas where the increase has been higher or lower than average, thus providing a way to prioritise intervention.

U2 - 10.1029/2019GL085142

DO - 10.1029/2019GL085142

M3 - Article

JO - Geophysical Research Letters

JF - Geophysical Research Letters

SN - 0094-8276

ER -