Detecting change in precipitation indices using observed (1977–2016) and modeled future climate data in Portland, Oregon, USA

This study addresses how regional changes to precipitation may be identified by exploring the effect of temporal resolution on trend detection. Climate indices that summarize precipitation characteristics are used with Mann–Kendall monotonic testing to investigate precipitation trends in Portland, O...

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Autores principales:	Alexis Kirsten Cooley, Heejun Chang
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	etccdi precipitation indices global climate models hourly precipitation scale trend Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/f3705e17d3ca42e0b7b2546a292f7793
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spelling	oai:doaj.org-article:f3705e17d3ca42e0b7b2546a292f77932021-11-05T18:52:18ZDetecting change in precipitation indices using observed (1977–2016) and modeled future climate data in Portland, Oregon, USA2040-22442408-935410.2166/wcc.2020.043https://doaj.org/article/f3705e17d3ca42e0b7b2546a292f77932021-06-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/4/1135https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354This study addresses how regional changes to precipitation may be identified by exploring the effect of temporal resolution on trend detection. Climate indices that summarize precipitation characteristics are used with Mann–Kendall monotonic testing to investigate precipitation trends in Portland, Oregon (OR) from 1977 to 2016. Observational records from rain gages are compared with downscaled global climate models to determine trends for the historic (1977–2005) and future (2006–2100) periods. Standard indices created by the Expert Team on Climate Change Detection and Indices (ETCCDI) are deployed. ETCCDI indices that summarize conditions at the annual level are generated alongside a limited number of ETCCDI indices summarized at the monthly level. For the future climate, the indices summarized at the annual level demonstrate trends indicative of an intensifying hydrologic cycle. The historical record depicted by annual indices does not show trends. The historical record is viewed differently by changing the indices to monthly summaries, which causes trend detection to increase and hallmark indicators of an intensifying hydrologic cycle to become apparent. HIGHLIGHTS We used the Expert Team on Climate Change Detection and Indices (ETCCDI) precipitation indices and the Mann–Kendall test to detect trends in precipitation.; We compared observed precipitation records (1977–2005) with future (2006–2100) projections from five downscaled global climate models for the Portland area.; While four of five climate models projected an intensifying hydrologic cycle from 2006 to 2100, trends were not detected in the 1977–2016 observational record.; When data were disaggregated from annual to monthly, many of the hallmarks of an intensified hydrologic cycle were observed in the 1977–2016 Portland record in spring, winter, and fall months.; Trend detection of increasing precipitation intensity was detected more at a finer temporal scale (i.e., hourly data), indicating a finer temporal analysis is critical for urban flood risk management.;Alexis Kirsten CooleyHeejun ChangIWA Publishingarticleetccdi precipitation indicesglobal climate modelshourly precipitationscaletrendEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 4, Pp 1135-1153 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	etccdi precipitation indices global climate models hourly precipitation scale trend Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	etccdi precipitation indices global climate models hourly precipitation scale trend Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Alexis Kirsten Cooley Heejun Chang Detecting change in precipitation indices using observed (1977–2016) and modeled future climate data in Portland, Oregon, USA
description	This study addresses how regional changes to precipitation may be identified by exploring the effect of temporal resolution on trend detection. Climate indices that summarize precipitation characteristics are used with Mann–Kendall monotonic testing to investigate precipitation trends in Portland, Oregon (OR) from 1977 to 2016. Observational records from rain gages are compared with downscaled global climate models to determine trends for the historic (1977–2005) and future (2006–2100) periods. Standard indices created by the Expert Team on Climate Change Detection and Indices (ETCCDI) are deployed. ETCCDI indices that summarize conditions at the annual level are generated alongside a limited number of ETCCDI indices summarized at the monthly level. For the future climate, the indices summarized at the annual level demonstrate trends indicative of an intensifying hydrologic cycle. The historical record depicted by annual indices does not show trends. The historical record is viewed differently by changing the indices to monthly summaries, which causes trend detection to increase and hallmark indicators of an intensifying hydrologic cycle to become apparent. HIGHLIGHTS We used the Expert Team on Climate Change Detection and Indices (ETCCDI) precipitation indices and the Mann–Kendall test to detect trends in precipitation.; We compared observed precipitation records (1977–2005) with future (2006–2100) projections from five downscaled global climate models for the Portland area.; While four of five climate models projected an intensifying hydrologic cycle from 2006 to 2100, trends were not detected in the 1977–2016 observational record.; When data were disaggregated from annual to monthly, many of the hallmarks of an intensified hydrologic cycle were observed in the 1977–2016 Portland record in spring, winter, and fall months.; Trend detection of increasing precipitation intensity was detected more at a finer temporal scale (i.e., hourly data), indicating a finer temporal analysis is critical for urban flood risk management.;
format	article
author	Alexis Kirsten Cooley Heejun Chang
author_facet	Alexis Kirsten Cooley Heejun Chang
author_sort	Alexis Kirsten Cooley
title	Detecting change in precipitation indices using observed (1977–2016) and modeled future climate data in Portland, Oregon, USA
title_short	Detecting change in precipitation indices using observed (1977–2016) and modeled future climate data in Portland, Oregon, USA
title_full	Detecting change in precipitation indices using observed (1977–2016) and modeled future climate data in Portland, Oregon, USA
title_fullStr	Detecting change in precipitation indices using observed (1977–2016) and modeled future climate data in Portland, Oregon, USA
title_full_unstemmed	Detecting change in precipitation indices using observed (1977–2016) and modeled future climate data in Portland, Oregon, USA
title_sort	detecting change in precipitation indices using observed (1977–2016) and modeled future climate data in portland, oregon, usa
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/f3705e17d3ca42e0b7b2546a292f7793
work_keys_str_mv	AT alexiskirstencooley detectingchangeinprecipitationindicesusingobserved19772016andmodeledfutureclimatedatainportlandoregonusa AT heejunchang detectingchangeinprecipitationindicesusingobserved19772016andmodeledfutureclimatedatainportlandoregonusa
_version_	1718444139621646336

Detecting change in precipitation indices using observed (1977–2016) and modeled future climate data in Portland, Oregon, USA

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