Effect of Time-Resolution of Rainfall Data on Trend Estimation for Annual Maximum Depths with a Duration of 24 Hours

The main challenge of this paper is to demonstrate that one of the most frequently conducted analyses in the climate change field could be affected by significant errors, due to the use of rainfall data characterized by coarse time-resolution. In fact, in the scientific literature, there are many st...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Renato Morbidelli, Carla Saltalippi, Jacopo Dari, Alessia Flammini
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	rainfall data measurements rainfall time resolution extreme rainfall annual maximum rainfall depths trend analysis Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
Acceso en línea:	https://doaj.org/article/4cd26a559ac24569a49c110d248e2f12
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:4cd26a559ac24569a49c110d248e2f12
record_format	dspace
spelling	oai:doaj.org-article:4cd26a559ac24569a49c110d248e2f122021-11-25T19:16:09ZEffect of Time-Resolution of Rainfall Data on Trend Estimation for Annual Maximum Depths with a Duration of 24 Hours10.3390/w132232642073-4441https://doaj.org/article/4cd26a559ac24569a49c110d248e2f122021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4441/13/22/3264https://doaj.org/toc/2073-4441The main challenge of this paper is to demonstrate that one of the most frequently conducted analyses in the climate change field could be affected by significant errors, due to the use of rainfall data characterized by coarse time-resolution. In fact, in the scientific literature, there are many studies to verify the possible impacts of climate change on extreme rainfall, and particularly on annual maximum rainfall depths, <i>H<sub>d</sub></i>, characterized by duration <i>d</i> equal to 24 h, due to the significant length of the corresponding series. Typically, these studies do not specify the temporal aggregation, <i>t<sub>a</sub></i>, of the rainfall data on which maxima rely, although it is well known that the use of rainfall data with coarse <i>t<sub>a</sub></i> can lead to significant underestimates of <i>H<sub>d</sub></i>. The effect of <i>t<sub>a</sub></i> on the estimation of trends in annual maximum depths with <i>d</i> = 24 h, <i>H<sub>d=</sub></i><sub>24 <i>h</i>,</sub> over the last 100 years is examined. We have used a published series of <i>H<sub>d=</sub></i><sub>24 <i>h</i></sub> derived by long-term historical rainfall observations with various temporal aggregations, due to the progress of recording systems through time, at 39 representative meteorological stations located in an inland region of Central Italy. Then, by using a recently developed mathematical relation between average underestimation error and the ratio <i>t<sub>a</sub></i>/<i>d</i>, each <i>H<sub>d=</sub></i><sub>24 <i>h</i></sub> value has been corrected. Successively, commonly used climatic trend tests based on different approaches, including least-squares linear trend analysis, Mann–Kendall, and Sen’s method, have been applied to the “uncorrected” and “corrected” series. The results show that the underestimation of <i>H<sub>d=</sub></i><sub>24 <i>h</i></sub> values with coarse <i>t<sub>a</sub></i> plays a significant role in the analysis of the effects of climatic change on extreme rainfalls. Specifically, the correction of the <i>H<sub>d=</sub></i><sub>24 <i>h</i></sub> values can change the sign of the trend from positive to negative. Furthermore, it has been observed that the innovative Sen’s method (based on a graphical approach) is less sensitive to corrections of the <i>H<sub>d</sub></i> values than the least-squares linear trend and the Mann–Kendall method. In any case, the analysis of <i>H<sub>d</sub></i> series containing potentially underestimated values, especially when <i>d</i> = 24 h, can lead to misleading results. Therefore, before conducting any trend analysis, <i>H<sub>d</sub></i> values determined from rainfall data characterized by coarse temporal resolution should always be corrected.Renato MorbidelliCarla SaltalippiJacopo DariAlessia FlamminiMDPI AGarticlerainfall data measurementsrainfall time resolutionextreme rainfallannual maximum rainfall depthstrend analysisHydraulic engineeringTC1-978Water supply for domestic and industrial purposesTD201-500ENWater, Vol 13, Iss 3264, p 3264 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	rainfall data measurements rainfall time resolution extreme rainfall annual maximum rainfall depths trend analysis Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
spellingShingle	rainfall data measurements rainfall time resolution extreme rainfall annual maximum rainfall depths trend analysis Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 Renato Morbidelli Carla Saltalippi Jacopo Dari Alessia Flammini Effect of Time-Resolution of Rainfall Data on Trend Estimation for Annual Maximum Depths with a Duration of 24 Hours
description	The main challenge of this paper is to demonstrate that one of the most frequently conducted analyses in the climate change field could be affected by significant errors, due to the use of rainfall data characterized by coarse time-resolution. In fact, in the scientific literature, there are many studies to verify the possible impacts of climate change on extreme rainfall, and particularly on annual maximum rainfall depths, <i>H<sub>d</sub></i>, characterized by duration <i>d</i> equal to 24 h, due to the significant length of the corresponding series. Typically, these studies do not specify the temporal aggregation, <i>t<sub>a</sub></i>, of the rainfall data on which maxima rely, although it is well known that the use of rainfall data with coarse <i>t<sub>a</sub></i> can lead to significant underestimates of <i>H<sub>d</sub></i>. The effect of <i>t<sub>a</sub></i> on the estimation of trends in annual maximum depths with <i>d</i> = 24 h, <i>H<sub>d=</sub></i><sub>24 <i>h</i>,</sub> over the last 100 years is examined. We have used a published series of <i>H<sub>d=</sub></i><sub>24 <i>h</i></sub> derived by long-term historical rainfall observations with various temporal aggregations, due to the progress of recording systems through time, at 39 representative meteorological stations located in an inland region of Central Italy. Then, by using a recently developed mathematical relation between average underestimation error and the ratio <i>t<sub>a</sub></i>/<i>d</i>, each <i>H<sub>d=</sub></i><sub>24 <i>h</i></sub> value has been corrected. Successively, commonly used climatic trend tests based on different approaches, including least-squares linear trend analysis, Mann–Kendall, and Sen’s method, have been applied to the “uncorrected” and “corrected” series. The results show that the underestimation of <i>H<sub>d=</sub></i><sub>24 <i>h</i></sub> values with coarse <i>t<sub>a</sub></i> plays a significant role in the analysis of the effects of climatic change on extreme rainfalls. Specifically, the correction of the <i>H<sub>d=</sub></i><sub>24 <i>h</i></sub> values can change the sign of the trend from positive to negative. Furthermore, it has been observed that the innovative Sen’s method (based on a graphical approach) is less sensitive to corrections of the <i>H<sub>d</sub></i> values than the least-squares linear trend and the Mann–Kendall method. In any case, the analysis of <i>H<sub>d</sub></i> series containing potentially underestimated values, especially when <i>d</i> = 24 h, can lead to misleading results. Therefore, before conducting any trend analysis, <i>H<sub>d</sub></i> values determined from rainfall data characterized by coarse temporal resolution should always be corrected.
format	article
author	Renato Morbidelli Carla Saltalippi Jacopo Dari Alessia Flammini
author_facet	Renato Morbidelli Carla Saltalippi Jacopo Dari Alessia Flammini
author_sort	Renato Morbidelli
title	Effect of Time-Resolution of Rainfall Data on Trend Estimation for Annual Maximum Depths with a Duration of 24 Hours
title_short	Effect of Time-Resolution of Rainfall Data on Trend Estimation for Annual Maximum Depths with a Duration of 24 Hours
title_full	Effect of Time-Resolution of Rainfall Data on Trend Estimation for Annual Maximum Depths with a Duration of 24 Hours
title_fullStr	Effect of Time-Resolution of Rainfall Data on Trend Estimation for Annual Maximum Depths with a Duration of 24 Hours
title_full_unstemmed	Effect of Time-Resolution of Rainfall Data on Trend Estimation for Annual Maximum Depths with a Duration of 24 Hours
title_sort	effect of time-resolution of rainfall data on trend estimation for annual maximum depths with a duration of 24 hours
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4cd26a559ac24569a49c110d248e2f12
work_keys_str_mv	AT renatomorbidelli effectoftimeresolutionofrainfalldataontrendestimationforannualmaximumdepthswithadurationof24hours AT carlasaltalippi effectoftimeresolutionofrainfalldataontrendestimationforannualmaximumdepthswithadurationof24hours AT jacopodari effectoftimeresolutionofrainfalldataontrendestimationforannualmaximumdepthswithadurationof24hours AT alessiaflammini effectoftimeresolutionofrainfalldataontrendestimationforannualmaximumdepthswithadurationof24hours
_version_	1718410088940568576

Effect of Time-Resolution of Rainfall Data on Trend Estimation for Annual Maximum Depths with a Duration of 24 Hours

Ejemplares similares