Addressing rainfall data selection uncertainty using connections between rainfall and streamflow

Abstract Studies of the hydroclimate at regional scales rely on spatial rainfall data products, derived from remotely-sensed (RS) and in-situ (IS, rain gauge) observations. Because regional rainfall cannot be directly measured, spatial data products are biased. These biases pose a source of uncertai...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Morgan C. Levy, Avery Cohn, Alan Vaz Lopes, Sally E. Thompson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/48d6c41893f34c7f9520eb856f350f5b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:48d6c41893f34c7f9520eb856f350f5b
record_format	dspace
spelling	oai:doaj.org-article:48d6c41893f34c7f9520eb856f350f5b2021-12-02T12:32:54ZAddressing rainfall data selection uncertainty using connections between rainfall and streamflow10.1038/s41598-017-00128-52045-2322https://doaj.org/article/48d6c41893f34c7f9520eb856f350f5b2017-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-00128-5https://doaj.org/toc/2045-2322Abstract Studies of the hydroclimate at regional scales rely on spatial rainfall data products, derived from remotely-sensed (RS) and in-situ (IS, rain gauge) observations. Because regional rainfall cannot be directly measured, spatial data products are biased. These biases pose a source of uncertainty in environmental analyses, attributable to the choices made by data-users in selecting a representation of rainfall. We use the rainforest-savanna transition region in Brazil to show differences in the statistics describing rainfall across nine RS and interpolated-IS daily rainfall datasets covering the period of 1998–2013. These differences propagate into estimates of temporal trends in monthly rainfall and descriptive hydroclimate indices. Rainfall trends from different datasets are inconsistent at river basin scales, and the magnitude of index differences is comparable to the estimated bias in global climate model projections. To address this uncertainty, we evaluate the correspondence of different rainfall datasets with streamflow from 89 river basins. We demonstrate that direct empirical comparisons between rainfall and streamflow provide a method for evaluating rainfall dataset performance across multiple areal (basin) units. These results highlight the need for users of rainfall datasets to quantify this “data selection uncertainty” problem, and either justify data use choices, or report the uncertainty in derived results.Morgan C. LevyAvery CohnAlan Vaz LopesSally E. ThompsonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Morgan C. Levy Avery Cohn Alan Vaz Lopes Sally E. Thompson Addressing rainfall data selection uncertainty using connections between rainfall and streamflow
description	Abstract Studies of the hydroclimate at regional scales rely on spatial rainfall data products, derived from remotely-sensed (RS) and in-situ (IS, rain gauge) observations. Because regional rainfall cannot be directly measured, spatial data products are biased. These biases pose a source of uncertainty in environmental analyses, attributable to the choices made by data-users in selecting a representation of rainfall. We use the rainforest-savanna transition region in Brazil to show differences in the statistics describing rainfall across nine RS and interpolated-IS daily rainfall datasets covering the period of 1998–2013. These differences propagate into estimates of temporal trends in monthly rainfall and descriptive hydroclimate indices. Rainfall trends from different datasets are inconsistent at river basin scales, and the magnitude of index differences is comparable to the estimated bias in global climate model projections. To address this uncertainty, we evaluate the correspondence of different rainfall datasets with streamflow from 89 river basins. We demonstrate that direct empirical comparisons between rainfall and streamflow provide a method for evaluating rainfall dataset performance across multiple areal (basin) units. These results highlight the need for users of rainfall datasets to quantify this “data selection uncertainty” problem, and either justify data use choices, or report the uncertainty in derived results.
format	article
author	Morgan C. Levy Avery Cohn Alan Vaz Lopes Sally E. Thompson
author_facet	Morgan C. Levy Avery Cohn Alan Vaz Lopes Sally E. Thompson
author_sort	Morgan C. Levy
title	Addressing rainfall data selection uncertainty using connections between rainfall and streamflow
title_short	Addressing rainfall data selection uncertainty using connections between rainfall and streamflow
title_full	Addressing rainfall data selection uncertainty using connections between rainfall and streamflow
title_fullStr	Addressing rainfall data selection uncertainty using connections between rainfall and streamflow
title_full_unstemmed	Addressing rainfall data selection uncertainty using connections between rainfall and streamflow
title_sort	addressing rainfall data selection uncertainty using connections between rainfall and streamflow
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/48d6c41893f34c7f9520eb856f350f5b
work_keys_str_mv	AT morganclevy addressingrainfalldataselectionuncertaintyusingconnectionsbetweenrainfallandstreamflow AT averycohn addressingrainfalldataselectionuncertaintyusingconnectionsbetweenrainfallandstreamflow AT alanvazlopes addressingrainfalldataselectionuncertaintyusingconnectionsbetweenrainfallandstreamflow AT sallyethompson addressingrainfalldataselectionuncertaintyusingconnectionsbetweenrainfallandstreamflow
_version_	1718393914784743424

Addressing rainfall data selection uncertainty using connections between rainfall and streamflow

Ejemplares similares