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FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015

Streamflow data is highly relevant for a variety of socio-economic as well as ecological analyses or applications, but a high-resolution global streamflow dataset is yet lacking. We created FLO1K, a consistent streamflow dataset at a resolution of 30 arc seconds (~1 km) and global coverage. FLO1K co...

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Autores principales: Barbarossa, Valerio, Huijbregts, Mark A.J., Beusen, Arthur H.W., Beck, Hylke E., King, Henry, Schipper, Aafke M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870339/
https://www.ncbi.nlm.nih.gov/pubmed/29583139
http://dx.doi.org/10.1038/sdata.2018.52
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author Barbarossa, Valerio
Huijbregts, Mark A.J.
Beusen, Arthur H.W.
Beck, Hylke E.
King, Henry
Schipper, Aafke M.
author_facet Barbarossa, Valerio
Huijbregts, Mark A.J.
Beusen, Arthur H.W.
Beck, Hylke E.
King, Henry
Schipper, Aafke M.
author_sort Barbarossa, Valerio
collection PubMed
description Streamflow data is highly relevant for a variety of socio-economic as well as ecological analyses or applications, but a high-resolution global streamflow dataset is yet lacking. We created FLO1K, a consistent streamflow dataset at a resolution of 30 arc seconds (~1 km) and global coverage. FLO1K comprises mean, maximum and minimum annual flow for each year in the period 1960–2015, provided as spatially continuous gridded layers. We mapped streamflow by means of artificial neural networks (ANNs) regression. An ensemble of ANNs were fitted on monthly streamflow observations from 6600 monitoring stations worldwide, i.e., minimum and maximum annual flows represent the lowest and highest mean monthly flows for a given year. As covariates we used the upstream-catchment physiography (area, surface slope, elevation) and year-specific climatic variables (precipitation, temperature, potential evapotranspiration, aridity index and seasonality indices). Confronting the maps with independent data indicated good agreement (R(2) values up to 91%). FLO1K delivers essential data for freshwater ecology and water resources analyses at a global scale and yet high spatial resolution.
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spelling pubmed-58703392018-04-06 FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015 Barbarossa, Valerio Huijbregts, Mark A.J. Beusen, Arthur H.W. Beck, Hylke E. King, Henry Schipper, Aafke M. Sci Data Data Descriptor Streamflow data is highly relevant for a variety of socio-economic as well as ecological analyses or applications, but a high-resolution global streamflow dataset is yet lacking. We created FLO1K, a consistent streamflow dataset at a resolution of 30 arc seconds (~1 km) and global coverage. FLO1K comprises mean, maximum and minimum annual flow for each year in the period 1960–2015, provided as spatially continuous gridded layers. We mapped streamflow by means of artificial neural networks (ANNs) regression. An ensemble of ANNs were fitted on monthly streamflow observations from 6600 monitoring stations worldwide, i.e., minimum and maximum annual flows represent the lowest and highest mean monthly flows for a given year. As covariates we used the upstream-catchment physiography (area, surface slope, elevation) and year-specific climatic variables (precipitation, temperature, potential evapotranspiration, aridity index and seasonality indices). Confronting the maps with independent data indicated good agreement (R(2) values up to 91%). FLO1K delivers essential data for freshwater ecology and water resources analyses at a global scale and yet high spatial resolution. Nature Publishing Group 2018-03-27 /pmc/articles/PMC5870339/ /pubmed/29583139 http://dx.doi.org/10.1038/sdata.2018.52 Text en Copyright © 2018, The Author(s) http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article.
spellingShingle Data Descriptor
Barbarossa, Valerio
Huijbregts, Mark A.J.
Beusen, Arthur H.W.
Beck, Hylke E.
King, Henry
Schipper, Aafke M.
FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015
title FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015
title_full FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015
title_fullStr FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015
title_full_unstemmed FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015
title_short FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015
title_sort flo1k, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870339/
https://www.ncbi.nlm.nih.gov/pubmed/29583139
http://dx.doi.org/10.1038/sdata.2018.52
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