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A 3 km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015

High-resolution soil moisture (SM) information is essential to many regional applications in hydrological and climate sciences. Many global estimates of surface SM are provided by satellite sensors, but at coarse spatial resolutions (lower than 25 km), which are not suitable for regional hydrologic...

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Autores principales: Naz, Bibi S., Kollet, Stefan, Franssen, Harrie-Jan Hendricks, Montzka, Carsten, Kurtz, Wolfgang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7125156/
https://www.ncbi.nlm.nih.gov/pubmed/32245972
http://dx.doi.org/10.1038/s41597-020-0450-6
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author Naz, Bibi S.
Kollet, Stefan
Franssen, Harrie-Jan Hendricks
Montzka, Carsten
Kurtz, Wolfgang
author_facet Naz, Bibi S.
Kollet, Stefan
Franssen, Harrie-Jan Hendricks
Montzka, Carsten
Kurtz, Wolfgang
author_sort Naz, Bibi S.
collection PubMed
description High-resolution soil moisture (SM) information is essential to many regional applications in hydrological and climate sciences. Many global estimates of surface SM are provided by satellite sensors, but at coarse spatial resolutions (lower than 25 km), which are not suitable for regional hydrologic and agriculture applications. Here we present a 16 years (2000–2015) high-resolution spatially and temporally consistent surface soil moisture reanalysis (ESSMRA) dataset (3 km, daily) over Europe from a land surface data assimilation system. Coarse-resolution satellite derived soil moisture data were assimilated into the community land model (CLM3.5) using an ensemble Kalman filter scheme, producing a 3 km daily soil moisture reanalysis dataset. Validation against 112 in-situ soil moisture observations over Europe shows that ESSMRA captures the daily, inter-annual, intra-seasonal patterns well with RMSE varying from 0.04 to 0.06 m(3)m(−3) and correlation values above 0.5 over 70% of stations. The dataset presented here provides long-term daily surface soil moisture at a high spatiotemporal resolution and will be beneficial for many hydrological applications over regional and continental scales.
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spelling pubmed-71251562020-04-13 A 3 km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015 Naz, Bibi S. Kollet, Stefan Franssen, Harrie-Jan Hendricks Montzka, Carsten Kurtz, Wolfgang Sci Data Data Descriptor High-resolution soil moisture (SM) information is essential to many regional applications in hydrological and climate sciences. Many global estimates of surface SM are provided by satellite sensors, but at coarse spatial resolutions (lower than 25 km), which are not suitable for regional hydrologic and agriculture applications. Here we present a 16 years (2000–2015) high-resolution spatially and temporally consistent surface soil moisture reanalysis (ESSMRA) dataset (3 km, daily) over Europe from a land surface data assimilation system. Coarse-resolution satellite derived soil moisture data were assimilated into the community land model (CLM3.5) using an ensemble Kalman filter scheme, producing a 3 km daily soil moisture reanalysis dataset. Validation against 112 in-situ soil moisture observations over Europe shows that ESSMRA captures the daily, inter-annual, intra-seasonal patterns well with RMSE varying from 0.04 to 0.06 m(3)m(−3) and correlation values above 0.5 over 70% of stations. The dataset presented here provides long-term daily surface soil moisture at a high spatiotemporal resolution and will be beneficial for many hydrological applications over regional and continental scales. Nature Publishing Group UK 2020-04-03 /pmc/articles/PMC7125156/ /pubmed/32245972 http://dx.doi.org/10.1038/s41597-020-0450-6 Text en © The Author(s) 2020 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 associated with this article.
spellingShingle Data Descriptor
Naz, Bibi S.
Kollet, Stefan
Franssen, Harrie-Jan Hendricks
Montzka, Carsten
Kurtz, Wolfgang
A 3 km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015
title A 3 km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015
title_full A 3 km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015
title_fullStr A 3 km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015
title_full_unstemmed A 3 km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015
title_short A 3 km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015
title_sort 3 km spatially and temporally consistent european daily soil moisture reanalysis from 2000 to 2015
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7125156/
https://www.ncbi.nlm.nih.gov/pubmed/32245972
http://dx.doi.org/10.1038/s41597-020-0450-6
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