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Forecasting dryland vegetation condition months in advance through satellite data assimilation

Dryland ecosystems are characterised by rainfall variability and strong vegetation response to changes in water availability over a range of timescales. Forecasting dryland vegetation condition can be of great value in planning agricultural decisions, drought relief, land management and fire prepare...

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Autores principales: Tian, Siyuan, Van Dijk, Albert I. J. M., Tregoning, Paul, Renzullo, Luigi J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6349931/
https://www.ncbi.nlm.nih.gov/pubmed/30692539
http://dx.doi.org/10.1038/s41467-019-08403-x
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author Tian, Siyuan
Van Dijk, Albert I. J. M.
Tregoning, Paul
Renzullo, Luigi J.
author_facet Tian, Siyuan
Van Dijk, Albert I. J. M.
Tregoning, Paul
Renzullo, Luigi J.
author_sort Tian, Siyuan
collection PubMed
description Dryland ecosystems are characterised by rainfall variability and strong vegetation response to changes in water availability over a range of timescales. Forecasting dryland vegetation condition can be of great value in planning agricultural decisions, drought relief, land management and fire preparedness. At monthly to seasonal time scales, knowledge of water stored in the system contributes more to predictability than knowledge of the climate system state. However, realising forecast skill requires knowledge of the vertical distribution of moisture below the surface and the capacity of the vegetation to access this moisture. Here, we demonstrate that contrasting satellite observations of water presence over different vertical domains can be assimilated into an eco-hydrological model and combined with vegetation observations to infer an apparent vegetation-accessible water storage (hereafter called accessible storage). Provided this variable is considered explicitly, skilful forecasts of vegetation condition are achievable several months in advance for most of the world’s drylands.
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spelling pubmed-63499312019-01-30 Forecasting dryland vegetation condition months in advance through satellite data assimilation Tian, Siyuan Van Dijk, Albert I. J. M. Tregoning, Paul Renzullo, Luigi J. Nat Commun Article Dryland ecosystems are characterised by rainfall variability and strong vegetation response to changes in water availability over a range of timescales. Forecasting dryland vegetation condition can be of great value in planning agricultural decisions, drought relief, land management and fire preparedness. At monthly to seasonal time scales, knowledge of water stored in the system contributes more to predictability than knowledge of the climate system state. However, realising forecast skill requires knowledge of the vertical distribution of moisture below the surface and the capacity of the vegetation to access this moisture. Here, we demonstrate that contrasting satellite observations of water presence over different vertical domains can be assimilated into an eco-hydrological model and combined with vegetation observations to infer an apparent vegetation-accessible water storage (hereafter called accessible storage). Provided this variable is considered explicitly, skilful forecasts of vegetation condition are achievable several months in advance for most of the world’s drylands. Nature Publishing Group UK 2019-01-28 /pmc/articles/PMC6349931/ /pubmed/30692539 http://dx.doi.org/10.1038/s41467-019-08403-x Text en © The Author(s) 2019 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/.
spellingShingle Article
Tian, Siyuan
Van Dijk, Albert I. J. M.
Tregoning, Paul
Renzullo, Luigi J.
Forecasting dryland vegetation condition months in advance through satellite data assimilation
title Forecasting dryland vegetation condition months in advance through satellite data assimilation
title_full Forecasting dryland vegetation condition months in advance through satellite data assimilation
title_fullStr Forecasting dryland vegetation condition months in advance through satellite data assimilation
title_full_unstemmed Forecasting dryland vegetation condition months in advance through satellite data assimilation
title_short Forecasting dryland vegetation condition months in advance through satellite data assimilation
title_sort forecasting dryland vegetation condition months in advance through satellite data assimilation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6349931/
https://www.ncbi.nlm.nih.gov/pubmed/30692539
http://dx.doi.org/10.1038/s41467-019-08403-x
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