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Global assessment of spatiotemporal variability of wet, normal and dry conditions using multiscale entropy-based approach

In recent decades, human-induced climate change has caused a worldwide increase in the frequency/intensity/duration of extreme events, resulting in enormous disruptions to life and property. Hence, a comprehensive understanding of global-scale spatiotemporal trends and variability of extreme events...

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Autores principales: Sreeparvathy, Vijay, Srinivas, V. V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9192772/
https://www.ncbi.nlm.nih.gov/pubmed/35697830
http://dx.doi.org/10.1038/s41598-022-13830-w
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author Sreeparvathy, Vijay
Srinivas, V. V.
author_facet Sreeparvathy, Vijay
Srinivas, V. V.
author_sort Sreeparvathy, Vijay
collection PubMed
description In recent decades, human-induced climate change has caused a worldwide increase in the frequency/intensity/duration of extreme events, resulting in enormous disruptions to life and property. Hence, a comprehensive understanding of global-scale spatiotemporal trends and variability of extreme events at different intensity levels (e.g., moderate/severe/extreme) and durations (e.g., short-term/long-term) of normal, dry and wet conditions is essential in predicting/forecasting/mitigating future extreme events. This article analyses these aspects using estimates of a non-stationary standardized precipitation evapotranspiration index corresponding to different accumulation periods for 0.5° resolution CRU grids at globe-scale. Results are analyzed with respect to changes in land-use/landcover and geographic/location indicators (latitude, longitude, elevation) at different time scales (decadal/annual/seasonal/monthly) for each continent. The analysis showed an (i) increasing trend in the frequency/count of both dry and wet conditions and variability of dry conditions, and (ii) contrasting (decreasing) trend in the variability of wet conditions, possibly due to climate change-induced variations in atmospheric circulations. Globally, the highest variability in the wet and dry conditions is found during the Northern hemisphere's winter season. The decadal-scale analysis showed that change in variability in dry and wet conditions has been predominant since the 1930s and 1950s, respectively and is found to be increasing in recent decades.
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spelling pubmed-91927722022-06-15 Global assessment of spatiotemporal variability of wet, normal and dry conditions using multiscale entropy-based approach Sreeparvathy, Vijay Srinivas, V. V. Sci Rep Article In recent decades, human-induced climate change has caused a worldwide increase in the frequency/intensity/duration of extreme events, resulting in enormous disruptions to life and property. Hence, a comprehensive understanding of global-scale spatiotemporal trends and variability of extreme events at different intensity levels (e.g., moderate/severe/extreme) and durations (e.g., short-term/long-term) of normal, dry and wet conditions is essential in predicting/forecasting/mitigating future extreme events. This article analyses these aspects using estimates of a non-stationary standardized precipitation evapotranspiration index corresponding to different accumulation periods for 0.5° resolution CRU grids at globe-scale. Results are analyzed with respect to changes in land-use/landcover and geographic/location indicators (latitude, longitude, elevation) at different time scales (decadal/annual/seasonal/monthly) for each continent. The analysis showed an (i) increasing trend in the frequency/count of both dry and wet conditions and variability of dry conditions, and (ii) contrasting (decreasing) trend in the variability of wet conditions, possibly due to climate change-induced variations in atmospheric circulations. Globally, the highest variability in the wet and dry conditions is found during the Northern hemisphere's winter season. The decadal-scale analysis showed that change in variability in dry and wet conditions has been predominant since the 1930s and 1950s, respectively and is found to be increasing in recent decades. Nature Publishing Group UK 2022-06-13 /pmc/articles/PMC9192772/ /pubmed/35697830 http://dx.doi.org/10.1038/s41598-022-13830-w Text en © The Author(s) 2022 https://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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Sreeparvathy, Vijay
Srinivas, V. V.
Global assessment of spatiotemporal variability of wet, normal and dry conditions using multiscale entropy-based approach
title Global assessment of spatiotemporal variability of wet, normal and dry conditions using multiscale entropy-based approach
title_full Global assessment of spatiotemporal variability of wet, normal and dry conditions using multiscale entropy-based approach
title_fullStr Global assessment of spatiotemporal variability of wet, normal and dry conditions using multiscale entropy-based approach
title_full_unstemmed Global assessment of spatiotemporal variability of wet, normal and dry conditions using multiscale entropy-based approach
title_short Global assessment of spatiotemporal variability of wet, normal and dry conditions using multiscale entropy-based approach
title_sort global assessment of spatiotemporal variability of wet, normal and dry conditions using multiscale entropy-based approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9192772/
https://www.ncbi.nlm.nih.gov/pubmed/35697830
http://dx.doi.org/10.1038/s41598-022-13830-w
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