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Lagged compound occurrence of droughts and pluvials globally over the past seven decades

Lagged compound occurrence of droughts and pluvials globally over the past seven decades
Lagged compound occurrence of droughts and pluvials globally over the past seven decades

The drought-pluvial seesaw—defined as the phenomenon of pluvials (wet spells) following droughts (dry spells)—magnifies the impact of individual pluvial and drought events yet has not been systematically evaluated, especially at the global scale. We apply an event coincidence analysis to explore the aggregated seesaw behavior based on land surface model simulations for the past seven decades (1950–2016). We find that globally, about 5.9% and 7.6% of the land surface have experienced statistically significant (p<0.10) drought-pluvial seesaw behavior during the boreal spring-summer and fall-winter, with an average 11.1% and 11.4% of all droughts being followed by pluvials in the following season, respectively. Although this global frequency pattern is modest and coherent changes cannot be detected at the subcontinental scale, local hot spots of drought-pluvial seesaw have become more frequent than either droughts or pluvials alone in the last three decades, albeit with a small percentage of area coverage.

Coincidence, Compounding events, Droughts, Pluvials, Seesaw
0094-8276
Sheffield, Justin
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He, Xiaogang
5fd2fdc9-b14e-4010-8490-c02950d0a62a
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
He, Xiaogang
5fd2fdc9-b14e-4010-8490-c02950d0a62a

Sheffield, Justin and He, Xiaogang (2020) Lagged compound occurrence of droughts and pluvials globally over the past seven decades. Geophysical Research Letters, 47 (14), [e2020GL087924]. (doi:10.1029/2020GL087924).

Record type: Article

Abstract

The drought-pluvial seesaw—defined as the phenomenon of pluvials (wet spells) following droughts (dry spells)—magnifies the impact of individual pluvial and drought events yet has not been systematically evaluated, especially at the global scale. We apply an event coincidence analysis to explore the aggregated seesaw behavior based on land surface model simulations for the past seven decades (1950–2016). We find that globally, about 5.9% and 7.6% of the land surface have experienced statistically significant (p<0.10) drought-pluvial seesaw behavior during the boreal spring-summer and fall-winter, with an average 11.1% and 11.4% of all droughts being followed by pluvials in the following season, respectively. Although this global frequency pattern is modest and coherent changes cannot be detected at the subcontinental scale, local hot spots of drought-pluvial seesaw have become more frequent than either droughts or pluvials alone in the last three decades, albeit with a small percentage of area coverage.

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2020GL087924 - Accepted Manuscript
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Accepted/In Press date: 17 April 2020
e-pub ahead of print date: 13 May 2020
Published date: 28 July 2020
Additional Information: Funding Information: This material was based upon work supported by NOAA grant NA14OAR4310218 and the Princeton Environmental Institute at Princeton University through the Mary and Randall Hack '69 Research Fund. This work was also funded through the 'Building REsearch Capacity for sustainable water and food security in drylands of Sub-Saharan Africa' (BRECcIA) which is supported by UK Research and Innovation as part of the Global Challenges Research Fund Grant NE/P021093/1. The latest Princeton Global Forcing (PGF) datasets, VIC land surface model simulations, and derived drought and pluvial indices are available at: https://hydrology.princeton.edu/data/hexg/GDFC/. Details can be found in He et al. (2020). Publisher Copyright: ©2020. American Geophysical Union. All Rights Reserved.
Keywords: Coincidence, Compounding events, Droughts, Pluvials, Seesaw

Identifiers

Local EPrints ID: 441637
URI: http://eprints.soton.ac.uk/id/eprint/441637
ISSN: 0094-8276
PURE UUID: 43748cb1-fb62-4d0d-9168-b06166060bad
ORCID for Justin Sheffield: ORCID iD orcid.org/0000-0003-2400-0630

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Date deposited: 23 Jun 2020 16:30
Last modified: 17 Mar 2024 05:39

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Author: Xiaogang He

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