Long-term, non-anthropogenic groundwater storage changes simulated by three global-scale hydrological models
Long-term, non-anthropogenic groundwater storage changes simulated by three global-scale hydrological models
This study examined long-term, natural (i.e., excluding anthropogenic impacts) variability of groundwater storage worldwide. Groundwater storage changes were estimated by forcing three global-scale hydrological models with three 50+ year meteorological datasets. Evaluation using in situ groundwater observations from the U.S. and terrestrial water storage derived from the Gravity Recovery and Climate Experiment (GRACE) satellites showed that these models reasonably represented inter-annual variability of water storage, as indicated by correlations greater than 0.5 in most regions. Empirical orthogonal function analysis revealed influences of the El Niño Southern Oscillation (ENSO) on global groundwater storage. Simulated groundwater storage, including its global average, exhibited trends generally consistent with that of precipitation. Global total (natural) groundwater storage decreased over the past 5–7 decades with modeled rates ranging from 0.01 to 2.18 mm year−1. This large range can be attributed in part to groundwater’s low frequency (inter-decadal) variability, which complicates identification of real long-term trends even within a 50+ year time series. Results indicate that non-anthropogenic variability in groundwater storage is substantial, making knowledge of it fundamental to quantifying direct human impacts on groundwater storage.
1-13
Li, Bailing
af6564bf-3e1d-41a1-a885-61ba52ca0550
Rodell, Matthew
8601b311-4098-4f9d-a19f-925ccea0a813
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Wood, Eric
8352c1b4-4fd3-42fe-bd23-46619024f1cf
Sutanudjaja, Edwin
b6e11f36-3b6b-4740-97ec-bc0a7e31ede2
1 December 2019
Li, Bailing
af6564bf-3e1d-41a1-a885-61ba52ca0550
Rodell, Matthew
8601b311-4098-4f9d-a19f-925ccea0a813
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Wood, Eric
8352c1b4-4fd3-42fe-bd23-46619024f1cf
Sutanudjaja, Edwin
b6e11f36-3b6b-4740-97ec-bc0a7e31ede2
Li, Bailing, Rodell, Matthew, Sheffield, Justin, Wood, Eric and Sutanudjaja, Edwin
(2019)
Long-term, non-anthropogenic groundwater storage changes simulated by three global-scale hydrological models.
Scientific Reports, 9 (1), , [10746].
(doi:10.1038/s41598-019-47219-z).
Abstract
This study examined long-term, natural (i.e., excluding anthropogenic impacts) variability of groundwater storage worldwide. Groundwater storage changes were estimated by forcing three global-scale hydrological models with three 50+ year meteorological datasets. Evaluation using in situ groundwater observations from the U.S. and terrestrial water storage derived from the Gravity Recovery and Climate Experiment (GRACE) satellites showed that these models reasonably represented inter-annual variability of water storage, as indicated by correlations greater than 0.5 in most regions. Empirical orthogonal function analysis revealed influences of the El Niño Southern Oscillation (ENSO) on global groundwater storage. Simulated groundwater storage, including its global average, exhibited trends generally consistent with that of precipitation. Global total (natural) groundwater storage decreased over the past 5–7 decades with modeled rates ranging from 0.01 to 2.18 mm year−1. This large range can be attributed in part to groundwater’s low frequency (inter-decadal) variability, which complicates identification of real long-term trends even within a 50+ year time series. Results indicate that non-anthropogenic variability in groundwater storage is substantial, making knowledge of it fundamental to quantifying direct human impacts on groundwater storage.
Text
s41598-019-47219-z
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Accepted/In Press date: 9 July 2019
e-pub ahead of print date: 24 July 2019
Published date: 1 December 2019
Identifiers
Local EPrints ID: 433044
URI: http://eprints.soton.ac.uk/id/eprint/433044
ISSN: 2045-2322
PURE UUID: fee4f1ca-57fb-4f39-874e-4746a0a898a3
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Date deposited: 07 Aug 2019 16:30
Last modified: 16 Apr 2024 01:47
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Author:
Bailing Li
Author:
Matthew Rodell
Author:
Eric Wood
Author:
Edwin Sutanudjaja
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