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| Titel |
Global terrestrial water storage connectivity revealed using complex climate network analyses |
| VerfasserIn |
A. Y. Sun, J. Chen, J. Donges |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
2198-5634
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics Discussions ; 2, no. 2 ; Nr. 2, no. 2 (2015-04-30), S.781-809 |
| Datensatznummer |
250115162
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| Publikation (Nr.) |
 copernicus.org/npgd-2-781-2015.pdf |
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| Zusammenfassung |
| Terrestrial water storage (TWS) exerts a key control in global
water, energy, and biogeochemical cycles. Although certain causal
relationships exist between precipitation and TWS, the latter also
reflects impacts of anthropogenic activities. Thus, quantification
of the spatial patterns of TWS will not only help to understand
feedbacks between climate dynamics and hydrologic cycle, but also
provide new model calibration constraints for improving the current
land surface models. In this work, the connectivity of TWS is
quantified using the climate network theory, which has received
broad attention in the climate modeling community in recent
years. Complex networks of TWS anomalies are built using two global
TWS datasets, a remote-sensing product that is obtained from the
Gravity Recovery and Climate Experiment (GRACE) satellite mission,
and a model-generated dataset from the global land data assimilation
system's NOAH model (GLDAS-NOAH). Both datasets have
1 ° × 1 ° resolutions and cover most global
land areas except for permafrost regions. TWS networks are built by
first quantifying pairwise correlation among all valid TWS anomaly
time series, and then applying a statistical cutoff threshold to
retain only the most important features in the network. Basinwise
network connectivity maps are used to illuminate connectivity of
individual river basins with other regions. The constructed network
degree centrality maps show TWS hotspots around the globe and the
patterns are consistent with recent GRACE studies. Parallel analyses
of networks constructed using the two datasets indicate that the
GLDAS-NOAH model captures many of the spatial patterns shown by
GRACE, although significant discrepancies exist in some
regions. Thus, our results provide important insights for
constraining land surface models, especially in data sparse regions. |
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