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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 |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 22, no. 4 ; Nr. 22, no. 4 (2015-07-30), S.433-446 |
| Datensatznummer |
250120992
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| Publikation (Nr.) |
 copernicus.org/npg-22-433-2015.pdf |
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| Zusammenfassung |
| Terrestrial water storage (TWS) exerts a key control in global water, energy,
and biogeochemical cycles. Although certain causal relationship exists
between precipitation and TWS, the latter quantity 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
the hydrologic cycle, but also provide new insights and model calibration
constraints for improving the current land surface models. This work is the
first attempt to quantify the spatial connectivity of TWS using the complex
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 data sets, a remote sensing product that is obtained from the
Gravity Recovery and Climate Experiment (GRACE) satellite mission, and a
model-generated data set from the global land data assimilation system's
NOAH model (GLDAS-NOAH). Both data sets have 1° × 1°
grid 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 cutoff threshold
derived from the edge-density function 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 the TWS anomaly hotspots
around the globe and the patterns are consistent with recent GRACE studies.
Parallel analyses of networks constructed using the two data sets reveal 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 further measures for constraining the current land surface models,
especially in data sparse regions. |
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