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Global terrestrial water storage connectivity revealed using complex climate network analyses
Stockholm University, Faculty of Science, Stockholm Resilience Centre. Potsdam Institute for Climate Impact Research, Germany.
Number of Authors: 3
2015 (English)In: Nonlinear processes in geophysics, ISSN 1023-5809, E-ISSN 1607-7946, Vol. 22, no 4, 433-446 p.Article in journal (Refereed) Published
Abstract [en]

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 feed-backs 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 degrees x 1 degrees 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.

Place, publisher, year, edition, pages
2015. Vol. 22, no 4, 433-446 p.
National Category
Earth and Related Environmental Sciences
URN: urn:nbn:se:su:diva-121189DOI: 10.5194/npg-22-433-2015ISI: 000360655400007OAI: diva2:857404
Available from: 2015-09-29 Created: 2015-09-28 Last updated: 2015-09-29Bibliographically approved

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Donges, Jonathan
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