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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 22, issue 4
Nonlin. Processes Geophys., 22, 433–446, 2015
https://doi.org/10.5194/npg-22-433-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Complex network approaches to analyzing and modeling nonlinear...

Nonlin. Processes Geophys., 22, 433–446, 2015
https://doi.org/10.5194/npg-22-433-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 30 Jul 2015

Research article | 30 Jul 2015

Global terrestrial water storage connectivity revealed using complex climate network analyses

A. Y. Sun1, J. Chen2, and J. Donges3,4 A. Y. Sun et al.
  • 1Bureau of Economic Geology, the Jackson School of Geosciences, University of Texas at Austin, University Station, Box X, Austin, Texas, USA
  • 2Center for Space Research, University of Texas at Austin, Austin, Texas, USA
  • 3Potsdam Institute for Climate Impact Research, Potsdam, Germany
  • 4Stockholm Resilience Center, Stockholm University, Stockholm, Sweden

Abstract. 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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Short summary
Terrestrial water storage (TWS) plays a key role in global water and energy cycles. This work applies complex climate networks to analyzing spatial patterns in TWS. A comparative analysis is conducted using a remotely sensed (GRACE) and a model-generated TWS data set. Our results reveal hotspots of TWS anomalies around the global land surfaces. Prospects are offered on using network connectivity as constraints to further improve current global land surface models.
Terrestrial water storage (TWS) plays a key role in global water and energy cycles. This work...
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