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Terrestrial water storage (TWS) exerts a key control in global water, energy,and biogeochemical cycles. Although certain causal relationship existsbetween precipitation and TWS, the latter quantity also reflects impacts ofanthropogenic activities. Thus, quantification of the spatial patterns of TWSwill not only help to understand feedbacks between climate dynamics andthe hydrologic cycle, but also provide new insights and model calibrationconstraints for improving the current land surface models. This work is thefirst attempt to quantify the spatial connectivity of TWS using the complexnetwork theory, which has received broad attention in the climate modelingcommunity in recent years. Complex networks of TWS anomalies are built usingtwo global TWS data sets, a remote sensing product that is obtained from theGravity Recovery and Climate Experiment (GRACE) satellite mission, and amodel-generated data set from the global land data assimilation system'sNOAH model (GLDAS-NOAH). Both data sets have 1° × 1°grid resolutions and cover most global land areas except for permafrostregions. TWS networks are built by first quantifying pairwise correlationamong all valid TWS anomaly time series, and then applying a cutoff thresholdderived from the edge-density function to retain only the most importantfeatures in the network. Basinwise network connectivity maps are used toilluminate connectivity of individual river basins with other regions. Theconstructed network degree centrality maps show the TWS anomaly hotspotsaround the globe and the patterns are consistent with recent GRACE studies.Parallel analyses of networks constructed using the two data sets reveal thatthe GLDAS-NOAH model captures many of the spatial patterns shown by GRACE,although significant discrepancies exist in some regions. Thus, our resultsprovide further measures for constraining the current land surface models,especially in data sparse regions

Global terrestrial water storage connectivity revealed using complex climate network analyses