Global terrestrial water storage connectivity revealed using complex climate network analyses

Abstract

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.

Using GRACE Satellite Gravimetry for Assessing Large-Scale Hydrologic Extremes

“…The results were used to build a coincidence matrix as shown in Figure 6. The meaning of this BP coincidence matrix is similar to the correlation-based TWSA connectivity map, which measures how TWS in different basins tend to co-vary [87,88]. The difference is that a nonparametric ECA measure is used in our analyses.…”

Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks

“…To construct the adjacency matrix A, we adopted a method commonly used in climate networks by first calculating the pairwise Euclidean distance (edge length) using the 27 static catchment attributes, and then applying a cutoff threshold of 0.98 on the resulting edge length cumulative distribution function (CDF) to trim the graph (Donges et al, 2009;Sun et al, 2015Sun et al, , 2018. This procedure resulted in an undirected graph with 10,706 edges.…”

Customized sea‐surface temperature indicators linking to streamflow at different timescales

“…(2012) employed complex networks to analyse extreme precipitation characteristics of the South American monsoon system and Indian summer monsoon. The complex network extends its application to evaluating streamflow dynamics' spatial connection (Sivakumar and Woldemeskel, 2014) and terrestrial water storage anomalies (Sun et al ., 2015). Overall in all the above applications, Complex networks effectively captured driver–response relationships and established typical spatiotemporal patterns in climate systems.…”