Network motifs shape distinct functioning of Earth’s moisture recycling hubs

Abstract

Earth’s hydrological cycle critically depends on the atmospheric moisture flows connecting evaporation to precipitation. Here we convert a decade of reanalysis-based moisture simulations into a high-resolution global directed network of spatial moisture provisions. We reveal global and local network structures that offer a new view of the global hydrological cycle. We identify four terrestrial moisture recycling hubs: the Amazon Basin, the Congo Rainforest, South Asia and the Indonesian Archipelago. Network motifs reveal contrasting functioning of these regions, where the Amazon strongly relies on directed connections (feed-forward loops) for moisture redistribution and the other hubs on reciprocal moisture connections (zero loops and neighboring loops). We conclude that Earth’s moisture recycling hubs are characterized by specific topologies shaping heterogeneous effects of land-use changes and climatic warming on precipitation patterns.

Local moisture recycling across the globe

“…As the LMR in the Congo Basin exceeds the LMR in the Amazon Basin, deforestation has a relatively large impact on local precipitation in the Congo Basin, suggesting a larger impact on droughts locally. This is further exacerbated by the fact that the Congo Basin, in comparison with the Amazon Basin, has many small-scale moisture feedback loops (Wunderling et al, 2022). Unlike the LMR, basin recycling is similar for both basins (Tuinenburg et al, 2020a).…”

“…However, the LMR can help us better predict the impact of land-cover changes on the local water cycle. Thus, it might help us identify regions where reforestation would not cause local drying due to enhanced evaporation (Hoek van Dijke et al, 2022;Tuinenburg et al, 2022). Overall, the LMR gives us better insight into the atmospheric part of the local water cycle and terrestrial evaporation as a source of local freshwater availability.…”

Networks of climate change: connecting causes and consequences

“…5, we show an example of a network of Granger causality of weather patterns from Hlinka et al (2013). Several papers on this topic focused on how to derive meaningful networks of weather dependencies, others showed that climate networks reflect well-known features of synoptic scale meteorology (Wang et al 2013;Yamasaki et al 2008;Sonone and Gupte 2021), or that climate network can help in predicting extreme events (Boers et al 2014(Boers et al , 2019Malik et al 2012), or the function of regions in the atmospheric circulation (Wunderling et al 2022). Some studies focuses on weather events rather than locations-Fan et al ( 2017) considered a network where nodes are El Niño events, linked by their similarity.…”

Reducing uncertainty of high-latitude ecosystem models through identification of key parameters

“…Motifs are small-scale substructures within interaction networks, which can be important for the overall functioning of a large-scale system and have as such been called the 'building blocks of complex networks' [42,43]. Some examples where motifs are decisive microstructures in larger networks are social networks, gene transcription networks, and networks in ecology [44,45]. One of the most influential microstructures for overall system behavior are feed forward loops (FFLs) (figure 2(a)), a structure in which two nodes are linked through both a direct and an indirect process via one intermediary node [41,46,47].…”

“…The final diagram consists of 30 nodes, which are connected via 127 edges, 71 of which represent positive and 56 of which represent negative interactions (figure 3, supporting file 2). We search for nodes that are targeted by a high number of FFLs, as they may be particularly vulnerable and/or influential in the system [41,45]. We define 'vulnerable' as a parameter that is likely to experience disproportionately large impacts due to changes in the system, and 'influential' as a parameter that is likely to cause large changes in the system.…”