Under global warming, the intensification of the global water cycle has led to an increased frequency of alternating drought and flood events, making such extreme conditions seemingly the new normal. These frequent events are causing more pronounced fluctuations in terrestrial water storage anomalies (TWSA). In this study, we introduce a novel yet straightforward metric—the standard deviation of TWSA (STDTWSA)—to quantify these fluctuations and gain insight into changes in the global water cycle. TWSA estimates from the Center for Space Research (CSR) are used to calculate STDTWSA, and the results are compared with fluctuations in precipitation anomalies (PA) and temperature anomalies (TA) to explore their connections with TWSA fluctuations. Our results indicate that 55.7% of the global land grid cells show a significant increase in TWSA fluctuations, while only 2.2% exhibit a noticeable decrease. Among the 40 large global basins analyzed, 11 basins display significant upward trends in STDTWSA, while only two basins show significant downward trends. Furthermore, 36 of the 40 basins show a positive correlation between STDTWSA and PA fluctuations, with 17 basins exhibiting a statistically significant correlation. Additionally, 6 basins show a significant positive correlation between STDTWSA and TA fluctuations, which corresponds with those basins that also have significant correlations between STDTWSA and PA fluctuations. This study provides valuable insights into global TWSA fluctuations and enhances our understanding of the impacts of global climate change on the water cycle.Figure 1. A simple schematic diagram demonstrates the large (red) and small (blue) fluctuations of TWSA in one basin.Figure 2. Spatial distribution of global grid trends in 17 STDTWSA series from 2002 to 2022. α < 0.05 is used to assess the significance of the estimated trend, and the grey shading indicates that the trend does not pass the significance test. It should be noted that the unit is expressed as 10-1mm considering the small trend term.Figure 3. Time series of STDTWSA, STDPA, and STDTA in 40 global large basins. The detrended and deseasonalized TWSAs are used to derive STDTWSA.
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Zhong et al. (2024) studied this question.
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