Re-assessing global water storage trends from GRACE time series

Bramha Dutt Vishwakarma*, Paul D Bates, Nico Sneeuw, Richard M Westaway, Jonathan L Bamber

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

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Monitoring changes in freshwater availability is critical for human society and sustainable economic development. To identify regions experiencing secular change in their water resources, many studies compute linear trends in the Total Water Storage (TWS) anomaly derived from the Gravity Recovery And Climate Experiment (GRACE) mission data. Such analyses suggest that several major water systems are under stress (1-6). TWS varies in space and time due to low frequency natural variability, anthropogenic intervention, and climate-change (7, 8). Therefore, linear trends from a short time series can only be interpreted in a meaningful way after accounting for natural spatiotemporal variability in TWS (9, 10). In this study, we first show that GRACE TWS trends from a short time series cannot determine conclusively if an observed change is unprecedented or severe. To address this limitation, we develop a novel metric, Trend to Variability Ratio (TVR), that assesses the severity of TWS trends observed by GRACE from 2003–2015 relative to the multi-decadal climate-driven variability. We demonstrate that the TVR combined with the trend provides a more informative and complete assessment of water storage change. We show that similar trends imply markedly different severity of TWS change, depending on location. Currently more than 3.2 billion people are living in regions facing severe water storage depletion w.r.t past decades. Furthermore, nearly 36% of hydrological catchments losing water in the last decade have suffered from unprecedented loss. Inferences from this study can better inform water resource management.
Original languageEnglish
JournalEnvironmental Research Letters
Publication statusAccepted/In press - 17 Dec 2020

Structured keywords

  • GlobalMass


  • linear trends
  • spatiotemporal variability

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