Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data

By Bridget R. Scanlon, Zizhan Zhang, H. Save, Alexander Y. Sun, Hannes Müller Schmied, L. V. van Beek, David Wiese, Yoshihide Wada, D. Long, R. Reedy and 3 more. Proceedings of the National Academy of Sciences of the United States of America, 2018

611 citations recorded by the source corpus.

Significance We increasingly rely on global models to project impacts of humans and climate on water resources. How reliable are these models? While past model intercomparison projects focused on water fluxes, we provide here the first comprehensive comparison of land total water storage trends from seven global models to trends from Gravity Recovery and Climate Experiment (GRACE) satellites, which have been likened to giant weighing scales in the sky. The models underestimate the large decadal (2002–2014) trends in water storage relative to GRACE satellites, both decreasing trends related to human intervention and climate and increasing trends related primarily to climate variations. The poor agreement between models and GRACE underscores the challenges remaining for global models to capture human or climate impacts on global water storage trends. Assessing reliability of global models is critical because of increasing reliance on these models to address past and projected future climate and human stresses on global water resources. Here, we evaluate model reliability based on a comprehensive comparison of decadal trends (2002–2014) in land water storage from seven global models (WGHM, PCR-GLOBWB, GLDAS NOAH, MOSAIC, VIC, CLM, and CLSM) to trends from three Gravity Recovery and Climate Experiment (GRACE) satellite solutions in 186 river basins (∼60% of global land area). Medians of modeled basin water storage trends greatly underestimate GRACE-derived large decreasing (≤−0.5 km3/y) and increasing (≥0.5 km3/y) trends. Decreasing trends from GRACE are mostly related to human use (irrigation) and climate variations, whereas increasing trends reflect climate variations. For example, in the Amazon, GRACE estimates a large increasing trend of ∼43 km3/y, whereas most models estimate decreasing trends (−71 to 11 km3/y). Land water storage trends, summed over all basins, are positive for GRACE (∼71–82 km3/y) but negative for models (−450 to −12 km3/y), contributing opposing trends to global mean sea level change. Impacts of climate forcing on decadal land water storage trends exceed those of modeled human intervention by about a factor of 2. The model-GRACE comparison highlights potential areas of future model development, particularly simulated water storage. The inability of models to capture large decadal water storage trends based on GRACE indicates that model projections of climate and human-induced water storage changes may be underestimated.

Subfields: Astronomy and Astrophysics, Molecular Biology, Oceanography

View Paper: https://www.semanticscholar.org/paper/be793b39db26ddb00cc0833fd7d03fca88cb1266

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Citations in the corpus

• Coupling between the terrestrial carbon and water cycles—a review (2019, methodology): “So far, GRACE observations have been largely used in water resources research (Rodell et al 2009, Scanlon et al 2018) and global hydrological modeling, but rarely in carbon cycle research, except in a few instances (Bloom et al 2010, Velicogna et al 2015, Andrew et al 2017).”
• Long‐Term (1979‐Present) Total Water Storage Anomalies Over the Global Land Derived by Reconstructing GRACE Data (2021, result): “evapotranspiration and soil percolation), and are often tuned to rather match observed discharge than storage changes, and as a result, they hardly match GRACE observations in trends and annual amplitudes (Scanlon et al., 2018).”
• GRACE-REC: a reconstruction of climate-driven water storage changes over the last century (2019, background): “…hydrological models and land surface models can provide long-term estimates of natural TWS variability; however, they are usually not calibrated against GRACE measurements and sometimes exhibit large biases in TWS amplitude (Schellekens et al., 2017; Zhang et al., 2017; Scanlon et al., 2018).”
• How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions (2019, result): “Because of missing processes or conceptualization errors, model-simulated TWS anomalies (TWSA) can be different from the GRACE-derived TWSA over many global river basins [173].”