4.8 Article

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

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NATL ACAD SCIENCES
DOI: 10.1073/pnas.1704665115

关键词

global hydrological models; land surface models; GRACE satellites; terrestrial total water storage anomalies; global mean sea level

资金

  1. Jackson School of Geosciences
  2. Fisher Endowed Chair in Geological Sciences

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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 (similar to 60% of global land area). Medians of modeled basin water storage trends greatly underestimate GRACE-derived large decreasing (<=-0.5 km(3)/y) and increasing (>= 0.5 km(3)/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 similar to 43 km(3)/y, whereas most models estimate decreasing trends (-71 to 11 km(3)/y). Land water storage trends, summed over all basins, are positive for GRACE (similar to 71-82 km(3)/y) but negative for models (-450 to -12 km(3)/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.

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