From MFKP_wiki

Jump to: navigation, search


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

Bridget R. Scanlon, Zizhan Zhang, Himanshu Save, Alexander Y. Sun, Hannes Müller Schmied, Ludovicus P. H. van Beek, David N. Wiese, Yoshihide Wada, Di Long, Robert C. Reedy, Laurent Longuevergne, Petra Döll, Marc F. P. Bierkens



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.

Abstract. 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.


Proceedings of the National Academy of Sciences (22 January 2018), 201704665, https://doi.org/10.1073/pnas.1704665115 
Key: INRMM:14522336

Keywords

             

Article-Level Metrics (Altmetrics)
Digital Object Identifier


Available versions (may include free-access full text)

DOI, HighWire, HighWire (PDF), Pubmed, Hubmed, Pubget

Versions of the publication are also available in Google Scholar.
Google Scholar code: GScluster:8406746587053099349

Works citing this publication (including grey literature)

An updated list of who cited this publication is available in Google Scholar.
Google Scholar code: GScites:8406746587053099349

Further search for available versions

Search in ResearchGate (or try with a fuzzier search in ResearchGate)
Search in Mendeley (or try with a fuzzier search in Mendeley)

Publication metadata

Bibtex, RIS, RSS/XML feed, Json, Dublin Core
Metadata search: CrossRef DOI, DataCite DOI

Digital preservation of this INRMM-MiD record

Internet Archive

Meta-information Database (INRMM-MiD).
This database integrates a dedicated meta-information database in CiteULike (the CiteULike INRMM Group) with the meta-information available in Google Scholar, CrossRef and DataCite. The Altmetric database with Article-Level Metrics is also harvested. Part of the provided semantic content (machine-readable) is made even human-readable thanks to the DCMI Dublin Core viewer. Digital preservation of the meta-information indexed within the INRMM-MiD publication records is implemented thanks to the Internet Archive.
The library of INRMM related pubblications may be quickly accessed with the following links.
Search within the whole INRMM meta-information database:
Search only within the INRMM-MiD publication records:
Full-text and abstracts of the publications indexed by the INRMM meta-information database are copyrighted by the respective publishers/authors. They are subject to all applicable copyright protection. The conditions of use of each indexed publication is defined by its copyright owner. Please, be aware that the indexed meta-information entirely relies on voluntary work and constitutes a quite incomplete and not homogeneous work-in-progress.
INRMM-MiD was experimentally established by the Maieutike Research Initiative in 2008 and then improved with the help of several volunteers (with a major technical upgrade in 2011). This new integrated interface is operational since 2014.