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ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
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Data sources for the manuscript on groundwater stress indicators published in Water Resources Research

Authors: Herbert, C.; Döll, P.;

Data sources for the manuscript on groundwater stress indicators published in Water Resources Research

Abstract

We computed seven global-scale groundwater stress indicators at the 0.5° grid-cell level and for transboundary aquifers> 20,000 km². All indicators were calculated for current conditions (1981-2010 or 2001-2010) based on a homogenized version of the concatenated WATCH Forcing Data ERA-40 (WFD) and WFD ERA-Interim data sets (WFDEI). In addition, four of the indicators were computed for the 2050s (2041-2070) under the worst-case greenhouse gas emissions scenario RCP8.5 applying ten climate and irrigation scenarios. The scenarios were derived by combining two irrigation scenarios (“AAI constant” and “AAI LandSHIFT”) with model output from the five global climate models GFDL-ESM2M, HadGEM2 -ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M. Here, we provide the WaterGAP model output used to compute the groundwater stress indicators. A description of the indicators and the underlying data can be found in the reference below. Moreover, a table with coordinates and grid-cell area [km²] used in WaterGAP is provided for the conversion of units. The model output comprises: 1. Monthly groundwater recharge (GWR and GWRswb) 1981-2010 and 2041-2070 [mm/month, km³/month] 2. Monthly groundwater withdrawals (WWg) 1981-2010 (constWU, transWU) and 2041-2070 (constWU) [m³/month] 3. Monthly net abstractions from groundwater (NAg) 1981-2010 (constWU, transWU) and 2041-2070 (constWU) [m³/month] 4. Monthly groundwater discharge (“gwrunoff”) 2001-2010 from a model run with human water use (transWU) [mm/month] 5. Monthly groundwater discharge (“gwrunoff”) 2001-2010 from a model run without human water use (NAT) [mm/month] 6. Monthly groundwater storage 2001-2010 from a model run with human water use (transWU) [mm] 7. Monthly groundwater storage 2001-2010 from a model run without human water use (NAT) [mm]

Purpose: Computation of groundwater stress indicators at the 0.5° grid-cell level or for larger spatial units such as transboundary aquifers. We advise the use for global assessments or for spatial units larger than 20,000 km².

{"references": ["Herbert, C. & D\u00f6ll, P. (2019): Global assessment of current and future groundwater stress with a focus on transboundary aquifers. Water Resour. Res., 55. doi: 10.1029/2018WR023321."]}

Keywords

global-scale modeling, WaterGAP, transboundary aquifers, Global-scale groundwater stress indicators

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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