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The global water resources and use model WaterGAP v2.2e: description and evaluation of modifications and new features.

Authors :
Schmied, Hannes Müller
Trautmann, Tim
Ackermann, Sebastian
Cáceres, Denise
Flörke, Martina
Gerdener, Helena
Kynast, Ellen
Peiris, Thedini Asali
Schiebener, Leonie
Schumacher, Maike
Döll, Petra
Source :
Geoscientific Model Development Discussions. 11/20/2023, p1-46. 46p.
Publication Year :
2023

Abstract

Water - Global Assessment and Prognosis (WaterGAP) is a modelling approach for quantifying water resources and water use for all land areas of the Earth that has served science and society since 1996. In this paper, the refinements, new algorithms and new data of the most recent model version v2.2e are described, together with a thorough evaluation of simulated water use, streamflow and total water storage anomaly against observation data. WaterGAP v2.2e improves the handling of inland sinks and now excludes not only large but also small man-made reservoirs when simulating naturalized conditions. The reservoir and non-irrigation water use data were updated. In addition, the model was calibrated against an updated and extended dataset of streamflow observations at 1509 gauging stations. The model can now be started using pre-scribed water storages and other conditions, which facilitates data assimilation as well as near real-time monitoring and forecast simulations. For specific applications, the model can consider the output of a glacier model, approximate the effect of rising CO2 concentrations on evapotranspiration or calculate the water temperature in rivers. In the paper, the publicly available standard model output is described and caveats of the model version are provided alongside the description of the model setup in the ISIMIP3 framework. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19919611
Database :
Academic Search Index
Journal :
Geoscientific Model Development Discussions
Publication Type :
Academic Journal
Accession number :
173730834
Full Text :
https://doi.org/10.5194/gmd-2023-213