Articles | Volume 10, issue 2
https://doi.org/10.5194/essd-10-787-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-10-787-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
17 Apr 2018
The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment
Lukas Gudmundsson
CORRESPONDING AUTHOR
Institute for Atmospheric and Climate Science, Department of
Environmental Systems Science, ETH Zurich,
Universitaetstrasse 16, Zurich 8092, Switzerland
Hong Xuan Do
School of Civil, Environmental and Mining Engineering, University of
Adelaide, Adelaide, Australia
Michael Leonard
School of Civil, Environmental and Mining Engineering, University of
Adelaide, Adelaide, Australia
Seth Westra
School of Civil, Environmental and Mining Engineering, University of
Adelaide, Adelaide, Australia
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- The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment L. Gudmundsson et al. 10.5194/essd-10-787-2018
- Human impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes: a multi-model validation study T. Veldkamp et al. 10.1088/1748-9326/aab96f
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- Globally observed trends in mean and extreme river flow attributed to climate change L. Gudmundsson et al. 10.1126/science.aba3996
Discussed (final revised paper)
Latest update: 27 May 2023
Short summary
Time-series indices characterizing streamflow at annual, seasonal and monthly resolution at more than 30 000 stations around the world are presented. The data belong to the Global Streamflow and Metadata Archive (GSIM) and allow for an assessment of water balance components, hydrological extremes and the seasonality of water availability. The quality of the data is tested using automated methods to aid potential users to gauge the suitability of the data for specific applications.
Time-series indices characterizing streamflow at annual, seasonal and monthly resolution at more...
