Articles | Volume 14, issue 8
https://doi.org/10.5194/gmd-14-5155-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-5155-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
18 Aug 2021
Model description paper |
| 18 Aug 2021
| 18 Aug 2021
Hydrostreamer v1.0 – improved streamflow predictions for local applications from an ensemble of downscaled global runoff products
Geoinformatics Research Group, Department of Built Environment, Aalto
University, Espoo, Finland
Water and Development Research Group, Department of Built
Environment, Aalto University, Espoo, Finland
Joseph H. A. Guillaume
Institute for Water Futures & Fenner School of Environment and
Society, Australian National University, Canberra, Australia
Vili Virkki
Water and Development Research Group, Department of Built
Environment, Aalto University, Espoo, Finland
Matti Kummu
Water and Development Research Group, Department of Built
Environment, Aalto University, Espoo, Finland
Kirsi Virrantaus
Geoinformatics Research Group, Department of Built Environment, Aalto
University, Espoo, Finland
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Short summary
Different runoff and streamflow products are freely available but may come with unsuitable spatial units. On the other hand, starting a new modelling exercise may require considerable resources. Hydrostreamer improves the usability of existing runoff products, allowing runoff and streamflow estimates at the desired spatial units with minimal data requirements and intuitive workflow. The case study shows that Hydrostreamer performs well compared to benchmark products and observation data.
Different runoff and streamflow products are freely available but may come with unsuitable...
