Articles | Volume 29, issue 19
https://doi.org/10.5194/hess-29-5099-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-5099-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Oct 2025
Research article |
| 10 Oct 2025
| 10 Oct 2025
Multi-decadal streamflow projections for catchments in Brazil based on CMIP6 multi-model simulations and neural network embeddings for linear regression models
Statistical Analysis and Machine Learning Department, Norsk Regnesentral STI, Oslo, Norway
Emilie Byermoen
Geophysical Institute, University of Bergen, Bergen, Norway
Water, Weather and Climate Department (MEW), Statkraft Energi AS, Oslo, Norway
Julia Ribeiro de Oliveira
Markets Brazil, Statkraft Energia do Brasil Ltda, Rio de Janeiro, Brazil
Thea Roksvåg
Statistical Analysis and Machine Learning Department, Norsk Regnesentral STI, Oslo, Norway
Dagrun Vikhamar Schuler
Water, Weather and Climate Department (MEW), Statkraft Energi AS, Oslo, Norway
Related authors
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Thea Roksvåg, Ingelin Steinsland, and Kolbjørn Engeland
Hydrol. Earth Syst. Sci., 26, 5391–5410, https://doi.org/10.5194/hess-26-5391-2022, https://doi.org/10.5194/hess-26-5391-2022, 2022
Short summary
Short summary
The goal of this work was to make a map of the mean annual runoff for Norway for a 30-year period. We first simulated runoff by using a process-based model that models the relationship between runoff, precipitation, temperature, and land use. Next, we corrected the map based on runoff observations from streams by using a statistical method. We were also able to use data from rivers that only had a few annual observations. We find that the statistical correction improves the runoff estimates.
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Short summary
Statkraft requires projections of future streamflow to plan hydropower investments. Setting up a hydrological model for new regions can be too time-consuming to meet the often short delivery deadlines. We have developed an interpretable machine learning method that links streamflow to precipitation and temperature, and can serve as a first screening approach. This method is then applied to climate model simulations of precipitation and temperature to obtain streamflow projections for Brazil.
Statkraft requires projections of future streamflow to plan hydropower investments. Setting up a...
