Articles | Volume 29, issue 19
https://doi.org/10.5194/hess-29-5099-2025
https://doi.org/10.5194/hess-29-5099-2025
Research article
 | 
10 Oct 2025
Research article |  | 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

Michael Scheuerer, Emilie Byermoen, Julia Ribeiro de Oliveira, Thea Roksvåg, and Dagrun Vikhamar Schuler

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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.
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