Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

Enhancing streamflow forecasting for the Brazilian electricity sector: a strategy based on a hyper-multimodel


Souza Filho,  F. A.
External Organizations;

Rocha,  R. V.
External Organizations;

Estacio,  A. B. S.
External Organizations;

Rolim,  L. R. Z.
External Organizations;

Pontes Filho,  J. D. A.
External Organizations;

Porto,  V. C.
External Organizations;


Guimarães,  Sullyandro Oliveira
Potsdam Institute for Climate Impact Research;

External Ressource
No external resources are shared
Fulltext (public)

(Publisher version), 3MB

Supplementary Material (public)
There is no public supplementary material available

Souza Filho, F. A., Rocha, R. V., Estacio, A. B. S., Rolim, L. R. Z., Pontes Filho, J. D. A., Porto, V. C., Guimarães, S. O. (2023): Enhancing streamflow forecasting for the Brazilian electricity sector: a strategy based on a hyper-multimodel. - Revista Brasileira de Recursos Hídricos, 28, e45.

Cite as: https://publications.pik-potsdam.de/pubman/item/item_28920
Streamflow forecasting plays an important role in ensuring the reliable supply of electricity in countries heavily reliant on hydropower. This paper proposes a novel framework that integrates various hydrological models, climate models, and observational data to develop a comprehensive forecasting system. Three families of models were employed: seasonal forecasting climate models integrated with hydrological rainfall-runoff models; stochastic or machine learning models utilizing endogenous variables, and stochastic or machine learning models that consider exogenous variables. The hyper-multimodel framework could successfully increase the overall performance of the scenarios generated through the use of the individual models. The quality of the final scenarios generated was directly connected to the performance of the individual models. Therefore, the proposed framework has potential to improve hydrological forecast for the Brazilian electricity sector with the use of more refined and calibrated individual models.