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A Framework to Evaluate and Compare Synthetic Streamflow Scenario Generation Models

Authors

Treistmann,  F.
External Organizations;

Penna,  D. D. J.
External Organizations;

Khenayfis,  L. de S.
External Organizations;

Cavalcante,  N. B. R.
External Organizations;

Souza Filho,  F. A.
External Organizations;

Rocha,  R. V.
External Organizations;

Estacio,  A. B. S.
External Organizations;

Rolim,  L. R. S.
External Organizations;

Pontes Filho,  J. D. A.
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Porto,  V. C.
External Organizations;

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Guimarães,  Sullyandro Oliveira
Potsdam Institute for Climate Impact Research;

Pessanha,  J. F. M.
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Almeida,  V. A.
External Organizations;

Chan,  P. D. S.
External Organizations;

Lappicy,  T.
External Organizations;

Lima,  C. H. R.
External Organizations;

Detzel,  D. H. M.
External Organizations;

Bessa,  M. R.
External Organizations;

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Citation

Treistmann, F., Penna, D. D. J., Khenayfis, L. d. S., Cavalcante, N. B. R., Souza Filho, F. A., Rocha, R. V., Estacio, A. B. S., Rolim, L. R. S., Pontes Filho, J. D. A., Porto, V. C., Guimarães, S. O., Pessanha, J. F. M., Almeida, V. A., Chan, P. D. S., Lappicy, T., Lima, C. H. R., Detzel, D. H. M., Bessa, M. R. (2023): A Framework to Evaluate and Compare Synthetic Streamflow Scenario Generation Models. - Revista Brasileira de Recursos Hídricos, 28, e43.
https://doi.org/10.1590/2318-0331.282320230115


Cite as: https://publications.pik-potsdam.de/pubman/item/item_28914
Abstract
Synthetic streamflow scenario generation is particularly important in countries like Brazil, where hydroelectric power generation plays a key role and properly handling the uncertainty of future streamflow is crucial. This paper showcases a collaborative effort within the Brazilian electrical sector to enhance streamflow scenario models, focusing on horizons up to one year. Five institutions proposed diverse methodologies, and their effectiveness was evaluated using a comparative framework. The results reveal the strengths and areas for improvement in each model. GHCen emerged as the top performer, excelling in both short-term and moving average analyses, while the PARX model demonstrated superior performance in specific regions. The PAR(p)-A, which is the official methodology in Brazil, was the second-best model in the moving average analysis. This research offers valuable insights for countries facing similar hydrothermal scheduling and scenario generation challenges.