GHCen: a stochastic-conceptual approach for generating synthetic streamflow scenarios
ABSTRACT In Brazil, energy production predominantly relies on hydropower generation, necessitating precise hydrological planning tools to manage the uncertainty inherent in river flows. While traditional hydrological models provide valuable deterministic forecasts, addressing the need for probabilis...
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| Format: | Article |
| Language: | English |
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Associação Brasileira de Recursos Hídricos
2023-11-01
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| Series: | Revista Brasileira de Recursos Hídricos |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312023000100601&lng=en&tlng=en |
| _version_ | 1797449380835360768 |
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| author | Felipe Treistman Lucas de Souza Khenayfis Débora Dias Jardim Penna |
| author_facet | Felipe Treistman Lucas de Souza Khenayfis Débora Dias Jardim Penna |
| author_sort | Felipe Treistman |
| collection | DOAJ |
| description | ABSTRACT In Brazil, energy production predominantly relies on hydropower generation, necessitating precise hydrological planning tools to manage the uncertainty inherent in river flows. While traditional hydrological models provide valuable deterministic forecasts, addressing the need for probabilistic information remains crucial. This paper introduces a novel approach, the Hybrid Generator of Synthetic Streamflow Scenarios (GHCen), which combines a conceptual SMAP/ONS model with stochastic simulation techniques to generate synthetic streamflow scenarios. The stochastic methodology employed in GHCen effectively reproduces the key characteristics of precipitation processes on daily to annual scales. Through a comprehensive case study, conducted for 2021, GHCen demonstrates its capability to accurately replicate the hydrological behaviors from historical data. The analysis reveals a strong alignment between the synthetic scenarios and observed Natural Energy Inflow for the National Interconnected System, both monthly and in accumulated terms. |
| first_indexed | 2024-03-09T14:25:16Z |
| format | Article |
| id | doaj.art-d4b987a7d04f4ea0ab85f27bba97fdeb |
| institution | Directory Open Access Journal |
| issn | 2318-0331 |
| language | English |
| last_indexed | 2024-03-09T14:25:16Z |
| publishDate | 2023-11-01 |
| publisher | Associação Brasileira de Recursos Hídricos |
| record_format | Article |
| series | Revista Brasileira de Recursos Hídricos |
| spelling | doaj.art-d4b987a7d04f4ea0ab85f27bba97fdeb2023-11-28T07:45:29ZengAssociação Brasileira de Recursos HídricosRevista Brasileira de Recursos Hídricos2318-03312023-11-012810.1590/2318-0331.282320230116GHCen: a stochastic-conceptual approach for generating synthetic streamflow scenariosFelipe Treistmanhttps://orcid.org/0000-0001-9948-8680Lucas de Souza KhenayfisDébora Dias Jardim PennaABSTRACT In Brazil, energy production predominantly relies on hydropower generation, necessitating precise hydrological planning tools to manage the uncertainty inherent in river flows. While traditional hydrological models provide valuable deterministic forecasts, addressing the need for probabilistic information remains crucial. This paper introduces a novel approach, the Hybrid Generator of Synthetic Streamflow Scenarios (GHCen), which combines a conceptual SMAP/ONS model with stochastic simulation techniques to generate synthetic streamflow scenarios. The stochastic methodology employed in GHCen effectively reproduces the key characteristics of precipitation processes on daily to annual scales. Through a comprehensive case study, conducted for 2021, GHCen demonstrates its capability to accurately replicate the hydrological behaviors from historical data. The analysis reveals a strong alignment between the synthetic scenarios and observed Natural Energy Inflow for the National Interconnected System, both monthly and in accumulated terms.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312023000100601&lng=en&tlng=enSynthetic streamflow scenario generationHybrid modelConceptual rainfall-runoff model |
| spellingShingle | Felipe Treistman Lucas de Souza Khenayfis Débora Dias Jardim Penna GHCen: a stochastic-conceptual approach for generating synthetic streamflow scenarios Revista Brasileira de Recursos Hídricos Synthetic streamflow scenario generation Hybrid model Conceptual rainfall-runoff model |
| title | GHCen: a stochastic-conceptual approach for generating synthetic streamflow scenarios |
| title_full | GHCen: a stochastic-conceptual approach for generating synthetic streamflow scenarios |
| title_fullStr | GHCen: a stochastic-conceptual approach for generating synthetic streamflow scenarios |
| title_full_unstemmed | GHCen: a stochastic-conceptual approach for generating synthetic streamflow scenarios |
| title_short | GHCen: a stochastic-conceptual approach for generating synthetic streamflow scenarios |
| title_sort | ghcen a stochastic conceptual approach for generating synthetic streamflow scenarios |
| topic | Synthetic streamflow scenario generation Hybrid model Conceptual rainfall-runoff model |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312023000100601&lng=en&tlng=en |
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