Análise do comportamento de uma bacia no semiárido contendo reservatórios via modelagem hidrológica
Bibliographic record
Abstract
O objetivo do artigo é aplicar o modelo SWAT (Soil and Water Assessment Tool) na bacia de contribuição do reservatório Epitácio Pessoa, na região semiárida do Estado da Paraíba e analisar suas respostas. Para tanto, foram utilizados dados de vazões diárias do período 1994 a 2016 das estações fluviométricas Caraúbas e Poço de Pedras. Dois cenários foram analisados: a presença ou não de reservatórios a montante das estações fluviométricas. Coeficientes como, Percentual de Tendência (PBIAS), Coeficiente de Nash-Sutcliffe (NSE), Coeficiente de determinação (R²) e a Razão entre a Raíz Quadrada do Erro Médio e o Desvio Padrão (RSR) foram calculados para avaliar o desempenho do modelo nos dois cenários. Os resultados mostram que a calibração foi considerada muito boa, com exceção para o coeficiente PBIAS, calculado para a estação Poço de Pedras, nos dois cenários, que considerou insatisfatório. Apenas o coeficiente PBIAS considerou a validação insatisfatória no posto Poço de Pedras, nos dois cenários, e no posto Caraúbas sem a presença de reservatórios. Os demais coeficientes consideraram as validações de muito boas à satisfatória. Logo, conclui-se que modelo SWAT foi capaz de gerar series de vazões confiáveis a referida bacia sendo uma importante ferramenta para a gestão dos recursos hídricos.ABSTRACT The objective of the article is to apply the SWAT (Soil and Water Assessment Tool) model in the watershed of the Epitácio Pessoa reservoir, located in the semi-arid region of Paraíba State, Brazil, and analyze its outcomes. Daily flow data from 1994 to 2016 from the Caraúbas and Poço de Pedras hydrological stations were used. Two scenarios were examined: with and without reservoirs upstream of the hydrological stations. Performance metrics including Percent Bias (PBIAS), Nash-Sutcliffe Efficiency (NSE), Coefficient of Determination (R²), and Root Mean Square Error to Standard Deviation Ratio (RSR) were computed to evaluate the model's accuracy in both scenarios. The findings show that overall calibration was considered very good, except for the PBIAS coefficient at the Poço de Pedras station, which was found unsatisfactory in both scenarios. Validation was unsatisfactory only for the PBIAS coefficient at the Poço de Pedras station in both scenarios and at the Caraúbas station without reservoir presence. Other coefficients ranged from very good to satisfactory. Thus, it is concluded that the SWAT model effectively generated reliable flow data for the studied basin, proving to be a valuable tool for water resource management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".