Técnica de calibração para modelagem da bacia hidrográfica do Rio São Francisco, Brasil, utilizando o SWAT
Bibliographic record
Abstract
O Soil and Water Assessment Tool (SWAT) é um modelo hidrossedimentológico que tem como foco principal avaliar os impactos das modificações no uso do solo sobre a qualidade e quantidade de água em bacias hidrográficas. Para que essas avaliações sejam adequadas, considerando a representatividade dos processos simulados pelo modelo, faz-se necessário a realização do procedimento de calibração. Muitos modeladores encontram grande desafio nesta etapa imprescindível da modelagem. Há uma grande variedade de parâmetros a serem considerados, diferentes algoritmos, bem como diferentes funções objetivas. Outro aspecto a ser destacado é o fato de que o tempo de processamento pode ser alto, quando se considera bacias hidrográficas representativas, como é o caso da bacia hidrográfica do Rio São Francisco, Brasil (639.219km²). Para atender a demanda de criação de um modelo de gestão integrada de águas para países BIS (Brasil, Índia e África do Sul) sob cenários de mudanças climáticas, a bacia do São Francisco foi calibrada. Esta nota científica objetiva fornecer uma visão do procedimento de calibração e confiabilidade do modelo, utilizando dados de vazão em tempo mensal no período de 1961 a 2016 para a bacia do São Francisco. O modelo SWAT calibrado para a bacia do Rio São Francisco pode ser utilizado para análises de cenários de mudanças climáticas e seus impactos sobre a disponibilidade hídrica da bacia, subsidiando assim, a tomada de decisão sobre os recursos hídricos, e a criação do modelo de gestão integrada com os demais países do BIS.Palavras-chave: modelagem hidrológica, SWAT-CUP, BIS Calibration technique for modeling the São Francisco watershed, Brazil, using SWAT A B S T R A C TThe Soil and Water Assessment Tool (SWAT) is a hydrosedimentological model whose main focus is to evaluate the impacts of changes in land use on the quality and quantity of water in watersheds. For these evaluations to be adequate, considering the representativeness of the processes simulated by the model, it is necessary to carry out the calibration procedure. Many modelers find a great challenge in this essential stage of modelling. There are a wide variety of parameters to consider, different algorithms, as well as different objective functions. Another aspect to be highlighted is the fact that the processing time can be high, when considering representative watersheds, as is the case of the São Francisco River basin, Brazil (639,219 km²). To meet the demand for the creation of an integrated water management model for BIS countries (Brazil, India and South Africa) under climate change scenarios, the São Francisco basin was calibrated. This scientific note aims to provide an overview of the calibration procedure using flow data in a monthly time step from 1961 to 2016 in the studied basin. The SWAT model calibrated for the São Francisco River basin can be used to analyze climate change scenarios and their impacts on the water availability of the basin, thus supporting decision-making on water resources and the creation of a management model. integrated with the other BIS countries.Keywords: hydrological modeling, SWAT-CUP, BIS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.012 |
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; both teacher heads agree on what is shown here.
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".