IMPACTO DOS AEROSSÓIS SOBRE FLUXOS RADIATIVOS EM REGIÃO DE TRANSIÇÃO CERRADO-PANTANAL
Evapotranspiration is crucial for the water balance, especially in irrigated agriculture. It can be measured directly using lysimeters or estimated through models such as the Penman-Monteith (PM) model, recommended by the FAO. This study aimed to analyze evapotranspiration measurements from a consta...
| Autores: | , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | Brasil |
| Institución: | Universidade Federal de Mato Grosso (UFMT) |
| Repositorio: | Nativa (Sinop) |
| Idioma: | portugués |
| OAI Identifier: | oai:periodicoscientificos.ufmt.br:article/18562 |
| Acceso en línea: | https://periodicoscientificos.ufmt.br/ojs/index.php/nativa/article/view/18562 |
| Access Level: | acceso abierto |
| Palabra clave: | agrometeorologia climatologia irrigação lisimetria manejo de recursos hídricos |
| Sumario: | Evapotranspiration is crucial for the water balance, especially in irrigated agriculture. It can be measured directly using lysimeters or estimated through models such as the Penman-Monteith (PM) model, recommended by the FAO. This study aimed to analyze evapotranspiration measurements from a constant water table lysimeter and verify their validity by comparing the measurements, taken between May and November 2023, with the estimates from the PM model. The experiment was conducted in Botucatu, SP, which has a tropical savanna climate (Aw), characterized by hot, rainy summers and cold, dry winters. For comparison, the following validation indicators were used: root mean square error (RMSE), mean absolute error (MAE), Willmott's index of agreement (d), mean bias error (MBE), Pearson’s correlation coefficient (r), relative mean bias error (rMBE), relative root mean square error (rRMSE), and the confidence coefficient (c). The results showed that in August, the PM model underestimated evapotranspiration measurements by 2.28%, the smallest variation in the analyzed period. In October, which had the greatest variation, the model overestimated the measurements by 46.39%. This highlights the need for seasonal adjustments when using the PM model in the region. |
|---|