XIII ENES - Encontro Nacional de Engenharia de Sedimentos e I PiA - Partículas nas Américas (Particles in the Americas)

Data: 24/09/2018 à 28/09/2018
Local: Vitória-ES
Mais informações: https://www.abrh.org.br/xiiienes/

AVALIAÇÃO DA MODELAGEM HIDROLÓGICA E DE SEDIMENTOS NA BACIA DO RIO MADEIRA CONSIDERANDO DIFERENTES FONTES DE PRECIPITAÇÃO

Autores

Brunella Sellitti Borges, Diogo Costa Buarque

Tema

MEDIÇÃO, MODELAGEM, TÉCNICAS E TECNOLOGIAS NO CONTEXTO DA HIDROSSEDIMENTOLOGIA

Resumo

The Madeira and Solimões rivers are the two main tributaries of the Amazon basin in terms of sediment discharge. The watershed of the Madeira River is responsible for almost 35% of the sediment load transported by the Amazon River to the Ocean, in additionto significantly influencing its sediment transport and flow. In order to study and understand the dynamics of sediments in the Madeira River basin, Buarque (2015) developed a sedimentological model, coupled with a hydrological model, and applied it to the basin. However, some limitations in its application in the Madeira River Basin were identified by Buarque (2015), such as the quality of the precipitation data used. In this sense, the present study evaluated the results of the model in the basin considering different rainfall data sources (Tropical Rainfall Measurement Mission (TRMM), National Water Agency (ANA), MERGE and Multi-Source Weighted-Ensenble Precipitation (MSWEP)). In general, the results obtained by the model with the selected sources of precipitation data were satisfactory, mainly in the Brazilian Amazon. However, it was verified the necessity of recalibration of the parameters of the model foreachdifferent source. Based on the results after an automatic calibration, it was found that the errors are more associated with the input data of the model than with the calibration. In order to improve the results of simulations with alternative sources, it is recommended to manually calibrate the model and at the same time evaluate the extent to which certain parameters can be extrapolated to compensate for the differences between observations and modelresults fromeach rainfalldata source evaluated.

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