Vinícius Siqueira Oliveira Carvalho
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Vinícius Siqueira Oliveira Carvalho has a total of 13 co-authors. Here are the top co-authors by number of shared publications:
- Javier Tomasella
Centro de Ciência do Sistema Terrestre, Instituto Nacional de Pesquisas Espaciais (INPE), Cachoeira Paulista 12630-970, Brazil
2 shared publications - Lívia Alves Alvarenga
Departamento de Recursos Hídricos, Universidade Federal de Lavras, Lavras 37203-202, Brazil
2 shared publications - Alberto Colombo
Water Resources Department, Federal University of Lavras, Campus Universitário, Lavras 37200-000, Brazil
1 shared publication - Ana Carolina N. Santos
National Institute for Space Research (INPE), National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), Postal Code 01, Cachoeira Paulista 12630-970, Brazil
1 shared publication - Carlos Rogério de Mello
Water Resources Department, Universidade Federal de Lavras, Lavras, Brazil
1 shared publication - Conceição de Maria Marques de Oliveira
Departamento de Engenharia Agrícola, Brasil
1 shared publication
Latest Publications
- Open Access
Hydrological Model Performance in the Verde River Basin, Minas Gerais, Brazil
In hydrological modelling, it is important to consider the uncertainties related to a model’s structures and parameters when different hydrological models are used to represent a system. Therefore, an adequate analysis of daily discharge forecasts that takes into account the performance of hydrological models can assist in identifying the best extreme discharge forecasts. In this context, this study aims to evaluate the performance of three hydrological models—Lavras Simulation of Hydrology (LASH), Variable Infiltration Capacity (VIC), and Distributed Hydrological Model (MHD-INPE) in the Verde River basin. The results demonstrate that LASH and MHD can accurately simulate discharges, thereby establishing them as crucial tools for managing water resources in the study region’s basins. Moreover, these findings could serve as a cornerstone for future studies focusing on food and water security, particularly when examining their connection to climate change scenarios.
Open AccessHydrological Model Performance in the Verde River Basin, Minas Gerais, Brazil
In hydrological modelling, it is important to consider the uncertainties related to a model’s structures and parameters when different hydrological models are used to represent a system. Therefore, an adequate analysis of daily discharge forecasts that takes into account the performance of hydrological models can assist in identifying the best extreme discharge forecasts. In this context, this study aims to evaluate the performance of three hydrological models—Lavras Simulation of Hydrology (LASH), Variable Infiltration Capacity (VIC), and Distributed Hydrological Model (MHD-INPE) in the Verde River basin. The results demonstrate that LASH and MHD can accurately simulate discharges, thereby establishing them as crucial tools for managing water resources in the study region’s basins. Moreover, these findings could serve as a cornerstone for future studies focusing on food and water security, particularly when examining their connection to climate change scenarios.
- Open Access
- 6 July 2020
- Published by Instituto de Pesquisas Ambientais em Bacias Hidrograficas (IPABHi) in Ambiente e Agua - An Interdisciplinary Journal of Applied Science
- Vol. 15 (4)
Hydrological simulation with SWAT and VIC Models in the Verde River Watershed, Minas Gerais
Successful streamflow forecasts depend on an adequate performance evaluation of the hydrological model. In this study, the hydrological responses were compared using two hydrological models, physic-based and semi-distributed, Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC), using input data from the Verde River Watershed, located in the Minas Gerais state in southern Brazil. This is a study of one of the most important headwater watershed regions of Brazil (Mantiqueira Range). Both models were suitable for streamflow simulation, with values of R2 (determination coefficient) and NSE (Nash-Sutcliffe) higher than 0.8, NSELog higher than 0.35 (Nash–Sutcliffe Efficiency of the logarithmic values of discharge) and PBIAS (percentage deviation) less than 25%. The integration of SWAT and VIC models can be useful in different water-resource assessment studies. Therefore, based upon this study further investigations should be conducted using various hydrological models and climate, land-use and land-cover changes scenarios in the region.
- 6 July 2020
- Published by Instituto de Pesquisas Ambientais em Bacias Hidrograficas (IPABHi) in Ambiente e Agua - An Interdisciplinary Journal of Applied Science
- Vol. 15 (4)
Open AccessHydrological simulation with SWAT and VIC Models in the Verde River Watershed, Minas Gerais
Successful streamflow forecasts depend on an adequate performance evaluation of the hydrological model. In this study, the hydrological responses were compared using two hydrological models, physic-based and semi-distributed, Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC), using input data from the Verde River Watershed, located in the Minas Gerais state in southern Brazil. This is a study of one of the most important headwater watershed regions of Brazil (Mantiqueira Range). Both models were suitable for streamflow simulation, with values of R2 (determination coefficient) and NSE (Nash-Sutcliffe) higher than 0.8, NSELog higher than 0.35 (Nash–Sutcliffe Efficiency of the logarithmic values of discharge) and PBIAS (percentage deviation) less than 25%. The integration of SWAT and VIC models can be useful in different water-resource assessment studies. Therefore, based upon this study further investigations should be conducted using various hydrological models and climate, land-use and land-cover changes scenarios in the region.