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.

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.