Water resources management is of primary importance for better understanding the impact on scenarios of climate change. The mean monthly runoff, soil moisture and aquifer recharge long-run forecast can support decisions to manage water demand, to recover degraded areas, water security, irrigation, electrical energy generation and urban water supply. The integrative and comprehensive analysis considering the spatial and temporal representation of hydrological process such as the distribution of rainfall, land cover and land use, ground elevation is a challenge. Therefore, these input data are important to modeling the water balance. We present the Rainfall-Runoff Balance Enhanced Model (RUBEM) as a grided hydrological model capable to represent the canopy interception, runoff, soil moisture on the non-saturated soil layer, baseflow and aquifer recharge. The RUBEM includes evapotranspiration and the interception based on the leaf area index (LAI), fraction of photosynthetically active radiation (FAPAR) and normalized difference vegetation index (NVDI). The land use and land cover are updated during the simulations. The RUBEM was tested for tree tropical watersheds in Brazil with different hydrological and soil properties zones. The Piracicaba River has 10,701 km² (latitude 22.7o S), Ipojuca River has 3,471 km² (latitude 8.3o S) and Alto Iguaçu River with 2,696 km² (latitude 25.6o S). The input data from 2000 to 2010 was used to calibrate the runoff and the Nash-Sutcliffe indicator (NSI) results in 0.63, 0.48 and 0.60, respectively. The data input from 2011 to 2018 was the validation model period and NSI results in 0.66, 0.43 and 0.77. According to the NSI results, the model had a suitable calibration and validation in different hydrological zones and soils constitutions. The RUBEM is an important grided hydrological model with capabilities to support researchers, policymakers, and decision-makers under spatial and temporal water balance analysis to water managements plans, recovery degradation areas and long-run forecast.