Applications of grid-based systems are widespread in many areas of environmental analysis. In this study, the concept is adapted to the modeling of water temperature by integrating a macroscale hydrologic model, variable infiltration capacity (VIC), with a computationally efficient and accurate water temperature model. The hydrologic model has been applied to many river basins at scales from 0.0625° to 1.0°. The water temperature model, which uses a semi-Lagrangian numerical scheme to solve the one-dimensional, time-dependent equations for thermal energy balance in advective river systems, has been applied and tested on segmented river systems in the Pacific Northwest. The state-space structure of the water temperature model described in previous work is extended to include propagation of uncertainty. Model results focus on proof of concept by comparing statistics from a study of a test basin with results from other studies that have used either process models or statistical models to estimate water temperature. The results from this study compared favorably with those of selected case studies using data-driven statistical models. The results for deterministic process models of water temperature were generally better than the grid-based method, particularly for those models developed from site-specific, data-intensive studies. Biases in the results from the grid-based system are attributed to heterogeneity in hydraulic characteristics and the method of estimating headwater temperatures.