We evaluated the effects of topographic complexity on landscape carbon and hydrologic process simulations within a rugged mixed hardwood forest by developing and applying a satellite-based hydroecological model at multiple spatial scales. The effects of topographic variability were evaluated by aggregating raster-based digital elevation model and satellite-derived leaf area index inputs across eight different spatial resolutions from 30 m (62 208 pixels) to 2160 m (12 pixels). Our modeling analysis showed that the effect of topography was the strongest on solar radiation and temperature, intermediate on soil water and evapotranspiration, and ambiguous on soil respiration. Spatial aggregation of model inputs smoothed heterogeneous spatial patterns of modeled output variables relative to fine-scale results. Model outputs varied nonlinearly with different levels of spatial aggregation, while spatial variability of model inputs and outputs were dampened at increasingly coarse aggregation levels. Biases in spatially aggregated model predictions were generally less than ±10%, except for solar radiation, which showed biases of up to +50% at coarser spatial scales. The large positive bias in the solar radiation implies that overestimation of biophysical variables that are sensitive to solar radiation (e.g., photosynthesis and net primary production) may be considerable in rugged forested landscapes unless subgrid scale effects are accounted for.