Several studies relating land cover to stream properties have used sample sizes of more than 100 watersheds, but the variance that they explain is moderate to low (R2 less than 50%), limiting the predictive value of these studies when their models are applied to watersheds that were not included in the models’ development. We hypothesize that this is due to the increases in variation that occur with increases in sample size and in the geographic scales of the areas in which the watersheds are distributed. Land cover alone cannot explain all of that variation; more predictors must be considered. Conversely, models with high explicative power would require relatively small sample sizes distributed over small areas. This hypothesis was evaluated sampling 17 watersheds from southern Chile’s Lake Region, for which we developed regressive models between land cover/watershed area/precipitation/geomorphology and stream properties (i.e., conductivity, temperature). With a maximum n = 15 watersheds, on a regional scale, a poorly explained variation in hydrologic variables (mean 37–49%) was obtained. The R2increased slightly, to 45–52%, when precipitation was included as a predictor. In half of the cases analyzed, the models improved when geomorphology was considered as an additional predictor (60–66%), supporting our hypothesis. Furthermore, when our analysis was restricted to a narrower latitudinal span (n = 9), the R2 was much stronger (68–87%) when only land cover and watershed area were included as predictors. These percentages also increased when more predictors were incorporated. Nevertheless, a portion of unexplained variance remained that would require the consideration of more predictors, such as geology and edaphology. The documented trade-off provides evidence that argues against the spatial generality of land cover/stream property models.