Atmospheric deposition has long been recognized as an important source of pollutants and nutrients to ecosystems. The need for reliable, spatially explicit estimates of total atmospheric deposition (wet + dry + cloud) is central, not only to air pollution effects researchers, but also for calculation of input-output budgets, and to decision makers faced with the challenge of assessing the efficacy of policy initiatives related to deposition. Although atmospheric deposition continues to represent a critical environmental and scientific issue, current estimates of total deposition have large uncertainties, particularly across heterogeneous landscapes such as montane regions. We developed an empirical modeling approach that predicts total deposition as a function of landscape features. We measured indices of total deposition to the landscapes of Acadia (121 km2) and Great Smoky Mountains (2074 km2) National Parks (USA). Using approximately 300-400 point measurements and corresponding landscape variables at each park, we constructed a statistical (general linear) model relating the deposition index to landscape variables measured in the field. The deposition indices ranged over an order of magnitude, and in response to vegetation type and elevation, which together explained approximately 40% of the variation in deposition. Then, using the independent landscape variables available in GIS data layers, we created a GIS-relevant statistical nitrogen (N) and sulfur (S) deposition model (LandMod). We applied this model to create park-wide maps of total deposition that were scaled to wet and dry deposition data from the closest national network monitoring stations. The resultant deposition maps showed high spatial heterogeneity and a four- to sixfold variation in "hot spots" and "cold spots" of N and S deposition ranging from 3 to 31 kg N x ha(-1) x yr(-1) and from 5 to 42 kg S x ha(-1) x yr(-1) across these park landscapes. Area-weighted deposition was found to be up to 70% greater than NADP plus CASTNET monitoring-station estimates together. Model-validation results suggest that the model slightly overestimates deposition for deciduous and coniferous forests at low elevation and underestimates deposition for high-elevation coniferous forests. The spatially explicit deposition estimates derived from LandMod are an improvement over what is currently available. Future research should test LandMod in other mountainous environments and refine it to account for (currently) unexplained variation in deposition.