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• Erosion models are often difficult to apply to large, diverse landscapes. • Provided a new approach for an established, process-based erosion model. • Can be combined with remote sensing and ecological models. • Combining with ecological models improved understanding of where to prioritize. Increasing fire activity in the American west heightens the need for natural resource managers to identify where risk associated with post-fire effects is greatest. This is particularly true for water resources, as many headwater forests are at risk for stand replacing fires. However, current methods to model post-fire erosion often have trade-offs between model area and spatial resolution. We introduce a novel approach to combine a process-based erosion model and state-and-transition simulation modeling to estimate post-fire sediment yield and identify areas of high risk. We demonstrate how this method can be applied at three scales: large watershed, sub-watershed, and single fire event. The combination of the erosion model and state-and-transition simulation model allowed us to identify areas of post-fire high sediment yield potential and increased likelihood of fire occurrence. This method can be used by land managers to prioritize pre-fire restoration practices or post-fire rehabilitation actions. [ABSTRACT FROM AUTHOR]

Land managers often need to predict watershed-scale erosion rates after disturbance or other land cover changes. This study compared commonly used hillslope erosion models to simulate post-fire sediment yields (SY) at both hillslope and watershed scales within the High Park Fire, Colorado, U.S.A. At hillslope scale, simulated SY from four models— RUSLE, AGWA/KINEROS2, WEPP, and a site-specific regression model—were compared to observed SY at 29 hillslopes. At the watershed scale, RUSLE, AGWA/KINEROS2, and WEPP were applied to simulate spatial patterns of SY for two 14–16 km2 watersheds using different scales (0.5–25 ha) of hillslope discretization. Simulated spatial patterns were compared between models and to densities of channel heads across the watersheds. Three additional erosion algorithms were implemented within a land surface model to evaluate effects of parameter uncertainty. At the hillslope scale, SY was only significantly correlated to observed SY for the empirical model, but at the watershed scale, sediment loads were significantly correlated to observed channel head densities for all models. Watershed sediment load increased with the size of the hillslope sub-units due to the nonlinear effects of hillslope length on simulated erosion. SY's were closest in magnitude to expected watershed-scale SY when models were divided into the smallest hillslopes. These findings demonstrate that current erosion models are fairly consistent at identifying areas with low and high erosion potential, but the wide range of predicted SY and poor fit to observed SY highlight the need for better field observations and model calibration to obtain more accurate simulations. • Evaluated performance of erosion models at hillslope and watershed scales. • Simulated hillslope sediment yields did not correlate with measured values. • Simulated watershed sediment yields were correlated with observed rill densities. • Longer hillslopes within watershed simulations led to greater sediment loads. • Parameter uncertainty caused >2 orders of magnitude variability in sediment yields. [ABSTRACT FROM AUTHOR]