Seasonally snow-covered regions are an important source of water and a crucial component of the yearly water balance for substantial areas of the world. The American River, located in the California Sierra Nevada presents an opportunity for evaluation of a distributed hydrologic framework, spanning a great range of elevation, land covers, climate, and degree of human influence. This project also details the first application of the SHyFT framework in North America. This study aims to collect and process available, relevant hydrometeorologic data for the region, and using currently available methods in SHyFT, assess the performance differences between regional approaches versus local and lumped methods, as well as the effect different snow-routines have on model results.The experimental design, employing five different region definitions for each of the respective snow-routines, found highly variable Nash-Sutcliffe efficiencies, ranging between -3.249 and 0.611. The poorest results occurring in problematic calibration approaches for local regions, and regulated sub-regions, the Middle Fork in particular. The overall best performing region was the entire Upper American River Watershed, gauged at Folsom lake using a full-natural-flow adapted time series. Between the three headwater regions, the free-flowing North Fork had the best overall performance between experimental setups, maximum NSE found during validation of 0.523. Between the two snow-routines included, the more advanced gamma-snow outperformed the HBV-snow adapted routine. Results show limited applicability for transfer of locally calibrated parameters to larger regions, for all but one method. The issues in model performance are attributed to several possible sources, spatial interpolation methods of point-based measurements, data quality issues, terrain capture difficulties, and the presence of regulated basins.