To assess spatial and temporal trends in annual atmospheric deposition, the Total Deposition Science Committee (TDep) under the National Atmospheric Deposition Program (NADP) developed a measurement-model fusion (MMF) method to estimate fluxes of total, wet, and dry deposition of sulfur, nitrogen, base cations, and chloride. The TDep method provides interpolated maps of annual deposition from the year 2000 to the present, using a modeling approach that fuses the Community Multiscale Air Quality (CMAQ) model data with data from ambient air quality and wet deposition monitoring sites. Due to its large spatial coverage, fine spatial resolution (i.e. 4 x 4 km²), and multiple data sources, the TDep approach involves complex geoprocessing steps that require an organized and iterable implementation to support ongoing data production and future process enhancements. In an effort to modernize and streamline the method and facilitate the development of new features, a new modularized Python geoprocessing TDep application was developed. In this presentation, we will focus on the methodology of the TDep MMF method and the development and structure of the updated Python geoprocessing application. The developed Python framework breaks down the TDep approach into six modules: (1) data ingestion, (2) data interpolation, (3) bias adjustment for modeled data, (4) data fusion, (5) data aggregation, and (6) product export. Through the modularized framework, the Python application has greatly reduced the complexity of the codebase and will support streamlined processing. The new TDep framework will facilitate future improvements to the TDep MMF method, such as inclusion of satellite data or CMAQ-modeled wet deposition to improve the accuracy of interpolation for locations without available monitoring sites.