Airborne light detection and ranging (LIDAR) technology now makes it possible to sample the Earth's surface with point spacings well below 1 m. It is, however, time consuming, costly, and technically challenging to directly use very high resolution LIDAR data for hydraulic modeling because of the computational requirements associated with solving fluid dynamics equations over complex boundary conditions in large data sets. For high relief terrain and urban areas, using coarse digital elevation models (DEMs) can cause significant degradation in hydraulic modeling, particularly when artificial obstructions, such as buildings, mask spatial correlations between terrain points. In this paper we present a strategy to reduce the computational complexity in the estimation of surface water discharge through a decomposition of the DEM data, wherein features have different characteristic spatial frequencies. Though the optimal DEM scale for a particular application will ultimately be decided by the user's tolerance for error, we present guidelines to choose a proper scale by balancing computer memory usage and accuracy. We also suggest a method to parameterize man-made structures, such as buildings in hydraulic modeling, to efficiently and accurately account for their effects on surface water discharge.