Compound flooding in low-relief coastal regions arises from interactions among coastal water levels, river discharge, and precipitation. Capturing these interactions typically requires coupled hydrodynamic models, which can be computationally intensive, limiting their use in high-resolution or large-ensemble analyses. In this study, we introduce rating surfaces: two-dimensional plots that provide contours of compound flood depth at points of interest given pairs of potentially interacting flood drivers. Using Southeast Texas as a testbed, we generate synthetic inundation scenarios with both efficient terrain-based models (c-HAND, GeoFlood, and Fill-Spill-Merge) and the reduced-physics hydrodynamic model SFINCS. Sampling these scenarios at points of interest yields rating surfaces that characterize how precipitation, discharge, and coastal water level jointly influence maximum compound flood depth. Across locations, simplified and hydrodynamic models produce similar depth patterns, but SFINCS captures finer-scale nonlinearities. The two approaches provide comparable depth estimates, and their prediction envelope typically includes the observed high-water marks. Rating surfaces provide an efficient tool for evaluating compound flooding in settings where computational constraints challenge traditional hydrodynamic modeling, offering a framework for scenario assessment and communication of compound flood hazards at points of interest.