A major concern resulting from the increased use and production of natural gas has been how to mitigate fugitive greenhouse gas emissions (predominantly methane) from natural gas infrastructure (e.g., leaky shallow pipelines). Subsurface migration and atmospheric loading of methane from pipeline leakage is controlled by source configurations and subsurface soil conditions (e.g., soil heterogeneity and soil moisture) and are further affected by atmospheric conditions (e.g., wind and temperature). However, the transport and attenuation of methane under varying subsurface and atmospheric conditions are poorly understood, making it difficult to estimate leakage fluxes from methane concentration measurements at and above the soil surface. Based on a series of controlled bench-scale experiments using a large porous media tank interfaced with an open-return wind tunnel, this study investigated multiphase processes controlling migration of methane from a point source representing a buried pipeline leaking at fixed flow rate (kg/s) under various saturation and soil-texture conditions. In addition, potential effects of atmospheric boundary controls, wind (0.5 and 2.0 m s−1) and temperature (22 and 35 °C), were also examined. Results showed the distinct effects of soil heterogeneity and, to a varying degree, of soil moisture on surface methane concentrations. In addition, results also showed the pronounced effects of wind and, to a lesser degree, of temperature on surface methane concentrations in the presence of varying soil and moisture conditions. The observed subsurface methane profiles were simulated using the multiphase transport simulator TOUGH2-EOS7CA. Observed agreement between measured and simulated data demonstrates that for the conditions studied, multiphase migration of a multicomponent gas mixture (including methane) under density-dependent flow can be adequately represented with a Fickian advection-diffusion (or dispersion) model (ADM) framework. The dominant effect of saturation over the soil texture, could also be inferred from numerical characterization.