Several factors influence power generation efficiency in wind farms, such as wind speed, turbulence levels, turbine size and arrangement, and the terrain’s topography. In particular, topography can affect turbine wake length, consequently impacting power generation of downstream turbines. In this work, we evaluate topographic effects by considering a particular complex topography resulting from the superposition of a Gaussian hill with small-scale sinusoidal roughness in which we can vary the amplitude (A) and wavelength (λ) to represent a hill with complex topography. This approach allows us to vary characteristics of the terrain in a controlled fashion and correlate topographic features with the length of turbine wakes. We use the commercial software ANSYS FluentTM to simulate the mean flow in which both the Gaussian hill and surface waviness are mesh-resolved. We use the k− ω SST turbulence model, and the Actuator Disk model to represent a wind turbine located on the top of the hill. We validate the numerical results with experimental data from Cao and Tamura [1]. The data suggest an optimal combination of amplitude (A) and wavelength (λ) to improve the wake’s recovery, i.e. shorten the wake. Shorter wavelengths diminish the entrainment of airflow with higher momentum into the wake, leading to poorer wake recovery. Similarly, excessively smooth terrain (smaller A and/or larger λ) reduces the momentum mixing due to turbulence.