We studied how to add site information to deep learning models that predict groundwater levels at many wells at once. Using data from Germany, we compared four simple ways to combine time varying weather with time invariant site characteristics. All methods gave similar average accuracy. Repeating site data at each time step was slightly best but used more computer power. The informativeness of site information mattered more than the method, guiding future model design.