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As often the wikipedia explains it quite well: https://en.wikipedia.org/wiki/Groundwater_model

As others have mentioned the availability of long timeseries of groundwater levels from multiple boreholes, nearby river/lake levels and spacial rainfall data is quite important as it is used to calibrate a model. This means that you develop a model based on your available aquifer data (soil/ground composition for a sufficiently large area) and then adjust it's calculated output according to the real data you have from the timelines. If you have high quality ground composition data and detailed spacial rainfall data, the calibation step (that usually involves a lot of guessing to "massage" the model's output to fit to the real data) is a bit less important and you can get away with shorter timeseries of the calibration data.