Both spatial and temporal information is important in agriculture. Information regarding the aboveground variables is ever increasing in terms of density and precision. On the contrary, belowground information lags behind and has been typically limited to time series. This study uses methods that map the subsurface spatial variability. Numerical simulations of aboveground and belowground water fluxes are then based on such spatial information and additional time-oriented datasets that are common in agriculture.