What model should I use for multiple time series input
You mention having 2 types of data:


Water temperature with a minute-granularity
Climate data taken 20 days prior


I'm not sure what you mean with "joining the series into one row/series", but I don't think that it make sense to combine these two types as the time-frame is just too different. In this case it would probably be better to combine different layers to deal with the discrepancy. I would use a RNN layer for the water temperature and experiment with different layers (for example regular dense layers) for the remaining input, treating it as fixed inputs (like the location variable) instead of dynamic time-step input like the water temperature. 

Since the climate data is 20 days prior, I don't think that it'll really influence the water temperature on a minute basis; the climate data is more like a foundation, similar to  the location variable.