Good morning, I know there are some similar questions, but I'm asking anyway since I didn't find a suitable answer for what I'm looking for. I've got some daily hydrological data about a spring, spanning for 5 years, and I'd like to analyse them in order to
- Find cause-effect relations between input series (aka rainfall, snow, external sources) and output series (mainly discharge, but also temperature and conductivity)
- Check for long-term memory effects (autocorrelation)
I'm unfortunately not a statistics-savvy guy, I'm an earth science student with some average knowledge of calculus, statistics and programming (web languages like PHP, JS, also some Python and C), but I need to perform these analyses and report them in a medium-to-short time frame (a couple of months).
I've already done the basic time plots, auto-correlation, cross-correlation and power spectral density charts for each series and they show some things, but seasonality (recurring cycles at 6 and 12 months) mostly belittles all else for now.
What are the best techniques for analyzing the data? What path would you follow? How and where can I educate myself enough to understand what has to be done? Please give me honest even if harsh feedback guys, thank you.