How to combine your own measurements with public data (weather, hydrology)
Most environmental measurements do not live in a vacuum. Water level responds to rainfall, turbidity to run-off, oxygen and blooms to temperature and wind, insect swarming to warmth. That is why your own data only gains full value when set against weather and hydrology - and these are publicly available (see IMGW-PIB data and GIOŚ data).
Why context changes everything
A few typical examples in which public data explains your result:
- water level / flow ↔ rainfall - whether the level is rising after rain or something unusual is happening,
- turbidity ↔ rainfall / run-off - where the sudden cloudiness comes from,
- cyanobacterial bloom / chlorophyll ↔ temperature, wind, insolation - anticipating the risk,
- oxygen ↔ temperature - warm water holds less of it,
- PM dust ↔ weather + a GIOŚ reference station - whether it is an inversion or a real episode,
- bark-beetle catch ↔ temperature - when swarming starts and intensifies.
Without this context it is easy to misinterpret - or to overlook that an “anomaly” is simply the result of yesterday’s downpour.
Two routes: by hand or automatically
By hand. You download data from the IMGW/GIOŚ portals, match it in time to your measurements and stitch it together in a spreadsheet. It works, but it is laborious and it is easy to make mistakes when matching dates and stations.
Automatically. The data flows in by itself and is immediately “glued” to your measurements on a shared time axis. This solution scales much better, especially with continuous monitoring.
How LimnoLog does it
Here the connectors in LimnoLog come to the rescue: they can automatically pull in data from public APIs (e.g. rainfall and hydrology from IMGW-PIB, air quality from GIOŚ) and record it alongside your measurements. Thanks to this you see a measurement and its context on a single chart - without manually stitching spreadsheets. The same goes for alerts: a warning threshold on your indicator can be read together with the weather that often explains it.
If you are just starting out, the simplest thing is to begin with your own measurements (see the Method Guide) and add the weather context when you need it.
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