In January 2013, Lake Michigan reached its lowest water level on record. Seven years later, in the summer of 2020, it broke the opposite record with widespread flooding, eroded shorelines and closed lakeside roads.

The difference between the two water levels was nearly two metres. Lakes Superior, Erie and Ontario had the same reversal within a few months of each other. Almost no one saw it coming.

These shifts build over months, through a combination of rain, snow, evaporation and human decisions. Unravelling this complex interplay of factors is one of the most persistent and important challenges in the field of hydrology today. Water levels control the depth of harbours, the stability of shorelines, drinking water intakes and the survival of coastal wetlands.

Our analysis of 40 years of fluctuations, which we published in the Science of the Total Environment journal, tackles this issue using tools from the field of artificial intelligence (AI).

The goal was not just to predict water levels, but to use AI to explain what causes them to vary.

A machine that predicts but doesn’t explain

For decades, the hydrologic balance was treated like a bank account: you add up the deposits (rain, runoff, inflows from upstream), subtract the withdrawals (evaporation, outflows), and the balance gives the water level. The approach is rigorous but requires extensive calibration and does not accurately capture unusual climate variations.

Machine learning does the opposite: it is fed 40 years of data and uses an algorithm to identify patterns. An algorithm consists of a series of calculations that........

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