Machine learning-based seasonal forecasting of dissolved organic matter to support drinking water management at the source

The amount of dissolved organic matter (DOM) in freshwaters impacts many processes in aquatic ecology and, therefore, on derived ecosystem services such as water supply. Surface drinking water sources (e.g., lakes and reservoirs), in particular, are increasingly subjected to unforeseen increases in both the concentration and variability of DOM. This makes it more difficult to deal with such water along the drinking water cycle (abstraction, treatment, storage and network distribution), which can affect tap quality and users through the unintentional formation of toxic disinfection by-products (DBPs). DBPs such as trihalomethanes (THMs) and haloacetic acids (HAAs) are known to affect human health under long-term exposure, increasing risks for different cancers and congenital malformations. Anticipating seasonal changes in DOM in source waters is therefore important for both improved drinking water source protection measures and a reduction of DBPs in supplies.

Ecological forecasting provides a way to support water quality management by generating predictions of future environmental conditions within decision-relevant timeframes. In lakes, DOM dynamics often vary strongly at intra-annual and seasonal scales, suggesting that seasonal forecasts could help managers anticipate periods of increased treatment risk and plan mitigation measures in advance. However, forecasting DOM remains challenging due to the complex interactions between in-lake processes and catchment-scale drivers. Recent applications of machine learning have shown skill in simulating historical DOM dynamics in lakes, offering opportunities to extend these approaches to forecasting.

In this study, we developed a seasonal forecasting framework to predict monthly average concentrations of surface fluorescent DOM (fDOM) one to seven months ahead. The framework consists of a machine-learning workflow that simulates daily fDOM using random forest regression, and is applied to two contrasting study sites: a lake in Ireland and a reservoir in Spain. Forecasting is driven by a set of predictors selected based on their relative importance in historical simulations and their availability in open-access seasonal forecast datasets.

The workflow integrates meteorological variables, soil conditions, hydrological outputs, lake model variables and a seasonal indicator. Forecast skill and uncertainty were evaluated over multiple periods (1993–2023, 1993–2016 and 2016–2023) to reflect changes in forecast input characteristics, and results were compared against a climatological baseline. The analysis highlights how seasonal forecasts of DOM can support drinking water management by providing information on expected conditions in source waters in advance. The framework is designed to be transferable to and tested in other lake and reservoir systems where similar data are available.