Ice-Regulated Water Quality Dynamics in Finnish Shallow Lakes: A Machine-Learning Reconstruction

Water quality in ice-covered lakes is strongly affected by winter physical conditions, particularly in shallow systems where ice cover influences mixing, oxygen availability, light conditions, and biogeochemical processes. Changes in ice thickness and duration can therefore have substantial impacts on key water quality parameters, including dissolved oxygen and nutrient dynamics. However, long-term observations of both water quality and ice conditions are sparse and unevenly distributed across Finnish lakes, limiting comprehensive assessments. In this study, we apply a machine-learning approach based on the gradient boosting algorithm to model water quality and ice conditions on shallow lakes in Finland over the period 1965–2024. The model demonstrates strong predictive performance, evaluated using the root mean square error (RMSE), enabling the reconstruction of water quality dynamics under data-scarce conditions.