Assessing physical and biological lake oxygen indicators using simulated environmental variables and machine learning algorithms

We integrate observations and simulated data from physics-based models with observations and machine learning (ML) algorithms to assess and predict lake dissolved oxygen (DO) and Apparent Oxygen Utilization (AOU). DO is a proxy of hypoxia, and AOU a proxy of respiration processes and biological activity. Weather, hydrology, and agroecosystem data were used to understand how various environmental drivers impact hypoxic conditions in Lake Erie for a 15-year period. We utilized outputs from various physics-based models and developed ML models with Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) to predict DO and AOU and rank the importance of influential explanatory variables. The ML models were able to accurately predict bottom DO and AOU, and identified thermal stratification as the most influential environmental variable, followed by mineralized phosphorus soil content. RF and XGBoost were not statistically different, therefore, we recommend the use of either ML algorithm to study hypoxic lake conditions. •Assessment of 15-years of physical and biological lake oxygen indicators.•Apparent Oxygen Utilization (AOU) for lake respiration processes and biological activity.•Integrating data from physics-based modeling systems with machine learning algorithms.•Random forest (RF) and XGBoost models for dissolved oxygen (DO) and AOU.•Thermal stratification and phosphorus (P) soil content most influential for DO and AOU prediction.