Impact of drought on de facto reuse and water quality in Lake Mead: Insights from hydrodynamic modeling versus machine learning

De facto reuse (DFR), where wastewater effluent is present at a drinking water source, can elevate levels of anthropogenic chemicals and pathogens. Wastewater effluent can travel through the water column as a well-defined plume, owing to density differences. This study evaluated the complex effects of drought on plume behavior and water quality in Lake Mead, an arid reservoir in the southwestern United States, using a hydrodynamic model, and compared its performance to a simpler machine learning model. Water quality remained high despite lake elevation declines if in-and outflow rates were maintained. DFR fluctuated seasonally following the plume entrainment depth in the lake thermal structure, with decreased lake elevation shifting peak DFR to occur earlier in the year. Though the hydrodynamic model (relative root mean square error (RRMSE) = 6.7 %) slightly outperformed the machine learning model (RRMSE = 10.8 %), both models can aid treatment and management decisions by predicting DFR at (drinking) water infrastructure. • Hydrodynamic and machine learning models give management insights for Lake Mead. • The percentage of de facto reuse remained relatively stable despite drought. • %DFR fluctuated seasonally, depending on lake stratification and thermocline depth. • The hydrodynamic model was slightly more accurate at predicting thermocline depth.