Integrated Random Forest and APEX-MODFLOW model for predicting and mapping river salinity in the Animas Watershed, Colorado River Basin

Study region The Animas watershed, located in Colorado and New Mexico, US Study focus Accurate prediction of river salinity is challenging due to complex nonlinear relationships between salinity and various environmental factors. Machine learning models have demonstrated remarkable predictive power for critical environmental factors. However, insufficient input data required by the model has challenged salinity prediction. Thus, this study proposed an integrated approach to predict river salinity by combining a Random Forest (RF) model and outputs from the APEX-MODFLOW model. Simulated streamflow, precipitation, air temperature, solar radiation, soil moisture, soil temperature, and groundwater discharge from a calibrated Agricultural Policy / Environmental eXtender integrated with MODFLOW (APEX-MODFLOW) model were used as inputs for the RF model to predict monthly river salinity. New hydrological insights for the region The predicted salinity was compared with the observed salinity at six monitoring stations. The prediction results showed a strong predictive performance, with an R 2 value of 0.81 and an RMSE of 79.88 µS/cm. The predicted river salinity increased gradually from upstream to downstream and showed seasonal variation, with lower salinity in warm seasons (May to July) and higher salinity in cold seasons (October to March). This study provides a novel integrated approach for predicting river salinity, particularly in ungauged basins.