Hydrology

An algorithm is developed that can accurately predict and forecast farm-scale regional daily out to 3 days. Daily ET forecast (3 days) model based on random forest (RF) has R2 and RMSE of 0.72 mm and 0.76mm respectively while recurrent neural network (RNN) ensemble forecast model was able to forecast 3 days ET with R2 and RMSE of 0.71 mm and 0.78 mm respectively.

Remotely piloted aircraft (RPA) was used to generate high-resolution maps of crop water stress using remotely sensed thermal and multi-spectral RPA imagery in the Central Sands region of Wisconsin.

The SWAT-MODFLOW coupled model approach was applied at large spatio-temporal scale to study the cumulative effects of changing precipitation patterns, groundwater withdrawals, and forest evapotranspiration to improve projections of the future of lake levels and water availability in agricultural regions.