Groundwater level assessment and prediction in the Nebraska Sand Hills using LIDAR-derived lake water level

The spatial variability of groundwater levels is often inferred from sparsely located hydraulic head observations in wells. correlation structure derived sparse associated with uncertainties that spread to estimates at unsampled locations. In areas where surface water represents the nearby level, remote sensing techniques can estimate and increase number measurements. This research uses light detection ranging (LIDAR) lake level characterize Nebraska Sand Hills (NSH), an area few observation LIDAR accuracy was within 40 cm mean square error (MSE) nearest were used predict locations using universal kriging (UK) external drift (KED). results indicate unbiased NSH. UK showed influence regional trends while KED revealed local variation present level. A 10-fold cross-validation demonstrated better squared (ME) [−0.003, 0.007], root (RMSE) [2.39, 4.46], residual prediction deviation (RPD) [1.32, 0.71] ratio (MSDR) [1.01, 1.49] than UK. highlights provides accurate cost-effective approach measure monitor subtle changes methodology be applied other bodies represent unconfined aquifer aid management modeling.