Groundwater level assessment and prediction in the Nebraska Sand Hills using LIDAR-derived lake water level
| Autor: | Yi Qi, Aaron R. Young, Aaron R. Mittelstet, C. Zeyrek, Nawaraj Shrestha, David C. Gosselin, Troy E. Gilmore |
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| Rok vydání: | 2021 |
| Předmět: |
Hydrology
geography geography.geographical_feature_category 010504 meteorology & atmospheric sciences Mean squared error 0207 environmental engineering Aquifer 02 engineering and technology 01 natural sciences Water level Hydraulic head Kriging Environmental science Spatial variability 020701 environmental engineering Surface water Groundwater 0105 earth and related environmental sciences Water Science and Technology |
| Zdroj: | Journal of Hydrology. 600:126582 |
| ISSN: | 0022-1694 |
| DOI: | 10.1016/j.jhydrol.2021.126582 |
| Popis: | The spatial variability of groundwater levels is often inferred from sparsely located hydraulic head observations in wells. The spatial correlation structure derived from sparse observations is associated with uncertainties that spread to estimates at unsampled locations. In areas where surface water represents the nearby groundwater level, remote sensing techniques can estimate and increase the number of hydraulic head measurements. This research uses light detection and ranging (LIDAR) to estimate lake surface water level to characterize the groundwater level in the Nebraska Sand Hills (NSH), an area with few observation wells. The LIDAR derived lake groundwater level accuracy was within 40 cm mean square error (MSE) of the nearest observation wells. The lake groundwater level estimates were used to predict the groundwater level at unsampled locations using universal kriging (UK) and kriging with an external drift (KED). The results indicate unbiased estimates of groundwater level in the NSH. UK showed the influence of regional trends in groundwater level while KED revealed the local variation present in the groundwater level. A 10-fold cross-validation demonstrated KED with better mean squared error (ME) [−0.003, 0.007], root mean square error (RMSE) [2.39, 4.46], residual prediction deviation (RPD) [1.32, 0.71] and mean squared deviation ratio (MSDR) [1.01, 1.49] than UK. The research highlights that the lake groundwater level provides an accurate and cost-effective approach to measure and monitor the subtle changes in groundwater level in the NSH. This methodology can be applied to other locations where surface water bodies represent the water level of the unconfined aquifer and the results can aid in groundwater management and modeling. |
| Databáze: | OpenAIRE |
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