|
|||||
|
|
Water Table Depth Estimates over the Contiguous United States Using a Random Forest Model |
|
| Authors: | Yueling Ma Elena Leonarduzzi Amy Defnet Peter Melchior Laura E Condon Reed M Maxwell |
| Institution: | 1. High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA;2. High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA
Integrated GroundWater Modeling Center, Princeton University, Princeton, NJ, USA;3. High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA Integrated GroundWater Modeling Center, Princeton University, Princeton, NJ, USA Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA;4. Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA;5. Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA |
| Abstract: | Water table depth (WTD) has a substantial impact on the connection between groundwater dynamics and land surface processes. Due to the scarcity of WTD observations, physically-based groundwater models are growing in their ability to map WTD at large scales; however, they are still challenged to represent simulated WTD compared to well observations. In this study, we develop a purely data-driven approach to estimating WTD at continental scale. We apply a random forest (RF) model to estimate WTD over most of the contiguous United States (CONUS) based on available WTD observations. The estimated WTD are in good agreement with well observations, with a Pearson correlation coefficient (r) of 0.96 (0.81 during testing), a Nash-Sutcliffe efficiency (NSE) of 0.93 (0.65 during testing), and a root mean square error (RMSE) of 6.87 m (15.31 m during testing). The location of each grid cell is rated as the most important feature in estimating WTD over most of the CONUS, which might be a surrogate for spatial information. In addition, the uncertainty of the RF model is quantified using quantile regression forests. High uncertainties are generally associated with locations having a shallow WTD. Our study demonstrates that the RF model can produce reasonable WTD estimates over most of the CONUS, providing an alternative to physics-based modeling for modeling large-scale freshwater resources. Since the CONUS covers many different hydrologic regimes, the RF model trained for the CONUS may be transferrable to other regions with a similar hydrologic regime and limited observations. |
| Keywords: | |