The Development of a ML-based Estimator to Reconstruct Water Table Depth over the Contiguous US
- 1High Meadows Environmental Institute, Princeton University, Princeton, USA
- 2Integrated GroundWater Modeling Center, Princeton University, Princeton, USA
- 3Department of Civil and Environmental Engineering, Princeton University, Princeton, USA
- 4Department of Astrophysical Sciences, Princeton University, Princeton, USA
- 5Center for Statistics and Machine Learning, Princeton University, Princeton, USA
- 6Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, USA
Groundwater is one of the most valuable resources in the US. The United States Geological Survey (USGS) reported that about 26% of the water used in 2015 came from groundwater. Due to the scarcity of groundwater observations, it is still challenging to monitor groundwater resources at the watershed scale (where local decision making occurs). In addition, for long-term high-resolution simulations, physically-based models become very computational demanding and data hungry. With much less computational time and physical knowledge, machine learning (ML) techniques are able to capture complex nonlinear connections between groundwater dynamics and atmospheric and land surface processes from historical data. Recent studies have shown their success in groundwater modeling.
In this study, we develop a ML-based estimator to produce water table depth (WTD) estimates over the Contiguous US (CONUS) at a spatial resolution of 1 km using USGS WTD observations and other hydrometeorological data. The WTD estimator consists of two components. One component captures spatial variations in WTD using random forests and the other component learns temporal variations in WTD by Long Short-Term Memory networks. We combine the results from the two components to obtain WTD estimates. The estimated WTD are compared to USGS WTD observations to show their reliability. Based on the WTD estimates, we study groundwater changes in the Upper Colorado River Basin (UCRB) in recent drought years, which is one of the principal headwater basins in the US. To better interpret the performance of the WTD estimator, we conduct sensitivity analyses on input variables for both components. Moreover, we assess the uncertainty of the WTD estimator in estimating WTD over the CONUS. Our study demonstrates that the WTD estimator can generate reasonable WTD estimates over CONUS, thereby facilitating understanding of groundwater systems in the US. The WTD estimator can also be transferred to other regions in the world that have a similar hydrologic regime to a region in the US.
How to cite: Ma, Y., Leonarduzzi, E., Defnet, A., Melchior, P., Condon, L., and Maxwell, R.: The Development of a ML-based Estimator to Reconstruct Water Table Depth over the Contiguous US , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10596, https://doi.org/10.5194/egusphere-egu23-10596, 2023.
