Spatial Downscaling of GRACE TWSA Data to Identify Spatiotemporal Groundwater Level Trends in the Upper Floridan Aquifer, Georgia, USA
| Autor: | Todd C. Rasmussen, Adam Milewski, Wondwosen M. Seyoum, Matthew B. Thomas |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Hydrology
geography geography.geographical_feature_category 010504 meteorology & atmospheric sciences Lineament Anomaly (natural sciences) Water storage 0207 environmental engineering Aquifer 02 engineering and technology Karst 01 natural sciences General Earth and Planetary Sciences Environmental science Satellite GRACE spatial downscaling downscaling machine learning boosted regression trees karst upper Floridan aquifer Flint River Basin Dougherty Plain 020701 environmental engineering Groundwater 0105 earth and related environmental sciences Downscaling |
| Zdroj: | Remote Sensing; Volume 11; Issue 23; Pages: 2756 |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs11232756 |
| Popis: | Accurate assessments of groundwater resources in major aquifers across the globe are crucial for sustainable management of freshwater reservoirs. Observations from the Gravity Recovery and Climate Experiment (GRACE) satellite have become invaluable as a means to identify regions groundwater change. While there is a large body of research that focuses on downscaling coarse (1°) GRACE products, few studies have attempted to spatially downscale GRACE to produce fine resolution (5 km) maps that are more useful to resource managers. This study trained a boosted regression tree model to statistically downscale GRACE total water storage anomaly to monthly 5 km groundwater level anomaly maps in the karstic upper Floridan aquifer (UFA) using multiple hydrologic datasets. Evaluation of spatial predictions with existing groundwater wells indicated satisfactory performance (R = 0.79, NSE = 0.61). Results demonstrate that groundwater levels were stable between 2002–2016 but varied seasonally. The data also highlights areas where groundwater pumping is exacerbating UFA water-level declines. While results demonstrate the applicability of machine learning based methods for spatial downscaling of GRACE data, future studies should account for preferential flowpaths (i.e., conduits, lineaments) in karstic systems. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |