Monitoring groundwater changes in southern Ontario using GRACE satellite gravity measurements
Bibliographic record
Abstract
The Gravity Recovery and Climate Experiment satellite mission(s) (GRACE) have been measuring variations in the Earth's gravitational field since 2002. Changes in groundwater, melting glaciers, ocean circulation, and large deformations associated with post-glacial rebound, continental tectonics and earthquakes all produce signals that can be detected and monitored. In this talk, I will provide an introduction to the GRACE missions, briefly mention some new analysis methods being developed at the Canadian Geodetic Survey, and show some new results related to groundwater variations in Southern Ontario. The new method uses watershed regions from the Canadian National Frameworks Dataset to build mascons (regions of interest) for Canada, ensuring that the boundaries of the numerical method correspond to real hydrological boundaries and reducing leakage between adjacent watersheds. Three distinct mascons are designed using watershed geometry that include Lake Huron, Lake Erie, and Lake Ontario and estimates of total water storage (TWS) change are obtained using GRACE data from the Center for Space Research (U. of Texas at Austin) spherical harmonic Release 5 dataset. These TWS estimates are split into a contribution from surface water (SW) change and a common large-scale background signal across Southern Ontario that represents groundwater, soil moisture, snow and ice. The surface water changes are almost entirely due to lake level changes within the Great Lakes and can be compared to water level observations from gauges on the lakes provided by the Canadian Hydrographic Service. The GRACE derived and observed lake level changes agree well with correlations of 0.95, 0.92, and 0.82 for the Lake Huron, Erie, and Ontario regions, respectively. The amplitudes also agree well and, to our knowledge, this is the first study to effectively detect the changing water levels of the different Great Lakes using GRACE. The common background signal is found to have peak to peak variations of ~20 cm equivalent water thickness (EWT) and is compared to 301 wells from across Southern Ontario (data from the Provincial Groundwater Monitoring Network). The signals agree well with a correlation of 0.85. This result has important implications for the relative size and/or dynamics of the groundwater and remaining surface terms (soil moisture, snow and ice). Furthermore, variations in water storage from groundwater are found to be comparable to variations in water storage from the Great Lakes themselves - highlighting the importance that groundwater plays in any water budget of the Great Lakes system. Comparisons between individual groundwater wells and that of GRACE reveal the spatial extent of wells that show regional signals (high correlation) and those that show more local variability (poor correlation). Wells that correlate poorly reveal additional spatial patterns and the influence of local topography, geology, and water usage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".