GRACE-derived groundwater storage estimation: Lake/Reservoir storage controls across Canada
Bibliographic record
Abstract
Abstract: Accurate estimation of groundwater storage is hindered by the lack of direct observations of groundwater over space and time. Gravity Recovery and Climate Experiment (GRACE) satellite observes total water storage, thus presenting issues in applying water budget approaches to extract GRACE-derived groundwater storage. This is especially true in regions with complicated hydrology, ranging from numerous small lakes/reservoirs, elevation variation, and changes in active layer thickness in regions with frozen ground. While the objective of many GRACE studies is to disaggregate total water storage budget, to separately estimate groundwater storage changes, the influence of reservoir storage change within a basin is generally ignored. Extraction of groundwater time series from GRACE, using hydrologic and land surface model output, fails to capture storage changes caused by changes in lake and reservoir storage. In significant surface water areas, reservoir storage may alter water storage changes by increasing leakage errors, and offsetting seasonal variability, leading to accumulation of errors in groundwater estimates. Here, we conducted data-driven experiments to understand the spatial influence of lake and reservoirs on GRACE-derived groundwater storage estimation, using independent information of recorded lake/reservoir water level. The study included comparisons with in-situ groundwater observations throughout Canada to validate our GRACE-derived groundwater storage signal. Accounting for reservoir storage combined with GRACE, improved out estimate of GRACE-derived groundwater storage changes for most basins. Identifying what factors did or did not influence goodness of fit will be addressed. Acknowledgement: The researcher, Mohamed Akl, is funded by a full scholarship from the Ministry of Higher Education of the Arab Republic of Egypt.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".