Detectability of groundwater storage change within the Great Lakes Water Basin using GRACE
Bibliographic record
Abstract
Groundwater is a primary hydrological reservoir of the Great Lakes Water Basin (GLB), which is an important region to both Canada and US in terms of culture, society and economy. Due to insufficient observations, there is a knowledge gap about groundwater storage variation and its interaction with the Great Lakes. The objective of this study is to examine the detectability of the groundwater storage change within the GLB using the monthly models from the Gravity Recovery And Climate Experiment (GRACE) satellite mission, auxiliary soil moisture, snow and lake (SMSL) data, and predictions from glacial isostatic adjustment (GIA) models. A two‐step filtering method is developed to optimize the extraction of GRACE signal. A two dimensional basin window weight function is also introduced to reduce ringing artifacts caused by the band‐limited GRACE models in estimating the water storage change within the GLB. The groundwater storage (GWS) as deviation from a reference mean storage is estimated for the period of 2002 to 2009. The average GWS of the GLB clearly show an annual cycle with an amplitude range from 27 to 91 mm in water thickness equivalent (WTE), and a phase range of about two months. The estimated phases of GWS variations have a half year shift with respect to the phase of SMSL water storage variations which show peaks in March and April. The least squares estimation gives a GWS loss trend of from 2.3 to 9.3 km3/yr within the GLB for the period of study. This wide range of the GRACE GWS results is caused largely by the differences of soil moisture and snow storage from different land surface models (LSMs), and to a lesser extent by the GRACE commission and omission errors, and the GIA model error.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".