Retrieving snow water equivalent from GRACE/GRACE-FO terrestrial water storage anomalies using modified spectral combination theory
Bibliographic record
Abstract
Snow Water Equivalent (SWE) refers to the quantity of water contained within the snowpack, which is a critical component of the seasonal water cycle in cold regions, notably Canada. The Gravity Recovery and Climate Experiment (GRACE) mission primarily focuses on quantifying Terrestrial Water Storage Anomalies (TWSA), which is the sum of anomalies in groundwater, soil moisture, surface water, and snow/ice. Separating the individual components with high precision is a challenging task due to the complex interactions of these parameters and their uncertainties involved. This study proposes an enhanced estimator which is modified based on the spectral combination theory, to extract the SWE component from GRACE/GRACE-FO (Follow-On) TWS measurements. This estimator uses a hydrological model and its uncertainty to optimally extract the SWE component from the GRACE monthly models in spectral domain. The approach was applied in eight selected basins across Canada, covering a diverse range of climatic and geographical conditions. Different winter seasons of each basin were considered, including the peak accumulation and ablation phases of the snowpack, from January 2003 to the end of 2022. Among the basins examined, the Fraser-Lower Mainland and Ottawa basins exhibited the most pronounced seasonal variations in SWE, with maximum value of about 200 mm. In contrast, the Saint John-St basin demonstrated the lowest SWE variability, with maximum amount of 50 mm. All the studied basins across Canada except for Okanagan-Similkameen basin and Saint John-St basin displayed a positive trend in SWE. The results from the proposed approach were compared to the SWE component derived from Canadian Historical Snow Water Equivalent dataset (CanSWE), Canadian Meteorological Centre (CMC), and GlobSnow. Varying levels of agreement were found depending on the basins (correlations between r = 0.40 and r = 0.83, and RMSE between 10 mm and 55 mm). The best agreements were found with CMC and CanSWE products. The inclusion of streamflow component highlighted the relationship between maximum SWE and the peak flow. The results found indicate significant correlations between SWE derived from our modified spectral combination approach and peak flow in several basins ( r varying from 0.42 to 0.80); thus emphasizing the critical role of snowmelt in influencing peak flows in the basins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".