Hydrology signal from GRACE gravity data in the Nelson River basin, Canada: a comparison of two approaches
Bibliographic record
Abstract
The Gravity Recovery and Climate Experiment (GRACE) satellite mission measures the combined gravity signal of several overlapping processes. A common approach to separate the hydrological signal in previous ice-covered regions is to apply numerical models to simulate the glacial isostatic adjustment (GIA) signals related to the vanished ice load and then remove them from the observed GRACE data. However, the results of this method are strongly affected by the uncertainties of the ice and viscosity models of GIA. To avoid this, Wang et al. (Nat Geosci 6(1):38–42, 2013. https://doi.org/10.1038/NGEO1652 ; Geodesy Geodyn 6(4):267–273, 2015) followed the theory of Wahr et al. (Geophys Res Lett 22(8):977–980, 1995) and isolated water storage changes from GRACE in North America and Scandinavia with the help of Global Positioning System (GPS) data. Lambert et al. (Postglacial rebound and total water storage variations in the Nelson River drainage basin: a gravity GPS Study, Geological Survey of Canada Open File, 7317, 2013a, Geophys Res Lett 40(23):6118–6122, https://doi.org/10.1002/2013GL057973 , 2013b) did a similar study for the Nelson River basin in North America but applying GPS and absolute gravity measurements. However, the results of the two studies in the Nelson River basin differ largely, especially for the magnitude of the hydrology signal which differs about 35%. Through detailed comparison and analysis of the input data, data post-processing techniques, methods and results of these two works, we find that the different GRACE data post-processing techniques may lead to this difference. Also the GRACE input has a larger effect on the hydrology signal amplitude than the GPS input in the Nelson River basin due to the relatively small uplift signal in this region. Meanwhile, the influence of the value of $$\alpha $$ , which represents the ratio between GIA-induced uplift rate and GIA-induced gravity-rate-of-change (before the correction for surface uplift), is more obvious in areas with high vertical uplift, but is smaller in the Nelson River basin. From Gaussian filtering of simulated data, we found that the magnitude of the peak gravity signal value can decrease significantly after Gaussian filtering with large average radius filter, but the effect in the Nelson River basin is rather small. More work is needed to understand the effect of amplitude restoration in the post-processing of GRACE g-dot signal. However, it is encouraging to find that both the methodologies of Wang et al. (2013, 2015) and Lambert et al. (2013a, b) can produce very similar results if their inputs are the same. This means that their methodologies can be applied to study the hydrology in other areas that are also affected by GIA provided that the effects of post-processing of their inputs are under control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".