Impact of climate change on the contribution of glaciers to the Upper Yukon River 
Bibliographic record
Abstract
In the Upper Yukon Watershed (UYW, 20,000 km2), seasonal melt from glaciers contribute significantly to annual runoff and operation of the Whitehorse power plant. This study aims to analyze the impact of climate change on the contribution of glaciers, which covers 5% of the UYW, to the annual runoff using hydrological modelling, GRACE data and machine learning algorithms. The spatial resolution of GRACE data remains too low to discriminate changes in glacier mass signal at the scale of the UYW. Thus, here we applied a spatial concentration function approach to build high resolution monthly time-series of glaciers mass changes over the UYW. To estimate glaciers mass changes, we decomposed four GRACE TWS solutions with different processing assumptions using monthly data from LSM GLDAS v2.1 (Rodell et al., 2004) and WGHM v2.2d (Müller Schmied et al., 2020). Spatial concentration functions were derived from two heterogeneous a priori of different resolutions and sources (Hugonnet et al., 2021; Larsen et al., 2015) and the leakage was subtracted by using glaciers over the Gulf Of Alaska (GOA). To analyze the accuracy of our assessments, we compared the trends resulting from the spatial concentration functions and the constrained forward approach (Doumbia et al., 2020) over the GOA and the Saint-Elias Mountains. To extend/reconstruct glacier mass change up to GRACE-FO (i.e. 2003-2020), we used the Automated Machine Learning (AML) H2O-AutoML (LeDell and Poirier, 2020). Then, glacier mass anomalies were used to calibrate the hybrid (i.e., degree-day/thermal energy balance) glacier melt model of HYDROTEL over UYW. For the period of 2003 to 2016, the trends in glaciers mass losses over the GOA and Saint-Elias Mountains varied from 41.61 to 53.43Gt/yr and 19.31 to 28.88Gt/yr, respectively. Our results compared well with the glaciers mass losses reported in other studies. The AML algorithms performed well with NSE values varying from 0.75 to 0.99; correlation coefficients from 0.93 to 0.99; P-bias from -2.4 to 4.8 and NMRSE from 0.8 to 49.6. The use of the glacier mass changes, in addition to stream flows, improved the calibration of HYDROTEL.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".