Mass balances of Yala and Rikha Samba glaciers, Nepal, from 2000 to 2017
Bibliographic record
Abstract
Abstract. The glacier mass balance is an important variable to describe the climate system and is used for various applications like water resource management or runoff modelling. The direct or glaciological method and the geodetic method are the standard methods to quantify glacier mass changes, and both methods are an integral part of international glacier monitoring strategies. In 2011, we established two glacier mass-balance programmes on Yala and Rikha Samba glaciers in the Nepal Himalaya. Here we present the methods and data of the directly measured annual mass balances for the first six mass-balance years for both glaciers from 2011/2012 to 2016/2017. For Yala Glacier we additionally present the directly measured seasonal mass balance from 2011 to 2017, as well as the mass balance from 2000 to 2012 obtained with the geodetic method. In addition, we analysed glacier length changes for both glaciers. The directly measured average annual mass-balance rates of Yala and Rikha Samba glaciers are −0.80 ± 0.28 and −0.39 ± 0.32 m w.e. a−1, respectively, from 2011 to 2017. The geodetically measured annual mass-balance rate of Yala Glacier based on digital elevation models from 2000 and 2012 is −0.74 ± 0.53 m w.e. The cumulative mass loss for the period 2011 to 2017 for Yala and Rikha Samba glaciers is −4.80 ± 0.69 and −2.34 ± 0.79 m w.e., respectively. The mass loss on Yala Glacier from 2000 to 2012 is −8.92 ± 6.33 m w.e. The winter balance of Yala Glacier is positive, and the summer balance is negative in every investigated year. The summer balance determines the annual balance. Compared to regional mean geodetic mass-balance rates in the Nepalese Himalaya, the mean mass-balance rate of Rikha Samba Glacier is in a similar range, and the mean mass-balance rate of Yala Glacier is more negative because of the small and low-lying accumulation area. During the study period, a change of Yala Glacier's surface topography has been observed with glacier thinning and downwasting. The retreat rates of Rikha Samba Glacier are higher than for Yala Glacier. From 1989 to 2013, Rikha Samba Glacier retreated 431 m (−18.0 m a−1), and from 1974 to 2016 Yala Glacier retreated 346 m (−8.2 m a−1). The data of the annual and seasonal mass balances, point mass balance, geodetic mass balance, and length changes are accessible from the World Glacier Monitoring Service (WGMS, 2021), https://doi.org/10.5904/wgms-fog-2021-05.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".