Linking Mountain Glacier Retreat and Hydrological Changes in Southwestern Yukon
Bibliographic record
Abstract
The project is linked to a paper by Chesnokova et al. (2020) “Linking Mountain Glacier Retreat and Hydrological Changes in Southwestern Yukon” and contains: 1. Glacier inventory data for three years for each study watershed (glacier_outlines.zip; files) are named based on the inventory year and the ID number from the discharge station at the watershed outlet provided by from Water Survey of Canada (Environment and Natural Resources, 2016; https://wateroffice.ec.gc.ca/). Glacier cover changes for each watershed were obtained by delineating glacier outlines for different years using freely available Landsat satellite images (Landsat Thematic Mapper 5, 7 and 8). Only August-September images with minimal snow and cloud cover were used for the inventory. Based on the temporal range of best available images suitable for the delineation, we choose years 1989, 1998 (1999 for White River watershed) and 2017, resulting in a time window of 28 years for the quantification of glacier cover changes. Initial glacier delineation was performed using the Global Land Ice Measurements from Space (GLIMS) (Kargel et al., 2014; available at https://www.glims.org/RGI/) shapefiles as a base. They were first modified in a way that each individual glacier was represented by an individual polygon instead of being merged with others. Delineating each individual glacier was required because glacier areas were used for glacier volume estimation in the SWBM. Glacier outlines were then modified according to the changes observed on satellite images for 1989, 1998 and 2017. Individual glacier area was subsequently calculated for the three years using ArcGIS®. 2. Simple water balance model (SWBM) (SWBM.zip; includes 8 models - one per study watershed) are named based on the ID numbers from the discharge station at the watershed outlet provided by from Water Survey of Canada (Environment and Natural Resources, 2016; https://wateroffice.ec.gc.ca/ ) (the modelling procedure, equations as well as model parameters can be found in the manuscript). SWBM was adapted from the model used by Baraer et al., (2012) to support the interpretation of trends in measured discharge in the Cordillera Blanca, Peru. As in the original version, based on water budget, the model generates synthetic hydrographs using the watershed area, the initial glacierized surface and the annual ice loss rate as an input. SWBM allows studying the direct impact of glaciers retreat on discharge thereby eliminating the uncertainties stemming from modeling the response of the glacier to changes in climatic forcing. In the present case, components of the energy balance, as well as physical characteristics of glacierized parts of the watersheds (glacier elevation range, slope orientation, ice flow etc.) that influence meltwater production, are integrated into an ice loss rate parameter. Moreover, most of the model parameters are taken from the literature, so if losses to evapotranspiration and deep infiltration can be estimated, the SWBM can even be used for ungauged watersheds. Modifications included rationalization of internal parameters, improvement of the watershed glaciers volume and glacier annual retreat calculation methods, and integration of extra processes such as the seasonality in groundwater and evapotranspiration fluxes. 3. Simple water balance model (SWBM) outputs that were used to create Figure 4 of the manuscript (SWBM_outputs.xlsx). For each studied watershed (column A) SWBM provides a peak water (PW) phase for the year 2017 (Phase, columns C & D) as well as a number of scenarios that assigned it (Number, column X;16 scenarios are summarized in Table 2 of the manuscript). The PW discharge relative to the original discharge, Qpeak/Q0, and the relative final discharge, Qend/Q0, is given annually (columns F & G) and for ablation period (columns H and I) for each scenario that predicted phases P1, P2 and P3. SWBM also calculates the potential of glaciers to further increase the watershed outflows in case of retreat acceleration, ∫Q+, (column J), estimated peak water year for scenario that did not lead to phase 3 or 4 (column K), and the precipitation increase rate Preq [mm/year] that would be required to reverse a negative trend in yearly discharge (or a positive trend in the coefficient of variation) arising from the PW analysis (column L).
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".