Overlooked and underrated: Influence of snowmelt runoff on lake-level rise rivals river floodwaters at a cold-region freshwater delta
Bibliographic record
Abstract
Snowmelt runoff is well-recognized as important for sustaining shallow waterbodies across semi-arid and sub-humid cold regions where water loss via evaporation is relatively high, but is often overlooked in cold-region floodplains where river floodwaters are considered to drive freshwater availability. At the Peace-Athabasca Delta (northwestern Canada), drawdown of ecologically and culturally important shallow lakes is a long-standing concern. The drawdown is widely attributed to alteration of the ice-jam flood regime, but the direct influence of snowmelt runoff remains largely unknown. Here, measurements of water depth and isotope composition are used to evaluate contributions from snowmelt versus river floodwaters to lake-level rise after widespread ice-jam flooding in spring 2020. Results reveal lake-level rise from snowmelt input at non-flooded lakes (median = 0.32 m; n = 27) is comparable to that from river floodwaters at flooded lakes (median = 0.26 m; n = 25). Snowmelt accounted for more than one-third of the rise at flooded lakes. Lake-level rise by snowmelt was greatest in areas where greater topographic relief and forest/shrub vegetation entrap wind-distributed snow from adjacent flat unforested terrain. In contrast, lake-level rise by floodwater was greatest in lower elevation flood-prone areas. Meteorological records of peak snow water equivalent and melt rate reveal that comparable contributions of snowmelt runoff to lake-level rise likely occurred regularly during 1963–1987 but values in 2020 (80 mm and 10.1 mm/day, respectively) have been exceeded once since 1987 (in 2018), suggesting drawdown of lakes during recent decades may also be associated with reduced input of snowmelt.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".