Nested Recharge Systems in Mountain Block Hydrology: High-Elevation Snowpack Generates Low-Elevation Overwinter Baseflow in a Rocky Mountain River
Bibliographic record
Abstract
The majority of each year′s overwinter baseflow (i.e., winter streamflow) in a third-order eastern slopes tributary is generated from annual melting of high-elevation snowpack which is transmitted through carbonate and siliciclastic aquifers. The Little Elbow River and its tributaries drain a bedrock system formed by repeated thrust faults that express as the same siliciclastic and carbonate aquifers in repeating outcrops. Longitudinal sampling over an 18 km reach was conducted at the beginning of the overwinter baseflow season to assess streamflow provenance. Baseflow contributions from each of the two primary aquifer types were apportioned using sulfate, δ34SSO4, and silica concentrations, while δ18OH2O composition was used to evaluate relative temperature and/or elevation of the original precipitation. Baseflow in the upper reaches of the Little Elbow was generated from lower-elevation and/or warmer precipitation primarily stored in siliciclastic units. Counterintuitively, baseflow generated in the lower-elevation reaches originated from higher-elevation and/or colder precipitation stored in carbonate units. These findings illustrate the role of nested flow systems in mountain block recharge: higher-elevation snowmelt infiltrates through fracture systems in the cliff-forming—often higher-elevation—carbonates, moving to the lower-elevation valley through intermediate flow systems, while winter baseflow in local flow systems in the siliciclastic valleys reflects more influence from warmer precipitation. The relatively fast climatic warming of higher elevations may alter snowmelt timing, leaving winter water supply vulnerable to climatic change.
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) | 0.001 | 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 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".