Impact of the spaciotemporal variability of the snowpack conditions on internal liquid water fluxes
Bibliographic record
Abstract
In cold regions, the seasonal snowpack plays an important hydrological role. By storing and releasing solid precipitation, the snowpack gives shape to the yearly hygrogram. In addition, by modulating liquid water pathway and residence time, snowpack internal conditions have a strong implication on the partitioning of meltwater among streamflow, groundwater recharge and soil moisture storage. During rain on snow (ROS) events, snowpack conditions influence timing and amount of liquid water inflow to the surface drainage system, with winter floods and ice jams as potential consequences. Recent observations and projections show an increase in ROS frequency in many cold regions of the world. This trend raises concern about a possible increase in winter floods and ice jams events with climate change. In order to better anticipate the hydrological consequences of the increasing ROS phenomenon, a good understanding of the processes and conditions influencing liquid water release from the snowpack is required. The present study articulates around a multimethod approach to characterize liquid water storage and movement in a snowpack in a non-mountainous environment. By combining drone-based high frequency GPR, NIR photogrammetry, time domain reflectometry, stable isotopes of water and other manual measurements throughout a winter season, we aim monitoring the spatiotemporal evolution of the snowpack liquid water content as well as the water fluxes at the snowpack margins. Preliminary results show that, combining the selected methods allows tracking liquid water storage and movements in the snowpack throughout an entire season.
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 | 0.000 |
| 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 | 0.001 | 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".