Variability of seasonnally frozen ground in an agricultural field using drone-based GPR
Bibliographic record
Abstract
Spring in cold regions is a critical time for floods, as snowmelt releases large amounts of water into watersheds. Seasonally frozen ground reduces soil infiltration and increases runoff by blocking pores in the soil. This limited infiltration causes rivers to respond faster to rain or meltwater, heightening flood risks. Most hydrological models used to project flood risks in a future climate are built on the assumption that, for a given land use, soil infiltrability is somewhat homogeneous. We challenge that assumption by measuring frozen ground thickness distribution in an agricultural field over an entire winter. For that purpose, we measured frost thickness at one specific point of the field at a sub hour frequency and over a +/- 120m transect on a weekly basis. Point measurements were done using TDR sensors. The transect measurements were performed with a drone-based ground penetrating radar (GPR). The use of a drone based GPR allowed repetitive measurements over a given transect in a nondestructive way. Unlike a drone based GPR, the use of a ground based GPR would have altered the snow cover over the studied transect with potential perturbations of the heat exchanges at the ground surface.Field measurements show that the ground frost depth is not spatially uniform all winter long. During the snowmelt period, the ground frost depth is particularly heterogeneous. We found that 78.11% of the transect that we were able to interpret had an unfrozen layer on top of the frozen ground. If the top layer of the ground is unfrozen during the snowmelt period, it forms a zone where there can be liquid water infiltration and/or storage. Furthermore, because of the spatial variability of ground frost, some areas thaw completely before others. The matric potential of these areas increases and allow preferential infiltration in the thawed zone while the ground is still considered frozen. We conclude that it is important to account for spatial variability of ground frost to better understand how seasonally frozen ground impacts infiltration and flooding. The study shows that drone based GPR is a well-adapted tool to evaluate frozen ground thickness variability in a repetitive and non-destructive way.
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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".