Addressing potential drought resiliency through high-resolution terrain and depression mapping
Bibliographic record
Abstract
Increasing occurrences of droughts across Europe and elsewhere require landscape-wide water-retention assessments to evaluate water-supply sustainabilities for local and regional use. This article reports on the results of a study designed to digitally delineate, connect and categorize recurring depression wetness across a rurally cultured morainal landscape, at 1 m resolution. To do this, a digital terrain model (DTM, 1 m resolution) was used to locate and characterize each terrain-detectable depression by type, depth, area, and volume, together with their flow-channel connections and upslope flow-accumulation areas. In addition, historical 2008–2017 Google Earth images and a local daily weather report were used to index and verify weather- and season-induced changes in depression wetness based on ground coloration, vegetation coverage, and image date. Developing and applying these procedures by way of a case study revealed (i) that about 90% of the image-indexed depression wetness variations could mostly be attributed to DTM-determined depression type, area and depth, and (ii) that image-recognized wetness variations were consistent with weather-modelled soil moisture projections. The results so obtained can be used to quantify potential drought resiliency in terms water retention volumes per depression. Since the procedures as described have a broad application potential, they can be used globally for drought resiliency evaluations and agricultural water management.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".