Spatial distribution of surface properties, runoff generation and landscape development in the Zin Valley Badlands, northern Negev, Israel
Bibliographic record
Abstract
Abstract Infiltration tests, soil mapping and soil property analysis were used to assess the effect of within‐storm rainfall conditions on spatial patterns of surface characteristics relevant for runoff generation, continuity and erosion in the Zin Valley Badlands. Runoff and erosion differ strongly between ridges and slopes. Soils at both locations are susceptible to sealing, but on the sideslopes deep desiccation cracks inhibit continuous flow, even during high magnitude rainstorms. The discontinuous nature of runoff has a feedback on surface conditions. Erosion on the ridges maintains shallow soils prone to sealing while infiltration and deposition on the sideslopes enhance soil depth, a prerequisite for stable desiccation cracks. Some runoff generated on the ridges is transmitted to the valley via rills. On straight sideslopes, rills are single and often discontinuous, indicating limited frequency of continuous runoff. Along concave valley heads, rill systems are well integrated and continuous, concentrating runoff and reducing infiltration losses along slopes. The longitudinal, V‐shaped valley morphology of small catchments in the Zin Valley Badlands reflects the long‐term effect of different erosion rates in valley heads and on sideslopes. Over time, valley incision lengthened the sideslopes, reducing the portion of annual rainfall that was runoff‐effective. Once sideslopes reached a critical length that inhibited frequent continuous flow, a colluvium with an increased infiltration capacity developed, reducing runoff frequency even further. Consequently, erosion on the valley sideslopes decreased. Continuous flow from ridges to the valley channel remained more common in integrated rill systems in concavities and valley heads, leading to more erosion and retreat of the valley heads. The spatial patterns of runoff and erosion in the Zin Valley Badlands demonstrate that landscape development is strongly affected by processes that lead to differentiation of soil properties on hillslopes with uniform lithology. The patterns of surface characteristics and their role in landscape development are strongly dependent on rainfall conditions, highlighting the need for geomorphologists to identify the dynamic spatial and temporal scales relevant for landscape development. Copyright © 2004 John Wiley & Sons, Ltd.
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) | 0.000 | 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 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".