Gully‐head erosion processes on a semi‐arid valley floor in Kenya: a case study into temporal variation and sediment budgeting
Bibliographic record
Abstract
Abstract A three year monitoring programme of gully‐head retreat was established to assess the significance of sediment production in a drainage network that expanded rapidly by gully‐head erosion on the low‐angled alluvio‐lacustrine Njemps Flats in semi‐arid Baringo District, Kenya. This paper discusses the factors controlling the large observed spatial and temporal variation in gully‐head retreat rates, ranging from 0 to 15 m a−1. The selected gullies differed in planform and in runoff‐contributing catchment area but soil material and land use were similar. The data were analysed at event and annual timescales. The results show that at annual timescale rainfall amount appears to be a good indicator of gully‐head retreat, while at storm‐event timescale rainfall distribution has to be taken into account. A model is proposed, including only rainfall (P) and the number of dry days (DD) between storms: which explains 56 per cent of the variation in retreat rate of the single‐headed gully of Lam1. A detailed sediment budget has been established for Lam1 and its runoff‐contributing area (RCA). By measuring sediment input from the RCA, the sediment output by channelized flow and linear retreat of the gully head for nine storms, it can be seen that erosion shifts between different components of the budget depending on the duration of the dry period (DD) between storms. Sediment input from the RCA was usually the largest component for the smaller storms. The erosion of the gully head occurred as a direct effect of runoff falling over the edge (GHwaterfall) and of the indirect destabilization of the adjacent walls by the waterfall erosion and by saturation (GHmass/storage). The latter component (GHmass/storage) was usually much larger that the former (GHwaterfall). The sediment output from the gully was strongly related to the runoff volume while the linear retreat, because of its complex behaviour, was not. Overall, the results show that the annual retreat is the optimal timescale to predict retreat patterns. More detailed knowledge about relevant processes and interactions is necessary if gully‐head erosion is to be included in event‐based soil erosion models. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 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".