Soil erosion and sediment interception by check dam in watershed under extreme rainstorm on the Loess Plateau, China
Bibliographic record
Abstract
The magnitude of soil erosion and sediment reduction efficiency of check dams under extreme rainstorms are long-standing concerns. This paper aims to use check dams to deduce the amount of soil erosion under extreme rainstorms in watersheds and to identify the difference of sediment intercepting efficiency of different types of check dams. Based on the sediment deposition of 12 check dams with 100% sediment intercepting efficiency and sub-catchment clustering by taking 12 check dams-controlled catchments as standard separately, the amount of soil erosion caused by an extreme rainstorm event on July 26th, 2017 (named “7·26” extreme rainstorm) was deduced in the Chabagou watershed in the hill and gully region of the Loess Plateau. The differences of sediment intercepting efficiency among check dams in the watershed were analysed according to the field observation 17 check dams. The results showed that the average erosion intensity under the ‘7·26’ extreme rainstorm was approximately 2.03×104 t·km-2, which was 5 times that in the second erosive rainfall in 2017 (4.15×103 t·km-2) and 11-384 times that in 2018 (0.53×102 t·km-2 - 1.81×103 t·km-2). Under the ‘7·26’ extreme rainstorm, the amount of soil erosion in the Chabagou watershed above Caoping hydrological station was 4.20×106 tons. The sediment intercepting efficiencies check dams with drainage canals (including the destroyed check dams) and with drainage culverts was 6.48% and 39.49%, respectively. The total actual sediment amount trapped by the check dam was 1.11×106 tons, accounting for 26.36% of the total soil erosion amount. In contrast, 3.09×106 tons of sediment was inputted to the downstream channel, and the sediment deposition in the channel was 2.23×106 tons, accounting for 53.15% of the total amount of soil erosion. The amount of sediment transport at the hydrological station was 8.60×105 tons. The sediment delivery ratio (SDR) under the “7·26” extreme rainstorm was 0.21. The results indicated that the amount of soil erosion was huge, and the sediment intercepting efficiency of check dams was greatly reduced under extreme rainstorms. It is necessary to strengthen the management and construction technology standards of check dams to improve the sediment intercepting efficiency and flood safety in the watershed.
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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 | 0.000 | 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".