Rainfall spatial-heterogeneity accelerates landscape evolution processes
Bibliographic record
Abstract
Catchment hydro-morphological response is mainly conditioned on rainfall properties, such as rainfall intensity, storm duration and frequency, and the timing of these events. Rainfall spatial variability is likewise a major determinant affecting streamflow, erosion, and sediment transport, and is explored largely in the context of heavy rain triggering floods and fast morphological changes on hillslopes and in channels. In this study, we examine how the spatial structure of rainfall influences landscape evolution at the catchment scale over hundreds of years. To achieve this, multiple realizations of hourly rainfall fields, each differing only by their spatial distribution but identical in all other respects, were simulated using a weather generator. The impact of storm spatial-heterogeneity on the catchment morphology was then assessed with a landscape evolution model (CAESAR-Lisflood). A virtual “open-book” type catchment was used for this numerical experiment. The mean streamflow and low-flows remained the same while the magnitude of the annual peak streamflow increased by up to 12% in response to higher rainfall spatial heterogeneity. However, the erosion and deposition rates significantly increased (up to 50%) and the net erosion and deposition areas changed (increased by up to 9% and decreased by 13.5%, respectively) when the rain became less uniform in space. Furthermore, new gullies were found to be longer, deeper, and more branched in response to increased rainfall heterogeneity. The results suggest that heterogeneity in rainfall spatial patterns speeds up landscape development, even when rainfall volumes and temporal structures are the same. This implies that the spatial structure of rainfall may have more of an influence on catchment morphology at long time scales than previously thought.
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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 | 0.001 |
| 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 | 0.001 | 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".