Modeling Sediment Yield in Land Surface and Earth System Models: Model Comparison, Development, and Evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Sediment yield (SY) plays an important role in the global carbon cycle for carrying particulate carbon into rivers and oceans, but it is rarely represented in Earth system models (ESMs). Existing SY models have mostly been tested over a few small catchments in specific regions or in large river basins globally. By comparing the performance of eight well‐known SY models in 454 small catchments with various land covers and uses across the United States, Canada, Puerto Rico, U.S. Virgin Islands, and Guam, we identified the simple Morgan model for its better performance in representing the spatial variability of continental scale SY at spatial scales relevant to ESMs (several to hundreds of square kilometers) than other models because of a more realistic representation of runoff‐driven erosion and sediment transport capacity in the context of current data availability. The results also indicated that runoff‐driven erosion should be formulated using a power function of runoff, shear stress, or stream power to better represent the total effect of concentrated flow if gully erosion and channel erosion are not explicitly modeled. We also demonstrated that the Morgan model can be further improved by removing snowmelt‐driven runoff in modeling runoff‐driven erosion and to a minor degree by integrating a landslide model. The improved Morgan model explains 57% of the spatial variability of the measured SY. The new model also demonstrated the capability to simulate SY in cross‐validation catchments at fine temporal scales, which is important for coupling SY with other biogeochemistry processes in ESMs.
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.
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 | 0.002 | 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 | 0.001 |
| 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 it