A comparison of rainfall-runoff modelling approaches for estimating impacts of rural land management on flood flows
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
There is a requirement for predictive tools to assist in land management and flood risk planning, and a variety of tools have been proposed recently. We compare four tools developed under various UK research programmes. The strengths and limitations of the tools are reviewed, model performances on historic data are assessed, and the methods are applied to estimating flood flows of 5- and 10-year return periods, and flow peaks under both recent land management conditions and speculative scenarios (grazing intensification and tree planting), using the Pontbren catchment, UK as a case study. Overall, the models agree on the direction of change, so that heavy grazing increases, and afforestation and tree strips decrease the flood flows. However, the estimated effects vary significantly between methods. It is concluded that method selection needs to carefully consider the type and scale of land management scenario being examined, and the sources of data available to support the modelling. Using an ensemble of suitable models is proposed as a useful way to represent a multi-expert opinion and to characterise the structural error associated with a single model.
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 | 0.001 | 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 it