Modelling the hydrological impacts of rural land use change
Bibliographic record
Abstract
The potential role of rural land use in mitigating flood risk and protecting water supplies continues to be of great interest to regulators and planners. The ability of hydrologists to quantify the impact of rural land use change on the water cycle is however limited and we are not able to provide consistently reliable evidence to support planning and policy decisions. This shortcoming stems mainly from lack of data, but also from lack of modelling methods and tools. Numerous research projects over the last few years have been attempting to address the underlying challenges. This paper describes these challenges, significant areas of progress and modelling innovations, and proposes priorities for further research. The paper is organised into five inter-related subtopics: (1) evidence-based modelling; (2) upscaling to maximise the use of process knowledge and physics-based models; (3) representing hydrological connectivity in models; (4) uncertainty analysis; and (5) integrated catchment modelling for ecosystem service management. It is concluded that there is room for further advances in hydrological data analysis, sensitivity and uncertainty analysis methods and modelling frameworks, but progress will also depend on continuing and strengthened commitment to long-term monitoring and inter-disciplinarity in defining and delivering land use impacts research.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".