Land cover influence on catchment scale subsurface water storage investigated by multiple methods: Implications for UK Natural Flood Management
Bibliographic record
Abstract
United Kingdom (UK). ‘Natural flood management’ (NFM) schemes manipulating land use and other catchment features to control runoff are increasingly promoted across the UK. Catchment water storage and mixing processes influence runoff, but our understanding of the effects of land cover change on these processes is still limited. This study combined hydrometric, isotopic and geochemical measurements to investigate land cover versus potential topographic, soil and geological controls. It compared storage-discharge dynamics in nine nested catchments within a 67 km2 managed upland catchment in southern Scotland. Storage and mixing dynamics were characterised from hydrometric data using recession analysis and from isotopic data using mean transit time and young water fraction estimates. To give information on water sources, groundwater fraction was estimated from end member mixing analysis based on acid neutralising capacity. The analysis showed low but variable sub-catchment scale dynamic storage (16–200 mm), mean transit times (134–370 days) and groundwater fractions (0.20–0.52 of annual stream runoff). Soil hydraulic conductivity was most significantly positively correlated with storage and mixing measures, whilst percentage forest cover was inversely correlated. Any effects of forest cover on increasing catchment infiltration and storage are masked by soil hydraulic properties even in the most responsive catchments. This highlights the importance of understanding dominant controls on catchment storage when using tree planting as a flood management strategy.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".