Characterising catchment water storage under different land covers using multiple methods: Informing modelling and natural flood management
Bibliographic record
Abstract
<!--!introduction!--><b></b> A key assumption of `natural flood management’ (NFM) and catchment restoration schemes is that land use is a major control of runoff. To assess the effectiveness of these schemes requires understanding of the influence of land use change on catchment water storage and mixing. However, few NFM schemes are resourced to also investigate the underpinning hydrological processes. This study combined hydrometric, isotopic and geochemical water properties to investigate land cover controls on catchment runoff and water storage in nine nested catchments within a 67 km<sup>2</sup> managed upland catchment in southern Scotland, UK. Forest cover in the sub-catchments ranged from 0.5 to 94%. Sub-catchment dynamic storage characterised from hydrometric data using recession analysis was low but variable (16–200 mm). Mean transit times estimated from isotopic data were 134–370 days and groundwater fractions estimated from end member mixing analysis based on acid neutralising capacity (ANC) were 0.20–0.52 of annual stream runoff. Correlation of measures of catchment water storage and mixing with land cover, topographic, soil and geological catchment attributes showed significant positive correlations with soil hydraulic properties, whilst percentage forest cover was inversely correlated. The results highlight the importance of understanding dominant controls on water storage in target catchments when using tree planting as a flood management strategy. The isotopic and ANC data are also being integrated into hydrological modelling of the catchment to constrain the number of potential models and reduce model equifinality.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".