The role of subsurface storage on departures from the Budyko curve
Bibliographic record
Abstract
The Budyko framework is a widely used empirical concept in hydrology and climatology. However, catchment water balances that plot along the curve are often noisy and scattered, with some catchments plotting above the curve and some below the curve. Here we examine one of the possible causes for such scatter: subsurface storage. We bring together data from 38 experimental catchments in Luxembourg where all climate and landuse factors are roughly constant, except for subsurface storage. We leverage diverse catchment geology represented by the large differences in bedrock porosity and permeability with resulting large differences in storage and streamwater transit times across our set of nested catchments. This setting enables us to test the null hypothesis that departures (offset) from the Budyko line along the evaporative index (i.e. actual evapotranspiration / potential evapotranspiration) axis has no relation to below ground storage. We then ask the following questions: Where do the 38 Luxembourg catchments plot in the Budyko space? How do subsurface storage metrics vary across the 38 Luxembourg catchments? How are these subsurface storage metrics related to the Budyko offset? And secondarily, What might explain scatter on the precipitation / PET axis in the Luxembourg catchments and how is this related to catchment area? Our main finding is that subsurface storage—driven by differences in catchment geology—explains approximately 60% of the departure from the Budyko curve. Furthermore, scatter along the aridity index axis (i.e. precipitation / potential evapo-transpiration) is explained by an east-west gradient in precipitation amount within an otherwise low seasonality environment.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".