Urban water storage capacity inferred from observed evapotranspiration recession 
Bibliographic record
Abstract
The amount and dynamics of urban water storage play an important role in mitigating urban flooding and heat. Assessment of the capacity of cities to store water remains challenging due to the extreme heterogeneity of the urban surface. Evapotranspiration (ET) recession after rainfall events during the period without precipitation, over which the amount of stored water gradually decreases, can provide insight on the water storage capacity of urban surfaces. Assuming ET is the only outgoing flux, the water storage capacity can be estimated based on the timescale and intercept of its recession. In this paper, we test the proposed approach to estimate the water storage capacity at neighborhood scale with latent heat flux data collected by eddy covariance flux towers in eleven contrasting urban sites with different local climate zones, vegetation cover and characteristics and background climates (Amsterdam, Arnhem, Basel, Berlin, Helsinki, Łódź, Melbourne, Mexico City, Seoul, Singapore, Vancouver). Water storage capacities ranging between 1 and 12 mm were found. These values correspond to e-folding timescales lasting from 2 to 10 days, which translate to half-lives of 1.5 to 7 days. We find ET at the start of a drydown to be positively related to vegetation fraction, and long timescales and large storage capacities to be associated with higher vegetation fractions. According to our results, urban water storage capacity is at least one order of magnitude smaller than the known water storage capacity in natural forests and grassland.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".