Precipitation event distribution in Central Argentina: spatial and temporal patterns
Bibliographic record
Abstract
Abstract The annual amount of precipitation inputs received by a site during a full year is considered a dominant spatial and temporal control of primary productivity and other related process in arid to subhumid ecosystems. However, to be effectively used by plants, these inputs have to escape runoff, favoured by large and less frequent precipitation events, and evaporation losses, favoured by small and more frequent events. Thus, available water for plant transpiration is not only influenced by the annual sum of precipitation events but also by their frequency‐size distribution. In this paper, we characterize this distribution and its association to total annual precipitation inputs through space (five sites along a tenfold precipitation gradient across 1000 km) and time (1961–2010) in the plains of central Argentina. We decomposed total precipitation into two structural components, which are the frequency and mean size of events, showing that they have similar contributions (log–log slopes ≈ 0·5) explaining precipitation shifts in space. Over time, however, we found a preponderance of mean event size explaining precipitation fluctuations, particularly towards wetter sites (log–log slopes increasing from 0·61 to 0·88). The relative variability of event sizes, independent of their mean size (i.e. inequality), was numerically characterized with Gini coefficients derived from Lorenz curves, which showed highly constant values in space and time. Assuming fixed event‐size thresholds for evaporation and runoff, and ignoring other controls beyond precipitation structure, the proportion of water potentially available for plant transpiration grew with total precipitation, raising from 0·45 to 0·71 from the driest to the wettest sites, but displaying stronger responses to total precipitation in time, particularly in drier sites. No long‐term trends in any of the precipitation structure variables were detected. Response functions of frequency and mean size of events to annual precipitation together with Lorenz curves appeared to be robust descriptors of precipitation regimes that, not requiring any a priori assumptions, are useful to assess how spatial and temporal shifts in total precipitation may concurrently affect its relative availability for plant transpiration. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".