Historical land-use-induced evapotranspiration changes estimated from present-day observations and reconstructed land-cover maps
Bibliographic record
Abstract
Abstract. Recent results from the LUCID model intercomparison project have revealed large discrepancies in the simulated evapotranspiration (ET) response to the historical land-use change. Distinct land-surface parameterizations are behind those discrepancies, but understanding those differences rely on evaluations using still very limited measurements. Model benchmarking studies with observed ET are required in order to reduce the current uncertainties in the impacts of land use in terrestrial water flows. Here we present new estimates of historical land-use-induced ET changes based on three observation-driven products of ET. These products are used to derive empirical models of ET as a function of land-cover properties and environmental variables. An ensemble of reconstructions of past ET changes are derived with the same set of land-cover maps used in LUCID, with which we obtain an average decrease in global terrestrial ET of 1260 ± 850 km3 yr−1 between the preindustrial period and the present-day. This estimate is larger in magnitude than the mean ET change simulated within LUCID with process-based models, and substantially weaker than other estimates based on observations. Although decreases in annual ET dominate in deforested regions, large summertime increases in ET are diagnosed over areas of large cropland expansion. The multiple ET reconstructions carried out here show a large spread that we attribute principally to the different land-cover maps adopted and to the crops' ET rates deduced from the various products assessed. We therefore conclude that the current uncertainties of past ET changes could be reduced efficiently with improved historical land-cover reconstructions and better estimates of cropland ET.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".