Global patterns of annual actual evapotranspiration with land-cover type: knowledge gained from a new observation-based database
Bibliographic record
Abstract
Abstract. The process of evapotranspiration (ET) plays a critical role in the earth system, driving key land-surface processes in the energy, water and carbon cycles. Land-cover (LC) exerts multiple controls on ET, yet the global distribution of ET by LC and the related physical variables are poorly understood. The lack of quantitative understanding of global ET variation with LC begets considerable uncertainties regarding how ET and key land-surface processes will change alongside ongoing anthropogenic LC transformations. Here we apply statistical analysis and models to a new global ET database to advance our understanding of how annual actual ET varies with LC type. We derive global fields for each LC using linear mixed effect models (LMMs) that use geographical and meteorological variables as possible independent regression variables. Our inventory of ET observations reveals important gaps in spatial coverage that overlie hotpots of global change. There is a spatial bias of observations towards the mid latitudes, and LCs with large areas in the high latitudes (lakes, wetlands and barren land) are poorly represented. From the distribution of points as well as the uncertainty analysis completed by bootstrapping we identify high priority regions in need of more data collection. Our analysis of the new database provides new insights into how ET varies globally, providing more robust estimates of global ET rates for a broad range of LC types. Results reveal that different LC types have distinct global patterns of ET. Furthermore, zonal ET means among LCs reveal new patterns: ET rates in low latitudinal bands are more sensitive to LC change than in higher latitude bands; LCs with a higher evaporation component show higher variability of ET at the global scale; and LCs with dispersed rather than contiguous global locations have a higher variability of ET at the global scale. Results from this study indicate two major advancements are required to improve our ability to predict how ET will vary with global change. First, further collection of ground truth observations of ET is needed to fill gaps in LC types and spatial location identified in this paper. Second, LC types need to be de-aggregated into finer categories to better characterize ET, to reduce uncertainty and weakened strength to predictor variables, associated by aggregation of heterogeneous LC types into one group; this will require the development of higher-resolution LC databases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".