Comparing evaporation from water balance framework and multiple models on a global scale
Bibliographic record
Abstract
• Unprecedented estimates of ET differences (DET) from water balance and global models. • Human activities including water use and reservoir construction contribute to DET. • Precipitation uncertainty also leads to DET deviation, especially in humid zones. Terrestrial evaporation (ET) estimates from the water balance framework and large-scale modeling have been widely used in the evaluation and prediction of hydrological regimes. However, each method has its inherent limitations, including the external bias introduced by forcing variables, simplified functional relationships, and unconsidered human modules. A systematic comparison between water balance ET and modeled ET remains unexplored. Here, we quantify and attribute the difference between water balance estimations of ET and model-simulated ET (i.e., DET) on a global scale. We apply an unprecedentedly unique probabilistic ensemble of 84,042 DET estimates (2002–2021) based on all currently available datasets on water balance components. Satisfactory performance is found from the validation of the water balance-derived ET against several benchmarking ET products. We identify the regions with significantly positive DET in South and East Asia, Southern and Northern Africa, and southwestern parts of North America, with a global mean of 7 mm/a (5 % spread range: –2 to 16 mm/a). The patterns are primarily contributed by human water use and reservoir construction. We also report negative DET in the majority of South America, which may be related to human-induced deforestation. In addition, the seasonality of DET reflects the significant role of irrigation in regional ET dynamics. Variance analysis indicates higher uncertainties of DET in humid zones, mainly contributed by precipitation and simulated ET. Our uncertainty-constrained DET estimates have potential implications for assessing global and regional water availability, benchmarking climate and hydrological models, and developing sustainable mitigation and adaptation strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".