Analysis of scale-dependent spatial correlations of actual evapotranspiration measured by lysimeters
Bibliographic record
Abstract
Accurate determination of actual evapotranspiration (ETa) is important in various research fields like hydrology, meteorology, ecology and agriculture. In situ ETa can be determined using weighing lysimeters and eddy covariance . However, despite being regarded as the most precise in situ method for measuring ETa, the information content of lysimeter measurements remains poorly understood. Here we examined the spatial correlations between ETa measured at different locations by lysimeter (ET-LYS) and at different locations by eddy covariance (ET-EC). This was done for the period 2015 - 2020 and the analysis was made for different spatial (range: 0 to 500 km) and temporal scales (range: 1 day to 1 year) using 23 lysimeters and 4 eddy covariance towers. We found that: (a) Same lysimeters at the plot scale show very high correlations of ET-LYS; (b) The Pearson correlation of daily standardized anomalies of ET-LYS between sites exhibit moderate to high correlations and were similar to that of ET-EC, indicating that lysimeter is generally as representative as EC regarding ETa, and can provide certain information at the landscape and larger regional scale. During winter, the spatial correlations for ET-LYS were smaller; (c) Wavelet analysis indicated that temporal correlations in ETa were strongest for distances in time around 12 months (yearly cycle) and less than three months. Spatial correlations were smaller under drought conditions (in the year 2018). Furthermore, combination of multiple ET-LYS from different sites improved the predictability of ET-LYS for another site, suggesting that ET-LYS can be predicted well using ET-LYS from different neighboring sites. Overall, lysimeter measurements can provide information at much larger scales compared to their small measurement area.
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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 | 0.001 | 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) | 0.000 | 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".