Satellite Based Analysis of Northern ET Trends and Associated Changes in Regional Water Balance from 1983 to 2005
Bibliographic record
Abstract
We developed an evapotranspiration (ET) algorithm driven by satellite remote sensing inputs, including AVHRR GIMMS NDVI, MODIS land cover and NASA/GEWEX solar radiation and albedo, and regionally corrected NCEP/NCAR Reanalysis daily surface meteorology. The algorithm was used to assess spatial patterns and temporal trends in ET over the pan-Arctic basin and Alaska from 1983 to 2005. We then analyzed associated changes in the regional water balance defined as precipitation (P) minus ET, where monthly P was defined from Global Precipitation Climatology Project (GPCP) and Global Precipitation Climatology Center (GPCC) sources. Monthly ET results derived from both in situ meteorological measurements and coarse resolution model reanalysis inputs agreed well (RMSE = 5.1–6.3 mm month−1; R2 = 0.91–0.92) with measurements from eight independent flux towers representing regionally dominant land cover types. ET showed generally positive trends over most of the pan-Arctic domain, though negative ET trends occurred over 32% of the region, primarily in boreal forests of southern and central Canada. Generally positive trends in ET, P and available long-term river discharge measurements imply that the pan-Arctic terrestrial water cycle is intensifying despite uncertainty in regional P and associated water balance estimates. Increasing water deficits in eastern Alaska, Canadian Yukon and western Prairie Provinces, and Northern Mongolia agree with regional drought records and recent satellite observations of vegetation browning and productivity decreases. Our results indicate that the pan-Arctic water balance is responding to a warming climate in complex ways with direct links to terrestrial carbon and energy cycles.
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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 | 0.000 |
| 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) | 0.001 | 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".