Evaluating drought effect on MODIS Gross Primary Production (GPP) with an eco‐hydrological model in the mountainous forest, East Asia
Bibliographic record
Abstract
Abstract Surface soil moisture dynamics is a key link between climate fluctuation and vegetation dynamics in space and time. In East Asia, precipitation is concentrated in the short monsoon season, which reduces plants water availability in the dry season. Furthermore, most forests are located in mountainous areas because of high demand for agricultural land, which results in increased lateral water flux and uneven distribution of plant available water. These climatic and topographic features of the forests make them more vulnerable to drought conditions. In this study, the eco‐hydrological model (Regional Hydro‐Ecological Simulation System) is validated with various water and carbon flux measurements in a small catchment in Korea. The model is then extended to the regional scale with fine‐resolution remote sensing data to evaluate the Moderate Resolution Imaging Radiometer (MODIS) leaf area index and gross primary productivity (GPP) products. Long‐term model runs simulated severe drought effect in 2001 well, which is clearly shown in the ring increment data. However, MODIS GPP does not capture this drought effect in 2001, which might be from a simplified treatment of water stress in the MODIS GPP algorithm. This study shows that the MODIS GPP products can potentially overestimate carbon uptake specifically during drought conditions driven by soil water stress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".