Remote sensing of ecosystem trajectories as a proxy indicator for watershed water balance
Bibliographic record
Abstract
Abstract Ecosystem trajectories are inextricably linked to hydrology; however, water availability is not easily observed within the landscape. The response of vegetation to soil water availability may provide an indicator of local hydrology and the resilience or sensitivity of ecosystems to long‐term changes in water balance. In this study, vegetation trajectories derived from Landsat Modified Soil Adjusted Vegetation Index over a 22‐year period are used as an indicator of spatio‐temporal changes of watershed water balance and surface water storage within 6 proximal watersheds of the Boreal Plains ecozone of Alberta, Canada. The interactions between hydrology, topography, geology, and land cover type are examined as they relate to vegetation change. In this study, we find that run‐off ratio (run‐off/precipitation) is greater within watersheds with greater variability of relief, compared within subhumid, wetland‐dominated watersheds (average = 0.35, 0.20, respectively), which experience lagged run‐off response. A 2‐year drought had a greater impact on vegetation trajectories within variable relief watersheds containing a high proportion of coarse‐grain surficial geology, compared with peatland‐dominated watersheds. The drought extended for an additional 1 to 2 years within 3 subhumid watersheds, reducing watershed run‐off to less than 50 mm per water year (November to October) in 2 watersheds. However, proportional area of decline of Modified Soil Adjusted Vegetation Index was significantly less within these watersheds. The results of this study indicate that wetlands play an important role for local water balance and ecosystem maintenance during periods of water stress and should be considered within land use planning and reclamation strategies of the Boreal Plains.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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 teacher head, 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".