Assessing hydrometeorological controls on subalpine plant community evapotranspiration and evaluating the METRIC method using high-resolution UAV imagery in the Canadian Rocky Mountains
Bibliographic record
Abstract
Subalpine wetlands in the Canadian Rocky Mountains function as buffers for snowmelt runoff towards downstream systems and communities. However, hydrological regimes are changing rapidly in these ecosystems due to climate warming in the region causing earlier snowmelt patterns and extended vegetation growth periods. Response of vegetation health and composition to temperature and precipitation increases can have a significant influence on evapotranspiration (ET), the main component of wetland water balances. Subalpine plant communities are especially sensitive to shading mechanisms over the growing season, which limits ET flux. However, as the composition of plant communities are expected to migrate with climate, it is important to monitor changes in primary water sinks such as ET in vulnerable ecosystems, such as subalpine wetlands. Due to difficult accessibility, few studies have been conducted to monitor these ecosystems. Recent technological advances in unmanned aerial vehicles (UAV) provide opportunities to monitor these ecosystems at a high spatial resolution. \nThis study aims to quantify plant community scale ET, assess the spatial variability and sensitivity of this ET to climate and vegetation health, and evaluate the Mapping Evapotranspiration with Internalized Calibration (METRIC) model for ET estimation in a subalpine wetland. ET was measured in-situ using a dynamic closed chamber method for the plant community scale at Fortress Mountain in Kananaskis, Alberta. Vegetation health, water content, and plant water stress was derived from spectral signatures using vegetation indices. High-resolution imagery with multispectral, thermal, and LiDAR sensors were collected during ground measurements to capture the spatial variability of ET throughout the wetland using the METRIC model. Modelled ET was compared with chamber ET measurements to assess the accuracy and applicability of the METRIC model using UAV imagery in a subalpine wetland. \nNet radiation and plant community type were the dominant controls on ET at the community scale. Variability in physiological differences between plant communities, such as depth of stomatal openings, cuticle thickness, leaf surface area to volume ratio, and root water uptake rates affect plant response of ET to radiation and temperature. Plant physiology as well as volumetric water content, proximity to surface water, and groundwater connections, also influenced spatial ET trends. \nMETRIC model results had high estimation accuracy when compared to chamber results. METRIC ET had strong relationship with hourly (R2=0.79) and daily (R2=0.82) chamber ET. Taller vegetation (trees and shrubs) had higher estimation accuracy than lower-lying vegetation (ground vegetation and moss). Spatial variability of ET using the local indicators of spatial association (LISA) with METRIC results showed clusters of high ET in the Southern and Western sections of the meadow and low ET in the Northern and Eastern sections of the meadow. \nThe results of this study demonstrate that as plant communities are expected to migrate with changing climate conditions in subalpine ecosystems, METRIC model applications using UAV imagery could be an effective solution to monitoring plant community ET at a high spatial resolution in vulnerable and inaccessible areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".