Effects of lateral hydrological processes on photosynthesis and evapotranspiration in a boreal ecosystem
Bibliographic record
Abstract
Abstract Landscape‐scale hydrological processes can greatly alter the local‐scale water balance and many ecological processes linked to it. We hypothesized that in humid forest ecosystems, topographically driven lateral subsurface flow (SSF) has significant influence on ecophysiological processes such as gross primary productivity (GPP) and evapotranspiration (ET). To investigate how simplified hydrological conceptualizations influence the simulated ET and GPP in space and time, we conducted a numerical experiment using a spatially explicit hydroecological model, BEPS‐TerrainLab V2.0. We constructed three modelling scenarios: (1) Explicit , where a realistic calculation of SSF was employed considering topographic controls, (2) Implict , where the SSF calculations were based on a bucket‐modelling approach and (3) NoFlow , where the SSF was turned‐off in the model. Statistical analyses of model outputs showed considerable differences among the three scenarios for the simulated GPP and ET. The NoFlow scenario generally underestimated GPP and ET, while the Implicit scenario overestimated them relative to the Explicit scenario, both in time and space. GPP was more sensitive to SSF than ET because of the presence of unique compensatory mechanisms associated with the subcomponents of the total ET. The key mechanisms controlling GPP and ET were manifested through nonlinear changes in stomatal conductance, unique contributions from GPP and ET subcomponents, alterations in rhizosphere wetting patterns and their impacts on upscaling mechanisms and variability in nitrogen dynamics (for GPP). Feedback and interactive relationships between hydrological and ecophysiological processes also exacerbated the biases. Thus, we conclude that ecological models that have simplified hydrological representations could have significant errors in the estimation of GPP and ET. Copyright © 2010 John Wiley & Sons, Ltd.
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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) | 0.000 | 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 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".