Ecohydrological dynamics in the Alps: Insights from a modelling analysis of the spatial variability
Bibliographic record
Abstract
Abstract Mountain ecosystems are experiencing rapid warming resulting in ecological changes worldwide. Projecting the response of these ecosystems to climate change is thus crucial, but also uncertain due to complex interactions between topography, climate, and vegetation. Here, we performed numerical simulations in a real and a synthetic spatial domain covering a range of contrasting climatic conditions and vegetation characteristics representative of the European Alps. Simulations were run with the mechanistic ecohydrological model Tethys–Chloris to quantify the drivers of ecosystem functioning and to explore the vulnerability of Alpine ecosystems to climate change. We correlated the spatial distribution of ecohydrological responses with that of meteorological and topographic attributes and computed spatially explicit sensitivities of net primary productivity, transpiration, and snow cover to air temperature, radiation, and water availability. We also quantified how the variance in several ecohydrological processes, such as transpiration, quickly diminishes with increasing spatial aggregation, which highlights the importance of fine spatial resolution for resolving patterns in complex topographies. We conducted controlled numerical experiments in the synthetic domain to disentangle the effect of catchment orientation on ecohydrological variables, such as streamflow. Our results support previous studies reporting an altitude threshold below which Alpine ecosystems are water‐limited in the drier inner‐Alpine valleys and confirm that the wetter areas are temperature‐limited. High‐resolution simulations of mountainous areas can improve our understanding of ecosystem functioning across spatial scales. They can also locate the areas that are the most vulnerable to climate change and guide future measurement campaigns.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".