Assessment of the impacts of climate change on groundwater evapotranspiration in mid-to-high latitude regions
Bibliographic record
Abstract
• Surface and groundwater (GW) contribution to AET was modelled by HydroGeoSphere. • Over 40% of the mean annual AET in riverine areas is supplied by GW. • GWET makes up over 80% of AET in areas with shallow GW, high PET, and perennial long-rooted plants. • Simulating CO 2 -related stomatal resistance is crucial for projecting future GWET. • Future AET rise in the Foothills and Plains depletes GW levels in these regions. In arid and semi-arid regions of mid-to-high latitude zones, actual evapotranspiration (AET) dominates the water balance, posing risks to the hydrologic budget and leading to potential groundwater depletion. Despite numerous studies on AET, the evapotranspiration from groundwater (GWET) and surface water (SWET) remains poorly understood at a regional scale. This study developed, calibrated, and validated an integrated surface and groundwater model to study AET in the North Saskatchewan River Basin (NSRB) in western Canada, covering historical (1983-2013) and mid-future (2043-2073) periods. The study addresses the temporal variation and feedback mechanisms affecting AET and its water sources across different ecohydro(geo)logical (EHG) regions often present in large watersheds, including Mountains, Foothills, and Plains. Results show that subsurface transpiration and evaporation are the primary contributors to AET, while surface water evaporation contributes the least across all EHG regions. In terms of water sources, SW is the largest contributor to AET in most areas across all EHG regions. However, GW is the primary source of water contributing to AET in riparian areas and regions with high atmospheric evapotranspiration demand, large Leaf Area Index, and deep-rooted plants accessing shallow groundwater. In the mid-future period, AET is projected to increase across the NSRB, with the greatest change occurring in the Mountains. In riparian areas and discharge zones of northeast Plains, GWET contribution is expected to increase. However, in the Foothills and Plains, the projected increases in AET may lead to groundwater depletion, reducing the amount of GWET. The most significant future impacts on groundwater are anticipated in the 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 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 | 0.000 |
| Scholarly communication | 0.001 | 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 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".