Quantifying coniferous subalpine tree transpiration and source water under seasonal and hydrological stress in the Canadian Rocky Mountains, Kananaskis, Alberta
Bibliographic record
Abstract
Fresh water supplies in mountainous regions are at risk as snow and ice stores continue to decline \nunder rising global temperatures, earlier winter snowmelt and changing climate regimes. Alpine \nforests are of particular importance due to their hydrological connectivity within watersheds \ncontrolling groundwater base flow, influencing evapotranspiration (ET) and snow storage dynamics. \nA change in the water availability to subalpine vegetation via changes in winter snowpack \naccumulation and quantities or differing summer precipitation (P) regimes could have a drastic effect \non the long-term health of these forests. This makes it imperative to understand and quantify their \nhydrological connectivity within these watersheds. Study sites located at Fortress Mountain in \nKananaskis, Alberta are composed of co-occurring coniferous tree stands of Abies lasiocarpa and \nPicea engelmannii. Little is known about water use dynamics of these species at high elevations, \nspecifically the quantity and timing of transpiration (T) in addition to the water sources most \nimportant for T during the entire length of the growing season. \nThis study used a combination of hydrological and meteorological tools to address coniferous \nsubalpine tree water use behaviours before, during and after the growing season (June-September). \nMethodologies focussed on determining seasonal T patterns using the non-invasive stem-heat balance \nmethod to determine sap flow and eddy covariance to capture stand ET. The source water of the \nstudied trees was determined using δ18O and δ2H stable water isotopes and further partitioned using \nthe MixSIAR Bayesian Mixing Model (BMM). Groundwater monitoring wells, soil tensiometers, P \ngauges, and meteorological stations were used to determine baseline environmental conditions. Stable \nwater isotopes δ18O and δ2H were collected from all source waters (P, snow cover, soil water, \ngroundwater) in addition to xylem water samples from the coniferous trees within the study area. \nUnderstanding tree response to P and drying events was the main objective addressed, \nyielding stark differences between the growing seasons of 2016 and 2017. Stand T was higher in 2017 \n(165 mm) than 2016 (118 mm) despite a much drier and warmer season (155 mm of rain in 2017 \ncompared to 283 mm in 2016). A deeper, sustained snowpack in 2017 coupled with higher net \nradiation allowed for higher T rates. Paired with δ18O and δ2H stable isotope source partitioning, this \nstudy was able to identify soil water as the most important source to season-long tree productivity, \nwith groundwater the most important for early growing season. Well-drained soils and shallow depth \nto bedrock inhibited groundwater access for the studied trees after the snowmelt period concluded. \nThus soil moisture supplied a majority of water to the tree population during mid growing season, \ndetermined both hydrometrically and isotopically. Dry conditions in 2017 showed a clear trend \nbetween soil moisture levels and tree water use, with 2016 having almost double the soil moisture and \ntree productivity in the tail end of the growing season. By closely examining the patterns of subalpine \ntree water use, we can begin to clarify how these important ecosystems services will be impacted \nunder a changing climate in addition to helping us better manage our forest and freshwater resources.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".