Multi-scale water balance analysis of a thawing boreal peatland complex near the southern permafrost limit in western Canada
Bibliographic record
Abstract
Abstract. Permafrost thaw profoundly changes landscapes in the Arctic-boreal region, affecting ecosystem composition, structure, function and services and their hydrological controls. The water balance provides insights into water movement and distribution within a specific area and thus helps understand how different components of the hydrological cycle interact with each other. However, the water balance of small- (<101 km²) and meso-scale basins (101–103 km²) in thawing landscapes remains poorly understood. Here, we conducted an observational study in three small-scale basins (0.1–0.3 km²) of a thawing boreal peatland complex. The three small-scale basins were situated in the Scotty Creek basin headwater portion, a meso-scale low-relief basin (drainage area estimates from 130 to 202 km²) near the southern permafrost limit in the Taiga Plains ecozone in western Canada. By measuring water losses (discharge, evapotranspiration [ET]), inputs (rainfall [R], snow water equivalent [SWE]) and storage change (ΔS), and calculating runoff (Q), we (1) aimed at quantifying growing season (May–September, 2014–2016) headwater small-scale basins water balances, i.e., sub-basins. After (2) comparing monthly sub-basin- and corresponding basin water losses through ET and Q, we aimed at (3) assessing the long-term (1996–2022) annual basin water balance using publicly available observations of discharge (and thus calculated Q), R and SWE in combination with simulated ET. (1) Growing season water balance residuals (RES) for the sub-basins ranged from -81 to +122 mm. The monthly growing season water balance for the sub-basin for which all the water balance components throughout the three-year study period were recorded exhibited large positive RES for May (+117 to +176 mm) since it included late-winter SWE routinely estimated in late March right before snowmelt. In contrast, lower monthly RES were obtained from June to September (-41 to 0 mm). For two sub-basins, we provide two different drainage area estimates highlighting the challenge of automated terrain analysis using digital elevation models in low-relief landscapes. Drainage areas were similar for one sub-basin but exhibited a fivefold difference for the other. This discrepancy was attributed to the high degree of landscape heterogeneity and resulting hydrological connectivity with implications for Q calculations and RES. (2) The spring freshet contributed 41 to 100 % (sub-basins) and 50 to 79 % (basin) of the April–September Q. Spring freshet peaks were comparable, except for the driest year (2014), when basin Q was more than ten times lower than in the sub-basins. At both scales ET was the dominating water loss, more than twice Q. (3) Over the long-term (1996–2022), the increase of basin runoff ratio (ratio of runoff to precipitation) from 1996 to 2012 (0.1 to 0.5) has been attributed to the increasing connectivity of wetlands to the drainage network caused by permafrost thaw. However, the smaller average and more variable runoff ratio from 2013 to 2022 may be due to wetland drying and/or changes in precipitation patterns. Long-term hydrological monitoring is crucial to identify and understand potential threshold effects (e.g., hydrological connectivity) and ecohydrological feedbacks affecting local (e.g., subsistence activities), regional (e.g., weather) and global ecosystem services (e.g., carbon storage) provided by thawing boreal peatland complexes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".