Seasonal transpiration source water and ecohydrological connectivity with streamflow sources in the Maimai M8 Catchment 
Bibliographic record
Abstract
Transpiration significantly depletes terrestrial subsurface water stores and plays a crucial role in the hydrological cycle. While extensive research has been conducted in the Maimai M8 catchment (New Zealand) and across many catchments on streamflow generation processes and streamflow sources, we still know little about the sources of transpiration and when transpiration and streamflow sources are hydrologically connected. Here we leverage M8, a long-term studied catchment with well-described streamflow generation mechanisms, to investigate the transpiration source water of Pinus radiata and its connectivity to streamflow sources. We combined monthly observations of isotopic signatures (δ18O and δ2H) of xylem, bulk soil water, mobile water, subsurface flow, and stream water with continuous monitoring of tree water stress across a hillslope to answer: (1) What is the seasonal source of transpiration at Maimai? And (2) how does transpiration source water interact with streamflow sources? Our data showed that transpiration sources across the hillslope were not distinct but changed seasonally. During summer, when trees showed greater periods of water stress, trees relied on shallow soil water. In contrast, during the winter, trees’ isotopic signatures plotted along the local meteoric water line (LMWL), overlapping with mobile soil and stream water. Xylem isotopic signatures were not statistically distinct from stream signatures in the winter, contrasting with distinct isotopic signatures during the summer. Our results showed that transpiration source water in the Maimai M8 catchment changes seasonally, influenced by tree water stress and wetness conditions. Overall, our findings suggest an ecohydrological connectivity between transpiration and streamflow sources during winter months in this wet temperate climate.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".