Assessing climate change impacts on hydrology and agriculture in a boreal watershed: a combined hydrological modeling, machine learning, and efficiency analysis
Bibliographic record
Abstract
Climate change poses significant challenges to water resources and agriculture sustainability, particularly in boreal watersheds. Understanding hydrological dynamics and their response to climate variability is essential for effective water management and sustainable farming practices. This dissertation uses various models and a transdisciplinary approach to investigate the hydrological dynamics, climate change impacts, and agricultural sustainability in the Upper Humber River Watershed (UHRW) of western Newfoundland, Canada. Using the soil and water assessment tool (SWAT) model, the study evaluates watershed hydrology and identifies key parameters influencing streamflow, achieving favorable performance metrics during calibration and evaluation. Seasonal and monthly flow patterns, water balance components, and flow duration analyses validate the model's suitability for sustainable water management. Similarly, a Long Short-Term Memory (LSTM) machine learning model was developed for UHRW. The comparative analysis of the SWAT and LSTM models for streamflow prediction highlights the superior accuracy in capturing streamflow prediction. The LSTM model’s integration of real-time data demonstrates its potential for effective water management in cold climates. Furthermore, this dissertation explores climate change impacts on soil water availability (SWA) in UHRW using Coupled Model Intercomparison Project Phase 5 (CMIP5) projections and Representative Concentration Pathways (RCP) scenarios. Results reveal up to an 11% decline in SWA under RCP 8.5, driven by increased evapotranspiration and streamflow, despite rising precipitation. Lastly, agricultural sustainability is assessed through Data Envelopment Analysis (DEA), identifying high technical efficiency yet notable disparities in allocative, cost, scale, and environmental efficiencies. Sustainable practices, such as permaculture, no-dig farming, and resource optimization, enhance productivity while minimizing environmental impacts. This dissertation offers insights for water management, climate adaptation, and sustainable farming in boreal ecosystems.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".