Hydroclimatic simulations sensitivity to land use changes 
Bibliographic record
Abstract
Historical changes in land use have shown different effects on climatic and hydrological processes across spatial and temporal scales. Among these, snow accumulation, snowmelt, and evapotranspiration are key processes sensitive to land use changes that can directly influence streamflow production at the catchment scale. The potential future effects of land use changes on streamflow production highlight the importance of assessing the sensitivity of modelling tools commonly used to produce hydrological projections, such as hydrological models (HMs) and regional climate models (RCMs). Therefore, this study aims to assess the individual and combined effects of RCM- and HM-simulated land use changes on the streamflow simulations of five North American catchments. To assess RCM-simulated land use change impacts, three simulations from the Canadian RCM version 5 (CRCM5) were used: a reference simulation (current land uses), a forested scenario (100% forest land use), and a grass scenario (100% grass land use), following the Land-Use and Climate Across Scales (LUCAS) protocol. Two distributed HMs, WASIM and HYDROTEL, were used to evaluate HM-simulated land use change effects on streamflow under the same reference, forest and grass scenarios. Results indicated that RCM-simulated land use changes had a greater impact on streamflow than those simulated by HMs alone. Regarding the differences between hydrological models, HYDROTEL showed higher sensitivity to land use changes in snow processes, while WASIM showed greater sensitivity in modelling evapotranspiration. Further comparisons with a modified version of the GR4J hydrological model provided additional insights into how model structures influence the level of sensitivity to land use, highlighting the importance of each hydrological model internal formulations. Moreover, this study underscores the need for further research into how HMs represent complex land use changes and emphasizes the importance of selecting appropriate tools for specific local hydroclimatic conditions and land use dynamics to improve hydrological modelling and water resources management.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".