Hydrologic Cycle Impacts of Large-Scale Reforestation at Global and Regional Scales
Bibliographic record
Abstract
Reforestation is a key nature-based solution for mitigating climate change. However, changes in land cover through reforestation can significantly influence the climate and hydrological cycle, affecting water availability and other critical components of the Earth system. Understanding these impacts is essential for developing effective climate adaptation strategies and ensuring sustainable land management in the coming decades.This study leverages simulations with the Canadian Earth System Model (CanESM5.1), a state-of-the-art Earth system model, to quantify hydrological responses to two large-scale reforestation scenarios. The first scenario reverses historical deforestation, restoring tree cover to pre-industrial levels by the year 2070, while the second implements a sustainable reforestation strategy by the same year. To isolate the effects of reforestation, a reference simulation with land-cover fixed at the year 2015 configuration is also conducted. The study employs a two-stage simulation framework—historical (1850–2015) and future (2015–2200)—with multiple ensemble members, using SSP1-2.6 forcing to align with the Paris Agreement’s climate goals.Preliminary results reveal that large-scale reforestation induces statistically significant climate and hydrological responses at both regional and global scales. These findings highlight the potential for unintended consequences of land-based climate mitigation strategies, emphasizing the need for holistic assessments to guide future land management and policy decisions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".