Global and regional hydrologic cycle impacts of forestation
Bibliographic record
Abstract
As nations plan to plant billions to trillions of trees to mitigate against climate change, it is essential to understand how large-scale re- or afforestation will impact the Earth system. Trees remove carbon dioxide (CO2) from the atmosphere through photosynthesis and can store the sequestered carbon for centuries if not disturbed. This has climate benefits, as CO2 removal contributes to reduced atmospheric CO2 concentration and is a key measure for limiting global average temperature increase to 1.5 ⁰C or 2 ⁰C relative to pre-industrial conditions. However, despite this favorable biochemical effect of net tree cover increase, there are global and regional biophysical effects which remain understudied. One example of this is the impact of afforestation, reforestation, and avoided deforestation (referred to as forestation henceforth) on the atmospheric and terrestrial portions of the hydrologic cycle at the global and regional scales. This study uses a process-based modelling framework and relevant simulations from the World Climate Research Programme's Sixth Coupled Model Intercomparison Project (CMIP6) to quantify the global and regional impacts of realistic forestation on the global hydrologic cycle for a high-emissions shared socio-economic pathway to 2100 (SSP3-7.0). To accomplish this, the CMIP6 Land Use Model Intercomparison Project's afforestation experiment is leveraged. Changes in key hydrologic cycle variables and metrics such as precipitation recycling and soil moisture deficit are investigated. While the global impact of large-scale forestation on the hydrologic cycle is difficult to detect, regional impacts—often but not exclusively within the regions where forestation occurs—are apparent. Impacts on atmospheric and terrestrial hydrologic cycle variables can be seen with potential implications for water availability in some regions. Findings highlight the potential unintended consequences of including forestation in climate mitigation strategies.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".