Future trends in freshwater planetary boundary transgressions
Bibliographic record
Abstract
Changes in the global freshwater cycle have become increasingly common around the world since mankind’s industrialization. The concept of planetary boundaries (PBs) is a framework defining safe limits for human activity in terms of changes in Earth subsystems, freshwater being one of the boundaries. Here the goal was to use the recently proposed new method for the freshwater change PB to determine the status of and changes in it during this century under two climate change scenarios (RCP2.6 and RCP6.0). Additionally, local changes were determined to identify areas where freshwater changes are likely to cause potential risks, especially to the agricultural sector. The impact of climate change was assessed by comparing the two chosen scenarios. \n \nData from ISIMIP2b was used to determine global and local changes in discharge and root-zone soil moisture. Changes were calculated as the frequency of monthly discharge or root-zone soil moisture values exiting variability based on pre-industrial conditions on each 0.5-degree grid cell. Each grid cell has its local variability bounds calculated from preindustrial data (1691-1860), with values below 5th percentile being considered a dry exit, and values above 95th percentile being wet exits. Additionally, dry exit frequency values were used in conjunction with crop yield and land-use data to determine risk to the agricultural sector by calculating a risk index. \n \nThe freshwater change planetary boundary has already been transgressed, and the trend continues upward in the future, especially in the higher-emissions RCP6.0 climate scenario where the share of global land area where discharge or root-zone soil moisture exits preindustrial variability bounds keeps rising through-out the century. In RCP2.6 this trend stabilizes around the 2050s. Discharge changes have fewer differences between climate scenarios than root-zone soil moisture. Notable regions with a high dry exit frequency include the Mediterranean, China, and India, while Canada, Russia, Northern Europe, and India have a high wet exit frequency. The agricultural sectors of some of the largest global crop producers are at risk in the future due to drying conditions, including the North American Corn Belt, Western Europe, Northern China, and India. \n \nMitigating climate change to reduce water cycle changes, as well as sustainable irrigation practices to cope with decreasing water resources, are key to ensuring continued food and water supply for humanity. The results of the thesis are valuable, as they help in identifying the potential areas where adaptation to change is most critical.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".