Present and future water scarcity hotspots for rainfed and irrigated agriculture under climate change: a global study. 
Bibliographic record
Abstract
Climate change disrupts weather patterns in various ways across the world, leading to an increased variability in rainfall and therefore water availability, which in turn exacerbates water scarcity. At the same time, a growing population and rising GDP increases the demand for food and the demand for water in agriculture and other sectors. While 40% of agricultural production comes from irrigated systems, and represents 20% the total cultivated land, large-scale assessments of climate change impacts on agricultural production and food security typically focus on direct crop yield effects only. The increased water scarcity through an increased demand and a decreased supply for irrigation water is likely to impact agricultural production, leading to cascading effects on consumption, markets, and food security. Using an integrated impact chain including climate, hydrology, crop, and economic models, we present the results of a fully integrated assessment of the climate change impacts on both crop yields and water availability relying on the most recent CMIP6 climate change projections to analyze the impacts of irrigation as an adaptation measure for climate-induced yield losses and socio-economic increased demands. Using the Community Water Model (CWatM) we simulate changes to water availability for irrigation under various climate and socio-economic scenarios. Using the Environmental Policy Integrated Climate model (EPIC) model, we assess the impact of climate on yield under irrigated and rainfed systems. The availability of water and requirements for irrigated and rainfed crop production are subsequently integrated in the Global Biosphere Management Model (GLOBIOM) model to assess the uptake of irrigation as an adaptation mechanism and the probability, location, and extent of agricultural water scarcity hotspots, where available water resources fail to meet the agricultural demand, considering also demands from non-agricultural sectors. The model further assesses the consequences of subsequent changes in production, consumption, market, and highly productive areas that coincide with water scarcity hotspots under climate change. Areas with a surplus of water are also identified as potential irrigation investment locations. Results show that, by the mid-century, water use for irrigation is projected to increase worldwide. Brazil, China, Canada, Europe, and South-East Asia are expected to use over 40% more water for irrigation compared to 2000 in the high-emissions RCP 8.5 scenario. In contrast, water available for irrigation is diminished in Brazil and other regions in South and Central America as non-agricultural water demand increased. Non-agricultural water demand constrained the water available for irrigation in India and Sub-Sharan Africa as well. The irrigation water use in Europe and Canada are expected to occur at expenses of environmental flow requirements.
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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".