Present and future water scarcity hotspots for rainfed and irrigated agriculture under climate change: a global study. 
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».