Determining Command Areas of Irrigation Reservoirs at a Global Scale to Support Sustainable Water Management under Climate Change
Notice bibliographique
Résumé
The increasing population, rising water demand, and the multifaceted impacts of climate change have exacerbated global water scarcity challenges. Water reservoirs serve as critical infrastructure to ensure a reliable supply for agricultural, domestic, industrial, and environmental purposes. Among single-purpose dams, 48% are dedicated to irrigation; however, a study conducted by the World Commission on Dams revealed that irrigation dams frequently fail to deliver the projected water supply for the initially planned areas, underscoring inefficiencies in reservoir management. Furthermore, climate change is projected to amplify these challenges by increasing crop water demand and reducing reservoir storage. This highlights the urgent need for sustainable irrigation reservoir management at the global scale, beginning with the crucial step of identifying the command area—the designated region receiving water from a reservoir for irrigation purposes. Accurately delineating this area is essential for precise estimation of irrigation water demand, facilitating optimal water release planning, mitigating risks of over- or under-supply, and enhancing overall reservoir management, particularly in the context of climate change impacts. Knowledge of the location and extent of command areas can inform large-scale systematic planning efforts to ensure water supply under climate change conditions by identifying those command areas that are likely to face water shortages and those reservoirs where future releases may fall below historical trends.This study presents a structured approach for delineating and allocating reservoir command areas at the global scale using geospatial analysis. Command areas are estimated within a range of up to 100 km from the reservoir, reflecting economically viable water transfer distances. To estimate potential command area locations, landscape pixels are ranked based on five criteria: elevation, proximity to the reservoir, terrain slope, hydrologic connectivity, and land use (i.e., irrigated areas and croplands). Pixels at lower elevations relative to the reservoir are prioritized, assuming that natural downward gradients in water transfer are preferred over artificial pumping to reach higher grounds. Close proximity to the reservoir is preferred as closer areas minimize water losses and reduce economic costs. Slope suitability is assessed by prioritizing flat terrain below a threshold of 10%. Hydrologic connectivity is determined by tracing the downstream part of the watershed in which the reservoir is located, avoiding command area allocations across higher terrain in neighboring catchments. Finally, areas that are identified on ancillary maps as irrigation areas or croplands are assumed to have a high likelihood of representing the command area of the nearest reservoir; however, it is recognized that groundwater and local streamflow abstractions can provide alternative water sources. These five criteria are combined using weighted overlays to iteratively allocate pixels to determine the potential command area. In cases where the command area extent is not known for a given reservoir, the irrigation capacity is estimated based on the storage volume of the reservoir, i.e., the command area extent is limited to the maximum area that can be supplied with enough water to sustain one crop cycle. The resulting command areas are validated using reported data and literature reviews to ensure accuracy.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 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 | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».