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Climate change impacts assessments and mitigation strategies for sustainable water and agricultural management in the Prince Edward Island

2022· article· en· W7036809597 sur OpenAlexaboutno aff

Notice bibliographique

RevueIslandScholar (University of Prince Edward Island) · 2022
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueClimate variability and models
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésClimate changeEvapotranspirationEffects of global warmingWater resourcesPrecipitationAgriculture
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Prince Edward Island (PEI) has abundant water resources and rainfed agriculture and is the largest producer of potatoes in Canada. However, the sustainability of these natural resources is at stake due to environmental changes for which science-based information was lacking; the project filled the research and knowledge gaps. The changes and impacts on temperature, precipitation, streamflows, groundwater recharge, potential evapotranspiration (PET), potatoes' water requirements (CWR), supplemental irrigation requirements (SIR), and sustainable water availability (SWA) were analyzed. The analyses were spatially segregated into eastern, central, and western PEI; and temporally into climate normals (continuous 30-year period) for the longest 150 years (1931–2080). Daily observed climatic data and validated modeled data of Pacific Climate Impacts Consortium (PCIC) etcetera were used. Rational methods, like Probability of Exceedance (PoE), Intensity-Duration-Frequency (IDF), and World Meteorological Organization’s (WMO) guidelines for computation of climatic-normal averages, were used etcetera. Hydrological modeling of the western (Mill and Wilmot rivers), central (West and Winter rivers), and eastern Bear river watersheds was performed using the Soil and Water Assessment Tool (SWAT). Statistical significance of the temporal changes in the parameters among the climatic normals and scenarios were determined using Analysis of Variance (ANOVA). The Island underwent statistically significant warming as the historical (1961–1990) average annual temperatures increased by +1.14°C in the east to +0.75°C in the west during 1991–2020. The trend will likely continue with a further rise of 3–5°C in the next 30–60 years. Historical warming was uniformly distributed throughout the year; however, prospectively, it would be more concentrated during January–July, which would moderate cold intensity. The warming would cause annual PET of 1.95–2 mm/day to insignificantly increase 3–6% during the next 30–60 years, with a 2–4 times increase in colder months (January–April) and reductions during August–December due to coastal climate. Therefore, the historical CWR of potatoes ~425 mm would decrease by 5–9%. That, and changes in effective rainfall, would cause potatoes' SIR (July–September) in normal years to fall up to 50–90 mm, which, however, be 2–3 times more during dry years, with almost no SIR in wet years. Annual precipitations increased by 6% in the east and decreased by 5% and 8% in the central and western parts, respectively, from 1961–1991 to 1991–2020, along with a significant snowfall reduction in the west (-20%). While rainfall intensities in the central and western parts significantly increased by 5–32% in recent years (2004–2017) than 1961–1990. Prospective precipitations (2021–2080) would not change significantly and would range ~1150–1200 mm/year. Nevertheless, in western PEI, precipitations would be 17% higher than that during 1991–2020. The interannual precipitation uncertainty between wet and dry years would reduce to ~300 mm/year from the current ~400 mm/year but will remain a water management challenge. Streamflows among the modeled watersheds ranged from 565–811 mm/year during 1991–2020. Streamflows are highly contributed by groundwater, up to ~70%. Therefore, pumping in the populous Winter river (191 mm/year) and Wilmot river (16 mm/year) watersheds has almost equally reduced streamflows to 565 mm/year and 652 mm/year respectively, against a weighted average of ~688 mm/year for all the five watersheds. Groundwater recharges in PEI are relatively higher (~35% of precipitation), the highest in the eastern (~600 mm/year), lesser in the western (~330 mm/year), and moderate in the central watersheds (~450 mm/year). Temporally, March–May produce the highest streamflows, whereas recharge is maximum during April–July, which underscores strong surfacewater-groundwater interactions. Climate change and an increase in pumping would further reduce streamflows and recharges, and significantly change its intra-annual distribution. More attenuation is likely with higher quantities in late winter and early spring and somewhat lesser during summers. Groundwater fulfills 100% water needs of the Island; wherein existing water policy allows pumping up to 20% of yearly recharges (annual-SWA) but not exceeding 35% of summers' stream-baseflows. The policy ensures sustainability if implemented at the watershed scale. Despite insignificant reductions in the annual-SWA (3–17%) in the next 30–60 years, summer-SWA would significantly reduce by 38–50% due to its intraannual redistribution. Groundwater pumping for irrigation to satisfy normal years' SIRs would consume: 5–6%, 27–37%, and 63–79% of annual-SWA in the eastern, central, and western watersheds, respectively but it would surpass summer-SWA at some places. Extension of sprinkler irrigation to meet SIR is challenging on economic and technical grounds, though groundwater is mostly available. Extension of streamflow and groundwater monitoring network, integrated hydrological modeling, watershed scaled \ninvestigations, and continuous policy review and adaptations are required for sustainable water and agricultural management in PEI.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,076
Score d'incertitude au seuil0,555

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,012
Tête enseignante GPT0,233
Écart entre enseignants0,220 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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