Recent changes in Great Lake hydrologic variability: an artifact of chance or a robust change?
Notice bibliographique
Résumé
Water levels in the Laurentian Great Lakes have fluctuated dramatically over recent decades. Since 2015, each of the lakes has reached a record high, often following a recent record or near-record low. These exceptional swings have motivated examinations of changes in lake level variability, particularly given the known climate change-driven intensification of the hydrologic cycle. Recent studies have presented evidence of rising lake level variability and changing water balance components (i.e., overlake precipitation, overlake evaporation, and basin runoff), however a full characterization of trends in variability is needed. Here, we build on previous analyses by quantitatively answering the question: are trends in hydrologic interannual variability over the Great Lakes over recent decades – both lake levels and individual hydrologic components – statistically robust, or simply the result of random chance? Using two non-parametric trend tests, we find that interannual variability of lake levels is significantly increasing in Lakes Superior, Michigan-Huron, and Erie, while decreasing in well-regulated Lake Ontario. We also find robust increasing variability in overlake precipitation, overlake evaporation, and basin runoff for the vast majority of lakes. These results suggest that critical work must follow to both attribute causes of detected trends and to determine if trends will continue increasing in the future with continued anthropogenic climate change. 1. IntroductionRecent extraordinary shifts in Great Lakes water levels have prompted questions about potential changes in year-over-year lake level variability. Changes in the variability of Great Lake levels, namely, how quickly lake levels fluctuate between higher and lower water levels, can have dramatic environmental and societal impacts. Examples include shifts in shoreline erosion patterns (Gronewold and Stow, 2014; Davidson-Arnott, 2016), shipping costs (Millerd, 2010; Lindeberg and Albercook, 2000; Wang et al., 2012), tourism and recreation (Wall, 1998; Hartmann, 1990), and risks to critical infrastructure like water resource management (de Loe and Kreutzwiser, 2000), hydropower (Meyer et al., 2017), and toxic waste facilities (Environmental Law and Policy Center, 2022). Researchers and the public alike have thus been captivated by the rapid transition of Great Lake levels between record low and high lake levels and the resultant impacts (e.g., Gronewold et al., 2021; Egan, 2021). This interest is further motivated by the observed and projected intensification of the hydrologic cycle due to anthropogenic climate change (IPCC, 2021; Seager, 2014). Within this context, Gronewold et al. (2021) presented evidence of rising lake level variability and described the situation caused by this hydrologic cycle intensification as a “continental-scale hydrological tug-of-war” between changing water balance components.Lake levels of large lakes are dominated by three net basin supply (NBS) components: overlake precipitation, overlake evaporation, and basin runoff, where the collective balance of these three components largely determine Great Lakes levels (Δlake storage = poverlake + rbasin - eoverlake) (Gronewold et al., 2021). Note that we define runoff here as the amount of water entering the lake from all incoming river systems in a respective Great Lakes basin, excepting flow from any upstream lakes. These components are all expected to change with the amplification of anthropogenic climate change and trends in these components have already been well observed. For instance, Javed et al. (2019) find increasing evaporation, spatially mixed results on precipitation, and no change in runoff, over Lakes Michigan and Huron. Harp and Horton (2022) characterize an increase in wet day precipitation intensity of ~5% over the U.S.-portion of the Great Lakes basin from 1951 to the present. Looking forward, Mailhot et al. (2019) found increases in net basin supply components with an intensifying annual cycle, but claimed “no long-term changes can be confidently estimated for lake levels.” Kayastha et al. (2022) used a regionally downscaled model to project future Great Lake levels and found a rise in both water levels and net basin supply components, particularly overlake precipitation and runoff. The climate change-driven increase in Great Lake levels was similarly projected by Van De Weghe et al. (2022). These findings differ with earlier work by Hayhoe et al. (2010) which projected falling Great Lakes levels based on increasing evaporation rates with increasing regional temperatures. Examining individual hydrologic components, Wang et al. (2018) project a 16% increase in lake evaporation by the end of the 21st century in a high greenhouse gas emissions scenario (RCP8.5). However, despite examination of trends of lake levels, little attention has been given to statistically characterizing observed trends in variability for either Great Lakes levels or their net basin supply components. Here, we address this knowledge gap by providing a statistically rigorous assessment of changes in interannual variability over the past five decades.
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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 | 0,027 | 0,091 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,005 |
| Communication savante | 0,002 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».