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Recent changes in Great Lake hydrologic variability: an artifact of chance or a robust change?

2023· preprint· en· W4319312851 on OpenAlexaboutno aff
Ryan D. Harp, Daniel T. Blanco, Ashish Sharma, Donald J. Wuebbles, Daniel E. Horton

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
FundersDivision of Chemical, Bioengineering, Environmental, and Transport SystemsNorthwestern UniversityNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsSurface runoffPrecipitationClimate changeEnvironmental scienceWater balanceHydrology (agriculture)Climatic variabilityStructural basinWater cycleWater levelPhysical geographyClimatologyGeographyGeologyEcologyOceanographyMeteorology

Abstract

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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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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.091
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.091
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.005
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.123
GPT teacher head0.285
Teacher spread0.161 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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