Dataset Information

A database of in situ water temperatures for large inland lakes across the coterminous United States.

ABSTRACT: Water temperature dynamics in large inland lakes are interrelated with internal lake physics, ecosystem function, and adjacent land surface meteorology and climatology. Models for simulating and forecasting lake temperatures often rely on remote sensing and in situ data for validation. In situ monitoring platforms have the benefit of providing relatively precise measurements at multiple lake depths, but are often sparser (temporally and spatially) than remote sensing data. Here, we address the challenge of synthesizing in situ lake temperature data by creating a standardized database of near-surface and subsurface measurements from 134 sites across 29 large North American lakes, with the primary goal of supporting an ongoing lake model validation study. We utilize data sources ranging from federal agency repositories to local monitoring group samples, with a collective historical record spanning January 1, 2000 through December 31, 2022. Our database has direct utility for validating simulations and forecasts from operational numerical weather prediction systems in large lakes whose extensive surface area may significantly influence nearby weather and climate patterns.

SUBMITTER: Sorensen T

PROVIDER: S-EPMC10924966 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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A database of in situ water temperatures for large inland lakes across the coterminous United States.

Sorensen Troy T Espey Eamon E Kelley John G W JGW Kessler James J Gronewold Andrew D AD

Scientific data 20240309 1

Water temperature dynamics in large inland lakes are interrelated with internal lake physics, ecosystem function, and adjacent land surface meteorology and climatology. Models for simulating and forecasting lake temperatures often rely on remote sensing and in situ data for validation. In situ monitoring platforms have the benefit of providing relatively precise measurements at multiple lake depths, but are often sparser (temporally and spatially) than remote sensing data. Here, we address the c ...[more]

PMID: 38461156

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Project description:BackgroundWildfires are increasing in magnitude, frequency, and severity. Populations in the wildland-urban interface and in downwind communities are at increased risk of exposure to elevated concentrations of fine particulate matter (PM2.5) and other harmful components of wildfire smoke. We conducted this analysis to evaluate the use of modeled predictions of wildfire smoke to create county-level measures of smoke exposure for public health research and surveillance.MethodsWe evaluated four years (2015-2018) of grid-based North American Mesoscale (NAM)-derived PM2.5 forecasts from the U.S. Forest Service BlueSky modeling framework with monitoring data from the Environmental Protection Agency Air Quality System (AQS), the Interagency Monitoring of Protected Visual Environments (IMPROVE), the Western Regional Climate Center (WRCC), and the Interagency Real Time Smoke Monitoring (AIRSIS) programs. To assess relationships between model-derived estimates and monitor-based observations, we assessed Spearman's correlations by spatial (i.e., county, level of urbanization, states in the western United States impacted by major wildfires, and climate regions) and temporal (i.e., month and wildfire activity periods) characteristics. We then generated county-level smoke estimates and examined spatial and temporal patterns in total and person-days of smoke exposure.ResultsAcross all counties in the coterminous United States and for all days, the correlation between county-level model- and monitor-derived PM2.5 estimates was 0.14 (p < 0.001). Correlations were stronger using data from temporary monitors and for areas and days impacted by high wildfire smoke, especially in the western United States. Correlations between county-level model- and monitor-derived estimates in non-metropolitan counties, and at higher concentrations ranged from 0.25 to 0.54 (p < 0.001).ConclusionsIn general, public health practitioners and health researchers need to consider the pros and cons associated with modeled data products for conducting health analyses. Our results support the use of model-derived smoke estimates to identify communities impacted by heavy smoke events, especially during emergency response and for communities located near wildfire episodes.

Dataset Information

A database of in situ water temperatures for large inland lakes across the coterminous United States.

Publications

A database of in situ water temperatures for large inland lakes across the coterminous United States.

Similar Datasets

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