Estimating future temperature maxima in lakes across the United States using a surrogate modeling approach

Abstract

A warming climate increases thermal inputs to lakes with potential implications for water quality and aquatic ecosystems. In a previous study, we used a dynamic water column temperature and mixing simulation model to simulate chronic (7-day average) maximum temperatures under a range of potential future climate projections at selected sites representative of different U.S. regions. Here, to extend results to lakes where dynamic models have not been developed, we apply a novel machine learning approach that uses Gaussian Process regression to describe the model response surface as a function of simplified lake characteristics (depth, surface area, water clarity) and climate forcing (winter and summer air temperatures and potential evapotranspiration). We use this approach to extrapolate predictions from the simulation model to the statistical sample of U.S. lakes in the National Lakes Assessment (NLA) database. Results provide a national-scale scoping assessment of the potential thermal risk to lake water quality and ecosystems across the U.S. We suggest a small fraction of lakes will experience less risk of summer thermal stress events due to changes in stratification and mixing dynamics, but most will experience increases. The percentage of lakes in the NLA with simulated 7-day average maximum water temperatures in excess of 30°C is projected to increase from less than 2% to approximately 22% by the end of the 21st century, which could significantly reduce the number of lakes that can support cold water fisheries. Site-specific analysis of the full range of factors that influence thermal profiles in individual lakes is needed to develop appropriate adaptation strategies.

Temperature but not nutrient addition affects abundance and assemblage structure of colonizing aquatic insects

“…Rising temperatures of shallow lentic systems (Butcher et al 2017) can have direct impacts on species if temperatures are already near critical thermal maxima, as well as indirect effects resulting from shifts in water quality and resources (Komatsu et al 2007, Dallas and Rivers‐Moore 2012). Oxygen is less soluble in warmer waters, which can change species interactions and the resulting food web, as many gilled organisms are unable to persist (Verberk and Bilton 2013), creating additional habitat space for species that typically avoid habitats with gilled predators such as fish and amphibian larvae.…”

Increasing maximum lake surface temperature under climate change

“…A review of the physiological basis of effects on fish by climate change is provided by Whitney et al (2016). Sharma et al (2007) predicted an increase of maximum summer temperatures for lakes in Canada until 2100 and Butcher et al (2017) for lakes in the United States. Many lakes may experience water temperatures as high as 30°C, with Neusiedler See already exceeding that in several years.…”

“…Many lakes may experience water temperatures as high as 30°C, with Neusiedler See already exceeding that in several years. Simulated 7-day average maximum water temperatures of more than 30°C are projected to increase from less than 2% to approximately 22% by the end of the twenty-first century (Butcher et al 2017). Consequently, thermal habitats will be altered potentially expanding the range of warm water fish species negatively impacting native cold water fish communities.…”

“…The increment in the duration and exceedance of potentially critical temperature limits, and their changes with depth, are ecologically important but poorly studied. Many lacustrine species are constrained by their physiological or developmental thermal tolerance during episodic high-temperature events (Butcher et al 2017). Comparison of the thermal tolerance of aquatic organisms with a potentially critical temperature can provide indicators that define the impacts of global warming on biodiversity (Bates et al 2013;Bennett et al 2018).…”

Introduction to Metamodeling for Reducing Computational Burden of Advanced Analyses with Health Economic Models: A Structured Overview of Metamodeling Methods in a 6-Step Application Process

“…Metamodeling methods are used widely across different fields of science and engineering, for example, to optimize designs of coronary stents, 12 high-speed trains, 13 and groundwater remediation, 14 as well as to estimate future water temperatures. 15 In health economics, de Carvalho et al 16 recently demonstrated that a metamodel can be used to perform probabilistic analysis, which was not possible in a feasible time frame using their original model. 16 A previous literature review identified only 13 additional applications of metamodeling methods in health economics, mostly aiming to perform value of information analysis and applying various, relatively basic metamodeling methods compared with those used in other fields of research, suggesting the field of metamodeling within health economics to be in its infancy.…”