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 pre

SUBMITTER: Butcher JB 

PROVIDER: S-EPMC5679518 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature