Development of a Midge-Based Summer Surface Water Temperature Inference Model for the Great Basin of the Western United States

Abstract

ABSTRACT Surface sediment recovered from 51 lakes in the Uinta Mountains of northeast Utah was analyzed for subfossil chironomid remains, and incorporated in a midge-based inference model for summer surface water temperature (SSWT). The lakes in the calibration set spanned elevation, depth, and summer surface water temperature ranges of 900 m, 12.7 m, and 11.3°C, respectively. Redundancy analysis (RDA) identified four variables, SSWT, depth, specific conductivity, and Al concentration, that could account for a statistically significant amount of variance in the chironomid distribution, with SSWT accounting for the largest amount of variance. The Uinta Mountain calibration set was merged with a previously developed calibration set from the Sierra Nevada, California, in order to develop a midge-based inference model for SSWT applicable to subfossil chironomid stratigraphies from the Great Basin. A variety of statistical approaches, such as weighted averaging (WA), weighted averaging-partial least squares (WA-PLS), and partial least squares (PLS) were used to assess model performance. The best inference model for SSWT, based on a 3-component WA-PLS approach, had robust performance statistics (r2 jack = 0.66, RMSEP = 1.4°C). The newly expanded inference model will enable more accurate estimates of late Pleistocene and Holocene thermal regimes and help address many outstanding questions relating to long-term and recent climate change in this region.