1State Key Laboratory of Lake Science and Environment, Nanjing
Institute of Geography and Limnology, Chinese Academy of Science, Nanjing
210008, P. R. China
2College of Resources and Environmental Science, South-Central
University for Nationalities, Wuhan 430074, P. R. China
3Research Centre of Hydrobiology, Jinan University, Guangzhou 510632,
P. R. China
4Geography and Environment, University of Southampton, Southampton
SO17 1BJ, UK
5School of Earth and Environmental Sciences, University of
Queensland, St Lucia, Brisbane, Qld 4072, Australia
Received: 23 Sep 2016 – Discussion started: 04 Oct 2016
Abstract. A chironomid-based calibration training set comprised of 100 lakes from south-western China was established. Multivariate ordination analyses were used to investigate the relationship between the distribution and abundance of chironomid species and 18 environmental variables from these lakes. Canonical correspondence analyses (CCAs) and partial CCAs showed that mean July temperature is one of the independent and significant variables explaining the second-largest amount of variance after potassium ions (K+) in 100 south-western Chinese lakes. Quantitative transfer functions were created using the chironomid assemblages for this calibration data set. The second component of the weighted-average partial least squares (WA-PLS) model produced a coefficient of determination (r2bootstrap) of 0.63, maximum bias (bootstrap) of 5.16 and root-mean-square error of prediction (RMSEP) of 2.31 °C. We applied the transfer functions to a 150-year chironomid record from Tiancai Lake (26°38′3.8 N, 99°43′ E; 3898 m a.s.l.), Yunnan, China, to obtain mean July temperature inferences. We validated these results by applying several reconstruction diagnostics and comparing them to a 50-year instrumental record from the nearest weather station (26°51′29.22′′ N, 100°14′2.34′′ E; 2390 m a.s.l.). The transfer function performs well in this comparison. We argue that this 100-lake large training set is suitable for reconstruction work despite the low explanatory power of mean July temperature because it contains a complete range of modern temperature and environmental data for the chironomid taxa observed and is therefore robust.
Revised: 10 Jan 2017 – Accepted: 23 Feb 2017 – Published: 08 Mar 2017
Zhang, E., Chang, J., Cao, Y., Tang, H., Langdon, P., Shulmeister, J., Wang, R., Yang, X., and Shen, J.: A chironomid-based mean July temperature inference model from the south-east margin of the Tibetan Plateau, China, Clim. Past, 13, 185-199, doi:10.5194/cp-13-185-2017, 2017.