A robust hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags

Conte Anadromous Fish Research Center, USGS, Turners Falls, USA
Department of Environmental Conservation, University of Massachusetts, Amherst, USA
Northern Research Station, US Forest Service, Amherst, Massachusetts, USA
DOI
10.7287/peerj.preprints.1578v1
Subject Areas
Biosphere Interactions, Conservation Biology, Ecology, Environmental Sciences
Keywords
stream temperature, ecology, air temperature, statistical model, climate change
Copyright
© 2015 Letcher et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
Cite this article
Letcher BH, Hocking DJ, O'Neill K, Whiteley AR, Nislow KH, O'Donnell MJ. 2015. A robust hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags. PeerJ PrePrints 3:e1578v1

Abstract

Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59 °C), identified a clear warming trend (0.063 °C · y-1) and a widening of the synchronized period (2.9 d · y-1). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (~ 0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (~ 0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network. Straightforward incorporation of external drivers (e.g. land cover, basin size) should allow this modeling framework to be readily applied across multiple sites and at multiple spatial scales.

Author Comment

This is a preprint submission to PeerJ Preprints.

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