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The University of Massachusetts Amherst

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

Authors:

Benjamin Letcher

Daniel Hocking

Kyle O'Neil

Andrew Whiteley

Keith Nislow

Matthew O'Donnell

Publication Type:
Journal Article
Year of Publication:
2016
Secondary Title:
PeerJ
DOI:
10.7717/peerj.1727
Pages:
e1727
Volume:
4
Year:
2016
Date:
Jan-01-2016
URL:
https://peerj.com/articles/1727

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\textdegreeC), identified a clear warming trend (0.63 \textdegreeC decade-1) and a widening of the synchronized period (29 d decade-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 \textdegreeC jump in RMSE), but this decrease was moderated when data were available from other streams in the network.