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

PeerJ
By: , and 

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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 (0.63 °C 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 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags
Series title PeerJ
DOI 10.7717/peerj.1727
Volume 4
Year Published 2016
Language English
Publisher PeerJ
Contributing office(s) Leetown Science Center
Description e1727: 26 p.
Online Only (Y/N) N
Additional Online Files (Y/N) N