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

Metadata Updated: November 12, 2020

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 D 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.

Citation: Letcher et al. (2016), A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags. PeerJ 4:e1727; DOI 10.7717/peerj.1727

This project was co-funded by the North Atlantic Landscape Conservation Cooperative and Northeast Climate Adaptation Science Center. An alternate reference to this product can be found here: https://www.sciencebase.gov/catalog/item/56d96027e4b015c306f726ad.

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License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Metadata Date September 26, 2018
Metadata Created Date November 12, 2020
Metadata Updated Date November 12, 2020
Reference Date(s) 2018-06-20T14:15:27+00:00 (creation), 2018-06-26T13:14:26+00:00 (lastUpdate), 2016 (publication)
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Resource Type Publication
Metadata Date September 26, 2018
Metadata Created Date November 12, 2020
Metadata Updated Date November 12, 2020
Reference Date(s) 2018-06-20T14:15:27+00:00 (creation), 2018-06-26T13:14:26+00:00 (lastUpdate), 2016 (publication)
Responsible Party (Point of Contact, Principal Investigator); Northeast Climate Adaptation Science Center (Collaborator); North Atlantic Landscape Conservation Cooperative (Point of Contact)
Contact Email
Guid 5b2a617fe4b059207627e7ac
Access Constraints
Coupled Resource
Frequency Of Update
Licence
Metadata Language
Metadata Type geospatial
Progress completed
Spatial Data Service Type
Spatial Reference System
Spatial Harvester True
Temporal Extent Begin 2016-01-01T05:00:00.000+00:00
Temporal Extent End 2016-01-01T05:00:00.000+00:00

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