Integrating thermal infrared stream temperature imagery and spatial stream network models to understand natural spatial thermal variability in streams. (August 2021)

• Record Type: Journal Article
• Authors: Fuller, Matthew R.; Ebersole, Joseph L.; Detenbeck, Naomi E.; Labiosa, Rochelle; Leinenbach, Peter; Torgersen, Christian E.
• Is Part Of: Journal of thermal biology. Volume 100(2021)
• Publication Date: 2021-08
• Subjects: Thermal regime -- Middle Fork John Day River -- Spatial autocorrelation -- Airborne remote sensing -- Cold-water habitat -- Thermal infrared imagery -- Spatial stream network model; Thermobiology -- Periodicals; Temperature -- Periodicals; Biology -- Periodicals; Thermobiologie -- Périodiques; Thermobiology; Periodicals; 571.46
• DOI: 10.1016/j.jtherbio.2021.103028
• Languages: English
• ISSNs: 0306-4565

Abstract

Abstract: Under a warmer future climate, thermal refuges could facilitate the persistence of species relying on cold-water habitat. Often these refuges are small and easily missed or smoothed out by averaging in models. Thermal infrared (TIR) imagery can provide empirical water surface temperatures that capture these features at a high spatial resolution (<1 m) and over tens of kilometers. Our study examined how TIR data could be used along with spatial stream network (SSN) models to characterize thermal regimes spatially in the Middle Fork John Day (MFJD) River mainstem (Oregon, USA). We characterized thermal variation in seven TIR longitudinal temperature profiles along the MFJD mainstem and compared them with SSN model predictions of stream temperature (for the same time periods as the TIR profiles). TIR profiles identified reaches of the MFJD mainstem with consistently cooler temperatures across years that were not consistently captured by the SSN prediction models. SSN predictions along the mainstem identified ~80% of the 1-km reach scale temperature warming or cooling trends observed in the TIR profiles. We assessed whether landscape features (e.g., tributary junctions, valley confinement, geomorphic reach classifications) could explain the fine-scale thermal heterogeneity in the TIR profiles (after accounting for the reach-scale temperature variability predicted by the SSN model) by fitting SSN models using the TIR profile observation points. Only the distance to the nearest upstream tributary was identified as a statistically significant landscape feature for explaining some of the thermal variability in the TIR profile data. When combined, TIR data and SSN models provide a data-rich evaluation of stream temperature captured in TIR imagery and a spatially extensive prediction of the network thermal diversity from the outlet to the headwaters. Highlights: Combining TIR data and SSN models provides more comprehensive thermal regime characterization. TIR profiles show interannual consistency for some cooler mainstem segments. SSN models predicted same temperature trends as TIR data for 80% of mainstem. Geomorphic covariates did not explain TIR thermal heterogeneity within 1-km reaches.