Science Inventory

Integrating thermal infrared stream temperature imagery and spatial stream network models to understand natural spatial thermal variability in streams

Citation:

Fuller, M., Joe Ebersole, N. Detenbeck, R. Labiosa, P. Leinenbach, AND C. Torgersen. Integrating thermal infrared stream temperature imagery and spatial stream network models to understand natural spatial thermal variability in streams. JOURNAL OF THERMAL BIOLOGY. Elsevier Science Ltd, New York, NY, 100:103028, (2021). https://doi.org/10.1016/j.jtherbio.2021.103028

Impact/Purpose:

Future climates are expected to be warmer than what we have recently experienced. In addition to climate, water temperatures across stream and river networks are controlled by many interacting physical and biological factors, which leads to temperatures that are highly variable in space and time. Therefore, predicting where cold-water habitats exist for cold-water species has been difficult. Our research uses two methods to help characterize water temperatures across a river network to demonstrate how they can be leveraged together as compliments to each other, which also demonstrates some of the weaknesses of each method alone. We used spatial stream network temperature models and forward-looking thermal infrared imagery as our two distinct methods to help describe patterns of water temperature in the Middle Fork John Day River network. Our results indicate that spatial stream network models can provide reasonable predictions for identifying when a 1km reach is warming or cooling in the downstream direction. Furthermore, we were unable to identify any hydrologic or geomorhpic features of the mainstem Middle Fork John Day River that could explain the high temperature variability observed in the forward-looking thermal infrared imagery. Lastly, we were able to demonstrate how forward-looking thermal infrared imagery was able to capture fine-resolution temperature variability within the mainstem reaches of the Middle Fork John Day River, while the spatial stream network models were able to capture the temperature variability across the entire network.

Description:

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.