Can spatial statistical river temperature models be transferred between catchments?

Abstract

Abstract. There has been increasing use of spatial statistical models to understand and predict river temperature (T w ) from landscape covariates. However, it is not financially or logistically feasible to monitor all rivers and the transferability of such models has not been explored. This paper uses T w data from four river catchments collected in August 2015 to assess how well spatial regression models predict the maximum 7-day rolling mean of daily maximum T w (T w max ) within and between catchments. Models were fitted for each catchment separately using (1) landscape covariates only (LS models) and (2) landscape covariates and an air temperature (T a ) metric (LS_T a models). All the LS models included upstream catchment area and three included a river network smoother (RNS) that accounted for unexplained spatial structure. The LS models transferred reasonably to other catchments, at least when predicting relative levels of T w max . However, the predictions were biased when mean T w max differed between catchments. The RNS was needed to characterise and predict finer-scale spatially correlated variation. Because the RNS was unique to each catchment and thus non-transferable, predictions were better within catchments than between catchments. A single model fitted to all catchments found no interactions between the landscape covariates and catchment, suggesting that the landscape relationships were transferable. The LS_T a models transferred less well, with particularly poor performance when the relationship with the T a metric was physically implausible or required extrapolation outside the range of the data. A single model fitted to all catchments found catchment-specific relationships between T w max and the T a metric, indicating that the T a metric was not transferable. These findings improve our understanding of the transferability of spatial statistical river temperature models and provide a foundation for developing new approaches for predicting T w at unmonitored locations across multiple catchments and larger spatial scales.Copyright statement. The works published in this journal are distributed under the Creative Commons Attribution 3.0 License. This license does not affect the Crown copyright work, which is re-usable under the Open Government Licence (OGL). The Creative Commons Attribution 3.0 License and the OGL are interoperable and do not conflict with, reduce or limit each other.

Spatial and temporal variability in stream thermal regime drivers for three river networks during the summer growing season

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Long-Term Daily Stream Temperature Record for Scotland Reveals Spatio-Temporal Patterns in Warming of Rivers in the Past and Further Warming in the Future

“…for the supply of industry cooling water (Roberts and Maslin, 2021), as well as for the fishing industry (Donnelly et al, 2020;Seeney, 2019). Air temperature (T a ) (Bolduc and Lamoureux, 2018;Briggs et al, 2018;Toffolon and Piccolroaz, 2015;Zhu et al, 2018), topography (Jackson et al, 2018;O'Sullivan et al, 2019), channel characteristics (Garner et al, 2017;Jackson et al, 2018;Justice et al, 2017), land use (Daraio and Bales, 2014;Garner et al, 2017;Jackson et al, 2018), distance to coast (Hrachowitz et al, 2010;Jackson et al, 2018) and temporal drivers like day of year (Jackson et al, 2018;Zhu et al, 2019), day length (Jackson et al, 2017a;Pohle et al, 2019) and hydrological conditions (Baker et al, 2018;Pohle et al, 2019;Toffolon and Piccolroaz, 2015) are all known to influence T w .…”

“…However, climate change has both direct and indirect impacts on stream temperature, which are spatially variable (Liu et al, 2020;Punzet et al, 2012;van Vliet et al, 2013). Furthermore, in addition to climate, changes in hydrological conditions, landscape/channel characteristics and natural/artificial thermal inputs (Arora et al, 2016;Caissie, 2006;Dick et al, 2017;Dugdale et al, 2018;Jackson et al, 2017a;Kelleher et al, 2021) have also been recognised to influence changes in T w . A general understanding of controls on T w and knowledge of sensitive sites is therefore important for management strategies now, but also in future.…”

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