Abstract
Abstract. There has been increasing use of spatial statistical models to understand and predict river temperature (Tw) 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 Tw 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 Tw (Twmax) 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 (Ta) metric (LS_Ta 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 Twmax. However, the predictions were biased when mean Twmax 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_Ta models transferred less well, with particularly poor performance when the relationship with the Ta 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 Twmax and the Ta metric, indicating that the Ta 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 Tw at unmonitored locations across multiple catchments and larger spatial scales.
Highlights
River temperature (Tw) is a key control on the health of aquatic systems (Webb et al, 2008) and is important for the growth, survival and demographic characteristics of cold-water-adapted species such as salmonids (Elliott and Elliott, 2010; Gurney et al, 2008; Jonsson and Jonsson, 2009; McCullough et al, 2001)
The principles explored in this paper are likely to be relevant to other water temperature metrics so, for brevity, this study focuses on maximum summer temperature, a metric which is prevalent in the recent literature, reflecting its importance for the survival of cold-water-adapted fish (Chang and Psaris, 2013; Hrachowitz et al, 2010; Jackson et al, 2017b; Malcolm et al, 2008; Marine and Cech, 2004)
This study demonstrated that landscape covariates can explain broad-scale patterns in Twmax and that such relationships are transferable between catchments, at least to predict relative levels of Twmax
Summary
River temperature (Tw) is a key control on the health of aquatic systems (Webb et al, 2008) and is important for the growth, survival and demographic characteristics of cold-water-adapted species such as salmonids (Elliott and Elliott, 2010; Gurney et al, 2008; Jonsson and Jonsson, 2009; McCullough et al, 2001). Rising Tw will influence fish populations by altering the thermal suitability of rivers (Comte et al, 2013; Isaak et al, 2010, 2012). Jackson et al.: Can spatial statistical river temperature models be transferred between catchments?
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