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There has been increasing use of spatial statistical models to\udunderstand and predict river temperature (Tw) from landscape covariates.\udHowever, it is not financially or logistically feasible to monitor all\udrivers and the transferability of such models has not been explored. This\udpaper uses Tw data from four river catchments collected in August 2015 to\udassess how well spatial regression models predict the maximum 7-day rolling\udmean of daily maximum Tw (Twmax) within and between catchments. Models\udwere fitted for each catchment separately using (1) landscape covariates\udonly (LS models) and (2) landscape covariates and an air temperature (Ta)\udmetric (LS_Ta models). All the LS models included upstream\udcatchment area and three included a river network smoother (RNS) that\udaccounted for unexplained spatial structure. The LS models transferred\udreasonably to other catchments, at least when predicting relative levels of\udTwmax. However, the predictions were biased when mean Twmax\uddiffered between catchments. The RNS was needed to characterise and predict\udfiner-scale spatially correlated variation. Because the RNS was unique to\udeach catchment and thus non-transferable, predictions were better within\udcatchments than between catchments. A single model fitted to all catchments\udfound no interactions between the landscape covariates and catchment,\udsuggesting that the landscape relationships were transferable. The\udLS_Ta models transferred less well, with particularly poor\udperformance when the relationship with the Ta metric was physically\udimplausible or required extrapolation outside the range of the data. A\udsingle model fitted to all catchments found catchment-specific relationships\udbetween Twmax and the Ta metric, indicating that the Ta metric was not\udtransferable. These findings improve our understanding of the\udtransferability of spatial statistical river temperature models and provide\uda foundation for developing new approaches for predicting Tw at unmonitored\udlocations across multiple catchments and larger spatial scales

Can spatial statistical river temperature models be transferred between catchments?