The thermal suitability of riverine habitats for cold water adapted species may be reduced under climate change. Riparian tree planting is a practical climate change mitigation measure, but it is often unclear where to focus effort for maximum benefit. Recent developments in data collection, monitoring and statistical methods have facilitated the development of increasingly sophisticated river temperature models capable of predicting spatial variability at large scales appropriate to management. In parallel, improvements in temporal river temperature models have increased the accuracy of temperature predictions at individual sites. This study developed a novel large scale spatio-temporal model of maximum daily river temperature (Twmax) for Scotland that predicts variability in both river temperature and climate sensitivity. Twmax was modelled as a linear function of maximum daily air temperature (Tamax), with the slope and intercept allowed to vary as a smooth function of day of the year (DoY) and further modified by landscape covariates including elevation, channel orientation and riparian woodland. Spatial correlation in Twmax was modelled at two scales; (1) river network (2) regional. Temporal correlation was addressed through an autoregressive (AR1) error structure for observations within sites. Additional site level variability was modelled with random effects. The resulting model was used to map (1) spatial variability in predicted Twmax under current (but extreme) climate conditions (2) the sensitivity of rivers to climate variability and (3) the effects of riparian tree planting. These visualisations provide innovative tools for informing fisheries and land-use management under current and future climate.

There is increasing demand for models that can accurately predict river temperature at the large spatial scales appropriate to river management. This paper combined summer water temperature data from a strategically designed, quality controlled network of 25 sites, with recently developed flexible spatial regression models, to understand and predict river temperature across a 3,000 km² river catchment. Minimum, mean and maximum temperatures were modelled as a function of nine potential landscape covariates that represented proxies for heat and water exchange processes. Generalised additive models were used to allow for flexible responses. Spatial structure in the river network data (local spatial variation) was accounted for by including river network smoothers. Minimum and mean temperatures decreased with increasing elevation, riparian woodland and channel gradient. Maximum temperatures increased with channel width. There was greater between-river and between-reach variability in all temperature metrics in lower-order rivers indicating that increased monitoring effort should be focussed at these smaller scales. The combination of strategic network design and recently developed spatial statistical approaches employed in this study have not been used in previous studies of river temperature. The resulting catchment scale temperature models provide a valuable quantitative tool for understanding and predicting river temperature variability at the catchment scales relevant to land use planning and fisheries management and provide a template for future studies.

Water temperature is an important control on processes in aquatic systems and particularly for freshwater fish, affecting growth, survival and demographic characteristics. In recognition of this importance, the Scottish Government has prioritised developing a robust national river temperature monitoring network. Advances in geographical information systems, spatial statistics and field data loggers make large-scale river temperature monitoring increasingly possible. However, duplication of environmental and thermal characteristics among monitoring sites means many networks have lower than expected statistical power. This paper describes a novel methodology for network design, illustrated by the development of the Scotland River Temperature Monitoring Network. A literature review identified processes controlling stream temperature and associated landscape controls. Metrics indicative of these landscape controls were calculated for points every 500 m along the river network. From these points, sites were chosen to cover the full range of observed environmental gradients and combinations of controlling variables. The resulting network contains sites with unique characteristics covering the range of relevant environmental characteristics observed in Scottish salmon rivers. The network will thus have minimal redundancy, often not seen in large networks, and high statistical power to separate the relative importance of predictor variables thereby allowing large-scale water temperature predictions.

High river water temperature (T_w) extremes have been widely reported during drought conditions as extreme low‐flows often coincide with high atmospheric energy inputs. This has significant implications for freshwater ecosystem health and sustainable river management practices globally. However, the extent to which different meteorological and hydrological processes interact during droughts to govern T_w dynamics, and how this varies between environmental contexts, remains poorly understood. Here, we review the mechanisms controlling T_w dynamics during droughts across temperate, maritime environments, using the United Kingdom as a detailed case study. We evidence that T_w spikes have widely occurred during extreme low‐flow events observed within droughts, but such trends have been inconsistent due to varying hydroclimatic conditions and river basin controls. To better understand this, we re‐conceptualize the mechanisms governing drought‐induced T_w dynamics operating across three ‘process sets’: (i) ‘energy flux dynamics’ as non‐advective controls on T_w; (ii) the role of ‘reach‐scale habitat conditions’ in mediating non‐advective controls on T_w, including hydraulic properties (e.g., residence time) and physical conditions (e.g., riparian vegetation coverages, wetted perimeters); (iii) ‘water source contributions’ (surface water and groundwater) as advective heat and water flow controls. We review natural and anthropogenic influences affecting T_w controls within each process set and discuss how such mechanisms are likely to change under drought conditions. More systematic research (spanning various river environments and drought severities) is required to test such concepts, with existing scientific knowledge on drought‐induced T_w dynamics being largely gleaned from studies examining non‐extreme low‐flow conditions or with broader focuses (e.g., annual thermal dynamics). We conclude by highlighting critical future research questions that need to be answered to better model T_w dynamics during future droughts and for unmonitored sites. Such scientific advances would more effectively inform how high T_w extremes could be better managed through evidence‐based mitigation and adaptation strategies.

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 (Tw_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 Tw_max. However, the predictions were biased when mean Tw_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 Tw_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.