Development of spatial regression models for predicting summer river temperatures from landscape characteristics: Implications for land and fisheries management

Abstract

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 km 2 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 lowerorder 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.

Can spatial statistical river temperature models be transferred between catchments?

“…Large-scale models are required to provide information at the spatial scales appropriate to management decisions, i.e. catchment (Chang and Psaris, 2013;Hrachowitz et al, 2010;Imholt et al, 2011Imholt et al, , 2013Jackson et al, 2017b;Steel et al, 2016), regional (Hill et al, 2013;Isaak et al, 2012;Ruesch et al, 2012) and national scales.…”

“…The development of affordable, reliable, accurate T w data loggers has led to a rapid increase in T w monitoring (Sowder and Steel, 2012), to the point that staff time, data storage and quality control are often now the greatest limitations on data collection . At the same time, there have been substantial developments in spatial statistical modelling approaches (Ver Hoef et al, 2006Ver Hoef and Peterson, 2010;Isaak et al, 2014;Jackson et al, 2017b;O'Donnell et al, 2014;Peterson et al, 2013;Rushworth et al, 2015), monitoring network design (Dobbie et al, 2008;Jackson et al, 2016;Som et al, 2014), spatial datasets (e.g. shapefiles incorporating covariates such as in "The National Stream Internet Project" (Isaak et al, 2011) or gridded air temperature datasets (Perry and Hollis, 2005a, b)) and spatial analysis tools (Isaak et al, 2011(Isaak et al, , 2014Peterson et al, 2013;Peterson and Ver Hoef, 2014).…”

“…For example, in Scotland, there are 16 006 river catchments (unique rivers running to the sea), including 629 catchments > 10 km 2 , but there was no systematic nationwide quality-controlled river temperature data collection until 2015 . For management purposes, it is therefore often necessary to predict T w at unmonitored locations, both within (Hrachowitz et al, 2010;Jackson et al, 2017b;Peterson and Urquhart, 2006) and between catchments (Isaak et al, 2014). In recent years, it has become increasingly common to develop and apply spatial statistical river network models that incorporate network covariance structure to predict spatial variability in river temperature (e.g.…”

“…In recent years, it has become increasingly common to develop and apply spatial statistical river network models that incorporate network covariance structure to predict spatial variability in river temperature (e.g. Isaak et al, 2014;Jackson et al, 2017b). It is widely acknowledged that these models can dramatically improve predictions of river temperature where sufficient observational data exist, but the covariance component of the predictions cannot typically be transferred between catchments.…”

“…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).…”