Published January 2018 | Version v1
Journal article

A spatio-temporal statistical model of maximum daily river temperatures to inform the management of Scotland's Atlantic salmon rivers under climate change

  • 1. School of Geography, Earth and Environmental Science, University of Birmingham, Birmingham B15 2TT, England (United Kingdom)
  • 2. Marine Scotland Science, Scottish Government, Freshwater Fisheries Laboratory, Faskally, Pitlochry, PH16 5LB, Scotland (United Kingdom)
  • 3. Marine Scotland Science, Scottish Government, Marine Laboratory, 375 Victoria Road, Aberdeen AB11 9DB, Scotland (United Kingdom)

Description

Highlights: • Data collected from strategic river temperature monitoring network • Novel spatio-temporal model of maximum daily river temperature developed • Models include air temperature, location, day and landscape characteristics • Model predictions show spatial temperature variability and climate sensitivity. • Maps provide tools for fisheries and river managers. 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.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.scitotenv.2017.09.010

Additional details

Identifiers

DOI
10.1016/j.scitotenv.2017.09.010;
PII
S0048969717323525;

Publishing Information

Journal Title
Science of the Total Environment
Journal Volume
612
Journal Page Range
p. 1543-1558
ISSN
0048-9697
CODEN
STENDL

Optional Information

Copyright
Copyright (c) 2017 Published by Elsevier B.V.