A spatio-temporal statistical model of maximum daily river temperatures to inform the management of Scotland's Atlantic salmon rivers under climate change
Creators
- 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.2017.09.010Additional 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
INIS
- Country of Publication
- Netherlands
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 53054087
- Subject category
- S54: ENVIRONMENTAL SCIENCES;
- Descriptors DEI
- AIR; CLIMATIC CHANGE; FISHERIES; HABITAT; LAND USE; MAPS; RIVERS; SALMON; SPACE DEPENDENCE; STATISTICAL MODELS; TEMPERATURE MONITORING; TIME DEPENDENCE; TREES; UNITED KINGDOM; WATER; WATER POLLUTION ABATEMENT
- Descriptors DEC
- ANADROMOUS FISHES; ANIMALS; AQUATIC ORGANISMS; DEVELOPED COUNTRIES; EUROPE; FISHES; FLUIDS; GASES; HYDROGEN COMPOUNDS; MATHEMATICAL MODELS; MONITORING; OXYGEN COMPOUNDS; PLANTS; POLLUTION ABATEMENT; SURFACE WATERS; VERTEBRATES; WESTERN EUROPE
Optional Information
- Copyright
- Copyright (c) 2017 Published by Elsevier B.V.