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

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 s...

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Bibliographic Details
Published in:Hydrological processes 2017-03, Vol.31 (6), p.1225-1238
Main Authors: Jackson, F.L., Hannah, David M., Fryer, R.J., Millar, C.P., Malcolm, I.A.
Format: Article
Language:English
Subjects:
Additives
Annual variations
Catchment area
Catchment areas
Catchment scale
Catchments
Channels
Environmental monitoring
Fisheries
Fisheries management
Fishery management
Freshwater
Heat exchange
Land use
Land use management
Land use planning
Landscape
landscape covariates
Management
Mathematical models
Maximum temperatures
Mean temperatures
Networks
prediction
Regression
Regression analysis
Regression models
Riparian environments
River catchments
River channels
River management
River networks
river temperature
River temperatures
Rivers
Scotland
spatial regression models
Spatial variations
Summer
Temperature
Temperature data
Temperature effects
Temperature variability
Variability
Water exchange
Water temperature
Water temperature data
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Summary: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.
ISSN:0885-6087
1099-1085
DOI:10.1002/hyp.11087