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

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Development of spatial regression models for predicting summer river temperatures from landscape characteristics : Implications for land and fisheries management. / Jackson, F. L.; Hannah, David M.; Fryer, R. J.; Millar, C. P.; Malcolm, I. A.

In: Hydrological Processes, Vol. 31, No. 6, 03.2017, p. 1225–1238.

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@article{1cf76e0eea024323a1b0c0768b2a3d4d,
title = "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 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.",
keywords = "Landscape covariates, Prediction, River temperature, Scotland, Spatial regression models",
author = "Jackson, {F. L.} and Hannah, {David M.} and Fryer, {R. J.} and Millar, {C. P.} and Malcolm, {I. A.}",
year = "2017",
month = mar,
doi = "10.1002/hyp.11087",
language = "English",
volume = "31",
pages = "1225–1238",
journal = "Hydrological Processes",
issn = "0885-6087",
publisher = "Wiley",
number = "6",

}

RIS

TY - JOUR

T1 - Development of spatial regression models for predicting summer river temperatures from landscape characteristics

T2 - Implications for land and fisheries management

AU - Jackson, F. L.

AU - Hannah, David M.

AU - Fryer, R. J.

AU - Millar, C. P.

AU - Malcolm, I. A.

PY - 2017/3

Y1 - 2017/3

N2 - 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.

AB - 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.

KW - Landscape covariates

KW - Prediction

KW - River temperature

KW - Scotland

KW - Spatial regression models

UR - http://www.scopus.com/inward/record.url?scp=85011317386&partnerID=8YFLogxK

U2 - 10.1002/hyp.11087

DO - 10.1002/hyp.11087

M3 - Article

VL - 31

SP - 1225

EP - 1238

JO - Hydrological Processes

JF - Hydrological Processes

SN - 0885-6087

IS - 6

ER -