Articles | Volume 21, issue 9
https://doi.org/10.5194/hess-21-4727-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-4727-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
21 Sep 2017
Research article |
| 21 Sep 2017
Can spatial statistical river temperature models be transferred between catchments?
Faye L. Jackson
CORRESPONDING AUTHOR
School of Geography, Earth and Environmental Science, University of
Birmingham, Birmingham, B15 2TT, UK
Marine Scotland Science, Scottish Government, Freshwater Laboratory,
Faskally, Pitlochry, PH16 5LB, UK
Robert J. Fryer
Marine Scotland, Marine Laboratory, 375 Victoria Road, Aberdeen, AB11 9DB, UK
David M. Hannah
School of Geography, Earth and Environmental Science, University of
Birmingham, Birmingham, B15 2TT, UK
Iain A. Malcolm
Marine Scotland Science, Scottish Government, Freshwater Laboratory,
Faskally, Pitlochry, PH16 5LB, UK
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Cited
19 citations as recorded by crossref.
- Simple modifications of the nonlinear regression stream temperature model for daily data A. Piotrowski & J. Napiorkowski 10.1016/j.jhydrol.2019.02.035
- Citizen science evidence from the past century shows that Scottish rivers are warming I. Pohle et al. 10.1016/j.scitotenv.2018.12.325
- Performance of the air2stream model that relates air and stream water temperatures depends on the calibration method A. Piotrowski & J. Napiorkowski 10.1016/j.jhydrol.2018.04.016
- Land-Cover and Climatic Controls on Water Temperature, Flow Permanence, and Fragmentation of Great Basin Stream Networks A. Gendaszek et al. 10.3390/w12071962
- Climate variability and implications for keeping rivers cool in England R. Wilby & M. Johnson 10.1016/j.crm.2020.100259
- A machine learning model for estimating the temperature of small rivers using satellite-based spatial data D. Philippus et al. 10.1016/j.rse.2024.114271
- River temperature research and practice: Recent challenges and emerging opportunities for managing thermal habitat conditions in stream ecosystems V. Ouellet et al. 10.1016/j.scitotenv.2020.139679
- Modelling of daily lake surface water temperature from air temperature: Extremely randomized trees (ERT) versus Air2Water, MARS, M5Tree, RF and MLPNN S. Heddam et al. 10.1016/j.jhydrol.2020.125130
- JAMES BUTTLE REVIEW: Quantifying the influence of forestry and forest disturbance on stream temperature: Methodologies and challenges R. Moore & R. MacDonald 10.1002/hyp.15223
- Integrating thermal infrared stream temperature imagery and spatial stream network models to understand natural spatial thermal variability in streams M. Fuller et al. 10.1016/j.jtherbio.2021.103028
- Possibilities of River Water Temperature Reconstruction Using Statistical Models in the Context of Long-Term Thermal Regime Changes Assessment M. Sojka & M. Ptak 10.3390/app12157503
- River modification reduces climate resilience of brown trout (Salmo trutta) populations in Ireland R. O'Briain et al. 10.1111/fme.12326
- Long-term patterns and changes of unglaciated High Arctic stream thermal regime M. Majerska et al. 10.1016/j.scitotenv.2024.171298
- Anthropogenic influence on the Rhine water temperatures A. Zavarsky & L. Duester 10.5194/hess-24-5027-2020
- Influence of landscape and hydrological factors on stream–air temperature relationships at regional scale A. Beaufort et al. 10.1002/hyp.13608
- Long-term daily stream temperature record for Scotland reveals spatio-temporal patterns in warming of rivers in the past and further warming in the future E. Loerke et al. 10.1016/j.scitotenv.2023.164194
- A spatio-temporal statistical model of maximum daily river temperatures to inform the management of Scotland's Atlantic salmon rivers under climate change F. Jackson et al. 10.1016/j.scitotenv.2017.09.010
- Modeling thermal metrics of importance for native vs non-native fish across stream networks to provide insight for watershed-scale fisheries management A. Marsha et al. 10.1086/713038
- Modelling of Daily Lake Surface Water Temperature from Air Temperature: Extremely Randomized Trees (ERT) versus Air2Water, MARS, M5Tree, RF and MLPNN S. Heddam et al. 10.1016/j.jhydrol.2020.125130
18 citations as recorded by crossref.
- Simple modifications of the nonlinear regression stream temperature model for daily data A. Piotrowski & J. Napiorkowski 10.1016/j.jhydrol.2019.02.035
- Citizen science evidence from the past century shows that Scottish rivers are warming I. Pohle et al. 10.1016/j.scitotenv.2018.12.325
- Performance of the air2stream model that relates air and stream water temperatures depends on the calibration method A. Piotrowski & J. Napiorkowski 10.1016/j.jhydrol.2018.04.016
- Land-Cover and Climatic Controls on Water Temperature, Flow Permanence, and Fragmentation of Great Basin Stream Networks A. Gendaszek et al. 10.3390/w12071962
- Climate variability and implications for keeping rivers cool in England R. Wilby & M. Johnson 10.1016/j.crm.2020.100259
- A machine learning model for estimating the temperature of small rivers using satellite-based spatial data D. Philippus et al. 10.1016/j.rse.2024.114271
- River temperature research and practice: Recent challenges and emerging opportunities for managing thermal habitat conditions in stream ecosystems V. Ouellet et al. 10.1016/j.scitotenv.2020.139679
- Modelling of daily lake surface water temperature from air temperature: Extremely randomized trees (ERT) versus Air2Water, MARS, M5Tree, RF and MLPNN S. Heddam et al. 10.1016/j.jhydrol.2020.125130
- JAMES BUTTLE REVIEW: Quantifying the influence of forestry and forest disturbance on stream temperature: Methodologies and challenges R. Moore & R. MacDonald 10.1002/hyp.15223
- Integrating thermal infrared stream temperature imagery and spatial stream network models to understand natural spatial thermal variability in streams M. Fuller et al. 10.1016/j.jtherbio.2021.103028
- Possibilities of River Water Temperature Reconstruction Using Statistical Models in the Context of Long-Term Thermal Regime Changes Assessment M. Sojka & M. Ptak 10.3390/app12157503
- River modification reduces climate resilience of brown trout (Salmo trutta) populations in Ireland R. O'Briain et al. 10.1111/fme.12326
- Long-term patterns and changes of unglaciated High Arctic stream thermal regime M. Majerska et al. 10.1016/j.scitotenv.2024.171298
- Anthropogenic influence on the Rhine water temperatures A. Zavarsky & L. Duester 10.5194/hess-24-5027-2020
- Influence of landscape and hydrological factors on stream–air temperature relationships at regional scale A. Beaufort et al. 10.1002/hyp.13608
- Long-term daily stream temperature record for Scotland reveals spatio-temporal patterns in warming of rivers in the past and further warming in the future E. Loerke et al. 10.1016/j.scitotenv.2023.164194
- A spatio-temporal statistical model of maximum daily river temperatures to inform the management of Scotland's Atlantic salmon rivers under climate change F. Jackson et al. 10.1016/j.scitotenv.2017.09.010
- Modeling thermal metrics of importance for native vs non-native fish across stream networks to provide insight for watershed-scale fisheries management A. Marsha et al. 10.1086/713038
Latest update: 21 Jan 2025
Short summary
River temperature (Tw) is important to fish populations, but one cannot monitor everywhere. Thus, models are used to predict Tw, sometimes in rivers with no data. To date, the accuracy of these predictions has not been determined. We found that models including landscape predictors (e.g. altitude, tree cover) could describe spatial patterns in Tw in other rivers better than those including air temperature. Such findings are critical for developing Tw models that have management application.
River temperature (Tw) is important to fish populations, but one cannot monitor everywhere....
