Articles | Volume 12, issue 7
https://doi.org/10.5194/tc-12-2175-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-12-2175-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Glacio-hydrological melt and run-off modelling: application of a limits of acceptability framework for model comparison and selection
School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
British Geological Survey, Environmental Science Centre, Keyworth, Nottingham, NG12 5GG, UK
Nicholas E. Barrand
School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
David M. Hannah
School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
Stefan Krause
School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
Christopher R. Jackson
British Geological Survey, Environmental Science Centre, Keyworth, Nottingham, NG12 5GG, UK
Jez Everest
British Geological Survey, Lyell Centre, Research Avenue South, Edinburgh, EH14 4AS, UK
Guðfinna Aðalgeirsdóttir
Institute of Earth Sciences, University of Iceland, 101 Reykjavík, Iceland
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Cited
12 citations as recorded by crossref.
- What Can We Learn from Comparing Glacio-Hydrological Models? E. Stoll et al. 10.3390/atmos11090981
- Air Temperature Characteristics, Distribution, and Impact on Modeled Ablation for the South Patagonia Icefield C. Bravo et al. 10.1029/2018JD028857
- Glacio‐hydrological model calibration and evaluation M. van Tiel et al. 10.1002/wat2.1483
- Assessing the hydrological and geomorphic behaviour of a landscape evolution model within a limits‐of‐acceptability uncertainty analysis framework J. Wong et al. 10.1002/esp.5140
- A combined data assimilation and deep learning approach for continuous spatio-temporal SWE reconstruction from sparse ground tracks M. Guidicelli et al. 10.1016/j.hydroa.2024.100190
- Proglacial groundwater storage dynamics under climate change and glacier retreat J. Mackay et al. 10.1002/hyp.13961
- Groundwater–glacier meltwater interaction in proglacial aquifers B. Ó Dochartaigh et al. 10.5194/hess-23-4527-2019
- Simulation and construction of the glacier mass balance in the Manas River Basin, Tianshan, China from 2000 to 2016 G. Zhao et al. 10.1007/s11442-020-1766-z
- A hybrid time- and signature-domain Bayesian inference framework for calibration of hydrological models: a case study in the Ren River basin in China S. Liu et al. 10.1007/s00477-022-02282-3
- Information content of snow hydrological signatures based on streamflow, precipitation and air temperature I. Horner et al. 10.1002/hyp.13762
- A review of hydrologic signatures and their applications H. McMillan 10.1002/wat2.1499
- Future evolution and uncertainty of river flow regime change in a deglaciating river basin J. Mackay et al. 10.5194/hess-23-1833-2019
12 citations as recorded by crossref.
- What Can We Learn from Comparing Glacio-Hydrological Models? E. Stoll et al. 10.3390/atmos11090981
- Air Temperature Characteristics, Distribution, and Impact on Modeled Ablation for the South Patagonia Icefield C. Bravo et al. 10.1029/2018JD028857
- Glacio‐hydrological model calibration and evaluation M. van Tiel et al. 10.1002/wat2.1483
- Assessing the hydrological and geomorphic behaviour of a landscape evolution model within a limits‐of‐acceptability uncertainty analysis framework J. Wong et al. 10.1002/esp.5140
- A combined data assimilation and deep learning approach for continuous spatio-temporal SWE reconstruction from sparse ground tracks M. Guidicelli et al. 10.1016/j.hydroa.2024.100190
- Proglacial groundwater storage dynamics under climate change and glacier retreat J. Mackay et al. 10.1002/hyp.13961
- Groundwater–glacier meltwater interaction in proglacial aquifers B. Ó Dochartaigh et al. 10.5194/hess-23-4527-2019
- Simulation and construction of the glacier mass balance in the Manas River Basin, Tianshan, China from 2000 to 2016 G. Zhao et al. 10.1007/s11442-020-1766-z
- A hybrid time- and signature-domain Bayesian inference framework for calibration of hydrological models: a case study in the Ren River basin in China S. Liu et al. 10.1007/s00477-022-02282-3
- Information content of snow hydrological signatures based on streamflow, precipitation and air temperature I. Horner et al. 10.1002/hyp.13762
- A review of hydrologic signatures and their applications H. McMillan 10.1002/wat2.1499
- Future evolution and uncertainty of river flow regime change in a deglaciating river basin J. Mackay et al. 10.5194/hess-23-1833-2019
Latest update: 14 Dec 2024
Short summary
We apply a framework to compare and objectively accept or reject competing melt and run-off process models. We found no acceptable models. Furthermore, increasing model complexity does not guarantee better predictions. The results highlight model selection uncertainty and the need for rigorous frameworks to identify deficiencies in competing models. The application of this approach in the future will help to better quantify model prediction uncertainty and develop improved process models.
We apply a framework to compare and objectively accept or reject competing melt and run-off...