Articles | Volume 15, issue 24
https://doi.org/10.5194/gmd-15-9177-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-9177-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
23 Dec 2022
Model description paper |
| 23 Dec 2022
| 23 Dec 2022
UniFHy v0.1.1: a community modelling framework for the terrestrial water cycle in Python
Thibault Hallouin
CORRESPONDING AUTHOR
National Centre for Atmospheric Science, Reading, UK
Department of Meteorology, University of Reading, Reading, UK
now at: HYCAR Research Unit, INRAE, Université Paris‐Saclay, Antony, France
Richard J. Ellis
UK Centre for Ecology & Hydrology, Wallingford, UK
Douglas B. Clark
UK Centre for Ecology & Hydrology, Wallingford, UK
Simon J. Dadson
UK Centre for Ecology & Hydrology, Wallingford, UK
School of Geography and the Environment, University of Oxford, Oxford, UK
Andrew G. Hughes
British Geological Survey, Keyworth, UK
Bryan N. Lawrence
National Centre for Atmospheric Science, Reading, UK
Department of Meteorology, University of Reading, Reading, UK
Department of Computer Science, University of Reading, Reading, UK
Grenville M. S. Lister
National Centre for Atmospheric Science, Reading, UK
Department of Meteorology, University of Reading, Reading, UK
Jan Polcher
Laboratoire de Météorologie Dynamique, IPSL, CNRS, Palaiseau, France
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Soil moisture estimates from land surface models are important for forecasting floods, droughts, weather, and climate trends. We show that by combining model estimates of soil moisture with measurements from field-scale, ground-based sensors, we can improve the performance of the land surface model in predicting soil moisture values.
Ewan Pinnington, Javier Amezcua, Elizabeth Cooper, Simon Dadson, Rich Ellis, Jian Peng, Emma Robinson, Ross Morrison, Simon Osborne, and Tristan Quaife
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Land surface models are important tools for translating meteorological forecasts and reanalyses into real-world impacts at the Earth's surface. We show that the hydrological predictions, in particular soil moisture, of these models can be improved by combining them with satellite observations from the NASA SMAP mission to update uncertain parameters. We find a 22 % reduction in error at a network of in situ soil moisture sensors after combining model predictions with satellite observations.
Simon J. Dadson, Eleanor Blyth, Douglas Clark, Helen Davies, Richard Ellis, Huw Lewis, Toby Marthews, and Ponnambalan Rameshwaran
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-60, https://doi.org/10.5194/hess-2021-60, 2021
Manuscript not accepted for further review
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Flood prediction helps national and regional planning and real-time flood response. In this study we apply and test a new way to make wide area predictions of flooding which can be combined with weather forecasting and climate models to give faster predictions of flooded areas. By simplifying the detailed floodplain topography we can keep track of the fraction of land flooded for hazard mapping purposes. When tested this approach accurately reproduces benchmark datasets for England.
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Short summary
A new framework for modelling the water cycle in the land system has been implemented. It considers the hydrological cycle as three interconnected components, bringing flexibility in the choice of the physical processes and their spatio-temporal resolutions. It is designed to foster collaborations between land surface, hydrological, and groundwater modelling communities to develop the next-generation of land system models for integration in Earth system models.
A new framework for modelling the water cycle in the land system has been implemented. It...
