Articles | Volume 25, issue 3
https://doi.org/10.5194/hess-25-1617-2021
https://doi.org/10.5194/hess-25-1617-2021
Research article
 | 
31 Mar 2021
Research article |  | 31 Mar 2021

Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data

Ewan Pinnington, Javier Amezcua, Elizabeth Cooper, Simon Dadson, Rich Ellis, Jian Peng, Emma Robinson, Ross Morrison, Simon Osborne, and Tristan Quaife

Related authors

Using data assimilation to optimize pedotransfer functions using field-scale in situ soil moisture observations
Elizabeth Cooper, Eleanor Blyth, Hollie Cooper, Rich Ellis, Ewan Pinnington, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 25, 2445–2458, https://doi.org/10.5194/hess-25-2445-2021,https://doi.org/10.5194/hess-25-2445-2021, 2021
Short summary
The Land Variational Ensemble Data Assimilation Framework: LAVENDAR v1.0.0
Ewan Pinnington, Tristan Quaife, Amos Lawless, Karina Williams, Tim Arkebauer, and Dave Scoby
Geosci. Model Dev., 13, 55–69, https://doi.org/10.5194/gmd-13-55-2020,https://doi.org/10.5194/gmd-13-55-2020, 2020
Short summary
Impact of remotely sensed soil moisture and precipitation on soil moisture prediction in a data assimilation system with the JULES land surface model
Ewan Pinnington, Tristan Quaife, and Emily Black
Hydrol. Earth Syst. Sci., 22, 2575–2588, https://doi.org/10.5194/hess-22-2575-2018,https://doi.org/10.5194/hess-22-2575-2018, 2018
Short summary

Related subject area

Subject: Hydrometeorology | Techniques and Approaches: Modelling approaches
Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River
Ningpeng Dong, Haoran Hao, Mingxiang Yang, Jianhui Wei, Shiqin Xu, and Harald Kunstmann
Hydrol. Earth Syst. Sci., 29, 2023–2042, https://doi.org/10.5194/hess-29-2023-2025,https://doi.org/10.5194/hess-29-2023-2025, 2025
Short summary
High-resolution land surface modelling over Africa: the role of uncertain soil properties in combination with forcing temporal resolution
Bamidele Oloruntoba, Stefan Kollet, Carsten Montzka, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 29, 1659–1683, https://doi.org/10.5194/hess-29-1659-2025,https://doi.org/10.5194/hess-29-1659-2025, 2025
Short summary
Investigating the global and regional response of drought to idealized deforestation using multiple global climate models
Yan Li, Bo Huang, Chunping Tan, Xia Zhang, Francesco Cherubini, and Henning W. Rust
Hydrol. Earth Syst. Sci., 29, 1637–1658, https://doi.org/10.5194/hess-29-1637-2025,https://doi.org/10.5194/hess-29-1637-2025, 2025
Short summary
Distribution, trends, and drivers of flash droughts in the United Kingdom
Iván Noguera, Jamie Hannaford, and Maliko Tanguy
Hydrol. Earth Syst. Sci., 29, 1295–1317, https://doi.org/10.5194/hess-29-1295-2025,https://doi.org/10.5194/hess-29-1295-2025, 2025
Short summary
Are dependencies of extreme rainfall on humidity more reliable in convection-permitting climate models?
Geert Lenderink, Nikolina Ban, Erwan Brisson, Ségolène Berthou, Virginia Edith Cortés-Hernández, Elizabeth Kendon, Hayley J. Fowler, and Hylke de Vries
Hydrol. Earth Syst. Sci., 29, 1201–1220, https://doi.org/10.5194/hess-29-1201-2025,https://doi.org/10.5194/hess-29-1201-2025, 2025
Short summary

Cited articles

Abbaszadeh, P., Gavahi, K., and Moradkhani, H.: Multivariate remotely sensed and in-situ data assimilation for enhancing community WRF-Hydro model forecasting, Adv. Water Resour., 145, 103721, https://doi.org/10.1016/j.advwatres.2020.103721, 2020. a
Anderson, J. L. and Anderson, S. L.: A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and Forecasts, Mon. Weather Rev., 127, 2741–2758, https://doi.org/10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2, 1999. a
Asfaw, D., Black, E., Brown, M., Nicklin, K. J., Otu-Larbi, F., Pinnington, E., Challinor, A., Maidment, R., and Quaife, T.: TAMSAT-ALERT v1: a new framework for agricultural decision support, Geosci. Model Dev., 11, 2353–2371, https://doi.org/10.5194/gmd-11-2353-2018, 2018. a
Baatz, R., Bogena, H., Hendricks Franssen, H.-J., Huisman, J., Qu, W., Montzka, C., and Vereecken, H.: Calibration of a catchment scale cosmic-ray probe network: A comparison of three parameterization methods, J. Hydrol., 516, 231–244, https://doi.org/10.1016/j.jhydrol.2014.02.026, 2014. a, b
Baatz, R., Hendricks Franssen, H.-J., Han, X., Hoar, T., Bogena, H. R., and Vereecken, H.: Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction, Hydrol. Earth Syst. Sci., 21, 2509–2530, https://doi.org/10.5194/hess-21-2509-2017, 2017. a
Download
Short summary
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.
Share