Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2445-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-2445-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 May 2021
Research article |
| 10 May 2021
| 10 May 2021
Using data assimilation to optimize pedotransfer functions using field-scale in situ soil moisture observations
UK Centre for Ecology and Hydrology, Wallingford, UK
Eleanor Blyth
UK Centre for Ecology and Hydrology, Wallingford, UK
Hollie Cooper
UK Centre for Ecology and Hydrology, Wallingford, UK
Rich Ellis
UK Centre for Ecology and Hydrology, Wallingford, UK
Ewan Pinnington
National Centre for Earth Observation, Department of Meteorology, University of Reading, Reading, UK
Simon J. Dadson
UK Centre for Ecology and Hydrology, Wallingford, UK
School of Geography and the Environment, South Parks Road, Oxford, OX1 3QY, UK
Related authors
Maliko Tanguy, Michael Eastman, Amulya Chevuturi, Eugene Magee, Elizabeth Cooper, Robert H. B. Johnson, Katie Facer-Childs, and Jamie Hannaford
Hydrol. Earth Syst. Sci., 29, 1587–1614, https://doi.org/10.5194/hess-29-1587-2025, https://doi.org/10.5194/hess-29-1587-2025, 2025
Short summary
Short summary
Our research compares two techniques, bias correction (BC) and data assimilation (DA), for improving river flow forecasts across 316 UK catchments. BC, which corrects errors after simulation, showed broad improvements, while DA, adjusting model states before forecast, excelled under specific conditions like snowmelt and high baseflows. Each method's unique strengths suit different scenarios. These insights can enhance forecasting systems, offering reliable and user-friendly hydrological predictions.
Ramesh Visweshwaran, Elizabeth Cooper, and Sarah L. Dance
EGUsphere, https://doi.org/10.5194/egusphere-2024-3980, https://doi.org/10.5194/egusphere-2024-3980, 2025
Preprint archived
Short summary
Short summary
We developed a new approach to improve soil moisture predictions, which are vital for managing floods, and droughts. Traditional methods focus on either adjusting the initial soil moisture condition or model parameters. Instead, we optimized both simultaneously using field-scale data from 16 UK sites. This combined approach improved prediction accuracy by 143 %, showing great potential for enhancing soil moisture forecasts to support agriculture, disaster response, and water management.
Elizabeth Cooper, Rich Ellis, Eleanor Blyth, and Simon Dadson
EGUsphere, https://doi.org/10.5194/egusphere-2023-1596, https://doi.org/10.5194/egusphere-2023-1596, 2023
Preprint archived
Short summary
Short summary
We have tested a different way of simulating soil moisture and river flow. Instead of dividing the land up into over 10,000 squares to run our numerical model, we cluster the land into fewer, irregular areas with similar landscape characteristics. We show that different ways of clustering the landscape produce different patterns of soil moisture. We also show that with this method we can we match observations as well as our usual gridded approach for ten times less computational resource.
Hollie M. Cooper, Emma Bennett, James Blake, Eleanor Blyth, David Boorman, Elizabeth Cooper, Jonathan Evans, Matthew Fry, Alan Jenkins, Ross Morrison, Daniel Rylett, Simon Stanley, Magdalena Szczykulska, Emily Trill, Vasileios Antoniou, Anne Askquith-Ellis, Lucy Ball, Milo Brooks, Michael A. Clarke, Nicholas Cowan, Alexander Cumming, Philip Farrand, Olivia Hitt, William Lord, Peter Scarlett, Oliver Swain, Jenna Thornton, Alan Warwick, and Ben Winterbourn
Earth Syst. Sci. Data, 13, 1737–1757, https://doi.org/10.5194/essd-13-1737-2021, https://doi.org/10.5194/essd-13-1737-2021, 2021
Short summary
Short summary
COSMOS-UK is a UK network of environmental monitoring sites, with a focus on measuring field-scale soil moisture. Each site includes soil and hydrometeorological sensors providing data including air temperature, humidity, net radiation, neutron counts, snow water equivalent, and potential evaporation. These data can provide information for science, industry, and agriculture by improving existing understanding and data products in fields such as water resources, space sciences, and biodiversity.
Ewan Pinnington, Javier Amezcua, Elizabeth Cooper, Simon Dadson, Rich Ellis, Jian Peng, Emma Robinson, Ross Morrison, Simon Osborne, and Tristan Quaife
Hydrol. Earth Syst. Sci., 25, 1617–1641, https://doi.org/10.5194/hess-25-1617-2021, https://doi.org/10.5194/hess-25-1617-2021, 2021
Short summary
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.
Gemma Coxon, Yanchen Zheng, Rafael Barbedo, Hollie Cooper, Felipe Fileni, Hayley J. Fowler, Matt Fry, Amy Green, Tom Gribbin, Helen Harfoot, Elizabeth Lewis, Germano Gondim Ribeiro Neto, Xiaobin Qiu, Saskia Salwey, and Doris E. Wendt
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-608, https://doi.org/10.5194/essd-2025-608, 2025
Preprint under review for ESSD
Short summary
Short summary
We present the second version of a large-sample catchment hydrology dataset for Great Britain. The dataset collates (1) climate, river flow and groundwater timeseries at monthly to hourly timescales, (2) catchment attributes characterising topography, climate, streamflow, land cover, soils, hydrogeology and human influences, and (3) catchment boundaries for 671 catchments across Great Britain. The dataset is publicly available to use in a wide range of environmental and modelling analyses.
Maliko Tanguy, Michael Eastman, Amulya Chevuturi, Eugene Magee, Elizabeth Cooper, Robert H. B. Johnson, Katie Facer-Childs, and Jamie Hannaford
Hydrol. Earth Syst. Sci., 29, 1587–1614, https://doi.org/10.5194/hess-29-1587-2025, https://doi.org/10.5194/hess-29-1587-2025, 2025
Short summary
Short summary
Our research compares two techniques, bias correction (BC) and data assimilation (DA), for improving river flow forecasts across 316 UK catchments. BC, which corrects errors after simulation, showed broad improvements, while DA, adjusting model states before forecast, excelled under specific conditions like snowmelt and high baseflows. Each method's unique strengths suit different scenarios. These insights can enhance forecasting systems, offering reliable and user-friendly hydrological predictions.
Ramesh Visweshwaran, Elizabeth Cooper, and Sarah L. Dance
EGUsphere, https://doi.org/10.5194/egusphere-2024-3980, https://doi.org/10.5194/egusphere-2024-3980, 2025
Preprint archived
Short summary
Short summary
We developed a new approach to improve soil moisture predictions, which are vital for managing floods, and droughts. Traditional methods focus on either adjusting the initial soil moisture condition or model parameters. Instead, we optimized both simultaneously using field-scale data from 16 UK sites. This combined approach improved prediction accuracy by 143 %, showing great potential for enhancing soil moisture forecasts to support agriculture, disaster response, and water management.
Emma L. Robinson, Matthew J. Brown, Alison L. Kay, Rosanna A. Lane, Rhian Chapman, Victoria A. Bell, and Eleanor M. Blyth
Earth Syst. Sci. Data, 15, 4433–4461, https://doi.org/10.5194/essd-15-4433-2023, https://doi.org/10.5194/essd-15-4433-2023, 2023
Short summary
Short summary
This work presents two new Penman–Monteith potential evaporation datasets for the UK, calculated with the same methodology applied to historical climate data (Hydro-PE HadUK-Grid) and an ensemble of future climate projections (Hydro-PE UKCP18 RCM). Both include an optional correction for evaporation of rain that lands on the surface of vegetation. The historical data are consistent with existing PE datasets, and the future projections include effects of rising atmospheric CO2 on vegetation.
Elizabeth Cooper, Rich Ellis, Eleanor Blyth, and Simon Dadson
EGUsphere, https://doi.org/10.5194/egusphere-2023-1596, https://doi.org/10.5194/egusphere-2023-1596, 2023
Preprint archived
Short summary
Short summary
We have tested a different way of simulating soil moisture and river flow. Instead of dividing the land up into over 10,000 squares to run our numerical model, we cluster the land into fewer, irregular areas with similar landscape characteristics. We show that different ways of clustering the landscape produce different patterns of soil moisture. We also show that with this method we can we match observations as well as our usual gridded approach for ten times less computational resource.
Camilla Mathison, Eleanor Burke, Andrew J. Hartley, Douglas I. Kelley, Chantelle Burton, Eddy Robertson, Nicola Gedney, Karina Williams, Andy Wiltshire, Richard J. Ellis, Alistair A. Sellar, and Chris D. Jones
Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, https://doi.org/10.5194/gmd-16-4249-2023, 2023
Short summary
Short summary
This paper describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle. We have created a new configuration and set-up for the JULES-ES land surface model, driven by bias-corrected historical and future climate model output provided by the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This allows us to compare projections of the impacts of climate change across multiple impact models and multiple sectors.
Thibault Hallouin, Richard J. Ellis, Douglas B. Clark, Simon J. Dadson, Andrew G. Hughes, Bryan N. Lawrence, Grenville M. S. Lister, and Jan Polcher
Geosci. Model Dev., 15, 9177–9196, https://doi.org/10.5194/gmd-15-9177-2022, https://doi.org/10.5194/gmd-15-9177-2022, 2022
Short summary
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.
Robert J. Parker, Chris Wilson, Edward Comyn-Platt, Garry Hayman, Toby R. Marthews, A. Anthony Bloom, Mark F. Lunt, Nicola Gedney, Simon J. Dadson, Joe McNorton, Neil Humpage, Hartmut Boesch, Martyn P. Chipperfield, Paul I. Palmer, and Dai Yamazaki
Biogeosciences, 19, 5779–5805, https://doi.org/10.5194/bg-19-5779-2022, https://doi.org/10.5194/bg-19-5779-2022, 2022
Short summary
Short summary
Wetlands are the largest natural source of methane, one of the most important climate gases. The JULES land surface model simulates these emissions. We use satellite data to evaluate how well JULES reproduces the methane seasonal cycle over different tropical wetlands. It performs well for most regions; however, it struggles for some African wetlands influenced heavily by river flooding. We explain the reasons for these deficiencies and highlight how future development will improve these areas.
Toby R. Marthews, Simon J. Dadson, Douglas B. Clark, Eleanor M. Blyth, Garry D. Hayman, Dai Yamazaki, Olivia R. E. Becher, Alberto Martínez-de la Torre, Catherine Prigent, and Carlos Jiménez
Hydrol. Earth Syst. Sci., 26, 3151–3175, https://doi.org/10.5194/hess-26-3151-2022, https://doi.org/10.5194/hess-26-3151-2022, 2022
Short summary
Short summary
Reliable data on global inundated areas remain uncertain. By matching a leading global data product on inundation extents (GIEMS) against predictions from a global hydrodynamic model (CaMa-Flood), we found small but consistent and non-random biases in well-known tropical wetlands (Sudd, Pantanal, Amazon and Congo). These result from known limitations in the data and the models used, which shows us how to improve our ability to make critical predictions of inundation events in the future.
Anna B. Harper, Karina E. Williams, Patrick C. McGuire, Maria Carolina Duran Rojas, Debbie Hemming, Anne Verhoef, Chris Huntingford, Lucy Rowland, Toby Marthews, Cleiton Breder Eller, Camilla Mathison, Rodolfo L. B. Nobrega, Nicola Gedney, Pier Luigi Vidale, Fred Otu-Larbi, Divya Pandey, Sebastien Garrigues, Azin Wright, Darren Slevin, Martin G. De Kauwe, Eleanor Blyth, Jonas Ardö, Andrew Black, Damien Bonal, Nina Buchmann, Benoit Burban, Kathrin Fuchs, Agnès de Grandcourt, Ivan Mammarella, Lutz Merbold, Leonardo Montagnani, Yann Nouvellon, Natalia Restrepo-Coupe, and Georg Wohlfahrt
Geosci. Model Dev., 14, 3269–3294, https://doi.org/10.5194/gmd-14-3269-2021, https://doi.org/10.5194/gmd-14-3269-2021, 2021
Short summary
Short summary
We evaluated 10 representations of soil moisture stress in the JULES land surface model against site observations of GPP and latent heat flux. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES. In addition, using soil matric potential presents the opportunity to include parameters specific to plant functional type to further improve modeled fluxes.
Hollie M. Cooper, Emma Bennett, James Blake, Eleanor Blyth, David Boorman, Elizabeth Cooper, Jonathan Evans, Matthew Fry, Alan Jenkins, Ross Morrison, Daniel Rylett, Simon Stanley, Magdalena Szczykulska, Emily Trill, Vasileios Antoniou, Anne Askquith-Ellis, Lucy Ball, Milo Brooks, Michael A. Clarke, Nicholas Cowan, Alexander Cumming, Philip Farrand, Olivia Hitt, William Lord, Peter Scarlett, Oliver Swain, Jenna Thornton, Alan Warwick, and Ben Winterbourn
Earth Syst. Sci. Data, 13, 1737–1757, https://doi.org/10.5194/essd-13-1737-2021, https://doi.org/10.5194/essd-13-1737-2021, 2021
Short summary
Short summary
COSMOS-UK is a UK network of environmental monitoring sites, with a focus on measuring field-scale soil moisture. Each site includes soil and hydrometeorological sensors providing data including air temperature, humidity, net radiation, neutron counts, snow water equivalent, and potential evaporation. These data can provide information for science, industry, and agriculture by improving existing understanding and data products in fields such as water resources, space sciences, and biodiversity.
Ewan Pinnington, Javier Amezcua, Elizabeth Cooper, Simon Dadson, Rich Ellis, Jian Peng, Emma Robinson, Ross Morrison, Simon Osborne, and Tristan Quaife
Hydrol. Earth Syst. Sci., 25, 1617–1641, https://doi.org/10.5194/hess-25-1617-2021, https://doi.org/10.5194/hess-25-1617-2021, 2021
Short summary
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.
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
Short summary
Short summary
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.
Cited articles
Antoniou, V., Askquith-Ellis, A., Bagnoli, S., Ball, L., Bennett, E., Blake,
J., Boorman, D., Brooks, M., Clarke, M., Cooper, H., Cowan, N., Cumming, A.,
Doughty, L., Evans, J., Farrand, P., Fry, M., Hewitt, N., Hitt, O., Jenkins,
A., Kral, F., Libre, J., Lord, W., Roberts, C., Morrison, R., Parkes, M.,
Nash, G., Newcomb, J., Rylett, D., Scarlett, P., Singer, A., Stanley, S.,
Swain, O., Thornton, J., Trill, E., Vincent, H., Ward, H., Warwick, A.,
Winterbourn, B., and Wright, G.: COSMOS-UK user guide: users' guide to sites,
instruments and available data (version 2.10), Tech. Rep., Wallingford,
http://nora.nerc.ac.uk/id/eprint/524801/ (last access: 5 August 2020), 2019. a, b, c, d
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 determination of soil
moisture: Measurements and theoretical approaches, J. Hydrol., 516, 231–244,
https://doi.org/10.1016/j.jhydrol.2014.02.026, 2014. a, b
Berghuijs, W. R., Harrigan, S., Molnar, P., Slater, L. J., and Kirchner, J. W.:
The Relative Importance of Different Flood-Generating Mechanisms Across
Europe, Water Resour. Res., 55, 4582–4593, https://doi.org/10.1029/2019WR024841,
2019. a
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a, b, c
Bogena, H. R., Huisman, J. A., Baatz, R., Hendricks Franssen, H.-J., and
Vereecken, H.: Accuracy of the cosmic-ray soil water content probe in humid
forest ecosystems: The worst case scenario, Water Resour. Res., 49,
5778–5791, https://doi.org/10.1002/wrcr.20463, 2013. a
Brooks, R. H. and Corey, A. T.: Hydraulic properties of porous media, Hydrological Papers 3, Colorado State Univ., Fort Collins, 1964. a
Brunetti, G., Šimůnek, J., Bogena, H., Baatz, R., Huisman, J. A.,
Dahlke, H., and Vereecken, H.: On the Information Content of Cosmic‐Ray
Neutron Data in the Inverse Estimation of Soil Hydraulic Properties, Vadose
Zone Journal, 18, 1–24, https://doi.org/10.2136/vzj2018.06.0123, 2019. a
Cooper, E. and Pinnington, E.: COSMOS-UK LAVENDAR Rose-suite repository, Met-Office trac system, available at: https://code.metoffice.gov.uk/trac/roses-u/browser/b/q/0/1/6/trunk (last access: 23 April 2021), 2020. a
De Lannoy, G. J. M. and Reichle, R. H.: Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model, Hydrol. Earth Syst. Sci., 20, 4895–4911, https://doi.org/10.5194/hess-20-4895-2016, 2016. a
Dharssi, I., Vidale, P.L.and Verhoef, A., Macpherson, B., Jones, C., and Best,
M.: New soil physical properties implemented in the Unified Model at PS18,
Met Office Technical Report Series,
available at: https://library.metoffice.gov.uk/Portal/Default/en-GB/RecordView/Index/251847( last access: 14 October 2020), 2009. a
Ehsan Bhuiyan, M. A., Nikolopoulos, E. I., Anagnostou, E. N., Polcher, J., Albergel, C., Dutra, E., Fink, G., Martínez-de la Torre, A., and Munier, S.: Assessment of precipitation error propagation in multi-model global water resource reanalysis, Hydrol. Earth Syst. Sci., 23, 1973–1994, https://doi.org/10.5194/hess-23-1973-2019, 2019. a
Evans, J. G., Ward, H. C., Blake, J. R., Hewitt, E. J., Morrison, R., Fry, M.,
Ball, L. A., Doughty, L. C., Libre, J. W., Hitt, O. E., Rylett, D., Ellis,
R. J., Warwick, A. C., Brooks, M., Parkes, M. A., Wright, G. M. H., Singer,
A. C., Boorman, D. B., and Jenkins, A.: Soil water content in southern
England derived from a cosmic-ray soil moisture observing system –
COSMOS-UK, Hydrol. Proc., 30, 4987–4999, https://doi.org/10.1002/hyp.10929,
2016. a, b, c
Fischer, G., Nachtergaele, F., Prieler, S., Van Velthuizen, H., Verelst, L.,
and Wiberg, D.: Global agro-ecological zones assessment for agriculture
(GAEZ 2008), IIASA, Laxenburg, Austria and FAO, Rome, Italy, 10,
available at: http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/ (last access: 5 August 2020), 2008. a
Fowler, A. M., Dance, S. L., and Waller, J. A.: On the interaction of
observation and prior error correlations in data assimilation, Q. J. Roy. Meteor. Soc., 144, 48–62,
https://doi.org/10.1002/qj.3183, 2018. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Han, X., Franssen, H.-J. H., Rosolem, R., Jin, R., Li, X., and Vereecken, H.: Correction of systematic model forcing bias of CLM using assimilation of cosmic-ray Neutrons and land surface temperature: a study in the Heihe Catchment, China, Hydrol. Earth Syst. Sci., 19, 615–629, https://doi.org/10.5194/hess-19-615-2015, 2015. a
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M.,
Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X.,
Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A.,
Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and
Kempen, B.: SoilGrids250m: Global gridded soil information based on machine
learning, PLOS ONE, 12, 1–40, 2017. a, b
Hilton, F., Collard, A., Guidard, V., Randriamampianina, R., and Schwaerz, M.:
ECMWF/EUMETSAT NWP-SAF Workshop on the assimilation of IASI in NWP,
Assimilation of IASI Radiances at European NWP Centres, Tech. Rep.,
available at: https://www.ecmwf.int/sites/default/files/elibrary/2009/9896-assimilation-iasi-radiances-european-nwp-centres.pdf (last access: 5 August 2020), 2009. a
Hodnett, M. G. and Tomasella, J.: Marked differences between van Genuchten
soil water-retention parameters for temperate and tropical soils: a new
water-retention pedo-transfer functions developed for tropical soils,
Geoderma, 108, 155–180, https://doi.org/10.1016/S0016-7061(02)00105-2,
2002. a, b
Holtan, H.: Moisture-tension data for selected soils on experimental
watersheds, Agricultural Research Service, U.S. Dept. of Agriculture, 1–11, 1968. a
JULES user guide, A.: JULES documentation,
available at: http://jules-lsm.github.io/latest/index.html, last access: 5 August 2020. a
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash-Sutcliffe and Kling-Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019. a, b
Köhli, M., Schrön, M., Zreda, M., Schmidt, U., Dietrich, P., and
Zacharias, S.: Footprint characteristics revised for field-scale soil
moisture monitoring with cosmic-ray neutrons, Water Resour. Res., 51,
5772–5790, https://doi.org/10.1002/2015WR017169, 2015. a, b
Koster, R., Mahanama, S., Livneh, B., Lettenmaier, D., and Reichle, R.: Skill
in streamflow forecasts derived from large-scale estimates of soil moisture
and snow, Nat. Geosci., 3, 613–616, https://doi.org/10.1038/ngeo944, 2010. a
Liu, C., Xiao, Q., and Wang, B.: An Ensemble-Based Four-Dimensional
Variational Data Assimilation Scheme, Part I: Technical Formulation and
Preliminary Test, Monthly Weather Rev., 136, 3363–3373,
https://doi.org/10.1175/2008MWR2312.1, 2008. a, b
Liu, Q., Reichle, R. H., Bindlish, R., Cosh, M. H., Crow, W. T., De Jeu, R.,
De Lannoy, G. J., Huffman, G. J., and Jackson, T. J.: The contributions of
precipitation and soil moisture observations to the skill of soil moisture
estimates in a land data assimilation system, J. Hydrometeorol.,
12, 750–765, https://doi.org/10.1175/JHM-D-10-05000.1, 2011. a
Marthews, T. R., Quesada, C. A., Galbraith, D. R., Malhi, Y., Mullins, C. E., Hodnett, M. G., and Dharssi, I.: High-resolution hydraulic parameter maps for surface soils in tropical South America, Geosci. Model Dev., 7, 711–723, https://doi.org/10.5194/gmd-7-711-2014, 2014. a, b
Martínez-de la Torre, A., Blyth, E. M., and Weedon, G. P.: Using observed river flow data to improve the hydrological functioning of the JULES land surface model (vn4.3) used for regional coupled modelling in Great Britain (UKC2), Geosci. Model Dev., 12, 765–784, https://doi.org/10.5194/gmd-12-765-2019, 2019. a
Mwangi, S., Zeng, Y., Montzka, C., Yu, L., and Su, Z.: Assimilation of
Cosmic-Ray Neutron Counts for the Estimation of Soil Ice Content on the
Eastern Tibetan Plateau, J. Geophys. Res.-Atmos., 125, e2019JD031529
https://doi.org/10.1029/2019JD031529, 2020. a
Pinnington, E.: pyearthsci/lavendar: First release of LaVEnDAR software (Version v1.0.0), Zenodo, https://doi.org/10.5281/zenodo.2654853 (last access: 23 April 2021), 2019. a
Pinnington, E., Quaife, T., and Black, E.: Impact of remotely sensed soil moisture and precipitation on soil moisture prediction in a data assimilation system with the JULES land surface model, Hydrol. Earth Syst. Sci., 22, 2575–2588, https://doi.org/10.5194/hess-22-2575-2018, 2018. a, b
Pinnington, E., Quaife, T., Lawless, A., Williams, K., Arkebauer, T., and Scoby, D.: The Land Variational Ensemble Data Assimilation Framework: LAVENDAR v1.0.0, Geosci. Model Dev., 13, 55–69, https://doi.org/10.5194/gmd-13-55-2020, 2020. a, b, c
Rawls, W.: Calibration of selected infiltration equations for the Georgia
Coastal Plain, 1–15, 1976. a
Ritchie, P. D. L., Harper, A. B., Smith, G. S., Kahana, R., Kendon, E. J.,
Lewis, H., Fezzi, C., Halleck-Vega, S., Boulton, C. A., Bateman, I. J., and
Lenton, T. M.: Large changes in Great Britain's vegetation and agricultural
land-use predicted under unmitigated climate change, Environ. Res. Lett., 14, 114012, https://doi.org/10.1088/1748-9326/ab492b, 2019. a
Schaap, M. G., Leij, F. J., and van Genuchten, M. T.: ROSETTA: a computer
program for estimating soil hydraulic parameters with hierarchical
pedotransfer functions, J. Hydrol., 251, 163–176,
https://doi.org/10.1016/S0022-1694(01)00466-8, 2001. a
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B.,
Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil
moisture–climate interactions in a changing climate: A review,
Earth-Sci. Rev., 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004,
2010. a
Shuttleworth, J., Rosolem, R., Zreda, M., and Franz, T.: The COsmic-ray Soil Moisture Interaction Code (COSMIC) for use in data assimilation, Hydrol. Earth Syst. Sci., 17, 3205–3217, https://doi.org/10.5194/hess-17-3205-2013, 2013. a
Stanley, S., Antoniou, V., Askquith-Ellis, A., Ball, L. A., Bennett, E. S., Blake, J. R., Boorman, D. B., Brooks, M., Clarke, M., Cooper, H. M., Cowan, N.; Cumming, A., Evans, J. G., Farrand, P., Fry, M., Hitt, O. E., Lord, W. D., Morrison, R., Nash, G. V., Rylett, D., Scarlett, P. M., Swain, O. D., Szczykulska, M., Thornton, J. L., Trill, E. J., Warwick, A. C., and Winterbourn, B.: Daily and sub-daily hydrometeorological and soil data (2013–2019) [COSMOS-UK], NERC Environmental Information Data Centre [data set], available at: https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185, last access: 23 April 2021. a, b, c, d
Van Looy, K., Bouma, J., Herbst, M., Koestel, J., Minasny, B., Mishra, U.,
Montzka, C., Nemes, A., Pachepsky, Y. A., Padarian, J., Schaap, M. G.,
Tóth, B., Verhoef, A., Vanderborght, J., van der Ploeg, M. J.,
Weihermüller, L., Zacharias, S., Zhang, Y., and Vereecken, H.:
Pedotransfer Functions in Earth System Science: Challenges and
Perspectives, Rev. Geophys., 55, 1199–1256,
https://doi.org/10.1002/2017RG000581, 2017.
a, b
Wösten, J. H. M., Lilly, A., Nemes, A., and Bas, C. L.: Development and
use of a database of hydraulic properties of European soils, Geoderma, 90,
169–185, https://doi.org/10.1016/S0016-7061(98)00132-3, 1999. a
Yang, K., Zhu, L., Chen, Y., Zhao, L., Qin, J., Lu, H., Tang, W., Han, M.,
Ding, B., and Fang, N.: Land surface model calibration through microwave
data assimilation for improving soil moisture simulations, J. Hydrol., 533, 266–276, https://doi.org/10.1016/j.jhydrol.2015.12.018, 2016. a
Zhao, H., Zeng, Y., Lv, S., and Su, Z.: Analysis of soil hydraulic and thermal properties for land surface modeling over the Tibetan Plateau, Earth Syst. Sci. Data, 10, 1031–1061, https://doi.org/10.5194/essd-10-1031-2018, 2018. a
Short summary
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.
Soil moisture estimates from land surface models are important for forecasting floods, droughts,...
