Articles | Volume 25, issue 3
https://doi.org/10.5194/hess-25-1617-2021
© 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-25-1617-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Mar 2021
Research article |
| 31 Mar 2021
| 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
CORRESPONDING AUTHOR
National Centre for Earth Observation, Department of Meteorology, University of Reading, Reading, UK
Javier Amezcua
National Centre for Earth Observation, Department of Meteorology, University of Reading, Reading, UK
Elizabeth Cooper
UK Centre for Ecology and Hydrology, Wallingford, UK
Simon Dadson
UK Centre for Ecology and Hydrology, Wallingford, UK
School of Geography and the Environment, University of Oxford, Oxford, UK
Rich Ellis
UK Centre for Ecology and Hydrology, Wallingford, UK
Jian Peng
Department of Remote Sensing, Helmholtz Centre for Environmental Research – UFZ, Permoserstraße 15, 04318 Leipzig, Germany
Remote Sensing Centre for Earth System Research, Leipzig University, 04103 Leipzig, Germany
Emma Robinson
UK Centre for Ecology and Hydrology, Wallingford, UK
Ross Morrison
UK Centre for Ecology and Hydrology, Wallingford, UK
Simon Osborne
Met Office Field Site, Cardington Airfield, Shortstown, Bedford, UK
Tristan Quaife
National Centre for Earth Observation, Department of Meteorology, University of Reading, Reading, UK
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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.
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Bethan L. Harris, Tristan Quaife, Christopher M. Taylor, and Phil P. Harris
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Solomon H. Gebrechorkos, Jian Peng, Ellen Dyer, Diego G. Miralles, Sergio M. Vicente-Serrano, Chris Funk, Hylke E. Beck, Dagmawi T. Asfaw, Michael B. Singer, and Simon J. Dadson
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Emma L. Robinson, Chris Huntingford, Valyaveetil Shamsudheen Semeena, and James M. Bullock
Earth Syst. Sci. Data, 15, 5371–5401, https://doi.org/10.5194/essd-15-5371-2023, https://doi.org/10.5194/essd-15-5371-2023, 2023
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Samantha Petch, Bo Dong, Tristan Quaife, Robert P. King, and Keith Haines
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Karina von Schuckmann, Audrey Minière, Flora Gues, Francisco José Cuesta-Valero, Gottfried Kirchengast, Susheel Adusumilli, Fiammetta Straneo, Michaël Ablain, Richard P. Allan, Paul M. Barker, Hugo Beltrami, Alejandro Blazquez, Tim Boyer, Lijing Cheng, John Church, Damien Desbruyeres, Han Dolman, Catia M. Domingues, Almudena García-García, Donata Giglio, John E. Gilson, Maximilian Gorfer, Leopold Haimberger, Maria Z. Hakuba, Stefan Hendricks, Shigeki Hosoda, Gregory C. Johnson, Rachel Killick, Brian King, Nicolas Kolodziejczyk, Anton Korosov, Gerhard Krinner, Mikael Kuusela, Felix W. Landerer, Moritz Langer, Thomas Lavergne, Isobel Lawrence, Yuehua Li, John Lyman, Florence Marti, Ben Marzeion, Michael Mayer, Andrew H. MacDougall, Trevor McDougall, Didier Paolo Monselesan, Jan Nitzbon, Inès Otosaka, Jian Peng, Sarah Purkey, Dean Roemmich, Kanako Sato, Katsunari Sato, Abhishek Savita, Axel Schweiger, Andrew Shepherd, Sonia I. Seneviratne, Leon Simons, Donald A. Slater, Thomas Slater, Andrea K. Steiner, Toshio Suga, Tanguy Szekely, Wim Thiery, Mary-Louise Timmermans, Inne Vanderkelen, Susan E. Wjiffels, Tonghua Wu, and Michael Zemp
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Thibault Hallouin, Richard J. Ellis, Douglas B. Clark, Simon J. Dadson, Andrew G. Hughes, Bryan N. Lawrence, Grenville M. S. Lister, and Jan Polcher
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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
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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.
Bimal K. Bhattacharya, Kaniska Mallick, Devansh Desai, Ganapati S. Bhat, Ross Morrison, Jamie R. Clevery, William Woodgate, Jason Beringer, Kerry Cawse-Nicholson, Siyan Ma, Joseph Verfaillie, and Dennis Baldocchi
Biogeosciences, 19, 5521–5551, https://doi.org/10.5194/bg-19-5521-2022, https://doi.org/10.5194/bg-19-5521-2022, 2022
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Evaporation retrieval in heterogeneous ecosystems is challenging due to empirical estimation of ground heat flux and complex parameterizations of conductances. We developed a parameter-sparse coupled ground heat flux-evaporation model and tested it across different limits of water stress and vegetation fraction in the Northern/Southern Hemisphere. The model performed particularly well in the savannas and showed good potential for evaporative stress monitoring from thermal infrared satellites.
Joshua Chun Kwang Lee, Javier Amezcua, and Ross Noel Bannister
Geosci. Model Dev., 15, 6197–6219, https://doi.org/10.5194/gmd-15-6197-2022, https://doi.org/10.5194/gmd-15-6197-2022, 2022
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In this article, we implement a novel data assimilation method for the ABC–DA system which combines traditional data assimilation approaches in a hybrid approach. We document the technical development and test the hybrid approach in idealised experiments within a tropical framework of the ABC–DA system. Our findings indicate that the hybrid approach outperforms individual traditional approaches. Its potential benefits have been highlighted and should be explored further within this framework.
Shijie Li, Guojie Wang, Chenxia Zhu, Jiao Lu, Waheed Ullah, Daniel Fiifi Tawia Hagan, Giri Kattel, and Jian Peng
Hydrol. Earth Syst. Sci., 26, 3691–3707, https://doi.org/10.5194/hess-26-3691-2022, https://doi.org/10.5194/hess-26-3691-2022, 2022
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We found that the precipitation variability dominantly controls global evapotranspiration (ET) in dry climates, while the net radiation has substantial control over ET in the tropical regions, and vapor pressure deficit (VPD) impacts ET trends in boreal mid-latitude climate. The critical role of VPD in controlling ET trends is particularly emphasized due to its influence in controlling the carbon–water–energy cycle.
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
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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.
Chandan Sarangi, TC Chakraborty, Sachchidanand Tripathi, Mithun Krishnan, Ross Morrison, Jonathan Evans, and Lina M. Mercado
Atmos. Chem. Phys., 22, 3615–3629, https://doi.org/10.5194/acp-22-3615-2022, https://doi.org/10.5194/acp-22-3615-2022, 2022
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Transpiration fluxes by vegetation are reduced under heat stress to conserve water. However, in situ observations over northern India show that the strength of the inverse association between transpiration and atmospheric vapor pressure deficit is weakening in the presence of heavy aerosol loading. This finding not only implicates the significant role of aerosols in modifying the evaporative fraction (EF) but also warrants an in-depth analysis of the aerosol–plant–temperature–EF continuum.
Jiao Lu, Guojie Wang, Tiexi Chen, Shijie Li, Daniel Fiifi Tawia Hagan, Giri Kattel, Jian Peng, Tong Jiang, and Buda Su
Earth Syst. Sci. Data, 13, 5879–5898, https://doi.org/10.5194/essd-13-5879-2021, https://doi.org/10.5194/essd-13-5879-2021, 2021
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This study has combined three existing land evaporation (ET) products to obtain a single framework of a long-term (1980–2017) daily ET product at a spatial resolution of 0.25° to define the global proxy ET with lower uncertainties. The merged product is the best at capturing dynamics over different locations and times among all data sets. The merged product performed well over a range of vegetation cover scenarios and also captured the trend of land evaporation over different areas well.
Xiaolu Ling, Ying Huang, Weidong Guo, Yixin Wang, Chaorong Chen, Bo Qiu, Jun Ge, Kai Qin, Yong Xue, and Jian Peng
Hydrol. Earth Syst. Sci., 25, 4209–4229, https://doi.org/10.5194/hess-25-4209-2021, https://doi.org/10.5194/hess-25-4209-2021, 2021
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Soil moisture (SM) plays a critical role in the water and energy cycles of the Earth system, for which a long-term SM product with high quality is urgently needed. In situ observations are generally treated as the true value to systematically evaluate five SM products, including one remote sensing product and four reanalysis data sets during 1981–2013. This long-term intercomparison study provides clues for SM product enhancement and further hydrological applications.
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
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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.
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
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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.
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.
Gemma Coxon, Nans Addor, John P. Bloomfield, Jim Freer, Matt Fry, Jamie Hannaford, Nicholas J. K. Howden, Rosanna Lane, Melinda Lewis, Emma L. Robinson, Thorsten Wagener, and Ross Woods
Earth Syst. Sci. Data, 12, 2459–2483, https://doi.org/10.5194/essd-12-2459-2020, https://doi.org/10.5194/essd-12-2459-2020, 2020
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We present the first large-sample catchment hydrology dataset for Great Britain. The dataset collates river flows, catchment attributes, and catchment boundaries for 671 catchments across Great Britain. We characterise the topography, climate, streamflow, land cover, soils, hydrogeology, human influence, and discharge uncertainty of each catchment. The dataset is publicly available for the community to use in a wide range of environmental and modelling analyses.
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
Bateni, S. M. and Entekhabi, D.: Relative efficiency of land surface energy
balance components, Water Resour. Res., 48, W04510, https://doi.org/10.1029/2011WR011357, 2012. a
Beljaars, A. C. M., Viterbo, P., Miller, M. J., and Betts, A. K.: The Anomalous
Rainfall over the United States during July 1993: Sensitivity to Land Surface
Parameterization and Soil Moisture Anomalies, Mon. Weather Rev., 124,
362–383, https://doi.org/10.1175/1520-0493(1996)124<0362:TAROTU>2.0.CO;2, 1996. 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
Beven, K.: How far can we go in distributed hydrological modelling?, Hydrol. Earth Syst. Sci., 5, 1–12, https://doi.org/10.5194/hess-5-1-2001, 2001. a
Beven, K. and Binley, A.: The future of distributed models: Model calibration
and uncertainty prediction, Hydrol. Process., 6, 279–298,
https://doi.org/10.1002/hyp.3360060305, 1992. a
Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical Aspects, Mon. Weather Rev., 129, 420–436, https://doi.org/10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2, 2001. a
Bocquet, M. and Sakov, P.: Joint state and parameter estimation with an iterative ensemble Kalman smoother, Nonlin. Processes Geophys., 20, 803–818, https://doi.org/10.5194/npg-20-803-2013, 2013. a
Bogena, H. R., Huisman, J. A., Güntner, A., Hübner, C., Kusche, J., Jonard,
F., Vey, S., and Vereecken, H.: Emerging methods for noninvasive sensing of
soil moisture dynamics from field to catchment scale: a review, WIREs Water,
2, 635–647, https://doi.org/10.1002/wat2.1097, 2015. a
Bormann, N., Bonavita, M., Dragani, R., Eresmaa, R., Matricardi, M., and
McNally, A.: Enhancing the impact of IASI observations through an updated
observation error covariance matrix, ECMWF Technical Memorandum Number 756, ECMWF, https://doi.org/10.21957/gq8j2gjp7, 2015. a
Botto, A., Belluco, E., and Camporese, M.: Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope, Hydrol. Earth Syst. Sci., 22, 4251–4266, https://doi.org/10.5194/hess-22-4251-2018, 2018. a
Chen, F., Crow, W. T., Bindlish, R., Colliander, A., Burgin, M. S., Asanuma,
J., and Aida, K.: Global-scale evaluation of SMAP, SMOS and ASCAT soil
moisture products using triple collocation, Remote Sens. Environ.,
214, 1–13, https://doi.org/10.1016/j.rse.2018.05.008, 2018. a
Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M. J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O., Harding, R. J., Huntingford, C., and Cox, P. M.: The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics, Geosci. Model Dev., 4, 701–722, https://doi.org/10.5194/gmd-4-701-2011, 2011. a, b
Clark, P., Roberts, N., Lean, H., Ballard, S. P., and Charlton-Perez, C.:
Convection-permitting models: a step-change in rainfall forecasting,
Meteorol. Appl., 23, 165–181, https://doi.org/10.1002/met.1538, 2016. a
Colliander, A., Jackson, T., Bindlish, R., Chan, S., Das, N., Kim, S., Cosh,
M., Dunbar, R., Dang, L., Pashaian, L., Asanuma, J., Aida, K., Berg, A.,
Rowlandson, T., Bosch, D., Caldwell, T., Caylor, K., Goodrich, D., al
Jassar, H., Lopez-Baeza, E., Martínez-Fernàndez, J.,
Gonzàlez-Zamora, A., Livingston, S., McNairn, H., Pacheco, A., Moghaddam,
M., Montzka, C., Notarnicola, C., Niedrist, G., Pellarin, T., Prueger, J.,
Pulliainen, J., Rautiainen, K., Ramos, J., Seyfried, M., Starks, P., Su, Z.,
Zeng, Y., van der Velde, R., Thibeault, M., Dorigo, W., Vreugdenhil, M.,
Walker, J., Wu, X., Monerris, A., O'Neill, P., Entekhabi, D., Njoku, E., and
Yueh, S.: Validation of SMAP surface soil moisture products with core
validation sites, Remote Sens. Environ., 191, 215–231,
https://doi.org/10.1016/j.rse.2017.01.021, 2017. a, b, c
Cosby, B. J., Hornberger, G. M., Clapp, R. B., and Ginn, T. R.: A Statistical
Exploration of the Relationships of Soil Moisture Characteristics to the
Physical Properties of Soils, Water Resour. Res., 20, 682–690,
https://doi.org/10.1029/WR020i006p00682, 1984. a, b
Courtier, P., Thépaut, J.-N., and Hollingsworth, A.: A strategy for
operational implementation of 4D-Var, using an incremental approach,
Q. J. Roy. Meteor. Soc., 120, 1367–1387,
https://doi.org/10.1002/qj.49712051912, 1994. 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
Desilets, D. and Zreda, M.: Footprint diameter for a cosmic-ray soil moisture
probe: Theory and Monte Carlo simulations, Water Resour. Res., 49,
3566–3575, https://doi.org/10.1002/wrcr.20187, 2013. a
Draper, C. S., Reichle, R. H., De Lannoy, G. J. M., and Liu, Q.: Assimilation
of passive and active microwave soil moisture retrievals, Geophys. Res. Lett., 39, L04401, https://doi.org/10.1029/2011GL050655, 2012. a
Duygu, M. B. and Akyürek, Z.: Using Cosmic-Ray Neutron Probes in Validating
Satellite Soil Moisture Products and Land Surface Models, Water, 11, 1362,
https://doi.org/10.3390/w11071362, 2019. a
Entekhabi, D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H., Crow,
W. T., Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson,
T. J., Johnson, J., Kimball, J., Piepmeier, J. R., Koster, R. D.,
Martin, N., McDonald, K. C., Moghaddam, M., Moran, S., Reichle, R.,
Shi, J. C., Spencer, M. W., Thurman, S. W., Tsang, L., and Van Zyl,
J.: The Soil Moisture Active Passive (SMAP) Mission, P. IEEE,
98, 704–716, 2010. a, b, c
Evensen, G.: The Ensemble Kalman Filter: theoretical formulation and practical
implementation, Ocean Dynam., 53, 343–367,
https://doi.org/10.1007/s10236-003-0036-9, 2003. a, b
Evensen, G.: Analysis of iterative ensemble smoothers for solving inverse
problems, Computat. Geosci., 22, 885–908,
https://doi.org/10.1007/s10596-018-9731-y, 2018. 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. Process., 30, 4987–4999, https://doi.org/10.1002/hyp.10929,
2016. a
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, and FAO, Rome, Italy, available at:
http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/
(last access: 28 April 2020), 2008. a, b, c, d, e
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
Fratini, G. and Mauder, M.: Towards a consistent eddy-covariance processing: an intercomparison of EddyPro and TK3, Atmos. Meas. Tech., 7, 2273–2281, https://doi.org/10.5194/amt-7-2273-2014, 2014. a
Han, X., Franssen, H.-J. H., Montzka, C., and Vereecken, H.: Soil moisture and
soil properties estimation in the Community Land Model with synthetic
brightness temperature observations, Water Resour. Res., 50,
6081–6105, https://doi.org/10.1002/2013WR014586, 2014. a, b, c, d
Hauser, M., Orth, R., and Seneviratne, S. I.: Investigating soil moisture–climate interactions with prescribed soil moisture experiments: an assessment with the Community Earth System Model (version 1.2), Geosci. Model Dev., 10, 1665–1677, https://doi.org/10.5194/gmd-10-1665-2017, 2017. a
Hilton, F., Collard, A., Guidard, V., Randriamampianina, R., and Schwaerz, M.: Assimilation of IASI radiances at European NWP centres, available at:
https://www.ecmwf.int/node/15331 (last access: 29 March 2021), 2009. a
Houtekamer, P. L. and Mitchell, H. L.: Data Assimilation Using an Ensemble
Kalman Filter Technique, Mon. Weather Rev., 126, 796–811,
https://doi.org/10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2, 1998. a
Howes, K. E., Fowler, A. M., and Lawless, A. S.: Accounting for model error in
strong-constraint 4D-Var data assimilation, Q. J.
Meteor. Soc., 143, 1227–1240, https://doi.org/10.1002/qj.2996, 2017. a
Hunt, B. R., Kostelich, E. J., and Szunyogh, I.: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica D, 230, 112–126, 2007. a
Kawanishi, T., Sezai, T., Ito, Y., Imaoka, K., Takeshima, T.,
Ishido, Y., Shibata, A., Miura, M., Inahata, H., and Spencer,
R. W.: The Advanced Microwave Scanning Radiometer for the Earth Observing
System (AMSR-E), NASDA's contribution to the EOS for global energy and water
cycle studies, IEEE T. Geosci. Remote, 41,
184–194, https://doi.org/10.1109/TGRS.2002.808331, 2003. a
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, c
Kolassa, J., Reichle, R., and Draper, C.: Merging active and passive microwave
observations in soil moisture data assimilation, Remote Sens.
Environ., 191, 117–130, 2017. a
Li, C., Lu, H., Yang, K., Han, M., Wright, J., Chen, Y., Yu, L., Xu, S., Huang,
X., and Gong, W.: The Evaluation of SMAP Enhanced Soil Moisture Products
Using High-Resolution Model Simulations and In-Situ Observations on the
Tibetan Plateau, Remote Sens., 10, 535, https://doi.org/10.3390/rs10040535, 2018. a
Lievens, H., Reichle, R. H., Liu, Q., De Lannoy, G. J. M., Dunbar, R. S., Kim,
S. B., Das, N. N., Cosh, M., Walker, J. P., and Wagner, W.: Joint Sentinel-1
and SMAP data assimilation to improve soil moisture estimates, Geophys.
Res. Lett., 44, 6145–6153, https://doi.org/10.1002/2017GL073904, 2017. a
Liu, Q., Reichle, R. H., Bindlish, R., Cosh, M. H., Crow, W. T., de Jeu, R.,
De Lannoy, G. J. M., 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
Lorenc, A. C. and Rawlins, F.: Why does 4D-Var beat 3D-Var?, Q. J.
Roy. Meteor. Soc., 131, 3247–3257, https://doi.org/10.1256/qj.05.85,
2005. 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
Martinez-de la Torre, A., Blyth, E., and Robinson, E.: Water, carbon and energy fluxes simulation for Great Britain using the JULES Land Surface Model and the Climate Hydrology and Ecology research Support System, meteorology dataset (1961–2015) [CHESS-land],
https://doi.org/10.5285/c76096d6-45d4-4a69-a310-4c67f8dcf096, 2018. a
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
Maurer, E. P. and Lettenmaier, D. P.: Potential Effects of Long-Lead Hydrologic Predictability on Missouri River Main-Stem Reservoirs, J. Climate, 17, 174–186, https://doi.org/10.1175/1520-0442(2004)017<0174:PEOLHP>2.0.CO;2, 2004. a
Minamide, M. and Zhang, F.: Adaptive Observation Error Inflation for
Assimilating All-Sky Satellite Radiance, Mon. Weather Rev., 145,
1063–1081, https://doi.org/10.1175/MWR-D-16-0257.1, 2017. a
Mizukami, N., Clark, M. P., Newman, A. J., Wood, A. W., Gutmann, E. D.,
Nijssen, B., Rakovec, O., and Samaniego, L.: Towards seamless large-domain
parameter estimation for hydrologic models, Water Resour. Res., 53,
8020–8040, https://doi.org/10.1002/2017WR020401, 2017. a
Montzka, C., Moradkhani, H., Weihermüller, L., Franssen, H.-J. H., Canty,
M., and Vereecken, H.: Hydraulic parameter estimation by remotely-sensed top
soil moisture observations with the particle filter, J. Hydrol.,
399, 410–421, https://doi.org/10.1016/j.jhydrol.2011.01.020, 2011. a
Montzka, C., Bogena, H. R., Zreda, M., Monerris, A., Morrison, R., Muddu, S.,
and Vereecken, H.: Validation of Spaceborne and Modelled Surface Soil
Moisture Products with Cosmic-Ray Neutron Probes, Remote Sens., 9, 103,
https://doi.org/10.3390/rs9020103, 2017. a
Moradkhani, H., Sorooshian, S., Gupta, H. V., and Houser, P. R.: Dual
state-parameter estimation of hydrological models using ensemble Kalman
filter, Adv. Water Resour., 28, 135–147,
https://doi.org/10.1016/j.advwatres.2004.09.002, 2005. a, b
Morrison, R., Cooper, H., Cumming, A., Evans, C., Thornton, J., Winterbourn,
J., Rylett, D., and Jones, D.: Eddy covariance measurements of carbon
dioxide, energy and water fluxes at a cropland and a grassland on lowland
peat soils, East Anglia, UK, 2016–2019, UK Centre for Ecology and Hydrology data set,
https://doi.org/10.5285/2fe84b80-117a-4b19-a1f5-71bbd1dba9c9, 2020. a
Nearing, G. S., Moran, M. S., Thorp, K. R., Collins, C. D. H., and Slack,
D. C.: Likelihood parameter estimation for calibrating a soil moisture model
using radar bakscatter, Remote Sens. Environ., 114, 2564–2574,
https://doi.org/10.1016/j.rse.2010.05.031, 2010. a, b
Osborne, S. R. and Weedon, G. P.: Observations and Modeling of
Evapotranspiration and Dewfall during the 2018 Meteorological Drought in
Southern England, J. Hydrometeorol., 22, 279–295, https://doi.org/10.1175/JHM-D-20-0148.1, 2021. a
Papale, D., Reichstein, M., Aubinet, M., Canfora, E., Bernhofer, C., Kutsch, W., Longdoz, B., Rambal, S., Valentini, R., Vesala, T., and Yakir, D.: Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation, Biogeosciences, 3, 571–583, https://doi.org/10.5194/bg-3-571-2006, 2006. a
Peng, J., Pinnington, E., Robinson, E., Evans, J., Quaife, T., Harris, P., and Blyth, E.: A high-resolution soil moisture dataset from merged model and Earth observation data in Great Britain, Remote Sens. Environ., in review, 2021. a
Pinnington, E.: pyearthsci/lavendar: First release of LaVEnDAR software (Version v1.0.0), Zenodo, https://doi.org/10.5281/zenodo.2654853, 2019. a
Pinnington, E.: LAVENDAR Rose-suite repository, Met-Office trac system, availalbe at: https://code.metoffice.gov.uk/trac/roses-u/browser/b/q/3/5/7/trunk (last access: 29 March 2021), 2020. 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, c
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
Pinnington, E. M., Casella, E., Dance, S. L., Lawless, A. S., Morison, J. I.,
Nichols, N. K., Wilkinson, M., and Quaife, T. L.: Investigating the role of
prior and observation error correlations in improving a model forecast of
forest carbon balance using Four-dimensional Variational data assimilation,
Agr. Forest Meteorol., 228/229, 299–314,
https://doi.org/10.1016/j.agrformet.2016.07.006, 2016. a
Pitman, A. J., Henderson-Sellers, A., Desborough, C. E., Yang, Z. L.,
Abramopoulos, F., Boone, A., Dickinson, R. E., Gedney, N., Koster, R.,
Kowalczyk, E., Lettenmaier, D., Liang, X., Mahfouf, J. F., Noilhan, J.,
Polcher, J., Qu, W., Robock, A., Rosenzweig, C., Schlosser, C. A., Shmakin,
A. B., Smith, J., Suarez, M., Verseghy, D., Wetzel, P., Wood, E., and Xue,
Y.: Key results and implications from phase 1(c) of the Project for
Intercomparison of Land-surface Parametrization Schemes, Clim. Dynam.,
15, 673–684, https://doi.org/10.1007/s003820050309, 1999. a
Reichle, R. H., De Lannoy, G. J. M., Liu, Q., Ardizzone, J. V., Colliander, A., Conaty, A., Crow, W., Jackson, T. J., Jones, L. A., Kimball, J. S., Koster, R. D., Mahanama, S. P., Smith, E. B., Berg, A., Bircher, S., Bosch, D., Caldwell, T. G., Cosh, M., Gonzàlez-Zamora, A., Holifield Collins, C. D., Jensen, K. H., Livingston, S., Lopez-Baeza, E., Martínez-Fernàndez, J., McNairn, H., Moghaddam, M., Pacheco, A., Pellarin, T., Prueger, J., Rowlandson, T., Seyfried, M., Starks, P., Su, Z., Thibeault, M., van der Velde, R., Walker, J., Wu, X., and Zeng, Y.: Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements, J. Hydrometeorol., 18, 2621–2645, https://doi.org/10.1175/JHM-D-17-0063.1, 2017. a
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier,
P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grünwald, T.,
Havránková, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila,
A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-M.,
Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G.,
Vaccari, F., Vesala, T., Yakir, D., and Valentini, R.: On the separation of
net ecosystem exchange into assimilation and ecosystem respiration: review
and improved algorithm, Glob. Change Biol., 11, 1424–1439,
https://doi.org/10.1111/j.1365-2486.2005.001002.x, 2005. a, b
Reichstein, M., Moffat, A., Wutzler, T., and Sickel, K.: REddyProc: Data
processing and plotting utilities of (half-) hourly eddy-covariance
measurements, R package version 0.6-0/r9, available at: https://cran.r-project.org/web/packages/REddyProc/index.html (last access: 29 March 2021), 2014. a
Ridler, M.-E., Zhang, D., Madsen, H., Kidmose, J., Refsgaard, J. C., and
Jensen, K. H.: Bias-aware data assimilation in integrated hydrological
modelling, Hydrol. Res., 49, 989–1004, https://doi.org/10.2166/nh.2017.117,
2017. a
Robinson, E., Blyth, E., Clark, D., Comyn-Platt, E., Finch, J., and Rudd, A.:
Climate hydrology and ecology research support system meteorology dataset for
Great Britain (1961–2015) [CHESS-met] v1.2,
https://doi.org/10.5285/b745e7b1-626c-4ccc-ac27-56582e77b900, 2017. a
Rosolem, R., Hoar, T., Arellano, A., Anderson, J. L., Shuttleworth, W. J., Zeng, X., and Franz, T. E.: Translating aboveground cosmic-ray neutron intensity to high-frequency soil moisture profiles at sub-kilometer scale, Hydrol. Earth Syst. Sci., 18, 4363–4379, https://doi.org/10.5194/hess-18-4363-2014, 2014. a
Samaniego, L., Kumar, R., and Attinger, S.: Multiscale parameter
regionalization of a grid-based hydrologic model at the mesoscale, Water
Resour. Res., 46, W05523, https://doi.org/10.1029/2008WR007327, 2010. a
Sawada, Y. and Koike, T.: Simultaneous estimation of both hydrological and
ecological parameters in an ecohydrological model by assimilating microwave
signal, J. Geophys. Res.-Atmos., 119, 8839–8857,
https://doi.org/10.1002/2014JD021536, 2014. a, b
Schaap, M. G., Nemes, A., and van Genuchten, M. T.: Comparison of Models for
Indirect Estimation of Water Retention and Available Water in Surface Soils,
Vadose Zone J., 3, 1455–1463, https://doi.org/10.2136/vzj2004.1455, 2004. 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., Ball, L., Bennett, E., Blake, J., Boorman, D.,
Brooks, M., Clarke, M., Cooper, H., Cowan, N., Evans, J., Farrand, P., Fry,
M., Hitt, O., Jenkins, A., Kral, F., Lord, W., Morrison, R., Nash, G.,
Rylett, D., Scarlett, P., Swain, O., Thornton, J., Trill, E., Warwick, A.,
and Winterbourn, J.: Daily and sub-daily hydrometeorological and soil data
(2013–2017) [COSMOS-UK], https://doi.org/10.5285/a6012796-291c-4fd6-a7ef-6f6ed0a6cfa5, 2019. a, b
Stewart, L. M., Dance, S. L., Nichols, N. K., Eyre, J. R., and Cameron, J.:
Estimating interchannel observation-error correlations for IASI radiance data
in the Met Office system†, Q. J. Roy. Meteor.
Soc., 140, 1236–1244, https://doi.org/10.1002/qj.2211, 2014. a
Tapley, B. D., Bettadpur, S., Ries, J. C., Thompson, P. F., and Watkins, M. M.:
GRACE Measurements of Mass Variability in the Earth System, Science, 305,
503–505, https://doi.org/10.1126/science.1099192, 2004. a
Thiemann, M., Trosset, M., Gupta, H., and Sorooshian, S.: Bayesian recursive
parameter estimation for hydrologic models, Water Resour. Res., 37,
2521–2535, https://doi.org/10.1029/2000WR900405, 2001. a
Tifafi, M., Guenet, B., and Hatté, C.: Large Differences in Global and
Regional Total Soil Carbon Stock Estimates Based on SoilGrids, HWSD, and
NCSCD: Intercomparison and Evaluation Based on Field Data From USA, England,
Wales, and France, Global Biogeochem. Cy., 32, 42–56,
https://doi.org/10.1002/2017GB005678, 2018. a
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
Vrugt, J. A., Gupta, H. V., Bouten, W., and Sorooshian, S.: A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters, Water Resour. Res., 39, 1201,
https://doi.org/10.1029/2002WR001642, 2003. a
Wagner, W., Hahn, S., Kidd, R., Melzer, T., Bartalis, Z., Hasenauer, S.,
Figa-Saldaña, J., de Rosnay, P., Jann, A., Schneider, S., Komma, J.,
Kubu, G., Brugger, K., Aubrecht, C., Züger, J., Gangkofner, U.,
Kienberger, S., Brocca, L., Wang, Y., Blöschl, G., Eitzinger, J.,
Steinnocher, K., Zeil, P., and Rubel, F.: The ASCAT Soil Moisture Product:
A Review of its Specifications, Validation Results, and Emerging
Applications, Meteorol. Z., 22, 5–33,
https://doi.org/10.1127/0941-2948/2013/0399, 2013. a
Walker, J. P., Willgoose, G. R., and Kalma, J. D.: In situ measurement of soil
moisture: a comparison of techniques, J. Hydrol., 293, 85–99,
https://doi.org/10.1016/j.jhydrol.2004.01.008, 2004. a
Wang, P., Li, J., Li, Z., Lim, A. H. N., Li, J., and Goldberg, M. D.: Impacts
of Observation Errors on Hurricane Forecasts When Assimilating Hyperspectral
Infrared Sounder Radiances in Partially Cloudy Skies, J. Geophys.
Res.-Atmos., 124, 10802–10813, https://doi.org/10.1029/2019JD031029,
2019.
a
Wang, X., Bishop, C. H., and Julier, S. J.: Which Is Better, an Ensemble of Positive–Negative Pairs or a Centered Spherical Simplex Ensemble?, Mon. Weather Rev., 132, 1590–1605, https://doi.org/10.1175/1520-0493(2004)132<1590:WIBAEO>2.0.CO;2, 2004. a
Wösten, J., Lilly, A., Nemes, A., and Le Bas, C.: 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, b
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, b, c, d
Zhang, R., Kim, S., and Sharma, A.: A comprehensive validation of the SMAP
Enhanced Level-3 Soil Moisture product using ground measurements over varied
climates and landscapes, Remote Sens. Environ., 223, 82–94,
https://doi.org/10.1016/j.rse.2019.01.015, 2019. a, b, c, d
Zheng, D., Li, X., Wang, X., Wang, Z., Wen, J., van der Velde, R., Schwank,
M., and Su, Z.: Sampling depth of L-band radiometer measurements of soil
moisture and freeze-thaw dynamics on the Tibetan Plateau, Remote Sens.
Environ., 226, 16–25, https://doi.org/10.1016/j.rse.2019.03.029,
2019. a
Zreda, M., Desilets, D., Ferré, T. P. A., and Scott, R. L.: Measuring soil
moisture content non-invasively at intermediate spatial scale using
cosmic-ray neutrons, Geophys. Res. Lett., 35, L21402,
https://doi.org/10.1029/2008GL035655, 2008. a, b
Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System, Hydrol. Earth Syst. Sci., 16, 4079–4099, https://doi.org/10.5194/hess-16-4079-2012, 2012. a
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.
Land surface models are important tools for translating meteorological forecasts and reanalyses...
