{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:51:19Z","timestamp":1772585479062,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,5,7]],"date-time":"2020-05-07T00:00:00Z","timestamp":1588809600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000844","name":"European Space Agency","doi-asserted-by":"publisher","award":["4000125399\/18\/I-BG"],"award-info":[{"award-number":["4000125399\/18\/I-BG"]}],"id":[{"id":"10.13039\/501100000844","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["198702"],"award-info":[{"award-number":["198702"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this study the impacts of Soil Moisture and Ocean Salinity (SMOS) soil moisture data assimilation upon the streamflow prediction of the operational Global Flood Awareness System (GloFAS) were investigated. Two GloFAS experiments were performed, one which used hydro-meteorological forcings produced with the assimilation of the SMOS data, the other using forcings which excluded the assimilation of the SMOS data. Both sets of experiment results were verified against streamflow observations in the United States and Australia. Skill scores were computed for each experiment against the observation datasets, the differences in the skill scores were used to identify where GloFAS skill may be affected by the assimilation of SMOS soil moisture data. In addition, a global assessment was made of the impact upon the 5th and 95th GloFAS flow percentiles to see how SMOS data assimilation affected low and high flows respectively. Results against in-situ observations found that GloFAS skill score was only affected by a small amount. At a global scale, the results showed a large impact on high flows in areas such as the Hudson Bay, central United States, the Sahel and Australia. There was no clear spatial trend to these differences as opposing signs occurred within close proximity to each other. Investigating the differences between the simulations at individual gauging stations showed that they often only occurred during a single flood event; for the remainder of the simulation period the experiments were almost identical. This suggests that SMOS data assimilation may affect the generation of surface runoff during high flow events, but may have less impact on baseflow generation during the remainder of the hydrograph. To further understand this, future work could assess the impact of SMOS data assimilation upon specific hydrological components such as surface and subsurface runoff.<\/jats:p>","DOI":"10.3390\/rs12091490","type":"journal-article","created":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T03:45:20Z","timestamp":1588909520000},"page":"1490","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["The Impact of SMOS Soil Moisture Data Assimilation within the Operational Global Flood Awareness System (GloFAS)"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3013-0370","authenticated-orcid":false,"given":"Calum","family":"Baugh","sequence":"first","affiliation":[{"name":"ECMWF, Shinfield Park, Reading RG2 9AX, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7374-3820","authenticated-orcid":false,"given":"Patricia","family":"de Rosnay","sequence":"additional","affiliation":[{"name":"ECMWF, Shinfield Park, Reading RG2 9AX, UK"}]},{"given":"Heather","family":"Lawrence","sequence":"additional","affiliation":[{"name":"ECMWF, Shinfield Park, Reading RG2 9AX, UK"}]},{"given":"Toni","family":"Jurlina","sequence":"additional","affiliation":[{"name":"ECMWF, Shinfield Park, Reading RG2 9AX, UK"}]},{"given":"Matthias","family":"Drusch","sequence":"additional","affiliation":[{"name":"European Space Agency European Space Research and Technology Centre, 2201 AZ Noordwijk, The Netherlands"}]},{"given":"Ervin","family":"Zsoter","sequence":"additional","affiliation":[{"name":"ECMWF, Shinfield Park, Reading RG2 9AX, UK"},{"name":"Department of Geography and Environmental Science, University of Reading, Whiteknights, PO Box 227, Reading RG6 6AB, UK"}]},{"given":"Christel","family":"Prudhomme","sequence":"additional","affiliation":[{"name":"ECMWF, Shinfield Park, Reading RG2 9AX, UK"},{"name":"UK Centre for Ecology and Hydrology, MacLean Building, Benson Lane, Crowmarsh Gifford, Wallingford, Oxfordshire OX10 8BB, UK"},{"name":"Geography department, Loughborough University, Epinal Way, Loughborough LE11 3TU, UK"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,7]]},"reference":[{"key":"ref_1","first-page":"14401","article-title":"An ensemble approach for attribution of hydrologic prediction uncertainty","volume":"34","author":"Wood","year":"2008","journal-title":"Geophys. Res. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2057","DOI":"10.5194\/hess-22-2057-2018","article-title":"Skillful seasonal forecasts of streamflow over Europe?","volume":"22","author":"Arnal","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_3","first-page":"182","article-title":"Applying SMOS soil moisture data into the National Weather Service (NWS)\u2019s Research Distributed Hydrologic Model (HL-RDHM) for flash flood guidance application","volume":"8","author":"Seo","year":"2017","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1038\/ngeo944","article-title":"Skill in streamflow forecasts derived from large-scale estimates of soil moisture and snow","volume":"3","author":"Koster","year":"2010","journal-title":"Nat. Geosci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1002\/wat2.1137","article-title":"Continental and global scale flood forecasting systems","volume":"3","author":"Emerton","year":"2016","journal-title":"WIREs Water"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"358","DOI":"10.2136\/vzj2007.0143","article-title":"Soil moisture measurement for ecological and hydrological watershed-scale observations: A review","volume":"7","author":"Robinson","year":"2008","journal-title":"Vadose Zone J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4079","DOI":"10.5194\/hess-16-4079-2012","article-title":"COSMOS: The COSmic-ray Soil Moisture Observing System","volume":"16","author":"Zreda","year":"2012","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4987","DOI":"10.1002\/hyp.10929","article-title":"Soil water content in southern England derived from a cosmic-ray soil moisture observing system COSMOS-UK","volume":"30","author":"Evans","year":"2016","journal-title":"Hydrol. Process."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Brocca, L., Ciabatta, L., Massari, C., Camici, S., and Tarpanelli, A. (2017). Soil Moisture for Hydrological Applications: Open Questions and New Opportunities. Water, 9.","DOI":"10.3390\/w9020140"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1109\/JPROC.2010.2043918","article-title":"The Soil Moisture Active Passive (SMAP) mission","volume":"98","author":"Entekhabi","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1109\/TGRS.2008.916264","article-title":"SMOS: The mission and system","volume":"46","author":"Barre","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.rse.2015.02.002","article-title":"A global comparison of alternate AMSR2 soil moisture products: Why do they differ?","volume":"161","author":"Kim","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111215","DOI":"10.1016\/j.rse.2019.111215","article-title":"Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: A comprehensive assessment using global ground based observations","volume":"231","author":"Ma","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1016\/j.rse.2006.10.014","article-title":"L-band microwave emission of the biosphere (L-MEB) model: Description and calibration against experimental data sets over crop fields","volume":"107","author":"Wigneron","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5991","DOI":"10.1109\/TGRS.2015.2430845","article-title":"Soil moisture retrieval using neural networks: Application to SMOS","volume":"53","author":"Aires","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5201","DOI":"10.5194\/hess-21-5201-2017","article-title":"SMOS near-real-time soil moisture product: Processor overview and first validation results","volume":"21","author":"Richaume","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1002\/qj.2023","article-title":"A simplified Extended Kalman Filter for the global operational soil moisture analysis at ECMWF","volume":"139","author":"Drusch","year":"2013","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3059","DOI":"10.5194\/hess-20-3059-2016","article-title":"Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations","volume":"20","author":"Wanders","year":"2016","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Massari, C., Camici, S., Ciabatta, L., and Brocca, L. (2018). Exploiting satellite-based soil moisture for flood forecasting in the Mediterranean area: State update versus rainfall correction. Remote Sens., 10.","DOI":"10.3390\/rs10020292"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2542","DOI":"10.1109\/TGRS.2011.2177468","article-title":"Assimilation of surface- and root-zone ASCAT soil moisture products into rainfall-runoff modelling","volume":"50","author":"Brocca","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.5194\/hess-19-1659-2015","article-title":"Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: Comparison between lumped and semi-distributed schemes","volume":"19","author":"Ryu","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, Y., Grimaldi, S., Walker, J.P., and Pauwels, V.R.N. (2016). Application of remote sensing data to constrain operational rainfall-driven flood forecasting: A review. Remote Sens., 8.","DOI":"10.3390\/rs8060456"},{"key":"ref_23","unstructured":"ECMWF (2019). ECMWF. ECMWF Part II: Data Assimilation. IFS Documentation CY46R1 Operational Implementation 6 June 2019, ECMWF. Available online: https:\/\/www.ecmwf.int\/en\/elibrary\/19306-part-ii-data-assimilation."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"111424","DOI":"10.1016\/j.rse.2019.111424","article-title":"SMOS brightness temperature forward modelling and long term monitoring at ECMWF","volume":"237","author":"Albergel","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2023","DOI":"10.1175\/JHM-D-19-0074.1","article-title":"The new stand-alone surface analysis at ECMWF: Implications for land-atmosphere DA coupling","volume":"20","author":"Fairbairn","year":"2019","journal-title":"J. Hydrometeorol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Fern\u00e1ndez, N., de Rosnay, P., Albergel, C., Richaume, P., Aires, F., Prigent, C., and Kerr, Y. (2019). SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact. Remote Sens., 11.","DOI":"10.20944\/preprints201904.0216.v1"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.5194\/hess-17-1161-2013","article-title":"GloFAS\u2014Global ensemble streamflow forecasting and flood early warning","volume":"17","author":"Alfieri","year":"2013","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Harrigan, S., Zsoter, E., Alfieri, L., Prudhomme, C., Salamon, P., Wetterhall, F., Barnard, C., Cloke, H., and Pappenberger, F. (2020). GloFAS-ERA5 operational global river discharge reanalysis 1979-present. Earth Syst. Sci. Data Discuss.","DOI":"10.5194\/egusphere-egu2020-15755"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1175\/2008JHM1068.1","article-title":"A revised hydrology for the ECMWF model: Verification from field site to terrestrial water storage and impact in the Integrated Forecast System","volume":"10","author":"Balsamo","year":"2009","journal-title":"J. Hydrometeorol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1080\/13658810802549154","article-title":"LISFLOOD: A GIS-based distributed model for river basin scale water balance and flood simulation","volume":"24","author":"Younis","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1175\/JHM-D-18-0086.1","article-title":"How well do operational numerical weather prediction configurations represent hydrology?","volume":"20","author":"Zsoter","year":"2019","journal-title":"J. Hydrometeorol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1109\/JPROC.2010.2043032","article-title":"The SMOS mission: New tool for monitoring key elements of the global water cycle","volume":"98","author":"Kerr","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1354","DOI":"10.1109\/TGRS.2012.2187666","article-title":"ESA\u2019s soil moisture and ocean salinity mission: Mission performance and operations","volume":"50","author":"Mecklenburg","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.1109\/TGRS.2012.2184548","article-title":"The SMOS soil moisture retrieval algorithm","volume":"50","author":"Kerr","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2524","DOI":"10.1002\/qj.3577","article-title":"Assimilation of SMOS brightness temperatures in the ECMWF integrated forecast system","volume":"145","author":"Lawrence","year":"2019","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_36","unstructured":"ECMWF (2019). IV: Physical Processes. IFS Documentation CY46R1 Operational Implementation 6 June 2019, ECMWF. Available online: https:\/\/www.ecmwf.int\/en\/elibrary\/19308-part-iv-physical-processes."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1063\/1.1745010","article-title":"Capillary conduction of liquids through porous mediums","volume":"1","author":"Richards","year":"1931","journal-title":"Physics"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"892","DOI":"10.2136\/sssaj1980.03615995004400050002x","article-title":"A closed-form equation for predicting the hydraulic conductivity of unsaturated soils","volume":"44","year":"1980","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_39","unstructured":"FAO (2003). Digital soil map of the world (DSMW). Technical Report, Food and Agriculture Organization of the United Nations, FAO. re-issued version."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"09701","DOI":"10.1029\/2012WR012313","article-title":"A new global river network database for macroscale hydrologic modelling","volume":"48","author":"Wu","year":"2012","journal-title":"Water Resour. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1029\/2008EO100001","article-title":"New global hydrography derived from spaceborne elevation data","volume":"89","author":"Lehner","year":"2008","journal-title":"Eos Trans."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2004","DOI":"10.1029\/2005RG000183","article-title":"The shuttle radar topography mission","volume":"45","author":"Farr","year":"2007","journal-title":"Rev. Geophys."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3467","DOI":"10.1002\/2013WR014664","article-title":"Development of the Global Width Database for Large Rivers","volume":"50","author":"Yamazaki","year":"2014","journal-title":"Water Resour. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1016\/j.jhydrol.2017.03.022","article-title":"The impact of lake and reservoir parameterisation on global streamflow simulation","volume":"548","author":"Zajac","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_45","unstructured":"Bollrich, G. (1992). Technische Hydromechanik: Grundlagen, Verlag Bauwesen."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/j.jhydrol.2018.09.052","article-title":"Calibration of the Global Flood Awareness System (GloFAS) using daily streamflow data","volume":"566","author":"Hirpa","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.jhydrol.2009.08.003","article-title":"Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling","volume":"377","author":"Gupta","year":"2009","journal-title":"J. Hydrol."},{"key":"ref_48","unstructured":"WMO (1989). The Global Water Runoff Data Project. Workshop on the Global Runoff Data Set and Grid Estimation, World Climate Programme Research. Available online: https:\/\/www.bafg.de\/GRDC\/EN\/01_GRDC\/11_rtnle\/WCRP22_WMO_TD302.pdf?__blob=publicationFile."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.jhydrol.2012.01.011","article-title":"Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios","volume":"424\u2013425","author":"Kling","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4323","DOI":"10.5194\/hess-23-4323-2019","article-title":"Technical Note: Inherent benchmark or not? Comparing Nash-Sutcliffe and Kling\u2013Gupta efficiency scores","volume":"23","author":"Knoben","year":"2019","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_51","unstructured":"Wilks, D.S. (2011). Statistical Methods in the Atmospheric Sciences, Academic Press. [3rd ed.]."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3507","DOI":"10.1109\/TGRS.2014.2378913","article-title":"Copula-based downscaling of coarse-scale soil moisture observations with implicit bias correction","volume":"53","author":"Verhoest","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","unstructured":"Murray\u2013Darling Basin Authority (2020, March 30). Basin Plan Annual Report 2017\u20132018, Available online: https:\/\/www.mdba.gov.au\/sites\/default\/files\/pubs\/basin-plan-annual-report-2017-18.pdf."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.rse.2015.06.025","article-title":"SMOS soil moisture assimilation for improved hydrologic simulation in the Murray\u2013Darling Basin, Australia","volume":"16","author":"Lievens","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.rse.2015.10.033","article-title":"Assimilation of SMOS soil moisture and brightness temperature products into a land surface model","volume":"180","author":"Lievens","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_56","unstructured":"ESA (2017). Land Cover CCI Product User Guide Version 2 Technical Report, UCL Geomatics. Available online: http:\/\/maps.elie.ucl.ac.be\/CCI\/viewer\/download\/ESACCI-LC-Ph2-PUGv2_2.0.pdf."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.jhydrol.2011.11.039","article-title":"Implementation of surface soil moisture data assimilation with watershed scale distributed hydrological model","volume":"416\u2013417","author":"Han","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.advwatres.2012.03.022","article-title":"Can ASCAT-derived soil wetness indices reduce predictive uncertainty in well-gauged areas? A comparison with in-situ observed soil moisture in an assimilation application","volume":"44","author":"Matgen","year":"2012","journal-title":"Adv. Water Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.5194\/hess-14-1881-2010","article-title":"Improving runoff prediction through the assimilation of the ASCAT soil moisture product","volume":"14","author":"Brocca","year":"2010","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1175\/JHM-D-14-0002.1","article-title":"Dual forcing and state correction via soil moisture assimilation for improved rainfall-runoff modelling","volume":"15","author":"Chen","year":"2014","journal-title":"J. Hydrometeorol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/9\/1490\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:26:35Z","timestamp":1760174795000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/9\/1490"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,7]]},"references-count":60,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["rs12091490"],"URL":"https:\/\/doi.org\/10.3390\/rs12091490","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,7]]}}}