Next Article in Journal
Applications of UAV Thermal Imagery in Precision Agriculture: State of the Art and Future Research Outlook
Next Article in Special Issue
Assimilating SMOS Brightness Temperature for Hydrologic Model Parameters and Soil Moisture Estimation with an Immune Evolutionary Strategy
Previous Article in Journal
Ice Production in Ross Ice Shelf Polynyas during 2017–2018 from Sentinel–1 SAR Images
Previous Article in Special Issue
Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau
Open AccessArticle

The Impact of SMOS Soil Moisture Data Assimilation within the Operational Global Flood Awareness System (GloFAS)

1
ECMWF, Shinfield Park, Reading RG2 9AX, UK
2
European Space Agency European Space Research and Technology Centre, 2201 AZ Noordwijk, The Netherlands
3
Department of Geography and Environmental Science, University of Reading, Whiteknights, PO Box 227, Reading RG6 6AB, UK
4
UK Centre for Ecology and Hydrology, MacLean Building, Benson Lane, Crowmarsh Gifford, Wallingford, Oxfordshire OX10 8BB, UK
5
Geography department, Loughborough University, Epinal Way, Loughborough LE11 3TU, UK
*
Author to whom correspondence should be addressed.
Now at Met Office, Fitzroy Road, Exeter, Devon EX1 3PB, UK.
Remote Sens. 2020, 12(9), 1490; https://doi.org/10.3390/rs12091490
Received: 4 April 2020 / Revised: 24 April 2020 / Accepted: 4 May 2020 / Published: 7 May 2020
(This article belongs to the Special Issue Microwave Remote Sensing for Hydrology)
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. View Full-Text
Keywords: hydrology; soil moisture; forecasting; data assimilation hydrology; soil moisture; forecasting; data assimilation
Show Figures

Figure 1

MDPI and ACS Style

Baugh, C.; de Rosnay, P.; Lawrence, H.; Jurlina, T.; Drusch, M.; Zsoter, E.; Prudhomme, C. The Impact of SMOS Soil Moisture Data Assimilation within the Operational Global Flood Awareness System (GloFAS). Remote Sens. 2020, 12, 1490. https://doi.org/10.3390/rs12091490

AMA Style

Baugh C, de Rosnay P, Lawrence H, Jurlina T, Drusch M, Zsoter E, Prudhomme C. The Impact of SMOS Soil Moisture Data Assimilation within the Operational Global Flood Awareness System (GloFAS). Remote Sensing. 2020; 12(9):1490. https://doi.org/10.3390/rs12091490

Chicago/Turabian Style

Baugh, Calum; de Rosnay, Patricia; Lawrence, Heather; Jurlina, Toni; Drusch, Matthias; Zsoter, Ervin; Prudhomme, Christel. 2020. "The Impact of SMOS Soil Moisture Data Assimilation within the Operational Global Flood Awareness System (GloFAS)" Remote Sens. 12, no. 9: 1490. https://doi.org/10.3390/rs12091490

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop