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Improving the Performance of an Operational Flood Early Warning System with the Assimilation of Satellite-Soil-Moisture Data

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12955))

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Abstract

Soil Moisture (SM) plays a vital role in the hydrologic cycle at watershed scale by influencing the partitioning of precipitation into infiltration, runoff, and evapotranspiration. Therefore, SM assimilation into rainfall-runoff models is considered a way to enhance their performance. Knowing if a storm event is occurring in dry or wet soil conditions means understanding if it may trigger or not a potentially hazardous flood event. Considering the scarcity of in situ SM records, satellite SM observations represent a reliable input for improving hydrologic model predictions. In this study, we assess whether the assimilation of satellite SM observations can enhance the water level simulations of an operational early warning system implemented in Uruguay for Durazno city. In particular, the possible improvement is evaluated taking into account the assimilation of SM retrievals from the Advanced SCATterometer (ASCAT). According to the results, an improvement higher than 100% was registered for peak level, and an enhancement higher than 30% was obtained for peak time.

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Authors and Affiliations

  1. Universidad de la República, Julio Herrera y Reissig 565, Montevideo, Uruguay

    Santiago Narbondo, Angela Gorgoglione & Christian Chreties

Authors
  1. Santiago Narbondo
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  2. Angela Gorgoglione
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  3. Christian Chreties
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Corresponding author

Correspondence to Christian Chreties .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Monash University, Clayton, VIC, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

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Narbondo, S., Gorgoglione, A., Chreties, C. (2021). Improving the Performance of an Operational Flood Early Warning System with the Assimilation of Satellite-Soil-Moisture Data. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12955. Springer, Cham. https://doi.org/10.1007/978-3-030-87007-2_3

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  • DOI: https://doi.org/10.1007/978-3-030-87007-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87006-5

  • Online ISBN: 978-3-030-87007-2

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