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  2. Ensemble Kalman-Filter-based seasonal runoff predictions for the Rio São Francisco River Basin
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    Dataset: Ensemble Kalman-Filter-based seasonal runoff predictions for the Rio São Francisco River Basin

    • RADAR Metadata
    • Content
    • Statistics
    • Technical Metadata
    Alternate identifier:
    -
    Related identifier:
    (Is Derived From) 10.26050/WDCC/SaWaM_D02_SEAS5_BCSD - DOI
    (Continues) 10.5194/essd-2020-177 - DOI
    (Continues) 10.1002/2014WR016794 - DOI
    Creator/Author:
    Borne, Maurus https://orcid.org/0000-0003-4656-5878 [Karlsruhe Institute of Technology - Institute of Meteorology and Climate Research - Department Troposphere Research (IMK-TRO)]
    Contributors:
    (Researcher)
    Lorenz, Christof https://orcid.org/0000-0001-5590-5470 [Karlsruhe Institute of Technology - Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU)]

    (Researcher)
    Portele, Tanja Christina https://orcid.org/0000-0001-9436-710X [Karlsruhe Institute of Technology - Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU)]

    (Project Leader)
    Kunstmann, Harald https://orcid.org/0000-0001-9573-1743 [Karlsruhe Institute of Technology - Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU)]

    (Researcher)
    Martins, Eduardo Sávio Passos Rodrigues https://orcid.org/0000-0002-9858-2541 [Research Institute of Meteorology and Water Resources (FUNCEME)]

    (Researcher)
    das Chagas Vasconcelos Júnior, Francisco https://orcid.org/0000-0002-1558-8383 [Research Institute of Meteorology and Water Resources (FUNCEME)]
    Title:
    Ensemble Kalman-Filter-based seasonal runoff predictions for the Rio São Francisco River Basin
    Additional titles:
    -
    Description:
    (Abstract) In semi-arid regions, interannual variability of seasonal rainfall and climate change is expected to stress water availability and increase the recurrence and intensity of extreme events such as droughts or floods. Local decision makers therefore need reliable long-term hydro-meteorological forecast... In semi-arid regions, interannual variability of seasonal rainfall and climate change is expected to stress water availability and increase the recurrence and intensity of extreme events such as droughts or floods. Local decision makers therefore need reliable long-term hydro-meteorological forecasts to support the seasonal management of water resources, reservoir operations and agriculture. In this context, an Ensemble Kalman Filter (EnKF) framework is applied to predict sub-basin-scale runoff employing global freely available datasets of reanalysis precipitation (ERA5-Land) as well as Bias-Corrected and Spatially Disaggregated seasonal forecasts (SEAS5-BCSD). Runoff is estimated using least squares predictions, exploiting the covariance structures between runoff and precipitation. This repository contains the runoff observations, the final EnKF-based runoff predictions, reference precipitation from ERA5-Land, bias-corrected and spatially disaggregated seasonal precipitation forecats from SEAS5-BCSD as well as shapefiles delineating the sub-basin-boundaries within the Rio São Francisco River Basin.

    In semi-arid regions, interannual variability of seasonal rainfall and climate change is expected to stress water availability and increase the recurrence and intensity of extreme events such as droughts or floods. Local decision makers therefore need reliable long-term hydro-meteorological forecasts to support the seasonal management of water resources, reservoir operations and agriculture. In this context, an Ensemble Kalman Filter (EnKF) framework is applied to predict sub-basin-scale runoff employing global freely available datasets of reanalysis precipitation (ERA5-Land) as well as Bias-Corrected and Spatially Disaggregated seasonal forecasts (SEAS5-BCSD). Runoff is estimated using least squares predictions, exploiting the covariance structures between runoff and precipitation. This repository contains the runoff observations, the final EnKF-based runoff predictions, reference precipitation from ERA5-Land, bias-corrected and spatially disaggregated seasonal precipitation forecats from SEAS5-BCSD as well as shapefiles delineating the sub-basin-boundaries within the Rio São Francisco River Basin.

    Show all
    Keywords:
    Hydrometeorology
    Seasonal runoff prediction
    River basin management
    Data assimilation
    Rio São Francisco
    Related information:
    -
    Language:
    English
    Publishers:
    Karlsruhe Institute of Technology (KIT)
    Production year:
    2022
    Subject areas:
    Geological Science
    Resource type:
    Dataset
    Data source:
    (Other) European Center for Medium Range Weather Forecasts (ECMWF), Copernicus, Brazilian National Water Agency (ANA)
    Software used:
    Resource processing
    Software:
    Python - 3.8
    Alternative software:
    -
    Data processing:
    -
    Publication year:
    2023
    Rights holders:
    Karlsruhe Institute of Technology (KIT)
    Funding:
    -
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    Name Storage Metadata Upload Action
    Status:
    Published
    Uploaded by:
    871893860b7f52e7214215615a0c1fcf
    Created on:
    2022-04-28
    Archiving date:
    2023-01-13
    Archive size:
    50.1 MB
    Archive creator:
    871893860b7f52e7214215615a0c1fcf
    Archive checksum:
    59ba3954ad2da26d745bda20484809db (MD5)
    Embargo period:
    -

    Geolocation

      Rio São Francisco River Basin, BRAZIL
      DOI: 10.35097/600
      Publication date: 2023-01-13
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      Rights statement for the dataset
      This work is licensed under
      CC BY-NC-SA 4.0
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      Cite Dataset
      Borne, Maurus (2023): Ensemble Kalman-Filter-based seasonal runoff predictions for the Rio São Francisco River Basin. Karlsruhe Institute of Technology (KIT). DOI: 10.35097/600
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