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Ensemble Kalman-Filter-based seasonal runoff predictions for the Rio São Francisco River Basin

Borne, Maurus ORCID iD icon 1; Lorenz, Christof ORCID iD icon 2; Portele, Tanja Christina 2; Kunstmann, Harald 2; Martins, Eduardo Sávio Passos Rodrigues; Das Chagas Vasconcelos Júnior, Francisco
1 Institut für Meteorologie und Klimaforschung Troposphärenforschung (IMKTRO), Karlsruher Institut für Technologie (KIT)
2 Institut für Meteorologie und Klimaforschung – Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

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.


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Originalveröffentlichung
DOI: 10.35097/600
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung – Atmosphärische Umweltforschung (IMK-IFU)
Institut für Meteorologie und Klimaforschung Troposphärenforschung (IMKTRO)
Publikationstyp Forschungsdaten
Publikationsdatum 13.01.2023
Identifikator KITopen-ID: 1000162309
HGF-Programm 12.11.33 (POF IV, LK 01) Regional Climate and Hydrological Cycle
Lizenz Creative Commons Namensnennung – Nicht kommerziell – Weitergabe unter gleichen Bedingungen 4.0 International
Projektinformation SaWaM - Grow (BMBF, 02WGR1421A)
Externe Relationen Siehe auch
Schlagwörter Geological Science, Hydrometeorology, Seasonal runoff prediction, River basin management, Data assimilation, Rio São Francisco
Art der Forschungsdaten Dataset
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Rechteinhaber Karlsruhe Institute Of Technology (KIT)
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
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