Alternativer Identifier:
-
Verwandter Identifier:
(Is Derived From) 10.26050/WDCC/SaWaM_D02_SEAS5_BCSD - DOI
(Continues) 10.5194/essd-2020-177 - DOI
(Continues) 10.1002/2014WR016794 - DOI
Ersteller/in:
Borne, Maurus https://orcid.org/0000-0003-4656-5878 [Karlsruhe Institute of Technology - Institute of Meteorology and Climate Research - Department Troposphere Research (IMK-TRO)]
Beitragende:
(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)]
Titel:
Ensemble Kalman-Filter-based seasonal runoff predictions for the Rio São Francisco River Basin
Weitere Titel:
-
Beschreibung:
(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 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.
Schlagworte:
Hydrometeorology
Seasonal runoff prediction
River basin management
Data assimilation
Rio São Francisco
Zugehörige Informationen:
-
Sprache:
Englisch
Herausgeber/in:
Karlsruhe Institute of Technology (KIT)
Erstellungsjahr:
Fachgebiet:
Geological Science
Objekttyp:
Dataset
Datenquelle:
(Other) European Center for Medium Range Weather Forecasts (ECMWF), Copernicus, Brazilian National Water Agency (ANA)
Verwendete Software:
Software für Datenbearbeitung
Software:
Python - 3.8
Alternative Software:
-
Datenverarbeitung:
-
Erscheinungsjahr:
Rechteinhaber/in:
Karlsruhe Institute of Technology (KIT)
Förderung:
-
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Status:
Publiziert
Eingestellt von:
871893860b7f52e7214215615a0c1fcf
Erstellt am:
Archivierungsdatum:
2023-01-13
Archivgröße:
50,1 MB
Archiversteller:
871893860b7f52e7214215615a0c1fcf
Archiv-Prüfsumme:
59ba3954ad2da26d745bda20484809db (MD5)
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