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A long-term monthly surface water storage dataset for the Congo basin from 1992 to 2015

hal.structure.identifierLaboratoire d'études en Géophysique et océanographie spatiales [LEGOS]
hal.structure.identifierInstitut des Géosciences de l’Environnement [IGE]
dc.contributor.authorKITAMBO, Benjamin
hal.structure.identifierLaboratoire d'études en Géophysique et océanographie spatiales [LEGOS]
hal.structure.identifierInstitut de Recherche pour le Développement [IRD]
hal.structure.identifierInstitut des Géosciences de l’Environnement [IGE]
dc.contributor.authorPAPA, Fabrice
hal.structure.identifierLaboratoire d'études en Géophysique et océanographie spatiales [LEGOS]
hal.structure.identifierInstitut de Recherche pour le Développement [IRD]
hal.structure.identifierInstitut des Géosciences de l’Environnement [IGE]
dc.contributor.authorPARIS, Adrien
hal.structure.identifierUniversity of Kinshasa [UNIKIN]
dc.contributor.authorTSHIMANGA, Raphael
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorFRAPPART, Frédéric
hal.structure.identifierLaboratoire d'études en Géophysique et océanographie spatiales [LEGOS]
hal.structure.identifierInstitut de Recherche pour le Développement [IRD]
hal.structure.identifierInstitut des Géosciences de l’Environnement [IGE]
dc.contributor.authorCALMANT, Stephane
hal.structure.identifierUniversity of Stuttgart
dc.contributor.authorELMI, Omid
hal.structure.identifierInstituto de Desenvolvimento Sustentável Mamirauá [IDSM]
dc.contributor.authorSANTOS FLEISCHMANN, Ayan
hal.structure.identifierLIttoral ENvironnement et Sociétés [LIENSs]
dc.contributor.authorBECKER, Melanie
hal.structure.identifierUniversity of Stuttgart
dc.contributor.authorTOURIAN, Mohammad
hal.structure.identifierLaboratoire d'études en Géophysique et océanographie spatiales [LEGOS]
hal.structure.identifierInstitut de Recherche pour le Développement [IRD]
hal.structure.identifierInstitut des Géosciences de l’Environnement [IGE]
dc.contributor.authorOLIVEIRA, Rômulo
hal.structure.identifierLaboratoire d'études en Géophysique et océanographie spatiales [LEGOS]
hal.structure.identifierInstitut de Recherche pour le Développement [IRD]
hal.structure.identifierInstitut des Géosciences de l’Environnement [IGE]
dc.contributor.authorWONGCHUIG, Sly
dc.date.accessioned2024-04-08T11:43:00Z
dc.date.available2024-04-08T11:43:00Z
dc.date.issued2023-07-12
dc.identifier.issn1866-3508
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/195092
dc.description.abstractEnThe spatio-temporal variation of surface water storage (SWS) in the Congo River basin (CRB), the second-largest watershed in the world, remains widely unknown. In this study, satellite-derived observations are combined to estimate SWS dynamics at the CRB and sub-basin scales over 1992-2015. Two methods are employed. The first one combines surface water extent (SWE) from the Global Inundation Extent from MultiSatellite (GIEMS-2) dataset and the long-term satellite-derived surface water height from multi-mission radar altimetry. The second one, based on the hypsometric curve approach, combines SWE from GIEMS-2 with topographic data from four global digital elevation models (DEMs), namely the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Observing Satellite (ALOS), MultiError-Removed Improved Terrain (MERIT), and Forest And Buildings removed Copernicus DEM (FABDEM). The results provide SWS variations at monthly time steps from 1992 to 2015 characterized by a strong seasonal and interannual variability with an annual mean amplitude of similar to 101 +/- 23 km(3). The Middle Congo sub-basin shows a higher mean annual amplitude ( similar to 71 +/- 15 km(3)). The comparison of SWS derived from the two methods and four DEMs shows an overall fair agreement. The SWS estimates are assessed against satellite precipitation data and in situ river discharge and, in general, a relatively fair agreement is found between the three hydrological variables at the basin and sub-basin scales (linear correlation coefficient > 0 :5). We further characterize the spatial distribution of the major drought that occurred across the basin at the end of 2005 and in early 2006. The SWS estimates clearly reveal the widespread spatial distribution of this severe event ( similar to 40% deficit as compared to their long-term average), in accordance with the large negative anomaly observed in precipitation over that period. This new SWS long-term dataset over the Congo River basin is an unprecedented new source of information for improving our comprehension of hydrological and biogeochemical cycles in the basin. As the datasets used in our study are available globally, our study opens opportunities to further develop satellitederived SWS estimates at the global scale. The dataset of the CRB's SWS and the related Python code to run the reproducibility of the hypsometric curve approach dataset of SWS are respectively available for download at https://doi.org/10.5281/zenodo. 7299823 and https://doi.org/10.5281/zenodo.8011607 (Kitambo et al., 2022b, 2023).
dc.language.isoen
dc.publisherCopernicus Publications
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.title.enA long-term monthly surface water storage dataset for the Congo basin from 1992 to 2015
dc.typeArticle de revue
dc.typeData paper
dc.identifier.doi10.5194/essd-15-2957-2023
dc.subject.halPlanète et Univers [physics]/Sciences de la Terre/Hydrologie
bordeaux.journalEarth System Science Data
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-04182592
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04182592v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Earth%20System%20Science%20Data&rft.date=2023-07-12&rft.eissn=1866-3508&rft.issn=1866-3508&rft.au=KITAMBO,%20Benjamin&PAPA,%20Fabrice&PARIS,%20Adrien&TSHIMANGA,%20Raphael&FRAPPART,%20Fr%C3%A9d%C3%A9ric&rft.genre=article&unknown


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