Cited by 19Hydrology and Watershed Management StudiesGeophysics and Gravity MeasurementsSoil Moisture and Remote Sensing

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

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Kitambo, Benjamin M., Papa, Fabrice, Paris, Adrien, Tshimanga, Raphael M., Frappart, Frederic, Calmant, Stephane, Elmi, Omid, Fleischmann, Ayan Santos +4 more·2023·Earth System Science Data

Published version of a preprint

Abstract

The 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 Multi-Satellite (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), Multi-Error-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 ∼101±23 km 3 . The Middle Congo sub-basin shows a higher mean annual amplitude (∼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 (∼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 satellite-derived 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).

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Authors
Kitambo, B. M., Papa, F., Paris, A., Tshimanga, R. M., Frappart, F., Calmant, S., Elmi, O., Fleischmann, A. S., Becker, M., Tourian, M. J., Jucá Oliveira, R. A., & Wongchuig, S.
Affiliations
  • Centre National de la Recherche Scientifique (FR)
  • University of Kinshasa (CD)
  • University of Stuttgart (DE)
  • Instituto de Desenvolvimento Sustentável Mamirauá (BR)
Venue
Earth System Science Data
Published
2023-07-12
Volume
15
Issue
7
Pages
2957-2982
ISSN
  • 1866-3508 (ISSN-L)
  • 1866-3516
OpenAlex
W4384034169
Cited by
19
References
107
Citations by year
2026: 5 · 2025: 5 · 2024: 6 · 2023: 3
FWCI
1.52
Type
journal article
Language
en
Open access
Locations
  • Earth system science data (publishedVersion, cc-by)
  • HAL (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Centre National d’Etudes Spatiales (DYBANGO)
Topics
  • Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
  • Physical Sciences › Earth and Planetary Sciences › Oceanography › Geophysics and Gravity Measurements
  • Physical Sciences › Environmental Science › Environmental Engineering › Soil Moisture and Remote Sensing
Keywords
surface water storage, Congo River basin, surface water extent, radar altimetry, hypsometric curve, digital elevation model, seasonal and interannual variability, precipitation deficit, river discharge, water cycle, ASTER, ALOS, MERIT DEM

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