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The African continent hosts some of the largest freshwater systems worldwide, characterized by a large distribution and variability of surface waters that play a key role in the water, energy and carbon cycles and are of major importance to the global climate and water resources. Freshwater availability in Africa has now become of major concern under the combined effect of climate change, environmental alterations and anthropogenic pressure. However, the hydrology of the African river basins remains one of the least studied worldwide and a better monitoring and understanding of the hydrological processes across the continent become fundamental. Earth Observation, that offers a cost-effective means for monitoring the terrestrial water cycle, plays a major role in supporting surface hydrology investigations. Remote sensing advances are therefore a game changer to develop comprehensive observing systems to monitor Africa’s land water and manage its water resources. Here, we review the achievements of more than three decades of advances using remote sensing to study surface waters in Africa, highlighting the current benefits and difficulties. We show how the availability of a large number of sensors and observations, coupled with models, offers new possibilities to monitor a continent with scarce gauged stations. In the context of upcoming satellite missions dedicated to surface hydrology, such as the Surface Water and Ocean Topography (SWOT), we discuss future opportunities and how the use of remote sensing could benefit scientific and societal applications, such as water resource management, flood risk prevention and environment monitoring under current global change.Article HighlightsThe hydrology of African surface water is of global importance, yet it remains poorly monitored and understoodComprehensive review of remote sensing and modeling advances to monitor Africa’s surface water and water resourcesFuture opportunities with upcoming satellite missions and to translate scientific advances into societal applications

River discharge is a crucial measurement, indicating the volume of water flowing through a river cross-section at any given time. However, the existing network of river discharge gauges faces significant issues, largely due to the declining number of active gauges and temporal gaps. Remote sensing, especially radar-based techniques, offers an effective means to this issue. This study introduces the Satellite Altimetry-based Extension of the global-scale in situ river discharge Measurements (SAEM) data set, which utilizes multiple satellite altimetry missions and estimates discharge using the existing worldwide networks of national and international gauges. In SAEM, we have explored 47 000 gauges and estimated height-based discharge for 8730 of them, which is approximately 3 times the number of gauges of the largest existing remote-sensing-based data set. These gauges cover approximately 88 % of the total gauged discharge volume. The height-based discharge estimates in SAEM demonstrate a median Kling–Gupta efficiency (KGE) of 0.48, outperforming current global data sets. In addition to the river discharge time series, the SAEM data set comprises three more products, each contributing a unique facet to better usage of our data. (1) A catalog of virtual stations (VSs) is defined by certain predefined criteria. In addition to each station's coordinates, this catalog provides information on satellite altimetry missions, distance to the discharge gauge, and relevant quality flags. (2) The altimetric water level time series of those VSs are included, for which we ultimately obtained good-quality discharge data. These water level time series are sourced from both existing Level-3 water level time series and newly generated ones within this study. The Level-3 data are gathered from pre-existing data sets, including Hydroweb.Next (formerly Hydroweb), the Database of Hydrological Time Series of Inland Waters (DAHITI), the Global River Radar Altimetry Time Series (GRRATS), and HydroSat. (3) SAEM's third product is rating curves for the defined VSs, which map water level values into discharge values, derived using a nonparametric stochastic quantile mapping function approach. The SAEM data set can be used to improve hydrological models, inform water resource management, and address nonlinear water-related challenges under climate change. The SAEM data set is available from https://doi.org/10.18419/darus-4475 (Saemian et al., 2024).

The Congo Basin is of global significance for biodiversity and the water and carbon cycles. However, its freshwater availability and distribution remain relatively unknown. Using satellite data, here we show that currently the Congo Basin’s Total Drainable Water Storage lies within a range of 476 km 3 to 502 km 3 , unevenly distributed throughout the region, with 63% being stored in the southernmost sub-basins, Kasaï (220–228 km 3 ) and Lualaba (109–169 km 3 ), while the northern sub-basins contribute only 173 ± 8 km 3 . We further estimate the hydraulic time constant for draining its entire water storage to be 4.3 ± 0.1 months, but, regionally, permanent wetlands and large lakes act as resistors resulting in greater time constants of up to 105 ± 3 months. Our estimate provides a robust basis to address the challenges of water demand for 120 million inhabitants, a population expected to double in a few decades.

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 km3. The Middle Congo sub-basin shows a higher mean annual amplitude (∼71±15 km3). 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).

The Congo River Basin (CRB), hosting the second-largest tropical forest on Earth, is of global significance for the water and carbon cycles. Its population and ecosystems are also strongly dependent on freshwater availability, which is increasingly threatened by current climate change and deforestation. Persistent drought conditions in CRB have been reported, but their drivers and impacts on the basin’s hydrology remain unknown. Here, we analyze 42 years (1981–2022) of atmospheric and hydrological variability to show that the drying trend in Central Congo is linked to reduced atmospheric moisture convergence and precipitation, primarily during the rainiest period. This trend correlates with a weakening of the Walker circulation and an increase in Sea Surface Temperature in the Central Eastern Tropical Atlantic Ocean which influences moisture convergence over the Central Congo with a ~3-month lag. Our findings emphasize the need for integrative atmospheric and hydrological approaches to address CRB’s freshwater and forest vulnerability to climate change.

The Congo River basin (CRB) is the second largest river system in the world, but its hydroclimatic characteristics remain relatively poorly known. Here, we jointly analyse a large record of in situ and satellite-derived observations, including a long-term time series of surface water height (SWH) from radar altimetry (a total of 2311 virtual stations) and surface water extent (SWE) from a multi-satellite technique, to characterize the CRB surface hydrology and its variability. First, we show that SWH from altimetry multi-missions agrees well with in situ water stage at various locations, with the root mean square deviation varying from 10 cm (with Sentinel-3A) to 75 cm (with European Remote Sensing satellite-2). SWE variability from multi-satellite observations also shows a plausible behaviour over a ∼25-year period when evaluated against in situ observations from the subbasin to basin scale. Both datasets help to better characterize the large spatial and temporal variability in hydrological patterns across the basin, with SWH exhibiting an annual amplitude of more than 5 m in the northern subbasins, while the Congo River main stream and Cuvette Centrale tributaries vary in smaller proportions (1.5 to 4.5 m). Furthermore, SWH and SWE help illustrate the spatial distribution and different timings of the CRB annual flood dynamic and how each subbasin and tributary contribute to the hydrological regime at the outlet of the basin (the Brazzaville/Kinshasa station), including its peculiar bimodal pattern. Across the basin, we estimate the time lag and water travel time to reach the Brazzaville/Kinshasa station to range from 0–1 month in its vicinity in downstream parts of the basin and up to 3 months in remote areas and small tributaries. Northern subbasins and the central Congo region contribute highly to the large peak in December–January, while the southern part of the basin supplies water to both hydrological peaks, in particular to the moderate one in April–May. The results are supported using in situ observations at several locations in the basin. Our results contribute to a better characterization of the hydrological variability in the CRB and represent an unprecedented source of information for hydrological modelling and to study hydrological processes over the region.

In late 2023, the Amazon River Basin experienced its most extreme drought to date, putting its population and ecosystem at risk. Gauges that were still functioning measured the lowest river water levels (RWL) on record. Here, satellite observations, including Surface Water Ocean Topography (SWOT), reveal the spread and timing of extremely low RWL across the entire river system. The majority of Nadir altimeter observations show that the 2023 minimum RWL in the Central Amazon were 3 m or more below their annual average, representing two to three times its mean variability. Additionally, SWOT captures the basin-scale reduction in RWL with a spatial resolution of 200 m and how it propagates with time. Large-scale evaluation with gauges suggests that SWOT outperforms classical altimetry in estimating RWL, despites differences that need further investigations. SWOT offers a new opportunity to understand hydroclimatic extremes and their broad impacts on the environment of the Amazon.

Inundation dynamics are the primary control on greenhouse gas emissions from peatlands. Situated in the central Congo Basin, the Cuvette Centrale is the largest tropical peatland complex. However, our knowledge of the spatial and temporal variations in its water levels is limited. By addressing this gap, we can quantify the relationship between the Cuvette Centrale’s water levels and greenhouse gas emissions, and further provide a baseline from which deviations caused by climate or land-use change can be observed, and their impacts understood. We present here a novel approach that combines satellite-derived rainfall, evapotranspiration and L-band Synthetic Aperture Radar (SAR) data to estimate spatial and temporal changes in water level across a sub-region of the Cuvette Centrale. Our key outputs are a map showing the spatial distribution of rainfed and flood-prone locations and a daily, 100 m resolution map of peatland water levels. This map is validated using satellite altimetry data and in situ water table data from water loggers. We determine that 50% of peatlands within our study area are largely rainfed, and a further 22.5% are somewhat rainfed, receiving hydrological input mostly from rainfall (directly and via surface/sub-surface inputs in sloped areas). The remaining 27.5% of peatlands are mainly situated in riverine floodplain areas to the east of the Congo River and between the Ubangui and Congo rivers. The mean amplitude of the water level across our study area and over a 20-month period is 22.8 ± 10.1 cm to 1 standard deviation. Maximum temporal variations in water levels occur in the riverine floodplain areas and in the inter-fluvial region between the Ubangui and Congo rivers. Our results show that spatial and temporal changes in water levels can be successfully mapped over tropical peatlands using the pattern of net water input (rainfall minus evapotranspiration, not accounting for run-off) and L-band SAR data.