Abstract The chapter provides a comprehensive review of the hydrology of the Congo River Basin (CRB), emphasizing its physical features, current data and models, hydrological processes, environmental pressures, and the evolving scientific understanding. Water resources of the CRB support vital ecosystem and societal services that include agriculture, fisheries, hydropower, navigation, water supply, biodiversity conservation, and maintenance of vulnerable ecosystems such as peatlands and flooded forests that are crucial for carbon storage and climate resilience. The CRB hydrology is shaped by its diverse physiographical and geomorphological features, linked through a complex river network encompassing wetlands, lakes, and groundwater systems. Connectivity between headwaters, the Cuvette Centrale, and major tributaries (Kasai, Lualaba, Oubangui, and Sangha) plays a central regulatory role. Our current understanding of these processes remains very limited, which restricts our ability to implement policies for water security and address the impacts of change on physical systems and society. Increasing pressures from deforestation, mining, land use, and climate change threaten hydrological stability, livelihoods, and ecosystem resilience. Heightened vulnerability to hydro-climatic extremes, such as floods, droughts, and landslides, and biological risks like Ebola outbreaks raises concerns of an approaching hydro-ecological tipping point. The chapter calls for urgent investment in monitoring networks, remote sensing, data integration, and predictive modeling to support sustainable water resources management and development. Fifteen major hydrological research challenges are identified , underscoring the need for robust scientific investment. Beyond the CRB, findings will enhance global understanding of tropical forest hydrology and reinforce the basin’s critical role in the Earth System.

Peatlands in the Congo Basin are not only critical ecological assets for carbon storage, hydrological regulation and biodiversity conservation; they are also deeply embedded in social, cultural and governance systems. Their sustainability therefore depends on their recognition as complex social-ecological systems, shaped by long-standing interactions between communities, customary institutions and natural processes. Yet, despite growing scientific attention, the vulnerability and resilience dynamics of these systems remain insufficiently understood. This study applies a multidisciplinary, system-based framework across seven peatland sites, including one peatland mangrove site in the Democratic Republic of the Congo, to analyse peatland dynamics through interacting dimensions of exposure, sensitivity and adaptive capacity. Using mixed methods: household surveys (n = 350), floristic inventories, satellite-based hydrological analysis and human-animal-environment analysis, we integrate evidence across hydrological, ecological, socio-economic, governance and health domains. The results show strong spatial heterogeneity in peatland integrity (water supply, vegetation composition and socio-economic activities) and human dependence, indicating that management strategies cannot be uniform across scales. Species-rich peat swamp forests of the CuvetteCentrale reveal greater functional redundancy and resilience than low-diversity coastal mangrove peatlands. High livelihood dependence, low educational attainment, weak infrastructure and limited economic alternatives increase social vulnerability to environmental change. Peatlands also emerge as critical human-wildlife interfaces, where intensive hunting and bushmeat trade elevate zoonotic disease risks. Overall, the Congo Basin peatlands function as complex coupled human-natural systems characterized by non-linear feedback and threshold behaviours. Sustainable management therefore requires preventive, integrated interventions that protect hydrological integrity, conserve biodiversity, strengthen livelihoods, align customary and statutory governance, and address health risks under accelerating climate and land-use change. A French translation of this abstract is available in the supplementary material. This article is part of the discussion meeting issue 'African tropical peatlands: function, value and vulnerability'.

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.

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 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 water in Earth's rivers propagates as waves through space and time across hydrographic networks. A detailed understanding of river dynamics globally is essential for achieving accurate knowledge of surface water storage and fluxes to support water resources management and water‐related disaster forecasting and mitigation. Global in situ information on river flows are crucial to support such an investigation but remain difficult to obtain at adequate spatiotemporal scales, if they even exist. Many expectations are placed on remote sensing techniques as key contributors. Despite a rapid expansion of satellite capabilities, however, it remains unclear what temporal revisit, spatial coverage, footprint size, spatial resolution, observation accuracy, latency time, and variables of interest from satellites are best suited to capture the space‐time propagation of water in rivers. Additionally, the ability of numerical models to compensate for data sparsity through model‐data fusion remains elusive. We review recent efforts to identify the type of remote sensing observations that could enhance understanding and representation of river dynamics. Key priorities include: (a) resolving narrow water bodies (finer than 50–100 m), (b) further analysis of signal accuracy versus hydrologic variability and relevant technologies (optical/SAR imagery, altimetry, microwave radiometry), (c) achieving 1–3 days observation intervals, (d) leveraging data assimilation and multi‐satellite approaches using existing constellations, and (e) new variable measurement for accurate water flux and discharge estimates. We recommend a hydrology‐focused, multi‐mission observing system comprising: (a) a cutting‐edge single or dual‐satellite mission for advanced surface water measurements, and (b) a constellation of cost‐effective satellites targeting dynamic processes.

Rivers play a crucial role in maintaining ecosystems and regional climates, while also providing essential water for irrigation and drinking. With the increasing impacts of climate change and human activities, rivers are becoming more prone to extreme events (droughts and floods) and induced erosion/deposition processes, making water-related risk management more challenging. The recent launch of the Surface Water and Ocean Topography (SWOT) mission, which focuses on continental surfaces with a spatial resolution of 100 m, has opened new avenues for applications in hydrology, hydrodynamics, and geomorphology. This study examines the initial results of the SWOT mission for sixteen rivers (ranging in width from 50 to 2,000 m) in the Cuvette Centrale of the Congo Basin, obtained in April 2023 during the fast sampling phase. The study assesses water surface elevations (WSE) and backscatter coefficient (σ0) measured from SWOT. Comparisons of WSE with data from other radar altimetry missions (Sentinel-3A and 3B, Jason-3, and Sentinel-6A) and GEDI data show high correlation coefficients of 0.977 (with a bias of 0.538 m) and 0.992 (with a bias of 1.011 m), respectively. The first maps of WSE slopes show realistic values, even in rivers less than 100 m wide, with steeper slopes upstream. Various WSE longitudinal profiles are retrieved with unprecedented spatial resolution, surpassing what other nadir altimetry missions have achieved. The σ0 values, between −10 and 20 dB on average, also appear consistent with other studies. These promising initial results pave the way for future studies on fluvial geomorphology dynamics and erosion/deposition processes from the new SWOT observations.

Lake Tanganyika in East Africa contains 17% of the free freshwater on the Earth's surface and provides important ecosystem services to similar to 13 million people in the region. It is one of the great lakes in East Africa for which a significant rise in water level between 2019 and 2020 led to flooding, with major environmental consequences and social impacts. This study focused on the Lake Tanganyika basin water balance between 2003 and 2021 to assess the influence of recent climate variability on lake water level variations (due in particular to the floods of 2020 and 2021) and to explore early warnings of flooding in the lake's surrounding lowlands. This process is performed using remote sensing data. For the computation of the basin's water balance, we compared variations in the watershed total water storage (TWS) with the basin water flux calculated using rainfall, evaporation (E), evapotranspiration (ET) and discharges data. The space-time variations in rainfall, E and ET were analyzed by decomposing their time series into trend and seasonal signals and applying (only for rainfall) multivariate statistical analysis to the decomposed signals. For flood mapping, we calculated the MNDWI spectral water index from Sentinel-2 images acquired between 2017 and 2022. Our study showed that the basin water balance is closed when rainfall from Era5 is combined with E and ET from GLEV and MOD16A2, respectively. During the 2003-2021 period, over the entire watershed, water losses of similar to 70 km3 due to lake E were offset by an increase in water inflows of similar to 100 km3 in the rest of the watershed. During the period from 2003 to 2021, the E rate from the lake was stable overall, while the ET and rainfall mainly in the Malagarasi basin increased significantly. The surface water storage (SWS), which represents the variation in lake water volume derived from altimetry measurements, corresponds to 41.8% of the TWS, groundwater storage corresponds to 57.7% of the TWS, and the soil moisture is less than 0.5%. The TWS strongly correlated with the SWS (similar to 91%), with a one-month lag in the SWS variations in response to the TWS fluctuations. Therefore, the SWS in May, when the flood risk is the highest, was estimated using TWS in February, March and April with accuracies of 85%, 94% and 95%, respectively. This valuable information could be integrated into flood management tools, particularly for areas such as Gatumba city and the Ruzizi Delta Nature Reserve, which were heavily affected by the May 2021 floods.

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).

Study region: The Congo River basin (CRB), the world's second-largest river system, is subject to extreme hydrological events that strongly impact its ecosystems and population. Study focus: Here we present an improved 40-year (1981-2020) hydrological reanalysis of daily CRB discharge and analyze the spatiotemporal dynamics of recent major CRB floods and droughts, and their teleconnection with El Nin similar to o-Southern Oscillation (ENSO), the dominant driver of tropical precipitation. We employ a large-scale hydrologic-hydrodynamic model (MGB) with lake storage dynamics representation and a data assimilation (DA) technique using in-situ and remote sensing observations. New Hydrological Insights: The MGB model demonstrates satisfactory performance, with KlingGupta efficiency metric of 0.84 and 0.71 for calibration and validation, respectively. Incorporating lake representation substantially enhances simulations, increasing the Pearson correlation coefficient from 0.3 to 0.63. Additionally, DA yields a -13% reduction in discharge errors via cross-validation. We find that the 1997-1998 flood impacting the south and central CRB is statistically linked to a major El Nin similar to o event during that period. However, no such association is found for the 2019-2020 flood. Severe droughts in 1983-1984 and 2011-2012, affecting northern and southern CRB respectively, exhibit strong correlation with preceding El Nin similar to o and La Nin similar to a events, with a -10-12 months lag. This study advances understanding of the intricate interplay between spatiotemporal hydrological variability in CRB and large-scale climate phenomena like ENSO.

The Congo River Basin (CRB), located in the central region of Africa, is of particular importance for regional and global climate and carbon studies. Being the second largest river basin after the Amazon, it is also the one with the most free-flowing rivers. However, despite these important characteristics, it has not attracted as much attention among the scientific communities as the Amazon Basin or other large tropical rivers in the world. Because of the lack of comprehensive and maintained in situ data networks over time, large-scale monitoring of hydroclimatic variables has not been properly conducted. In this context, near real-time observations of the CRB, such as water surface elevation (WSE) and river discharge, as well as understanding the impacts of climate change in a spatiotemporally distributed manner across the basin present a major challenge. In the last few years, however, the scientific community, supported by the leading operational organism in the CRB (the CICOS), has worked on applying innovative tools, from hydrological and hydrodynamic modeling to the use of space data, to improve this monitoring and understanding of hydrological processes.Our work illustrates how space Earth Observation (EO) datasets used jointly with a hydrological model improve both near-real-time monitoring and past-period revisiting (from 1980). First, we built and validated an extensive database on long-term time series of water levels (WL) from satellite altimetry using a comprehensive unprecedented in situ database (root mean square error varying between 10 cm to 75 cm). Crossing this database with the Global Inundation Extent from Multi-Satellites (GIEMS) database, we analyzed the normal behavior of surface water in the CRB, and worked towards understanding the genesis of recent extreme events. The observations permitted to highlight the different travel time of waters from one to three months depending on its origin, and to discriminate the relative contribution of southern and northern sub-basins to the first and second peaks at the outlet of the basin Kinshasa/Brazzaville station. These datasets are then used to calibrate/validate the setting of a large-scale hydrologic and hydrodynamic model, the MGB model, in which lakes representation parameters are tuned using all the aforementioned databases and the long term CHIRPS precipitation product. In terms of discharge estimates, the model run resulted in an average KGE efficiency index value of 0.84 and 0.71 for the calibration (2001-2020) and validation (1981-2000) periods respectively.When included within a scheduler, this model run validated by space EO datasets now permits the inference of discharge and depths all over the basin in real-time. In addition, data assimilation techniques applied to ingest remote sensing datasets, into the MGB model, improves such real-time estimates. Long term modeling also provides a new look and understanding on recent hydrological extreme events that occurred in the CRB, and permits analyzing the impact of recent global and regional climate change on freshwater in one of the most free-flowing watersheds.

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.

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.

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.

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.

Despite being the second-largest watershed and tropical forest worldwide, with significant impacts on the global water cycle and in regulating Earth’s climate, the Congo River Basin’s (CRB) hydroclimatology remains among the least studied worldwide due to the lack of situ observations. To better characterize CRB surface hydrology and the variability of its different components at large scale, we jointly used a trove of large records of in situ and satellite-derived observations, specifically, Surface Water Level (SWL) from radar altimetry (a total of ~2,300 virtual stations) and Surface Water Extent (SWE) from the Global Inundation Extent from Multi-Satellite (GIEMS) dataset. A good performance is found between SWL from multi- satellite missions and in situ water height of historical and contemporary observations at different locations. The root mean square error varies from 10 cm for Sentinel-3A to 75 cm for European Remote Sensing-2. SWL annual amplitude exhibits large spatial variability across the basin, with Northern sub-basins varying more than 5 m while the central and the southern sub-basins vary in smaller proportions (1.5 to 4.5 m). The assessment of SWE also agreed relatively well over a ~25-year period with in situ discharge from sub-basin to basin scale. At the basin scale, SWE shows that cuvette centrale is flooded at its maximum in October/November. The northern part of the basin reaches its maximum in September/October, and the southern eastern one in January/February. Furthermore, SWL and SWE help capture the water travel time across the basin that varies from 0 to 3 months and the regional relative contribution to the flow at Brazzaville station characterized by a bimodal hydrological regime. Northern sub-basins and the cuvette centrale contribute much to the large peak in December-January while the southern sub-basins contribute to both peaks. We further combine these two datasets to estimate the variability of Surface Water Storage (SWS) in rivers, lakes, floodplains, and wetlands across the entire basin over the period 1992–2015. The CRB SWS shows an annual amplitude varying between ~74 km3 and ~112 km3. Moreover, the combination of SWS and the annual variations of GRACE/GRACE-FO-derived terrestrial water storage permits us to estimate the long-term variation of sub-surface water storage. The use of these new long-term satellite-derived observations are an invaluable source of information for hydrological modeling and will allow to properly characterize and reproduce the hydro-climate variability of the CRB, and a better representation of local and regional hydrological processes. These results ensure therefore an improved monitoring of CRB hydrological variables from space, and open new perspectives towards a better evaluation of the impact of climate variability on water availability in the region.

Despite being the second-largest watershed with significant impacts on the global water cycle, the Congo River Basin’s (CRB) hydroclimatology remains among the least studied worldwide due to the insufficient in situ observations. To better characterize CRB surface hydrology and the variability of its different components at large scale, we jointly used a trove of large records of in situ and satellite-derived observations, specifically, Surface Water Level (SWL) from radar altimetry (a total of ~2,300 virtual stations) and Surface Water Extent (SWE) from the Global Inundation Extent from Multi-Satellite (GIEMS) dataset. A good performance is found between SWL and in situ water height at different locations, with root mean square error varying from 10 cm for Sentinel-3A to 75 cm for European Remote Sensing-2. The assessment of SWE also agreed relatively well over a ~25-year period with in situ discharge from sub-basin to basin scale. SWL annual amplitude exhibits large spatial variability across the basin, with Northern sub-basins varying more than 5 m while the central and the southern sub-basins vary in smaller proportions (1.5 to 4.5 m). Furthermore, SWL and SWE help capture the water travel time across the basin that varies from 0 to 3 months and the regional relative contribution to the flow at Brazzaville station characterized by a bimodal hydrological regime. Northern sub-basins and the cuvette centrale contribute much to the large peak in December-January while the southern sub-basins contribute to both peaks. We further combine these two datasets to estimate the quantity of the variability of Surface Water Storage (SWS) using two methods, one method used hypsometric curves approach combining topographic data and SWE, and the second method used SWL variation and SWE. SWS in rivers, lakes, floodplains, and wetlands of the CRB is estimated over the period 1992–2015. The CRB SWS shows an annual amplitude ranging between ~74 km3 and ~112 km3. Moreover, the combination of SWS and the annual variations of GRACE/GRACE-FO-derived terrestrial water storage permits us to estimate the long-term variation of groundwater storage. Our results provide a better understanding of the hydrological variables in the CRB and their link with climate.