# Hydrogeological control of groundwater variations in the Congo River Basin revealed by GRACE water storage change decomposition

Benjamin M Kitambo, Sly Wongchuig, Raphael M Tshimanga, Adrien Paris, Alejandro Blazquez, Daniel Moreira, Frederic Frappart, Ayan Santos Fleischmann, Jean-Marie O Kileshye, Mohammad J Tourian, et al.

► To cite this version:

Benjamin M Kitambo, Sly Wongchuig, Raphael M Tshimanga, Adrien Paris, Alejandro Blazquez, et al. Hydrogeological control of groundwater variations in the Congo River Basin revealed by GRACE water storage change decomposition. Journal of Hydrology: Regional Studies, 2025, 62, pp.102810. 10.1016/j.ejrh.2025.102810. hal-05528720

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# Hydrogeological control of groundwater variations in the Congo River Basin revealed by GRACE water storage change decomposition

Benjamin M. Kitambo<sup>a,b,c,d,*</sup>, Sly Wongchuig<sup>d,e</sup>, Raphael M. Tshimanga<sup>b</sup>, Adrien Paris<sup>e,f</sup>, Alejandro Blazquez<sup>d</sup>, Daniel Moreira<sup>f</sup>, Frederic Frappart<sup>g</sup>, Ayan Santos Fleischmann<sup>h,g</sup>, Jean-Marie O. Kileshye<sup>i,j,g</sup>, Mohammad J. Tourian<sup>a,d</sup>, Fabrice Papa<sup>d</sup>

## ARTICLE INFO

**Keywords:**

Congo River Basin
Groundwater water storage anomaly
Hydrogeological control
Baseflow index
Observation-based surface water storage anomaly
Congo River Basin's groundwater variations

## ABSTRACT

**Study region:** The Congo River Basin (CRB) holds significant groundwater (GW) resources that play a crucial role as the primary source of drinking water for CRB's human population.

**Study focus:** Here, we provide the quantification of the temporal dynamics and, for the first time, the spatial distribution of monthly Groundwater Storage Anomaly (GWSA) in CRB from 2002 to 2015, through the decomposition of the Total Water Storage Anomaly (TWSA) measured by the Gravity Recovery and Climate Experiment (GRACE) mission. Observation based-surface water storage anomaly (SWSA) developed from multi-satellite missions was a game changer in providing reasonable GWSA seasonal dynamics.

**New hydrological insights:** The contributions of each hydrological reservoir, including SWSA, root-zone soil moisture, and GWSA, represent, respectively, ~18 %, ~59 %, and ~23 % of the seasonal amplitude of TWSA (~365 ± 105 km³), in line with previous model-based estimates. Additionally, at sub-basin scale, our study reveals that GWSA ranges between 42 % (for Ubangui and Kasaï) and 16 % (for Lower-Congo) of TWSA. The spatial distribution of GWSA confirms the expected hydrogeological behavior for each type of aquifer related to the four main hydro-geological formations encountered in CRB with high GWSA variability and high Base-Flow Index (BFI) in unconsolidated (BFI = 0.68) and consolidated (BFI = 0.67) sedimentary aquifers in

<footnote><sup>a</sup> Institute of Geodesy, University of Stuttgart, Stuttgart 70174, Germany</footnote>

<footnote><sup>b</sup> Congo Basin Water Resources Research Center (CRREBaC), the Regional School of Water (ERE), University of Kinshasa (UNIKIN), Kinshasa, Democratic Republic of the Congo</footnote>

<footnote><sup>c</sup> Faculty of Sciences, Department of Geology, Geotechnical - Hydrogeology laboratory, University of Lubumbashi (UNILU), Route Kasapa, Lubumbashi, Democratic Republic of the Congo</footnote>

<footnote><sup>d</sup> Laboratoire d'Etudes en Géophysique et Océanographie Spatiales (LEGOS), Université de Toulouse, CNES/CNRS/IRD/UT, Toulouse, France</footnote>

<footnote><sup>e</sup> Hydro Matters, 1 Chemin de la Pousaraque, Le Faget 31460, France</footnote>

<footnote><sup>f</sup> Geological Survey of Brazil, Rio de Janeiro, Brazil</footnote>

<footnote><sup>g</sup> ISPA, UMR1391 INRAE, Bordeaux Sciences Agro, 71 Avenue Edouard Bourlaux, Villenave d'Ornon 33882 CEDEX, France</footnote>

<footnote><sup>h</sup> Instituto de Desenvolvimento Sustentável Mamirauá, Tefé, AM, Brazil</footnote>

<footnote><sup>i</sup> WaterNet, North-West University's (NWU) Faculty of Natural and Agricultural Sciences, South Africa</footnote>

<footnote><sup>j</sup> Faculty of Engineering and School of Industrial Engineering, University of Lubumbashi (UNILU), Route Kasapa, Lubumbashi, Democratic Republic of the Congo</footnote>

<footnote>* Corresponding author.
E-mail address: b.kitambo@gis.uni-stuttgart.de (B.M. Kitambo).</footnote>
contrast with lower GWSA variability and BFI in fracture flow (BFI = 0.14) and basement (BFI = 0.12) aquifers.

# 1. Introduction

Freshwater constitutes an essential resource for human needs and ecosystem environments, while concurrently contributing to economic sustainability (Ahmed and Wiese, 2019). The largest freshwater stock is groundwater (GW), which represents ~99 % of liquid freshwater globally (Shiklomanov and Rodda, 2003; Stone et al., 2019; Pointet, 2022) and contributes to numerous natural cycles and processes. Nearly a quarter of the total human water consumption comes from GW (Albert et al., 2021; Pointet, 2022), and a significant proportion of the world's largest cities (~50 % of the global urban population) rely on this vital resource (Foster, 2020) to satisfy their population water requirements (UNESCO, 2022). A substantial proportion (~49 %) of the world's water supply that is allocated for domestic use, including drinking water, is derived from GW withdrawals, a scenario observed in both urban and rural settings (Döll et al., 2012; Margat and Van der Gun, 2013). Furthermore, ~38 % of the world's irrigated land is serviced by nearly 25 % of water withdrawn from GW (UNESCO, 2022). Over the past century, global water use has increased sixfold, and in the context of the current increasing population and economic development, the demand will continue to grow steadily (Wada et al., 2016). In the context of global climate change, this phenomenon may be exacerbated (Van der Gun, 2012), leading to a worsening GW depletion on a global scale (Döll, 2009; Green et al., 2011). This situation represents a significant threat to global water security (Konikow and Kendy, 2005; Wada et al., 2012). However, despite its huge importance, GW resources remain poorly understood in contrast to surface waters, consequently resulting in their undervaluation, mismanagement and inadequate protection (Pointet, 2022). Additionally, its variations and temporal dynamics remain under-explored at large to global scales due to a lack of comprehensive monitoring frameworks (Frappart et al., 2019). Furthermore, the critical state of current monitoring systems hinders local knowledge of hydro-geological characteristics and proper assessments of groundwater sustainability (UN-Water, 2021; UNESCO, 2022).

Today, the main reliable method for inferring changes in GW storage on a large scale is through the use of satellite gravimetry in combination with auxiliary land surface water storage estimates (Springer et al., 2023). Since the launch of the Gravity Recovery and Climate Experiment (GRACE) mission in March 2002, which measures temporal variations of Terrestrial Water Storage Anomaly (TWSA), efforts have been devoted to measuring GW storage changes for the world's major aquifers (Frappart and Ramillien, 2018).

The Congo River Basin (CRB), located in Central Africa, is home to the world's second-largest river basin and tropical rainforest. This region holds significant GW resources, which play a crucial role in the basin's water balance and ecosystems (Kasongo-Numbi, 2008). Furthermore, the majority of the population also relies on GW as their primary source of water (Braune and Xu, 2010). For instance, in the Democratic Republic of Congo (DRC), domestic water consumption is the predominant water use, accounting for ~52 % of total water withdrawal, mainly from GW. This contrasts with most African countries, where agricultural usage occupies the predominant sector (Partow, 2011; Global Water Partnership, 2022). Moreover, according to estimates by the Food and Agriculture Organization's AQUASTAT (FAO-AQUASTAT), the amount of GW in the DRC constitutes approximately 47 % of the country's total internal renewable water resources (Partow, 2011). The strategic role that GW assumes in the CRB emphasizes the relevance of systematic data collection and hydrogeological studies in the efficient and sustainable planning of GW resources use. Recent efforts by the scientific community have enabled progress in the estimation of the seasonal and interannual variability of freshwater stocks, including GW, at the CRB basin scale. This process is generally achieved through a modeling framework and the decomposition of GRACE's signal using mainly model-based water masses, such as snow, surface water storage anomaly (SWSA), and root-zone soil moisture (SMroot) at the global scale (e.g., Getirana et al., 2017; Güntner et al., 2024; Papa and Frappart, 2021). GRACE observations have indeed provided key hydrological information within the CRB. Crowley et al. (2006) quantified the dynamics of the terrestrial water storage from April 2002 to May 2006. Lee et al. (2011) employed a multi-sensor approach (i.e., GRACE, satellite radar altimeter, GPCP, JERS-1, SRTM, and MODIS) to estimate the amount of water that annually fills and drains the Congo main wetlands. Richey et al. (2015) revealed the low GW stress from the renewable GW stress ratio and pressure on water resources. Becker et al. (2018) determined the sub-surface water storage, defined as the SMroot and GW, for the period from 2003 to 2007, and showed that the CRB's SWSA contributes to 19 ± 5 % of the annual variations in GRACE-derived terrestrial water storage. Tourian et al. (2023) estimated the CRB's Total Drainable Water Storage (defined as a proxy measure, offering insights into the capacity and storage capabilities of a basin to retain water), which is unevenly distributed throughout the basin and ranges from 476 km³ to 502 km³. Employing other hydro-geological tools, Mouakoumbat et al. (2022) characterized the continuous aquifers of the Congolese Cuvette basin, which are primarily composed of sands, clayey sands, and clay, using the MODFLOW model.

Despite these recent advances, quantifying GW Storage Anomaly (GWSA) changes remains challenging due to the lack of adequate datasets regarding surface water dynamics. This limitation restricts the capability of global land surface and hydrological models to properly simulate GW dynamics (Yamazaki et al., 2011; Getirana et al., 2017; Papa and Frappart, 2021). This is particularly evident in tropical environments, such as CRB, where SWSA plays a significant role in the hydrological processes (Getirana et al., 2017; Papa et al., 2015; Papa and Frappart, 2021). To overcome such limitations, several approaches have been developed that take into account the quantification of SWSA changes to decompose GRACE's signal (Frappart et al., 2019; Rodell and Reager, 2023). Recent estimates of the monthly changes in SWSA in the CRB (1992–2015) have been made using a combination of surface water extent data from the Global Unindation Extent from Multi-Satellite with topographic data (Kitambo et al., 2023). This development is a step forward to the first large-scale GWSA characterization at the basin scale. The novelty of this study is the assessment of the hydrogeological behavior related to the aquifer's types and the spatial variability characterization of GWSA in the CRB. Our objective is twofold: 1) To
characterize GWSA dynamics at the basin and sub-basin levels and to map, for the first time, the spatial distribution of the GWSA stored in the CRB aquifers from April 2002 to December 2015 (~14 years). We further compared our GWSA estimates with other external model-based estimates; 2) To analyze the spatial distribution of GWSA changes for the main aquifer types identified in CRB. These aquifer types include unconsolidated sedimentary aquifers, consolidated sedimentary aquifers dominated by intergranular and fracture flow, and basement aquifers. Additionally, an investigation of their relationships with baseflow contributions is performed.

The structure of this study is delineated as follows. 2 presents the study area. 3 describes the datasets and 4 the methodological framework employed in this study. 5 is dedicated to the presentation of results, discussions, and validation. 6 provides the conclusions, limitations of the study, and future perspectives. Finally, the Section related to Open Research presents the repository from which the GWSA dataset can be accessed freely.

## 2. Study area

The CRB (Fig. 1) with its contiguous tropical forests is the world's second-largest river basin with an estimated drainage area of ~3.7 × 10⁶ km², a mean annual river discharge to the ocean of ~40,500 m³ s⁻¹, a coverage of ~45 % by tropical forest, and ~28 % of the total tropical peat carbon (Verhegghen et al., 2012; Laraque et al., 2020, Crezee et al., 2022). The drainage area of the CRB spans over nine countries due to its vast hydrographic system, which comprises the Congo main stem, Lualaba (i.e., from the source up to Boyoma Falls, Kisangani) and the Congo River (i.e., from Kisangani to the outlet). This system extends for a total of 4700 km from its source in the savanna highland plateaus located in the southeastern part of the Democratic Republic of Congo (DRC) to the Atlantic Ocean (Runge, 2007). The basin's substantial resources have been demonstrated to exert a significant impact on the socioeconomic growth at the regional scale and the regulation of the Earth's climate (Tshimanga et al., 2022). According to Inogwabini (2020) and White et al. (2021), more than 80 % of the riparian population relies on basin water resources for their livelihoods.

Regarding the basin's physiography, CRB is divided into six main sub-basins (Fig. 1) (Laraque et al., 2020): Lower-Congo (southwest), Middle-Congo (center), Sangha (northwest), Ubangui (northeast), Kasaï (south-center), and Lualaba (southeast). The mean annual precipitation is 2000 mm in the central region of the basin, decreasing to 1100 mm in regions distant from the equator. The hydrological regime at the outlet of CRB is characterized by the alternation of two periods of high flows (October-January; April-May) and two periods of low flows (February-March; June-September), a phenomenon attributed to the twice-annual equatorial transit of the Intertropical Convergence Zone (Laraque et al., 2020; Nicholson, 2022). The mean surface air temperature over the basin

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** The Congo River basin (CRB) and its main sub-basins: Lower-Congo, Sangha, Ubangui, Middle-Congo, Kasaï, and Lualaba (small dark line). Light blue lines and polygon colour represent major rivers and lakes respectively. The green background refers to the major wetlands (Source: Gumbricht et al#., 2017; world_map.gpkg).</figcaption></figure>
is estimated to be 25° C. For the annual potential evapotranspiration, the peak is ~1500 mm per year near the equator, and it decreases northwards and southwards to less than 1000 mm per year (Sridhar et al., 2022).

The dense hydrographic system of the CRB is dominated by the presence of several lakes (i.e., lake Bangweulu, Mweru Wantipa, Tanganyika, Kivu, Upemba, etc., Fig. 1), with the majority located within the southeast Lualaba sub-basin (Runge, 2007). From a geomorphological point of view, the basin is also characterized by its major wetlands, including an extensive internal drainage basin, known as the Cuvette Centrale, which is almost surrounded by peripheral relief at relatively high altitudes. The topography of the Cuvette Centrale is characterized by a flat expanse, with a predominance of a large tropical rainforest along with large wetlands and floodplains (Laraque et al., 2009, 2013; Bwangoy et al., 2010; Betbeder et al., 2013). The topography all around the Cuvette Centrale region is bordered by plateaus and mountain ranges (e.g. Mayombe, Chaillu, and Batéké). The distribution of vegetation in the Cuvette Centrale region is typically concentric. In the center of the basin lies the majestic equatorial forest with its multiple facies. In the border, almost outside the basin, appears the grassy savannah with Sahelian climate. The area between these two zones is characterized by the vegetation typical of the Sudanian climate, featuring wooded areas and grassy savannas (Bricquet, 1993). Regarding geology, CRB is distinguished by a long and complex history of sedimentation, erosion, and preservation, governed by an interplay of tectonic and climatic factors. It belongs to the Central African Shield (i.e., one of the four Africa's major Proterozoic shields tectonically amalgamated along Neoproterozoic orogens [West African Shield, Saharan Shield, Southern African Shield]). The aforementioned formation constitutes the basement of the basin, which is composed of Precambrian crystalline rocks and covered by Phanerozoic sedimentary rocks, recent sediments, laterites, and vegetation (Kadima et al., 2011, Linol et al., 2015, De Wit and Linol, 2015).

CRB's GW resources are distributed across four key large-scale hydrogeological units (Fig. 2; [Partow, 2011; Africa Groundwater Atlas, 2019]), including: (1) The precambrian basement aquifer, which is composed of crystalline and metasedimentary rocks with typical weathered/fractured aquifer properties. The aquifers in this region are characterized by their low to moderate productivity levels, which occasionally reach high levels. These aquifers are mainly located in the eastern, western, and northern regions of the basin; (2) The unconsolidated sedimentary aquifer with predominantly intergranular flow. This aquifer is composed of rocks from Quaternary to the Tertiary period, and it is distinguished by its variable nature, with productivity levels ranging from low to high.

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Key large-scale Congo River basin hydrogeological units provide information on the aquifer type and productivity (Africa Groundwater Atlas, 2019), as well as the locations of in situ river discharge stations (red triangle). Their characteristics are reported in Table S1 related to their number (in supplementary material). White features in the map refer mostly to water bodies (i.e., rivers, lakes) or to the lack of geological information.</figcaption></figure>
These aquifers underlie almost all of the basin's central part, including the Cuvette Centrale and some parts of the southern basin. The recharge for this type of aquifer is direct from rainfall as well as the river system; (3) Cretaceous Consolidated Sedimentary Inter-granular aquifer dominated by intergranular flow, along with low to moderate productivity. These aquifers occupied a major part of the basin's central part, including the Kasaï sub-basin; (4) Consolidated Sedimentary aquifer dominated by fracture flow with sig-nificant karstic features (e.g., southern Lualaba sub-basin characterized by faulted heterogeneous aquifers), with generally low to high productivity but is noticeably variable. The aquifer is composed of Precambrian metasedimentary rocks (Partow, 2011; Chishugi et al., 2018; Upton, et al., 2018a, 2018b, 2018c, 2018d; Sanga et al., 2018; Nkhuwa et al., 2018; Africa Groundwater Atlas, 2019). Therefore, according to the lithological characteristics of the geological formations of the aquifers which set GW storage, flow pathways and recharge potential, unconfined aquifers are more expected in unconsolidated (2) and consolidated (3) sedimentary aquifers dominated by intergranular flow and confined, somehow semi-confined aquifers in Precambrian basement aquifer (1) and consolidated sedi-mentary aquifer dominated by fracture flow (4). Generally, it is hard to find in nature an aquifer that is completely confined or un-confined; usually, it is a mix of the two (Todd and Mays, 2005; Mejías and Plata, 2007; Karamouz et al., 2020).

# 3. Datasets

## 3.1. Total water storage anomaly

The Gravity Recovery and Climate Experiment (GRACE) was a joint NASA and German Aerospace Center (DLR) mission launched in March 2002 (Tapley et al., 2004). It provided observations of changes in Total Water Storage Anomaly (TWSA) at the global scale from March 2002 to September 2017. Its successor GRACE Follow-On (GRACE-FO) was launched in May 2018 (Landerer et al., 2022). For our case study, only estimates from GRACE are used. There are several products already available to be used with focus on different applications. For hydrology applications, mascon gridded data solutions are recommended due to the advantages of reducing the leakage errors (https://grace.jpl.nasa.gov/data/choosing-a-solution/, last access 22/09/2025). Though, in our study, we decided to compare several products to assess the uncertainty in the TWSA and the derived GWSA. The difference in multiple different GRACE solutions is due to the choice in many parameters and solution strategies during the conversion of the Earth gravity field into Level-3 mass change maps (gridded data product). We used both spherical harmonics products (JPL-SH [1.0-degree of spatial resolution], GFZ-SH [1.0-degree of spatial resolution]) and mascons solutions (JPL-M [0.5-degree of spatial resolution] and CSR-M [1.0-degree of spatial resolution]) (https://grace.jpl.nasa.gov/data/choosing-a-solution/, last access 15/09/2025) as well as CNES-L3 [1.0-degree of spatial resolution] hydrological ensemble (Blazquez et al., 2018). The latter is an ensemble product that explores the uncertainty in the processing and post processing of GRACE data (hydroweb.next, last access 22/09/2025).

For computation purposes, GRACE datasets have been rescaled using the nearest-neighbour resampling method to 0.25-degree of spatial resolution. The 1.0 and 0.5-degree pixel values have been redistributed respectively over 16 and 4 pixels of 0.25-degree. The sum of the derived pixel values enables the recovery of the original value at its native spatial resolution. The surface mass change expressed in units of water-height-equivalent (m) has been converted into volume (km³). To do this, we multiply the water-height-equivalent by the pixel area according to the spatial resolution of each product (i.e., 111.12 km x 111.12 km for 1.0-degree and 55.56 km x 55.56 km for 0.5-degree at the Equator).

## 3.2. Surface water storage anomaly

We use the estimates of surface water storage anomaly (SWSA) over CRB (Kitambo et al., 2023) derived from a hypsometric curve approach (Papa and Frappart, 2021), which combines surface water extent from the Global Inundation Extent from Multi-Satellite (Prigent et al., 2020) and Digital Elevation Model-based topographic data as described in the methodology of Papa et al. (2013), Salameh et al. (2017), and Kitambo et al. (2023). This dataset provides regional coverage of SWSA over the CRB at a monthly time step for various water bodies, including wetlands, rivers, and lakes at 0.25-degree (~ 27 km) spatial resolution. The dataset spans over a long-term period, from 1992 to 2015, encompassing a total of 24 years. It is expressed in volume (km³). This dataset is freely accessible on the Zenodo repository: https://doi.org/10.5281/zenodo.7299823. For more details about the aforementioned technique, we refer the reader to the work by Kitambo et al. (2023).

The 24-year SWS dataset characterization and evaluation have been conducted in the CRB against independent hydrological variables, such as precipitation data, in situ discharges, and existing SWSA (Becker et al., 2018). Regarding the evaluation of this product, the 24-year SWS dataset performed well in regards with the existing SWS datasets over the CRB and its mean annual amplitude confidence interval (p-value > 0.01) included all the existing mean annual amplitude computed from different approaches (i.e., multi-satellite and hypsometric curve approaches) and different digital elevation models. For a detailed description of the technique, its evaluation, and assessment, we refer to Kitambo et al. (2023) – in particular, see Fig. 8 and Table 3 and 4 of Kitambo et al. (2023).

## 3.3. Root zone soil moisture

We use the root-zone soil moisture (SMroot) estimates derived from the Global Land Evaporation Amsterdam Model (GLEAM), which provides at a global scale the estimation of terrestrial evaporation and root-zone soil moisture from satellite data (Martens et al., 2017). The dataset was developed based on a reanalysis of radiation and air temperature, a combination of gauge-based, reanalysis, and satellite-based precipitation, and satellite-based vegetation optical depth (Martens et al., 2017). The version used in this study (i.e.,
GLEAM v3.6a) spans over a long-term period from 1980 to 2021 (42-year) and is gridded on 0.25-degree of spatial resolution with a daily temporal resolution (https://www.cen.uni-hamburg.de/en/icdc/data/land/docs-land/readme-gleam-v3-6.pdf, last access 22/09/2025). The monthly time step has been used in our case study. The root depth depends on the land-cover type and GLEAM v3 includes three model layers for the tall vegetation fraction (0–10, 10–100, and 100–250 cm), two for the low vegetation fraction (0–10, 10–100 cm), and just one for the bare soil fraction (0–10 cm) (Martens et al., 2017). The validation of GLEAM v3 has been conducted against measurements from 91 eddy-covariance towers and 2325 soil moisture sensors in different environments, and the performance indicated satisfactory results (Martens et al., 2017). The assimilation of satellite observations of surface soil moisture into the soil profile in GLEAM v3 (https://www.gleam.eu/, last access 22/09/2025) raises our confidence in the use of this product out of others due to the fact that assimilating surface soil moisture improves SMroot (Reichle et al., 2019; Seo et al., 2021). For computation purposes, the SMroot given in m³/m³ has been converted into volume (km³). To achieve this, we multiply the SMroot value (i.e., soil water content) by the pixel area (i.e., 27.75 km x 27.75 km) and the soil depth (i.e., we consider the mean from all model layers considering the main vegetation types in the CRB).

## 3.4. Auxiliary data

### 3.4.1. Africa groundwater atlas

The Africa Groundwater Atlas is an online resource that provides a summary of the GW resources of 51 African countries (Africa Groundwater Atlas, 2019). It includes information on the hydrogeology (i.e., *aquifer type* and *productivity*) and geology (MacDonald et al., 2010) of the main aquifers of each country. The Atlas country hydrogeology maps were developed by modifying and attributing a digital Africa-wide geology map at 1:5 000 000 scale, using available hydrogeological and geological information from hydro-geologists from national agencies. Information about the *aquifer productivity* was synthesized from the existing available quantitative borehole yield data from across Africa (Africa Groundwater Atlas, 2019). This Atlas has been validated where possible and constitutes a valuable continental hydrogeological overview of Africa. However, this Atlas cannot be used to infer detailed information on hydrogeology and geology at local scale. For more details about its limitations, refer to Africa Groundwater Atlas (2019). The Africa Groundwater Atlas is freely accessible and downloaded in shapefile format via <https://www.bgs.ac.uk/africagroundwateratlas/index.cfm> (last accessed: 22/09/2025).

The term *aquifer type* refers to the hydrogeological environment or the geological characteristics that largely control the nature of groundwater flow and storage in the aquifer. On the other hand, *aquifer productivity* expresses the average yields that might be obtained from a single, effectively sited and developed borehole, to a specific depth, in the relevant aquifer. The *aquifer productivity* categories used in the Atlas maps are a largely qualitative assessment due to several factors raised in the Atlas country maps user guide (Africa Groundwater Atlas, 2019).

### 3.4.2. In situ river discharge

For validation purposes, we use historical and contemporary observations of in situ river discharge available at a daily and monthly scale across CRB. These data are used to estimate baseflow with a baseflow separation method. Most of these data are located at the outlet of sub-basins (see Fig. 2 for their locations and Table S1 for their characteristics in supplementary material). In situ river discharge data were obtained from the Congo Basin Water Resources Research Center (CRREBaC, https://www.crrebac.org/, last access: 22/09/2025), from the Environmental Observation and Research project (SO-HyBam, https://hybam.obs-mip.fr/fr/, last access: 22/09/2025), the Global Runoff Data Centre (GRDC, https://grdc.bafg.de/, last access: 22/09/2025), and the Geographical Institute of Burundi (IGEBU, https://www.igebu.bi/, last access: 22/09/2025).

### 3.4.3. The Global Gravity-based Groundwater Product

For validation purposes, we used the Global Gravity-based Groundwater Product (G3P), which provides GWSA (in millimeters) by subtracting the storage compartments of the water cycle that include glacier, snow, soil moisture, and surface water bodies from GRACE/GRACE-FO-based TWSA. To guarantee the accuracy of these products, each storage component is provided with their uncertainties (https://gravis.gfz.de/gws, last access: 22/09/2025). The dataset spans from April 2002 to December 2020, with monthly temporal resolution at 0.5-degree spatial resolution (Güntner et al., 2024). The SWSA used in the G3P project was derived from the Copernicus Global Flood Awareness System (GloFAS) version 4.0 (Prudhomme et al., 2024). Consequently, it is classified as a model-based SWSA. For SMroot, the data was derived from the Copernicus Climate Change Service (C3S) Surface Soil Moisture dataset.

### 3.4.4. Terrestrial water storage variability from a coupled Land Surface Model and River Routing Scheme

For validation purposes, we also use the estimations of terrestrial water storage (TWS) variability (in millimeters equivalent to the height of water) conducted by Getirana et al. (2017) for its main components: surface water storage, groundwater, soil moisture, and snow water equivalent. These components were estimated from the coupling of Noah-MP Land Surface Model and HyMAP River Routing Scheme which provided acceptable outputs (e.g., long-term mean simulated streamflow and global volume of water in rivers and floodplains) as compared to the existing information in the literature. The estimates span over 35 years (1980–2014) with a temporal resolution of one hour and a spatial resolution of one-degree.

## 4. Methods for estimating GWSA variation

To estimate GWSA variations over the CRB, the following approach for the decomposition of TWSA is used (Frappart and Ramillien,
2018; Frappart et al., 2019; Springer et al., 2023).

## 4.1. Groundwater storage estimates using the decomposition method

TWSA is the sum of the contributions of different hydrological storage compartments, including changes in SWSA (ΔSWSA), SMroot (ΔSMroot), Snow (ΔSnow), and GWSA (ΔGWSA):

$$ \Delta TWSA = \Delta SWSA + \Delta SMroot + \Delta Snow + \Delta GWSA \quad (1) $$

From Eq. (1), changes in GWSA can be derived from the TWSA following the formula:

$$ \Delta GWSA = \Delta TWSA - (\Delta SWSA + \Delta SMroot + \Delta Snow) \quad (2) $$

where TWSA stands for the total water storage anomaly; SWSA represents surface water storage anomaly over wetlands, rivers, and lakes; SMroot refers to the soil moisture in the root zone; and Snow stands for snow storage.

This mass balance Eq. (2) has been employed and validated in several studies (Frappart et al. 2011, 2019; Becker et al. 2018; Pham-duc et al. 2019, 2020; Springer et al. 2023). Since there is no contribution of snow storage in the Congo tropical region, this term is excluded. Notice that all the storage terms are expressed in volume (km³) per month. The monthly GWSA at 0.25-degree spatial resolution was computed using Eq. 2 from 2002 to 2015, the period of overlap of all the considered datasets originally at different grid resolutions. To match the resolution of each dataset (i.e., SWSA and SMroot), we implemented a resampling process of different TWSA datasets using the nearest-neighbour resampling method (i.e., JPL-M, JPL-SH, GFZ-SH, CSR-M, CNES-L3) from 0.5 and 1.0-degree to 0.25-degree resolution.

## 4.2. Relative contribution of each water storage component

To define the contribution of each water storage component (i.e., SWSA, SMroot, GWSA) to TWSA, we used the impact index I described as follows (Getirana et al., 2017):

$$ C_i = \sum_{t=1}^{12} |S_{i,t} - \bar{S}_i| \quad (3) $$

$$ I_i = \frac{C_i}{\sum_{l=1}^{nc} C_l} \quad (4) $$

where C_i stands for the sum of absolute monthly climatological anomaly values of a given water storage component i (i.e., SWSA, SMroot, GWSA). t represents the time step. Specifically for each component i, the climatology values S_{i,t} are compared to the mean of the climatology for that component S_i, and the absolute differences are summed over the 12 months of the year. nc is equal to three, corresponding to the three types of water storage (S).

Note that the sum of all I_i is equal to one. The impact evaluation is performed over the intersection period of the TWSA, SWSA, SMroot data, from 2002 to 2015. The missing months in the TWSA dataset have also been removed in the other two datasets during the estimation.

## 4.3. Baseflow separation method

Baseflow is defined as the slow portion of streamflow that is derived from GW storage and other delayed sources (Hall, 1968). Several methods, including the isotopic/chemical tracer (Munoz-Villers et al., 2016), the graphic (Tallaksen, 1995), and the digital filter (Lu et al., 2022) can be used to estimate baseflow. Previous studies compared these different methods and suggested that the digital filter method produces objective results (Nathan and McMahon, 1990; Furey and Gupta, 2003; Gonzales et al., 2009). Digital filter and graphic methods only use streamflow data as an input to estimate baseflow (Baratelli et al., 2018). Regarding the digital filter method, the assumption made for estimating the baseflow is that the latter is the low-frequency part of streamflow (Eckhardt, 2005). In the present study, the digital filter method is employed, particularly the Eckhardt digital filter method, which has been found to provide better performance in comparison to alternative methods (Gonzales et al., 2009; Xie et al., 2020). The baseflow index (hereafter BFI) is defined as the ratio of baseflow to the total streamflow, which indicates the catchment flow regime. Tallaksen and van Lanen (2004) reported that higher indices (i.e., > 0.9) refer to permeable catchments with a very stable flow regime, while low indices (i.e., 0.15–0.2) indicate impermeable catchments with a flashy flow regime.

The Eckhardt digital filter for baseflow separation used is defined as follows (Eckhardt, 2005):

$$ b_t = \frac{(1 - BFI_{max}) \times \alpha \times b_{t-1} + (1 - \alpha) \times BFI_{max} \times Q_t}{1 - \alpha \times BFI_{max}} \quad (6) $$

where b stands for baseflow (m³ s⁻¹); Q stands for streamflow (m³ s⁻¹); t the time (e.g. day) for which the baseflow is calculated; α stands for the GW recession constant; BFI_max refers to the long-term ratio of baseflow to total streamflow. Eckhardt (2005), (2008) proposed the procedure to determine α using the recession analysis and suggested that BFI_max value is equal to 0.8 for perennial streams with porous aquifers, 0.5 for ephemeral streams with porous aquifers, and 0.25 for perennial streams with hard rock aquifers.
Eckhardt (2008) stated that a streamflow $Q_t$ is eligible for the recession analysis over a five-day interval if it satisfied the following equation:

$$Q_{t-3} > Q_{t-2} > Q_{t-1} > Q_t > Q_{t+1} > Q_{t+2} \qquad (7)$$

Where $t$ is time step in days.

To determine $\alpha$, for each streamflow $Q_t$ that satisfy the Eq. (7), a pair of $Q_t$ and $Q_{t+1}$ was built and helped to construct a scatter plot from which a trend line was fitted from the origin such that the gradient (slope) of the straight line was the recession constant $\alpha$.

The estimated values used for $\alpha$ vary from 0.49 to 0.99. According to the CRB's hydrogeological characteristics presented above, we used a $BFI_{max}$ of 0.8 for unconsolidated and consolidated sedimentary aquifers dominated by intergranular flow, and 0.25 for consolidated sedimentary and basement aquifers dominated by fracture flow. After integrating the values $\alpha$ and $BFI_{max}$, the Eckhardt digital filter Eq. (6) can be summarized as:

$$b_t = Ab_{t-1} + BQ_t,$$

which behaves like a deterministic AR(1) model where baseflow remembers its past state while also responding to current streamflow, but only weakly, capturing both groundwater memory and instantaneous recharge dynamics. A and B stand for the derived constant number after using $\alpha$ and $BFI_{max}$ values.

# 5. Results and discussion

## 5.1. Temporal and spatial variability of groundwater storage across the Congo River basin

At the basin scale, Fig. 3 displays the GWSA temporal dynamics at the CRB scale at 0.25-degree of spatial resolution, along with the TWSA, SWSA, and SMroot time series presented at a monthly time scale from April 2002 to December 2015 (~14-year). Fig. 3a shows the TWSA time series, which has been averaged over the entire CRB from different GRACE solutions used in this study (i.e., JPL-M, JPL-SH, GFZ-SH, CSR-M, CNES-L3).

All TWSA show fair agreement in their temporal variations (Fig. 3a), as found in other studies (Wang et al., 2023). However, we observed that TWSA from the CSR-M solution (grey color) deviates from the others, providing higher TWSA values, mainly during the period 2007–2010. This might be attributed to the fact that the intercomparisons based on synthesized TWSA (e.g., JPL-SH and GFZ-SH) are generally 15 % lower than those based on CSR mascon (Scanlon et al., 2016; Ferreira et al., 2023). Figs. 3b and 3c present, respectively, the long-term and substantial interannual variability of observation-based SWSA and SMroot, depicting both the bimodal patterns that characterize the hydrological regime of the CRB. The first large peak spans over August–February and the second minor

<figure><img src="image_6.png" /><figcaption>Fig. 3. (left column) Monthly variations of freshwater storage for the different hydrological reservoirs in the CRB from 2002 to 2015. (right column) Comparative analysis for all the hydrological reservoirs. (a) Time series of TWSA from different GRACE solutions (JPL-M, JPL-SH, GFZ-SH, CSR-M, CNES-L3). (b) Time series of SWSA. (c) Time series of SMroot. (d) derived GWSA from different GRACE solutions. (e) Interannual comparison between TWSA (i.e., mean TWSA from different GRACE solutions), SWSA, SMroot, and GWSA (i.e., mean GWSA from different GRACE solutions). (f) Seasonal comparison between TWSA (i.e., mean TWSA), SWSA, SMroot, and GWSA (mean GWSA). (g) Contributions of SWSA, SMroot, and GWSA (i.e., mean GWSA) to TWSA (i.e., mean TWSA).</figcaption></figure>
peak over March–July. The spatio-temporal characterization of the observation-based SWSA has been analyzed in detail by Kitambo et al. (2023) within the CRB over the period 1992–2015. Fig. 3d shows the resulting GWSA obtained from Eq. 2 using the different datasets overlapping in time (i.e., April 2002 to December 2015). It provides the first time series of GWSA estimations for the entire CRB, showing a large year-to-year variability. Following TWSA, GWSA also shows a higher deviation from CSR-M solution. The results also highlight a spectacular, sudden positive variation in the GWSA time series at the end of 2006 and the beginning of 2007. A negative variation precedes this prominent variation observed both in the TWSA and SWSA at the same period as GWSA. For the SMroot anomaly, this positive rise occurred two years before 2006 (i.e., 2004). Regarding previous analysis, this variability might be related to the drought and floods that recently affected the CRB, particularly the severe drought that occurred in 2005–2006 (Kitambo et al., 2022; 2023). Overall, all GWSA derived from different GRACE TWSA solutions provide almost a quinquennial multi-year variability during the analyzed period (i.e., 2002–2015) starting by a negative variation from 2002 to 2006, followed by a steady variation from 2006 to 2011, and ending by a positive variation from 2011 to 2015. Based on the good agreement between GRACE-based GWSA products, we decided to reduce the analysis to the CNES-L3 product, which is reasonable since this product represents an ensemble-mean from an ensemble of 1500 post-processed GRACE solutions (i.e., resulted from the combinations of six different GRACE processing parameters), which is assumed to encompass a significant part of the uncertainty range of GRACE estimates (Blazquez et al., 2018).

TWSA, SWSA, and SMroot show similar behavior, reaching their maximum in December and April, respectively for the first and second peaks. An exception is made for SWSA that reaches the maximum one month prior as compared to both TWSA and SMroot. Their minimum is all in phase for the second peak in February and for the first peak reached in August, with a month lag of SWSA in July. Fig. 3f depicts a double peak also in GWSA, with an opposite behavior in the dynamics of the other three reservoirs. Thus, the outcomes of this study revealed the time lag of three to four months between GWSA and the other hydrological reservoirs (SMroot and SWSA) when considering the increase of these storages respectively from November and July-August. For GWSA, the maximum occurs for the first and second peak, respectively, in January and May, showing a one-month lag as compared to the three reservoirs. Regarding the minimum, there is an important decrease from May, reaching the lowest level in November. Another minimum occurs in March, showing a month ahead as compared to the minimum of the three reservoirs for the second peak. The relative contributions of each hydrological reservoir (SWSA, SMroot, and GWSA) represent around 18%, 59%, and 23% of the seasonal amplitude of TWSA (~365 ± 105 km³; Fig. 3g and Table 1).

Areas along the main stem of the Congo River and the lakes' region in the Lualaba sub-basin (southern part) show moderate (i.e., 0.1–0.2 km³) to high amplitude (i.e., 0.25 km³ and above) (Fig. 4a). Overall, the western parts of CRB, mainly in the Lower-Congo sub-basin and the north-eastern part of the Lualaba sub-basin, present very low amplitude (i.e., 0.09 km³ and less). In the Kasaï sub-basin, the pattern is well marked, with the northern part characterized by large variations (0.1–0.15 km³) while the southern part showing slightly larger amplitudes (0.15–0.2 km³). Sangha and Middle-Congo sub-basins are characterized in general by 0.1–0.15 km³ of amplitude, whereas in Ubangui, the magnitude of amplitude ranges between 0.15 and 0.2 km³.

There are major contrasts in hydrological behaviors between the north and south (Fig. 4b). The maximum in GWSA occurs in November–December in the north, sometimes in September–October in the extreme north, while in the south, the maximum is reached in general March–April. In some regions of the middle part (between 0° and 5°) of the basin, the maximum is reached in January–February. In the western part of Kasaï sub-basin, an exception occurs with the maximum happening in May–June, similar to a portion of the eastern-southern region of the Lualaba sub-basin. In the heart of the cuvette centrale, we also notice the occurrence in July–August of the GWSA's maximum.

Conversely to the mean month of maximum, the period of low GWSA in the northern part is May–June (Fig. 4c). In the central part, it occurs in September–October and in the southern part, in November–December. Overall, the spatial distribution highlights three major hydrological patterns regarding the magnitude of the amplitude and the time period.

Regarding the spatial distribution of GWSA over the CRB, the results showed a large variability related to a clear hydrological pattern. However, in the Lualaba sub-basin there is a higher amplitude over Tanganyika Lake and other lakes that a priori were not expected to display such amplitude, assuming that under lakes, GWSA does not vary as much (Fig. 4a). The explanation for this might be related to the lag observed between SWSA and TWSA (Gbetkom et al., 2024), the only hydrological reservoirs considered in the expression (2) for deriving GWSA over lakes. It is important to point out that SMroot pixels are generally masked over lakes (i.e., no root zone soil moisture values are provided). Figs. 4b and 4c also presented an interesting hydrological pattern. This pattern matches

<caption>Table 1</caption>

Mean annual amplitude of the CRB and its sub-basins TWSA, SWSA, SMroot, GWSA over the period 2002–2015, and the contribution of each hydrological reservoir to TWSA.

<table><thead><tr><th rowspan="2">Basin and sub-basin</th><th colspan="4">Mean annual amplitude (km³)</th><th colspan="3">Contribution to TWSA (%)</th></tr><tr><th>TWSA</th><th>SWSA</th><th>SMroot</th><th>GWSA</th><th>SWSA</th><th>SMroot</th><th>GWSA</th></tr></thead><tbody><tr><td>CRB</td><td>366 ± 105</td><td>100 ± 23</td><td>259 ± 70</td><td>121 ± 30</td><td>18</td><td>59</td><td>23</td></tr><tr><td>Sangha</td><td>48 ± 12</td><td>28 ± 6</td><td>22 ± 5</td><td>13 ± 3</td><td>38</td><td>39</td><td>24</td></tr><tr><td>Ubangui</td><td>176 ± 47</td><td>16 ± 4</td><td>98 ± 26</td><td>84 ± 22</td><td>8</td><td>50</td><td>42</td></tr><tr><td>Kasaï</td><td>249 ± 68</td><td>24 ± 6</td><td>138 ± 39</td><td>131 ± 34</td><td>7</td><td>51</td><td>42</td></tr><tr><td>Middle-Congo</td><td>105 ± 26</td><td>59 ± 13</td><td>50 ± 11</td><td>37 ± 10</td><td>37</td><td>34</td><td>29</td></tr><tr><td>Lower-Congo</td><td>21 ± 6</td><td>2 ± 0.47</td><td>18 ± 5</td><td>6 ± 1</td><td>6</td><td>78</td><td>16</td></tr><tr><td>Lualaba</td><td>262 ± 69</td><td>78 ± 21</td><td>153 ± 44</td><td>74 ± 17</td><td>25</td><td>56</td><td>19</td></tr></tbody></table>
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Characterization of the spatial distribution of GWSA over the CRB. (a) GWSA amplitude expressed in km³. (b) and (c) Mean month of the maximum and minimum of GWSA, respectively, expressed in months.</figcaption></figure>

the one found from satellite altimetry data (Figs. 5b and 5c of Kitambo et al., 2022). Moreover, between these two satellite datasets (GWSA and satellite-based surface water elevation), we depict a lag describing the delay between the occurrence of the maximum and minimum of satellite-based surface water elevation and the maximum and minimum of GWSA. This might explain the progressive
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Monthly time series of different hydrological reservoirs in the CRB from 2002 to 2015 for each sub-basin. (a, d, g, j, m, p) in the left column, interannual comparison between TWSA (i.e., mean TWSA from different GRACE solutions), SWSA, SMroot, and GWSA (i.e., mean GWSA from different GRACE solutions). (b, e, h, k, n, q) in the middle column, seasonal comparison between TWSA (i.e., mean TWSA), SWSA, SMroot, and GWSA (i.e., mean GWSA). (c, f, i, l, o, r) in the right column, contributions of SWSA, SMroot, and (i.e., Mean GWSA) to TWSA (i.e., Mean TWSA).</figcaption></figure>
process of the aquifer recharge or depletion.

## 5.2. Temporal and spatial variability of groundwater storage at the sub-basin level

Here we characterize and analyze the temporal dynamics of GWSA of each of the CRB sub-basins (hereafter Sangha, Ubangui, Kasai, Middle-Congo, Lower-Congo, and Lualaba, see Fig. 1). Sangha shows a substantial interannual variation. A significant decrease in the low period of GWSA is observed in 2009 and 2011, whereas SWSA shows a large increase for the same period (Fig. 5a). The bimodal patterns are slightly observed in SWSA and GWSA while TWSA and SMroot present a unimodal regime.

This is well depicted at the annual cycle (Fig. 5b) where the major peak occurs in November for TWSA, SWSA, and SMroot. This large peak is followed by the smaller one in May for SWSA. When it comes to periods with low values in these reservoirs, it occurs in sequence, starting with SWSA in February, followed by SMroot in March, then finally TWSA in April. GWSA shows two peaks, with one occurring in September and the other in December. The low period of GWSA in November is totally out of phase in comparison with the three other reservoirs. The second low period occurs in April for the TWSA. Concerning the contribution of each reservoir to TWSA, SWSA, SMroot, and GWSA contribute respectively with 38 %, 39 %, and 24 % (Fig. 5c).

In the Ubangui sub-basin, a peak-to-peak variation for the four reservoirs being all in phase, is observed at the interannual time scale with a delay of one month from the SMroot (Fig. 5d). Exceptionally, we observe an occurrence of the small peak in May from the SWSA. At the seasonal scale (Fig. 5e), one mode in the hydrological dynamics is dominant, with the peak occurring in November for TWSA, SWSA, and GWSA, while for the SMroot it occurs in October. The low period, as in the Sangha sub-basin, displays a successive occurrence. SWSA reaches its lowest volume in February, followed by the SMroot in March, the TWSA in April, and ends with the GWSA in May. In Ubangui sub-basin, we note a preponderant contribution of SMroot (i.e., 50 %) and GWSA (i.e., 42 %) to TWSA as compared to SWSA (i.e., 8 %) (Fig. 5f).

In the Kasai sub-basin, at the interannual variation (Fig. 5g), we highlight the significance of the sudden positive variation in 2006 remarkably in the TWSA and GWSA from a negative change whereas this is not observed in SMroot nor SWSA, even though we observe a positive change starting two years (i.e., 2004) before the highlighted break in 2006 and variation in SWSA during the first six months of this year. At the annual time scale (Fig. 5h), the unimodal regime is still being picked up with the maximum occurring in April for TWSA, SMroot, and GWSA while the SWSA shows a triple peak one in January, the second one in March, and the third one in May. For the minimum, the occurrence is successively, in July for SWSA, in August for SMroot, September for TWSA, and October for GWSA. Similar as for the Ubangui in the same order of magnitude, we note a small contribution of SWSA (i.e., 7 %) to TWSA, whereas SMroot (i.e., 51 %) and GWSA (i.e., 42 %) contribute largely (Fig. 5i).

In contrast with the precedent sub-basins, Middle-Congo presents a higher interannual variation with a clear interannual variability superimposed on variations at a larger timescale, over few years, such as the significant decrease in both TWSA and GWSA until 2006 followed by a steady variation until 2011 breaks by a large low volume in TWSA and GWSA (Fig. 5j). In SWSA, we can observe a steady trend over several years while SMroot displays clear interannual variability as mentioned above in the other sub-basin. The bimodal regime is well depicted in the seasonal cycle characteristic of the CRB's hydrological mode (Fig. 5k). The dynamics observed here are closely related to those for the entire CRB. SWSA (i.e., 37 %) contributes more to the TWSA, followed by the SMroot (i.e., 34 %), and finally by GWSA (i.e., 29 %) (Fig. 5l). Figs. 5m, 5n, and 5o present respectively the interannual comparison, seasonal comparison of TWSA, SWSA, SMroot, and GWSA, and the contribution of SWSA, SMroot, and GWSA to TWSA for the Lower-Congo sub-basin. As it is the outlet sub-basin, the bimodal hydrological regime characteristic of the CRB is well depicted at the interannual scale in all the hydrological reservoirs. The dynamics of GWSA underlined previously (e.g., Fig. 5g) regarding the multi-year changes are observed similarly here, with a positive variation in GWSA from 2006 up to 2011. At the seasonal scale (Fig. 5n), TWSA and SMroot show a unimodal regime with the highest peak reached in April and lowest level occurred in September, while for the SWSA, the bimodal regime can be picked up, with the highest peak occurring in November followed by the small peak in March and the lowest level in June and February. Exceptionally, GWSA shows a triple peak along with the highest in February, followed by the one in May and October. For the lowest level of GWSA, it happens consecutively in November, March, and September. Regarding the contribution of SWSA, SMroot, and GWSA to TWSA, the largest proportion goes to SMroot (78 %), followed by GWSA (16 %), then SWSA (6 %).

Finally, the Lualaba sub-basin, at the interannual scale, presents a unimodal temporal variation in all the hydrological reservoirs. This is well shown at the seasonal scale where the maximum is reached in March for SWSA and SMroot, April for TWSA, and May for GWSA, highlighting the time lag between different reservoirs of one month and two months. For the minimum in different reservoirs, it happens in September for SMroot, October for SWSA and TWSA, and lastly in November for GWSA. In terms of the contribution to the TWSA, SWSA contributes 25 %, SMroot 56 %, and GWSA 19 %.

Following the results at the sub-basin level, a substantial variation of GWSA is observed at each sub-basin. Though two broad categories can be distinguished, the first category with a higher variation of GWSA time series (e.g., Middle-Congo sub-basin), and the second one with a smooth curve of time series of GWSA (e.g., Sangha, Ubangui sub-basin, and so on). These different fluctuations might be related to the geological formation within each sub-basin. For instance, in the Middle-Congo sub-basin, two-thirds of the sub-basin is made of unconsolidated sedimentary formations. Additionally, the boxplot related to this hydrogeological unit displays an elevated mean storage as compared to the other mean storage values from the three hydrogeological units. If we consider its correspondence in terms of water-height-equivalent (m), this simply means a high-water table facilitating a seasonal recharge. On the other hand, for sub-basins with a smooth curve of time series of GWSA, we do not have a direct interaction between the groundwater system and the recharge. This could be for several reasons, among them the geological formation, the interconnection between pores, and so on. This analysis could be further developed using spectral analysis with the Fourier transform during the study regarding the recharge zone when using a cutting-edge coupled set of satellite remote sensing-based datasets, including satellite rainfall products, satellite
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Characterization of the spatial distribution of GWSA with regard to the four main hydrogeological units over the CRB. (a) GWSA standard variation is expressed in km³. (b) GWSA maximum expressed in km³. (c) Spatial distribution of the baseflow index (BFI), defined as the ratio of baseflow to the total streamflow.</figcaption></figure>
altimetry, and the developed current dataset of GWSA.

Overall, regarding the variation of the mean annual amplitude of each hydrological reservoir at the basin and sub-basin level, their
contributions to TWSA are summarized in Table 1.

5.3. *Spatial hydrogeological control of groundwater storage over the Congo River basin*

We further investigate if the estimated GWSA patterns reflect the basin’s hydrogeology characteristics and aquifer types.

Fig. 6 presents the spatial distribution of GWSA superimposed by the CRB's main hydrogeological units and the BFI estimated at each gauging river station available across the basin. Fig. 6a displays the standard deviation (STD) of GWSA dataset for each pixel over April 2002 to December 2015 across the CRB, showing a realistic spatial pattern to the basin hydrogeological units' spatial distribution. The middle northern part and south eastern part (comprising between -5° and -10° of latitude and 15° and 30° of longitude) of the basin show moderate to high variation (e.g., 0.05–0.07 km³). Ubangui and Kasaï sub-basins include most of these parts and are overlayed in large proportion by unconsolidated sedimentary and consolidated formations, dominated by intergranular flow for Kasaï and Ubangui by basement and unconsolidated formations. The western, middle, and eastern parts of the basin present low to very low variation (e.g., 0.03–0.04 km³). Except along the main stem of the Congo River, we highlight a high variation (e.g., 0.07 km³). The western and eastern parts are dominated by basement formation. Fig. 6b shows the maximum of GWSA for each pixel over April 2002 to December 2015, revealing a similar pattern as in Fig. 6a. In addition to Ubangui, the Sangha sub-basin also displays a high value of GWSA maximum in areas of basement and unconsolidated formations.

At each river gauge available across CRB (Table S1, in supplementary material), we compute the BFI from in situ river discharge (Fig. 6c and Table S3 in supplementary material). Concerning the hydrogeology properties, stations with low BFI (0–0.3) are located in the basement and consolidated sedimentary formation dominated by fracture flow, whereas the ones with high BFI (0.7–1) are mainly located in unconsolidated formation. River gauges with moderate BFI (0.3–0.7) are mainly located in consolidated sedimentary formations dominated by intergranular flow, but with occurrences in basement and consolidated sedimentary formations dominated by fracture flow.

Fig. 7 shows the boxplot of the spatial distribution of GWSA (expressed in km³) separated per hydrogeological unit (related to the

<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Hydrogeological characterization of the spatial distribution of GWSA with regard to the four main hydrogeological units over the CRB. (a) Boxplot of the spatial distribution of GWSA (left axis) along with the bar plot of BFI (right axis) per hydrogeological unit. (b) Block diagram of the type of aquifers encountered in the basin at the large scale associated with the hydrogeological units (Africa Groundwater Atlas, 2019).</figcaption></figure>
type of aquifers), along with the bar plot of the BFI (the average BFI per hydrogeological unit as well, without unit). The figure is complemented with the diagrams of the type of aquifers encountered in CRB at the large scale and their main characteristics and processes. We found a higher variation or spread in terms of STD for unconsolidated sedimentary aquifers (i.e., 30 km³) and consolidated sedimentary aquifers dominated by intergranular flow (i.e., 23 km³), for which both types of aquifers have a large BFI, respectively 0.58 and 0.60. Conversely, for the consolidated sedimentary aquifers dominated by fracture flow and basement aquifers, we found low variations in GSWA (respectively 3 km³ and 13 km³) along with small BFI values (respectively 0.28 and 0.22).

This aforementioned analysis shows that the maximum of GWSA is located mainly in two sub-basins, Kasaï and Ubangui, which are composed in large proportion by, respectively, unconsolidated and sedimentary intergranular/fracture and basement formations. This result corroborates the findings by Tourian et al. (2023) which showed a large Total Drainable Water Storage (TDWS, volume of water storage that is connected to the drainage system expressing groundwater and other delay storage fluxes) in Kasaï sub-basin (220 ± 4 km³), where aquifers likely contribute to streams in most geologic settings. Considering the geological formations in the Ubangui sub-basin (Precambrian Basement formations composed of crystalline and metasedimentary rocks), the storage capacity is controlled by weathering/fracturing processes (Fig. 7b displaying the block diagram of the aquifer types), enabling a good storage capacity probably without interaction between the groundwater system and streams. This interpretation might be sustained by a very low BFI within the Ubangui sub-basin (i.e., mean BFI = 0.1) (Fig. 6c). Moreover, as far as BFI is concerned, several studies have found that geology is the key variable to explain baseflow dynamics (Neff et al., 2005; Longobardi and Villani, 2008; Bloomfield et al., 2009). This observation is also sustained by our results in Fig. 6c where BFI varies much in terms of the hydrogeology units with high to moderate values located in the unconsolidated and consolidated (dominated by the intergranular flow) sedimentary formations and small to sometimes moderate values in the fractured sedimentary and basement formations.

## 6. Conclusions and limitations

Here, we use the mass balance equation of TWSA to temporally and spatially characterize the GWSA over the CRB at the unprecedented spatial resolution of 0.25-degree at the period spanning 2002–2015. Three datasets, mainly based on satellite data, have been employed, hereafter, TWSA from GRACE satellite, SWSA from the observation-based multi-satellite, and SMroot from GLEAM v3 to derive GWSA. GWSA has been computed using five TWSA solutions: JPL-M, JPL-SH, GFZ-SH, CSR-M, CNES-L3. Overall, they show satisfactory agreements at both interannual and annual time scales, with minor exceptions related to the overestimation of CSR-M. For further analysis, only CNES-L3 has been considered, and this is sustained by the fact that its STD included the variations of the other GRACE solutions.

This study revealed at the interannual time scale the consecutive multi-year changes with a negative, steady, and then positive variation during the period of analysis (2002–2015). At the seasonal scale, a lag between GWSA and other hydrological variables expressing the progressive process of replenishing GW storage was observed, in contrast to previous studies, and likely associated with a better depiction of surface water dynamics in the TWSA decomposition. At the basin level, the mean annual amplitude of GWSA was found to be 121 ± 30 km³. In terms of contribution to the TWSA, we found GWSA to contribute with 23 %, SWSA with 18 %, and SMroot with the largest percentage of 59 %. Spatially, the analysis of the amplitude and the mean month of the maximum and minimum of GWSA revealed clear hydrological patterns. Regarding amplitude, the main stem of the Congo River and the lakes’ region in the Lualaba sub-basin presented moderate to higher amplitude, whereas the western and north-eastern part of the Lualaba sub-basin presented very low amplitude. Over lakes, the higher amplitude is possibly due to the lag between TWSA and SWSA. The mean month of the maximum and minimum of GWSA also presents a hydrological pattern that matches the ones obtained with satellite altimetry in a previous study (Becker et al., 2018).

At the sub-basin level, two categories of time series have been identified. First, time series with a high variation, for instance, in the Middle-Congo sub-basin, possibly due to the direct recharge of the aquifers. Second, time series with a smooth curve, with an example in the Ubangui sub-basin. This observed behavior was related to the dominant hydrogeological units in the sub-basin. Regarding the proportion of different hydrologic reservoirs, particularly GWSA to TWSA, Kasaï and Ubangui sub-basins are the ones with the most significant contribution (~42 %). The least sub-basins are Lower-Congo (~16 %) and Lualaba (~19 %). This is confirmed by the analysis on the spatial distribution of the maximum of GWSA which locates the largest storage in the aforementioned sub-basins. Finally, this study further investigated the spatial hydrogeological controls of groundwater storage over the CRB as a means of validation of our GWSA product. The analysis showed both a high variation in STD and a high BFI (based on in situ river discharge data) for both the unconsolidated and consolidated (dominated by intergranular flow) sedimentary aquifers. On the other hand, it shows both a small variation in STD and a small BFI for both the consolidated (dominated by fractured flow) sedimentary and basement aquifers.

This study faces some limitations. The first limitation when using GRACE data is its coarse spatial resolution (3.0-degree [provided at 0.5-degree] to 1.0-degree). We made the choice of resampling the spatial resolution to 0.25-degree to match the SWSA and SMroot spatial resolution datasets. Secondly, the other biggest limitation of our study, as often in the CRB is the lack of in situ data, particularly the piezometric water level, to compare with our GWSA estimates. To overcome this critical situation, we performed an indirect validation, first by comparing our GWSA estimates with existing independent global datasets of GWSA from the modeling framework (Getirana et al., 2017) and the other from the G3P project resulting from the decomposition method of TWSA (Günter et al., 2024) at the basin level, and secondly by looking at the variability in GWSA changes and the BFI computed using in situ river discharge. Despite these limitations, satisfactory results came out of this study, enabling the characterization of GWSA over the CRB at the spatial resolution of 0.25-degree spanning the period 2002–2015.
CRediT authorship contribution statement

**Benjamin M. Kitambo:** Writing – original draft, Visualization, Methodology, Formal analysis, Data curation, Conceptualization.
**Sly Wongchuig:** Writing – review & editing, Investigation. **Raphael M. Tshimanga:** Writing – review & editing. **Adrien Paris:** Writing – review & editing. **Alejandro Blazquez:** Writing – review & editing, Formal analysis. **Daniel Moreira:** Writing – review & editing. **Frederic Frappart:** Writing – review & editing. **Ayan Santos Fleischmann:** Writing – review & editing. **Jean-Marie Onema Kileshye:** Writing – review & editing. **Mohammad J. Tourian:** Writing – review & editing, Supervision, Investigation. **Fabrice Papa:** Writing – review & editing, Supervision, Investigation, Data curation.

Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Benjamin M. Kitambo reports financial support was provided by French Space Agency (CNES). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research has been supported by the CNES TOSCA project DYnamique hydrologique du BAssin du CoNGO (DYBANGO) (2020–2023) related to the doctoral grant that supported Benjamin Kitambo during his thesis (2020–2023). We thank Catherine Prigent from Sorbonne Université, Observatoire de Paris, Université PSL Paris, France, for sharing the GIEMS-2 database that allowed us to compute the estimates of surface water storage anomaly over the Congo Basin. We thank the Congo Basin Water Resources Research Center (CRREBaC) for providing in situ river discharge.

Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.ejrh.2025.102810.

Data availability

The GWSA estimates from the five GRACE solutions (JPL-M, JPL-SH, GFZ-SH, CSR-M, CNES-L3) resulted from the approach following the decomposition method of TWSA considering the period April 2002 - december 2015, are publicly available for non commercial use and are distributed via the following URL onn Zenodo platform https://doi.org/10.5281/zenodo.15512149.

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