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.

Abstract Evaporation plays an essential role in the water cycle, influencing local and regional climates while directly impacting water availability in lakes. However, directly measuring evaporation over water bodies remains challenging due to the high costs of installing and maintaining the required in situ instrumentation. Although several remote sensing algorithms have been providing evaporation estimates, the lack of a global validation hinders our understanding of their relative uncertainties and performances across different regions. Here, we analyze the performance of a suite of models that leverage satellite data and meteorological reanalysis to estimate evaporation over lakes worldwide. We compare 3 remote sensing‐based models, 1 reanalysis‐driven model and 1 ensemble approach, using in situ observations from 27 lakes representing a diverse range of geographic and climatic regions. Our results demonstrate that, overall, the ensemble outperformed any individual model in terms of accuracy, with a RMSE and a bias of 1.3 and 0.3 mm day−1, respectively. These findings highlight the benefits of using an ensemble approach to estimate open water evaporation with satellite‐based models at the global scale, leveraging the unique strengths of each model. For the individual models, differences in the representation of heat storage changes and advection effects led to lower values of RMSE and bias, depending on the location and depth of the lakes. This study sets the path for future improvement of open water evaporation algorithms globally, while remote sensing techniques are proven satisfactory to monitoring of water loss in lakes globally, an essential step toward effective large‐scale water resources management.

The long-term trends and prominent cycles in the variability of the Negro River basin hydrological data for 43 years are examined. The basin-mean precipitation does not show any significant change due to mixed trends at individual stations. The river levels at SM Boia & ccedil;u, Cucui and Manaus stations show a positive trend of the order of 1.0 m in 43 years. The river discharge datasets show negative trends in the Negro sub-basin and positive trends in the Branco River sub-basin. Besides the annual, semiannual and ENSO-related (2-7 years) cycles, there is an interdecadal-scale (similar to 15 years) oscillation in the annual precipitation, which explains about 12% of the variance. But the river level and discharge data have decadal or near decadal-scale cycles, indicating possible influence of the Pacific Decadal Oscillation and/or the Atlantic Dipole Oscillation on the hydrology of the basin. The dominating annual cycle in the basin-mean monthly precipitation explains 31% of the variance. The wavelet analysis shows the same cycles as in the Fourier analysis. The power of the annual cycle in the water level at Manaus increased from the 1980s to the 2020s, showing a widening trend in the annual range of water level. The basin-mean precipitation has small but significant positive correlation with the Southern Oscillation Index (SOI) and no correlation with either the Atlantic Dipole Index (ADI) or with Southern Annular Mode (SAM). In general, if the water level data at a station correlates significantly with a given climate index, the discharge dataset at the station also correlates significantly. The trends in the variables, along with the amplitude spectra obtained here, constitute the signature of the hydrometeorological variability in the Negro River basin in the 43-year datasets.

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.

Sedimentary processes cause large changes in riverscapes and alter river banks and margins, leading to major hazards for riverine communities. However, regional mapping of the resulting risk remains scarce, especially in remote Amazon regions. Here, we combine environmental observations with regional socioeconomic information to map erosion and sedimentation risk for Central Amazon river-floodplain communities. We combine long-term trends of open water changes (sedimentation/erosion hazard) with exposure (population size) and social vulnerability (socio-economic indices) to estimate risk at each community. Half of the region's population, located either in uplands or floodplains, is subject to an unstable riverscape, with 18.5% of the communities affected by sedimentation processes and 26% by erosion. We identified four communities (out of 51 assessed) at very high risk, and seven at high-to-moderate risk. We highlight the need to include sedimentary processes in disaster management strategies in the changing river landscapes of the Amazon.

Tropical forests are critical regulators of global water and energy cycles, with evapotranspiration (ET) being a key ecohydrological process. However, monitoring ET over tropical forests is a challenge due to their complex structure, and the logistical difficulties in obtaining observations that are both spatially representative and have wide coverage. Remote sensing data offer an alternative to these limitations, although the effectiveness of ET remote sensing-based models over these areas is not well-known. Thus, this study evaluates the performance of four remote sensing-based ET models (SSEBop, geeSEBAL, PT-JPL and T-SEB) in tropical forests. We compared models’ estimations against flux tower observations and assessed the uncertainty in models’ outputs driven by different meteorological input forcings. Additionally, we conducted a spatial–temporal analysis of models’ response to the impact of deforestation on ET patterns. Our results showed a good agreement between modeled and observed ET using the most accurate meteorological input dataset (RMSEs ranging from 1.1 to 1.3 mm.day−1 for ERA5-Land). The deforestation analysis for sites in Africa, America and Asia revealed an agreement of the models in demonstrating the impact of deforestation on ET, though performance varied due to different deforestation patterns. For the long-term results, models showed different responses to forest removal, highlighting the uncertainties of the individual models and underscoring the necessity of multi-model approaches in providing more accurate information. These findings demonstrate that current high-resolution remote sensing models can effectively monitor ET in tropical forests on a global scale, especially for assessing the impacts of deforestation in data-scarce regions.

While the 2023 record-breaking drought led to widespread social-ecological impacts across Amazonia, local impacts of such extreme events are rarely described in detail. Here we leverage a large interdisciplinary data collection related to social and ecological impacts in the Central Amazon. Compound hazards (reduced river water levels, lack of rainfall, high water/air temperatures, river erosion and fire smoke) led to major impacts, including an unprecedented mortality of 209 river dolphins and blooms of the potentially ichthyotoxic Euglena sanguinea phytoplankton. Fish kills in lakes and changes in caiman relative abundance along floodplain channels were observed, as well as lower-than-usual production of flowers and fruits in floodplain trees. Impaired river transportation was the main socio-economic impact, affecting important value chains such as the arapaima fisheries and manioc flour production, as well as access to healthcare, drinking water and urban markets. Our results also show the contrasting impacts between rural and urban populations, with the latter presenting a higher resilience throughout the event. Continuous records of impacts like those presented here are fundamental to guide future disaster management policies in Amazonia. This is particularly important to help vulnerable remote people and ecosystems during extreme hydro-climatic events, which are likely to increase in the near future.

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, similar to 18 %, similar to 59 %, and similar to 23 % of the seasonal amplitude of TWSA (similar to 365 +/- 105 km(3)), in line with previous model-based estimates. Additionally, at sub-basin scale, our study reveals that GWSA ranges between 42 % (for Ubangui and Kasa & iuml;) 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 hydrogeological 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 contrast with lower GWSA variability and BFI in fracture flow (BFI = 0.14) and basement (BFI = 0.12) aquifers.

A Amazônia é um sistema complexo de ecossistemas vibrantes e interconectados e culturas humanas, abrigando a maior diversidade de espécies da Terra. As muitas conexões na Amazônia, que ligam a biosfera e a atmosfera, a hidrologia e a biodiversidade, são fundamentais para a biodiversidade global, a estabilidade do clima global e, portanto, para o bem-estar da humanidade. A floresta amazônica recicla entre 30% e 50% de suas chuvas e exporta umidade que molda os padrões de precipitação em toda a América do Sul por meio de "rios aéreos". O aumento do desmatamento, os incêndios florestais extremos e a frequência de eventos compostos de seca e calor geram preocupações quanto à possibilidade de grandes porções da floresta amazônica sofrerem degradação significativa. Essas mudanças exacerbam a crise climática em escalas locais, regionais e globais que, por sua vez, comprometem a conectividade amazônica e aumentam a vulnerabilidade humana mundial. O clima da Amazônia influencia e é influenciado por fenômenos atmosféricos de grande escala que ligam o tempo e o clima a grandes distâncias, conhecidos como teleconexões. A floresta amazônica sustenta outros biomas e atividades econômicas para regiões como o Pantanal, a Bacia do Rio da Prata e a Bacia do Rio Orinoco (conectividade N-S e S-N). O sistema hidroclimático Andino-Amazônico (conectividade E-W e W-E) está em conformidade com um sistema de interação bidirecional, no qual a Amazônia exporta vapor de água para os Andes por meio de rios aéreos, e os Andes exportam fluxos fluviais, sedimentos e nutrientes para a Amazônia de baixa altitude. A diminuição da conectividade dos rios durante as secas extremas isola as comunidades locais e compromete sua segurança alimentar e hídrica; as enchentes abaixo da média também podem prejudicar as atividades dependentes da planície de inundação, como a pesca. Sem ações que impeçam uma maior degradação, as florestas amazônicas estão se aproximando de limites ambientais críticos que ameaçam suas funções ecológicas, sua biodiversidade e suas conexões culturais.

In 2023, an unprecedented drought and heat wave severely affected Amazon waters, leading to high mortality of fishes and river dolphins. Five of 10 lakes monitored had exceptionally high daytime water temperatures (over 37°C), with one large lake reaching up to 41°C in the entire approximately 2-meter-deep water column and up to 13°C of diel variation. Modeling showed that high solar radiation, reduced water depth and wind speed, and turbid waters were the main drivers of the high temperatures. This extreme heating of Amazon waters follows a long-term increase of 0.6°C/decade revealed by satellite estimates across the region's lakes between 1990 and 2023. With ongoing climate change, temperatures that approach or exceed thermal tolerances for aquatic life are likely to become more common in tropical aquatic systems.

The Amazon is a complex system of vibrant and interconnected ecosystems and human cultures, housing the largest species diversity on Earth. The many connections across the Amazon, linking the biosphere and atmosphere, the hydrology and biodiversity, are fundamental for global biodiversity, the stability of the global climate, and this, the well-being of humanity. The Amazon forest recycles between 30%–50% of its rainfall and exports moisture that shapes precipitation patterns across South America via “aerial rivers”. The increases in deforestation, extreme wildfires, and the frequency of compound drought-heat events raise concerns around the possibility that large portions of the Amazon forest will experience significant degradation. These changes exacerbate the climate crisis at local, regional, and global scales that in turn compromise Amazonian connectivity and increase worldwide human vulnerability. The Amazon’s climate both influences and is influenced by large-scale atmospheric phenomena that link weather and climate across vast distances, known as teleconnections. The Amazon forest sustains other biomes and economic activities for regions such as the Pantanal wetlands, the La Plata River Basin, and the Orinoco River Basin (N-S and S-N connectivity). The Andean-Amazon hydroclimatic system (E-W and W-E connectivity) conforms to a two-way interacting system, whereby the Amazon exports water vapor to the Andes through aerial rivers, and the Andes export river flows, sediments, and nutrients to the low-lying Amazon. Decreased river connectivity during extreme droughts isolates local communities and compromises their food and water security; below average floods can also impair floodplain-dependent activities, such as fishing. Without actions that prevent further degradation, Amazon forests are approaching critical environmental thresholds that threaten its ecological functions, biodiversity, and cultural connections.

Recent studies highlight the critical role of methane emissions from tropical wetlands in driving the accelerated atmospheric CH4 growth rate observed in the last decade. The Amazon lowland region, where up to 30% of the area can be seasonally flooded, is one of the largest natural methane sources. The total methane flux estimates for the Amazon basin from top-down and bottom-up approaches converge at 31–46 TgCH₄/year. However, understanding methane emission trends and interannual variability—such as inundation extent and seasonality—requires improved attribution of emissions to specific wetland types and habitats. In this study, we present a refined bottom-up estimate of methane fluxes for the lowland Amazon that addresses key challenges to regionalizing fluxes in the basin: i) the large seasonal variation in inundated areas and habitats, ii) the diversity of aquatic ecosystems across the Amazon, and iii) the spatiotemporal variability of methane fluxes. We link local methane flux measurements collected during more than 20 years of field campaigns to specific river and wetland types and incorporate seasonal variability in inundation extent using dynamic remote sensing products (i.e. open water data from the Global Surface Water for lakes, Global River Width from Landsat (GRWL) for rivers, and wetland inundation extent from the High-Resolution Surface WAterFraction (SWAF-HR, based on SMOS L-band imagery) for the Amazon basin, and (4) GIEMS-D15 (merge of multiple satellites) for the remaining portions of South America). Wetland types (herbaceous and woody vegetation) were obtained from the JERS-1 L-band based classification of Hess et al. (2015) for the Amazon Basin and ESA-CCI land cover for the rest of South America. The magnitude and seasonal variability of our bottom-up fluxes are evaluated against fluxes derived from atmospheric CH4 mole fraction measurements at two Amazonian sites, whose footprints go beyond the Amazon Basin. While our product successfully captures the seasonal variability at both sites, it underestimates the overall magnitude of emissions compared to other estimates, even when accounting for emissions from flooded forest tree stems. Our findings represent an important improvement of bottom-up estimates representing the diversity of wetland habitats and processes driving methane emissions, but further work is needed to understand the mismatch with other methane emissions products.

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.

Protected Areas (PAs) are pivotal instruments in natural resource conservation and maintaining or enhancing ecosystem services, including hydrological functions. In the Amazon, the impact of PAs on the quality of river waters remains largely overlooked. Therefore, this study aims to evaluate whether Environmental Protected Areas (EPAs) and Indigenous Lands (ILs) efficiently protect the quality of surface waters in the Brazilian Amazon. Water quality variables from river gauge stations distributed across the lowlands Amazon are analyzed according to baseline river hydrogeochemistry classification, both inside and outside PAs. This study found that whitewater and clearwater rivers coursing within EPAs and ILs show lower turbidity and electrical conductivity compared to those outside PAs, likely due to the buffering effect of dense forest cover within protected landscapes. Moreover, data indicate that protected areas enhance the water quality from upstream unprotected landscapes in the Brazilian Amazon. These results highlight the need to further invest on effective mechanisms of water resource conservation across lowland tropical regions, and particularly in the Earth’s largest watershed.

AbstractGround weather observations are scarce in many parts of the globe, hampering effective climate monitoring and disaster management. In the Amazon basin, this occurs due to its remoteness and the challenging measurement of rainfall within the forest. Innovative rainfall estimation methods are thus requested to fill this gap. Here we present an approach to estimate rainfall based on sound measurements. We identified the best frequency range to estimate rainfall occurrence and intensity, trained classification and regression models with sound and rain gauge data collected in the Central Amazon during 9 months. By training a random forest classifier/regression model based on power spectrum values it was possible to identify and satisfactorily estimate hourly rainfall rates in two vegetation environments distinct from the training site, located 30 km from it. The proposed method is a promising approach for future weather monitoring in remote tropical areas.

Estimating evaporative losses from reservoirs is essential for water resource management. While remote sensing provides great opportunities for better constrain this process, there are still large uncertainties related to the behavior of evaporative losses across large domains. Here, we present a multi-model approach combined with remote sensing data to estimate evaporation losses at a national scale for dozens of reservoirs in Brazil. Open water evaporation (Ew) rates were obtained using two surface energy balance (SEB) models (the Google Earth Engine implementation of the Surface Energy Balance Algorithm for Land (geeSEBAL) and the Simplified Surface Energy Balance (SSEBop)). Due to the lack of in situ evaporation data, the estimations were cross-validated with results from the validated product Moderate-Resolution Imaging Spectroradiometer (MODIS) Water Reservoir Monthly Level 3 Global (MOD28C3) and the widely used open water evaporation equation described by Penman. Ew estimates were obtained for 74 large reservoirs, representing almost 70% of the area occupied by artificial water bodies in Brazil. Monthly estimates of Ew showed satisfactory agreement among the models, especially for geeSEBAL, SSEBop, and MOD28C3. Reservoirs with higher Ew were located in the semi-arid Caatinga Biome. The total annual evaporation and net evaporation (difference between lake Ew and surrounding evapotranspiration) for the assessed reservoirs were 46.6 ± 1.0 km³ and 16.4 ± 0.5 km³, respectively, for the period 2003–2021, based on an average of geeSEBAL and SSEBop. We also found that net evaporation is very high in dry climate zones and lower in tropical and humid subtropical climate zones. Our results show the advantage of integrating remote sensing datasets and evaporation models with cloud computing to obtain evaporation estimates in a tropical region and over a large domain. These multi-model evaporation estimates can improve our understanding of water resources in ungauged regions at a large spatial scale.

ABSTRACT We developed and analyzed the performance of an ensemble forecasting system for the Madeira River basin, the largest sub-basin of the Amazon, with forecasts up to 30 days under different hydrometeorological conditions. We used outputs from the regional Eta model of precipitation and global climatological data as inputs to a large-scale hydrological model. Bias correction of precipitation through quantile mapping significantly improved the results, achieving a hit rate >70%. The system demonstrated the ability to discriminate between high, medium, and low flow conditions. Forecast performance is better for larger catchment areas. This system is expected to increase decision-making efficiency for flood and drought situations in the largest Amazon tributary.