Although the Congo Basin is still one of the least studied river basins in the world, this paper attempts to provide a multidisciplinary but non-exhaustive synthesis on the general hydrology of the Congo River by highlighting some points of interest and some particular results obtained over a century of surveys and scientific studies. The Congo River is especially marked by its hydrological regularity only interrupted by the wet decade of 1960, which is its major anomaly over nearly 120 years of daily observations. Its interannual flow is 40,500 m³ s⁻¹. This great flow regularity should not hide important spatial variations. As an example, we can cite the Ubangi basin, which is the most northern and the most affected by a reduction in flow, which has been a cause for concern since 1970 and constitutes a serious hindrance for river navigation. With regard to material fluxes, nearly 88 × 10⁶ tonnes of material are exported annually from the Congo Basin to the Atlantic Ocean, composed of 33.6 × 10⁶ tonnes of TSS, 38.1 × 10⁶ tonnes of TDS and 16.2 × 10⁶ tonnes of DOC. In this ancient flat basin, the absence of mountains chains and the extent of its coverage by dense rainforest explains that chemical weathering (10.6 t km⁻² year⁻¹ of TDS) slightly predominates physical erosion (9.3 t km⁻² year⁻¹ of TSS), followed by organic production (4.5 t km⁻² year⁻¹ of DOC). As the interannual mean discharges are similar, it can be assumed that these interannual averages of material fluxes, calculated over the longest period (2006–2017) of monthly monitoring of its sedimentology and bio-physical-chemistry, are therefore representative of the flow record available since 1902 (with the exception of the wet decade of 1960). Spatial heterogeneity within the Congo Basin has made it possible to establish an original hydrological classification of right bank tributaries, which takes into account vegetation cover and lithology to explain their hydrological regimes. Those of the Batéké plateau present a hydroclimatic paradox with hydrological regimes that are among the most stable on the planet, but also with some of the most pristine waters as a result of the intense drainage of an immense sandy-sandstone aquifer. This aquifer contributes to the regularity of the Congo River flows, as does the buffer role of the mysterious “Cuvette Centrale”. As the study of this last one sector can only be done indirectly, this paper presents its first hydrological regime calculated by inter-gauging station water balance. Without neglecting the indispensable in situ work, the contributions of remote sensing and numerical modelling should be increasingly used to try to circumvent the dramatic lack of field data that persists in this basin.

We used the lumped rainfall–runoff hydrologic models Génie Rural à 4, 5, 6 paramètres Journalier (GR4J, GR5J and GR6J) to evaluate the most robust model for simulating discharge on four forested small catchments (<40 ha) in south-central Chile. Different evapotranspiration methods were evaluated: Oudin, Hargreaves–Samani and Priestley–Taylor. Oudin’s model allows the achievement of the highest efficiencies in the flow simulation. The more sensitive parameters for each model were identified through a Generalized Probability Uncertainty Estimation (GLUE) model. Our results demonstrate that the three hydrological models were capable of efficiently simulating flow in the four study catchments. However, the GR6J model obtained the most satisfactory results in terms of simulated to measured streamflow closeness. In general, the three models tended to underestimate peak flow, as well as underestimate and overestimate flow events in most of the in situ observations, according to the probability of non-exceedance. We also evaluated the models’ performance in a simulation of summer discharge due to the importance of downstream water supply in the months of greatest scarcity. Again, we found that GR6J obtained the most efficient simulations.

In this study, we combined machine learning and remote sensing techniques to estimate the value of chlorophyll-a concentration in a freshwater ecosystem in the South American continent (lake in Southern Chile). In a previous study, nine artificial intelligence (AI) algorithms were tested to predict water quality data from measurements during monitoring campaigns. In this study, in addition to field data (Case A), meteorological variables (Case B) and satellite data (Case C) were used to predict chlorophyll-a in Lake Llanquihue. The models used were SARIMAX, LSTM, and RNN, all of which showed generally good statistics for the prediction of the chlorophyll-a variable. Model validation metrics showed that all three models effectively predicted chlorophyll as an indicator of the presence of algae in water bodies. Coefficient of determination values ranging from 0.64 to 0.93 were obtained, with the LSTM model showing the best statistics in any of the cases tested. The LSTM model generally performed well across most stations, with lower values for MSE (<0.260 (μg/L)²), RMSE (<0.510 ug/L), MaxError (<0.730 μg/L), and MAE (<0.442 μg/L). This model, which combines machine learning and remote sensing techniques, is applicable to other Chilean and world lakes that have similar characteristics. In addition, it is a starting point for decision-makers in the protection and conservation of water resource quality.

This study aims to develop and implement a methodology for retrieving bio-optical parameters in a lagoon located in the Biobío region, South-Central Chile, by analyzing time series of Landsat-8 OLI satellite images. The bio-optical parameters, i.e., chlorophyll-a (Chl-a, in mg·m⁻³) and turbidity (in NTU) were measured in situ during a satellite overpass to minimize the impact of atmospheric distortions. To calibrate the satellite images, various atmospheric correction methods (including ACOLITE, C2RCC, iCOR, and LaSRC) were evaluated during the image preprocessing phase. Spectral signatures obtained from the scenes for each atmospheric correction method were then compared with spectral signatures acquired in situ on the water surface. In short, the ACOLITE model emerged as the best fit for the calibration process, reaching R² values of 0.88 and 0.79 for Chl-a and turbidity, respectively. This underlies the importance of using inversion models, when processing water surfaces, to mitigate errors due to aerosols and the sun-glint effect. Subsequently, reflectance data derived from the ACOLITE model were used to establish correlations between various spectral indices and the in situ data. The empirical retrieval models (based on band combinations) yielding superior performance, with higher R² values, were subjected to a rigorous statistical validation and optimization by applying a bootstrapping approach. From this process the green chlorophyll index (GCI) was selected as the optimal choice for constructing the Chl-a retrieval model, reaching an R² of 0.88, while the red + NIR spectral index achieved the highest R² value (0.79) for turbidity analysis, although in the last case, it was necessary to incorporate data from several seasons for an adequate model training. Our analysis covered a broad spectrum of dates, seasons, and years, which allowed us to search deeper into the evolution of the trophic state associated with the lake. We identified a striking eight-year period (2014–2022) characterized by a decline in Chl-a concentration in the lake, possibly attributable to governmental measures in the region for the protection and conservation of the lake. Additionally, the OLI imagery showed a spatial pattern varying from higher Chl-a values in the northern zone compared to the southern zone, probably due to the heat island effect of the northern urban areas. The results of this study suggest a positive effect of recent local regulations and serve as the basis for the creation of a modern monitoring system that enhances traditional point-based methods, offering a holistic view of the ongoing processes within the lake.

This paper addresses the geomorphic characterization and classification of large rivers in a framework of scarce information. This is inspired by the River Styles Framework with some modifications that make the process more straightforward and accessible to practitioners and more applicable to large basins, while reducing the subjective, expert-based inputs, as the process is now more systematic. To this aim, it utilizes innovative criteria and some computer-aided procedures and tools based on GIS, Excel and Python. This approach sheds light on the character and the behavior of rivers, which is key to informing planning, management and restoration. The application to the Magdalena River (Colombia) illustrates the characterization and classification process and the type of results, which ultimately highlight the great geomorphic diversity of that river. The process is applicable to many other rivers worldwide.