The Congo Basin exhibits tremendous heterogeneities, out of which it emerges as an intricate system where complexity will vary consistently over time and space. Increased complexity in the absence of adequate knowledge will always result in increased uncertainties. One way of simplifying this complexity is through an understanding of organisational relationships of the landscape features, which is termed here as catchment classification. The need for a catchment classification framework for the Congo Basin is obvious given the basin’s inherent heterogeneities, the ungauged nature of the basin, and the pressing needs for water resources management that include the quantification of current and future supplies and demands, which also encompass the impacts of future changes associated with climate and land use, as well as water resources operational policies. The need is also prompted by many local-scale management concerns within the basin. This study uses an a priori approach to determine homogenous climatic-physiographic regions that are expected to underline dominant hydrological processes characteristics. A set of 1740 catchment units are partitioned across the whole basin, based on a set of comprehensive criteria, including natural break of the elevation gradient (199 units), inclusion of socio-economic and anthropogenic systems (204 units), and water management units based on traditional nomenclature of the rivers within the basin (1337 units). The identified catchment units are used to assess existing datasets of the basin physical properties, necessary to derive descriptors of the catchments characteristics. An unsupervised classification, based on Hierarchical Agglomerative Cluster algorithm is used, that yields 11 homogenous groups that are consistent with the current perceptual understanding of the Congo Basin physiographic and climatic settings. These regions represent therefore an a priori classification that will be further used to derive functional relationships of the catchments, necessary to enable hydrological prediction and water management in the basin.

The Congo River provides potential for socio-economic growth at the regional scale, but with limited information on the river dynamics it is difficult for basin countries to benefit from this potential, and to invest in the development of water resources. In recent years, the number of hazards related to navigation and flooding has sharply increased, resulting in high loss of human lives as well as economic losses. Associated problems of river management in the Congo also include inefficiency in hydropower production, an increase in rate of river sedimentation and land use changes. Accurate information is needed to support adequate management strategies such as prediction of navigation water levels and sediment movement, and assessment of environmental impacts and engineering implications of water resources infrastructure. Modelling approaches and space observations have been used to understand the Congo River dynamics, but their effective application has proved difficult due to a lack of ground-based observational data for validation. Recent developments in data capture with acoustic Doppler technologies have considerably improved measurements of river dynamics. As well measuring river discharge, they also allow the analysis of the multiple hydrodynamic features occurring in fluvial systems. This paper presents the results of field measurement campaigns carried out in the middle reach of the Congo River and the Kasai tributary using state of the art measurement technology (ADCP, Sonar, GNSS) for investigation of large rivers. The measurements relate to river flow at multiple transects, river bathymetry, static and continuous water surface elevation, and targeted sediment sampling along the river. The paper provides a descriptive summary of the measurement results, a discussion on the application and performance of the equipment used in the Congo River, and lessons for future use of this equipment for measurements of large rivers in a data scarce environment such as the Congo Basin.

The main objective of this study was to use an uncertainty version of a widely used monthly time step, semi-distributed model (the Pitman model) to explore the equifinalities in the way in which the main hydrological processes are simulated and any identifiable linkages with uncertainties in the available observational data. The study area is the Zambezi River basin and 18 gauged sub-basins have been included in the analyses. Unfortunately, it is not generally possible to quantify some of the observational uncertainties in such a data scarce area and mostly we are limited to identifying where these data are clearly deficient (i.e. erroneous or non-representative). The overall conclusion is that the equifinalities in the model are hugely dominant in terms of the uncertainties in the relative occurrence of different runoff generating processes, although water use uncertainties in the semi-arid parts of the basin can contribute to these uncertainties. The identification of landscape features that suggest the occurrence of saturation excess surface runoff provides some information to constrain the model. Improved independent estimates of groundwater recharge is also identified as a key source of observational data that would help a great deal in constraining the model parameter space and therefore reducing some of the model equifinality.