River transport, with more than 17,000 km of navigable channels in the Congo, is a crucial part of the economy for many of the countries sharing the river basin and allows the transport of many goods (timber, charcoal, minerals etc.) and enables access to many areas where roads do not exist. However, river transport falls short of the role it could play in development of the region and has actually declined since the Congo basin countries became independent in the 1960s. This is in part due to years of civil unrest, aging equipment, a lack of infrastructure maintenance, and the poor support and operation of public waterway agencies. River navigation maps are a specialist form of map specifically designed to allow safe navigation of river traffic such as for barges carrying cargo. Boat captains use them as they travel along the river to follow the advised navigation route and avoid hazards such as submerged rocks and shallow channels. The navigation maps for the 1,700 km of river between Kinshasa and Kisangani are issued by RVF (Régie de Voie Fluvial), the state river navigation authority, and are therefore used by all boat captains. These maps originate from the early 1900s and have not been updated since colonial times. As part of the CRuHM project we are exploring the possibility of updating these maps using modern remote sensing methods, together with RVFs experienced input. As part of the update process, RVF have provided us with detailed digital scans of the original navigation maps and we are geo-referencing these to modern geospatial projections, in line with the remote sensing data. This provides us with a unique opportunity to compare snapshots of the river system geomorphology separated by nearly 100 years. We will show the current state of the project and some of the river secrets we have discovered so far.

Landslide dams can result in substantial flood hazards caused by dam formation, overtopping, and dam failure. Previous studies have established datasets on a regional or global scale and identified indices to estimate the probability of landslide dam formation. These datasets are collections of landslide dam records from multiple data sources. However, the precision and accuracy of the landslide dam record’s spatial information hinder the completeness of data and prevents the possibility of linking with other relevant datasets, and thus hinders the exploration of factors affecting landslide dam formation. We established a new global-scale landslide dam dataset, named River Augmented Global Landslide Dams (RAGLAD), which geolocates those records whose location was vaguely known or completely unknown and combined this with additional data from global fluvial datasets to make the data record more comprehensive. We use RAGLAD to study the processes of landslide dam formation. The spatial distribution of landslide dam records, data distribution, triggering processes and preconditions, and the relationships between geomorphological parameters directly derived from RAGLAD help understanding areas prone to landslide dam conditions, and delineate potential thresholds for landslide dam formation. The results are compared with relationships achieved from general landslides studies to find the specific conditions of landslide dam formation. These conditions can be further applied for filtering the potential hazard occurrence area and calculating the landslide dam formation susceptibility.

A reach-scale high resolution digital elevation model (DEM) of the Congo’s main stem bathymetry is presented. The Bathymetry DEM covers a multichannel reach of the main stem situated in the Cuvette Centrale, and is developed from a series of in-situ measurements of bathymetry, water surface elevation and discharge that were obtained during a CRuHM fieldtrip in summer 2017. The main stem’s complex network of channel threads requires a bathymetry modelling methodology that is capable of intelligently interpolating the raw bathymetry measurements. The methodology must also estimate a significant portion of the bathymetry, since it is not feasible to measure the entire extent of the massive and complex channel network that this study reach is comprised of. This methodology is also presented. Remote sensing from satellites is increasingly being used to resolve the scarcity of contemporary hydrological and hydrographic measurements in the Congo Basin. However, river channel bathymetry information cannot yet be reliably obtained from remote sensing methods. This is problematic since river channel representation has been shown to be an essential input into a hydraulic model. Analyses of satellite observations suggest that, relative to other global rivers, in-channel flows on the Congo’s main stem represent a relatively large proportion of total flows through the river-floodplain system. This implies the Congo’s in-channel bathymetry may play a relatively large role in controlling Congo main stem hydrodynamics. When used in a hydraulic model, the bathymetry DEM presented here will provide new information on Congo in-channel hydraulics and the extent to which bathymetry controls the Congo’s middle reach hydrodynamics. It will help better quantify the capacity of the Congo main stem channels through the Cuvette Centrale, and thus provide further insights into the extent to which the main stem channel floods in this region. It is also intended to be used for testing simplified methods of Congo bathymetry representation that are necessary for larger scale hydraulic models.

Flood disasters have regularly been reported in the Congo Basin with significant damages to human lives, food production systems and infrastructure. Losses incurred by these damages are huge and represent a major challenge for economic expansion in developing nations. In the Congo River Basin, where availability of in-situ data is a significant challenge, new approaches are needed to investigate flood risks and enable effective management strategies. This study uses recently developed global flood prediction data in order to produce flood risk maps for the Congo River Basin, where flood information currently does not exist. Flood hazard maps that estimate fluvial flooding at a grid cell resolution of 3 arc-seconds (~ 90 m), gridded population density data of 1 arc-second (~ 30 m) spatial resolution, and a spatial layer of infrastructure dataset are used to addresses flood risk at the scale of the Congo Basin. The global flood data provides different return periods of exposure to flooding in the Congo Basin and identifies flood extents. The risk analysis results are presented in terms of the percentage of population and infrastructure at flood risk for six return periods (5, 10, 20, 50, 75 and 100 year). Of the 525 administrative territories, 374 are AGU Books

Over the last two decades, several datasets have been developed to assess flood risk at the global scale. In recent years, some of these datasets have become detailed enough to be informative at national scales. The use of these datasets nationally could have enormous benefits in areas lacking existing flood risk information and allow better flood management decisions and disaster response. In this study, we evaluate the usefulness of global data for assessing flood risk in five countries: Colombia, England, Ethiopia, India, and Malaysia. National flood risk assessments are carried out for each of the five countries using global datasets and methodologies. We also conduct interviews with key water experts in each country to explore what capacity there is to use these global datasets nationally. To assess national flood risk, we use 6 datasets of global flood hazard, 7 datasets of global population, and 3 different methods for calculating vulnerability that have been used in previous global studies of flood risk. We find that the datasets differ substantially at the national level, and this is reflected in the national flood risk estimates. While some global datasets could be of significant value for national flood risk management, others are either not detailed enough, or too outdated to be relevant at this scale. For the relevant global datasets to be used most effectively for national flood risk management, a country needs a functioning, institutional framework with capability to support their use and implementation.

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.

Land use change due to population growth and an increase in anthropogenic activities such as logging and mining (mostly illegal) have led to an increase in the sediment load of the Kasai River in the Congo River Basin. However, even with recent developments in technologies such as remote sensing and improvements in erosion prediction models to study sediment transport in water bodies; this phenomenon has been little studied in this poorly gauged catchment and the wider Congo River Basin due to lack of field-based sediment concentration measurements required to calibrate the results of such studies. The present study describes recently concluded field excursions to set up a high frequent sediment sampling station on one of the major tributaries of Congo River, the Kasai River. The station is fitted with an ISCO 6712 automatic pumping sampler to enable high frequency sampling. The ISCO is coupled with an OBS 501 turbidity sensor to collect even higher frequent sediment concentration data. The site is also fitted with other instruments to measure other hydrologic and climatic variables such as a manual staff gauge and an automatic water levels logger. The preliminary sampling efforts have been designed to guide a comprehensive sediment sampling programme which is part of a wider study to build a sediment yield model for the Congo River Basin in order to study the impacts of sedimentation on hydropower planning.