In the sparsely investigated region of the Congo Basin (CB), flood seasonality and flood regime shift are established through relative frequency, cluster analysis, directional statistics, and non-overlapping block methods based on block maxima and peak over threshold (POT) series. Two months of significantly rich floods are observed at all gauging stations. The spatial distribution of floods presents three patterns: the north and northwest pattern, south and southeast pattern, and west/east pattern. It is observed that unimodal flood distribution is coherent in the northern and southern parts, as opposed to the bimodal flood distribution observed along the large band of the Equator from west to east of the basin. The time lag of flood indices suggests that the flood regime is not stationary. In addition, the time series show periods of high flooding, with POT frequencies and amplitudes higher during the 1960s and early 1970s than any other time period.

A multi-model hydrological assessment in the Congo Basin is performed to assess water availability conditions for historical and future periods (1913–2099). With models limited by scarce in situ observations, a combination of GRACE satellite data and soil-moisture-based drought indices is shown to be capable of estimating water budget, streamflow, and drought and storage variability. Changes in land use and land cover played a role in modifying the hydrologic responses but were found to be within the uncertainties of other inputs, including weather, soil, and model parameters. Seasonal and annual variability in total water storage anomalies (TWSAs) and the modified Palmer drought severity index (MPDSI) display a good correlation with each other. A selected set of global climate models is used to characterize the future temperature and precipitation patterns. It is expected that subbasin-scale variability in future temperature and precipitation increases will result in increased evapotranspiration, decreased runoff, and more drought events in the Congo Basin.

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.

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

Gode Bola1,4, Raphael M. Tshimanga1, Jeff Neal2,  Laurence Hawker2, Mark A. Trigg3, Lukanda Mwamba4 , Paul Bates21 Congo Basin Water Resources Research Center (CRREBaC) & Department of Natural Resources Management, University of Kinshasa, DR Congo2School of Geographical Sciences, University of Bristol, United Kingdom3School of Civil Engineering, University of Leeds, United Kingdom4General Commission of Atomic Energy, Regional Center for Nuclear Study, Kinshasa, DR CongoFlood disasters have always 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 the 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 address flood risk at the scale of the Congo Basin. The global flood data provide 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 years). Of the 525 administrative territories, 374 are exposed to fluvial floods, and 38 (10 %) of them are categorised as risk hotspots. Analysis shows that the most exposed territories represent 1% of total exposure which is estimated at 2.65% of the basin’s population. This study demonstrates the first and potentially most important stage in developing flood responses by determining the flood hazards areas and the population/infrastructures that would be exposed. The flood risk maps produced in this study provide information necessary to support policy decisions of flood disasters prevention, including prioritisation of interventions to reduce flood risk in the CRB.Keywords: Flood hazard, Risk assessment, Return period, Congo River Basin 

Information on flood seasonality is required in many practical applications of hydrology and water resources management. However, an understanding of flood seasonality and how flood frequencies may have changed over time has not been established for the Congo Basin. The main objective of this study is therefore to identify flood seasonality and change in frequency the Congo Basin (CB). The analysis based on six major drainage areas of the CB, where we used a Peaks Over Threshold (POT) flood time-series with three peaks per year. The relative frequency method is applied to identify flood seasons, and then a cluster analysis is performed to classify flood into type based on monthly frequency. The directional statistics method is used to determine the mean day of flood and the flood variability measure. To identify flood frequency changes, the analysis of variance was applied to test the difference between two flood frequency time series blocks before and after the change point year. Results show that four gauging stations exhibit a unimodal flood seasonality distribution while two gauging stations have bimodal flood seasonality distribution, and two significant flood rich months are observed in all studied gauging stations.  The cluster analysis results in four spatially flood types with distinct seasonality characteristics. Mean flood dates show that the time interval between adjacent flood events in the south and south-east is shorter compared to time interval between flood events in the north and north-west. It is observed that, in almost all gauging stations, there is strong flood seasonality, and the geographical location of a watershed is indicative of its flood pattern. Most of significant decreasing frequencies are found in the southern part of the Congo Basin. There are no significant changes in flood frequency identified in the northern and eastern part of the Basin. However, flood frequencies have been increasing in the centre and western part of the Basin. This study suggest that, exploring flood generating factors and the drivers of change can provide insights for understanding the influence of these factors on floods as climate models projected changes in extreme precipitation and aridity in the future.