article · Journal of Hydrology Regional Studies
The Congo River Basin in central Africa lacks comprehensive natural hydrological information across its sub-basins, restricting standard regionalisation techniques for ungauged catchments. To address this, research across the five drainage systems established uncertainty ranges for hydrologic indices by linking climate and physiographic attributes. Predictive equations were developed across all climate and physiographic regions using only the aridity index to enable the transfer of hydrological information from gauged to ungauged sub-basins. Derived uncertainty bounds remained below 41 percent for both Q10/MMQ and Q50/MMQ indices across the entire basin. In contrast, higher uncertainty emerged for the runoff ratio and Q90/MMQ indices. These uncertainties are attributed to errors in rainfall and evapotranspiration data, sparse streamflow records, and secondary controls such as geology. These established bounds provide initial estimates to constrain hydrological model outputs and quantify risks.
Large river basins often suffer from a severe shortage of monitoring stations, leaving planners without accurate flow records. Establishing mathematical bounds for river indices helps scientists and planners understand baseline water behaviour in ungauged areas. This allows decision-makers to evaluate uncertainty and risk more realistically when designing water infrastructure, managing drought, or setting environmental management policies in data-scarce catchments.
The findings could assist hydrologic consultancies, environmental engineering firms, and regional water resource authorities that develop catchment models for ungauged areas. The predictive equations can serve as calibration constraints to improve risk quantification in planning software. As this is early-stage analytical research, further validation and the inclusion of extra variables, such as local geology and improved rainfall data, would be required before implementation in commercial modelling software.
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The five drainage systems of the Congo River Basin in central Africa. This study aims to establish uncertainty ranges of hydrologic indices and to provide a basis for transferring hydrologic indices from gauged to ungauged sub-basins by identifying the most influential climate and physiographic attributes. Only limited information on individual sub-basins natural hydrology exists across the Congo River Basin, limiting the application of commonly used regionalization approaches for prediction in ungauged sub-basins. This study uses predictive equations for the hydrologic indices across all climate and physiographic regions based only on the aridity index. The degree of uncertainty in the derived uncertainty bounds is less than 41% for both Q10/MMQ and Q50/MMQ indices across the basin. A greater degree of uncertainty is associated with the runoff ratio and the Q90/MMQ indices. The uncertainty is assumed to be due to uncertainty in rainfall and evapotranspiration estimates, a lack of spatial representativeness of the available observed streamflow data and other factors (e.g., geology) that might control the hydrologic indices rather than the aridity index alone. The uncertainty ranges provide the first estimates of hydrologic indices that are intended to constrain the outputs from hydrologic models and appropriately quantify prediction uncertainty and risks associated with water resources decision making.
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DOI: 10.1016/j.ejrh.2020.100710
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