This dataset provides a set of hydrometric indices for about 1500 stations across Africa with daily discharge data. These indices represent mean flow characteristics and extremes (low flows and floods), allowing us to study the long-term evolution of hydrology in Africa and support the modeling efforts that aim at reducing the vulnerability of African countries to hydro-climatic variability.

Input data (and model parameters) are significant sources of uncertainty that should be quantified. In southern Africa, water use data are among the most unreliable sources of model input data because available databases generally consist of only licensed information and actual use is generally unknown. The study assesses how uncertainty impacts the estimation of surface water resources of the Mogalakwena and Shashe sub-basins when using the databases that are currently available.

The paper investigates the use of GIS to come up with model parameters. This is part of a process of simplifying model use. The findings show that existing GIS data can be used for estimating model parameters as the outcomes of the research show that model outputs are consistent with previously estimated measures. This research is part of a development of a model which can estimate soil loss and sediment delivery at broad spatial and temporal scales to improve catchment management.

Flow regimes of rivers will be different in the future, but how different is uncertain. Water resources decisions will rely on practical simulation tools that are sensitive to changes, can assimilate change information and flexible enough to accommodate new understanding. This paper presents some tools that can address these issues in southern Africa. Appropriate tools are available but we need more reliable forcing and model validation data and methods for making decisions with uncertain data.