Initially, the Rossby Centre Regional Climate Model RCA3 was used to simulate the present-day climate for a control period (1961-1990) with forcing conditions taken from a reanalysis-driven experiment. Three transient climate change experiments were performed, using lateral boundary conditions from two GCMs (ECHAM4, CCSM3) for the time period 1961-2050. Forcings in the future scenarios followed the SRES B2 (both models) and A2 (only ECHAM4) from IPPC. By looking at the tran¬sient evolution of climate change in the simulations the natural variability on decadal scales was estimated both in a continuous fashion and by comparing two different time periods (1991-2020 and 2021-2050) to the control period. The climate change projections were used to drive the hydrological model HBV, with the aim to identify plausible changes to water availability and extreme events, with consideration to spatial variability on a sub-basin scale, as well as to variability within and between years.
The balanced results from the three scenario experiments indicate that "dry conditions get drier". For wet periods, however, the ECHAM4-A2 driven experiment indicated a slight increase of precipitation and river flow for some of the wet months, and in some sub-basins increase of occurrence of very high floods. Perceived robustness from using only three climate change experiments can also be questioned.
However, as concluded by national actors from Mozambique and Zimbabwe during the final workshop, also if including uncertainties, available information need to be considered, together with other aspects in IWRM, since adaptation takes time and consequences might be severe if nothing is done. Most relevant adaptation measures are beneficial already to cope with the present climate variability, but there will be a need for prioritizing, e.g., between actions to prevent impacts of water deficit and actions to prevent flooding. This pinpoint the need for a better representation of the uncertainty cloud, which could be done by including more climate change experiments, e.g., by using a combination of available dynamical and statistical downscaling of GCMs.