Abstract
ABSTRACT A recent study in headwater sub-basins of the Zambezi River concluded that a machine learning model (HydroForecast) outperformed a deterministic monthly time-step water balance model (Pitman) when observed streamflow data were used as part of the training data for the machine learning approach. This was partly attributed to non-stationary bias in the input rainfall data that has a greater impact on a model that has mass balance constraints. The current study investigated methods of adjusting the input rainfall data based on an analysis of the streamflow simulation errors and found that the results for the water balance model could be improved so that they compared more favourably with the machine learning model. The improvements were achieved without changing the long-term statistical characteristics of the original monthly rainfall time series. It is acknowledged that there is some evidence that differences in daily rainfall patterns might explain some of the simulation errors.
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