New Approaches to Modelling an Ungauged Basin

In this paper, we address a number of typical problems in ungauged basins: first, currently available rainfall sources from satellites seem promising, but are biased and noisy; second, streamflow records, if available, often have no overlap with modern rainfall products and are unreliable; third, the question is how to incorporate innovative satellite-based data sources (for instance SEBAL evaporation maps) in model calibration? Experiences are presented from a truly ungauged sub-catchment of the Zambezi, the Luangwa river basin (~150 000 km2) in Zambia. The assumption is that all data, whether noisy, non-concomitant or biased, contains information that may constrain our model solution space. A set of old discharge records revealed: 1) a clear recession curve, 2) an estimate of the long-term water balance, and 3) the auto- regressive properties of the streamflow process. This information has been mapped into a number of innovative maximum likelihood estimators that are introduced step-wise in a Bayesian updating procedure for the development of a conceptual hydrological model. As a last step, the updated parameter distribution is introduced in a classical automatic Pareto optimization, where land-surface related parameters are distributed, justified by evaporation constraints based on the SEBAL evaporation maps. This information is included in the form of fuzzy measures for parameter acceptability. The results show that it is possible to constrain the parameter space considerably based on the combined information. It is shown that the likelihood estimators are not very sensitive to the quality of the rainfall product used for constraining model parameters, as long as the autoregressive properties of the rainfall are present in the time series. The use of the evaporation maps in the optimization process considerably reduces equifinality, preventing parameters to compensate for each other.