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[1] Using a possibilistic approach, this study develops a methodology for the evaluation of predictive uncertainty in rainfall-runoff modeling. The methodology proposed herein can be regarded as a further extension of the well-known generalized likelihood uncertainty estimation (GLUE) methodology. Both methods are based on the equifinality paradigm, under which it is accepted that there may be many model structures and parameter sets (within a given model structure) that are compatible with the knowledge available about the real system. In both methodologies, uncertainty bounds of the model predictions are obtained using Monte Carlo simulations. The essential difference between them is that while in the GLUE methodology the likelihood weights of behavioral simulations are used to obtain prediction quantiles, in the possibilistic methodology the possibility distributions of the model outputs are used to derive the prediction uncertainty bounds. The methodology presented in this study is applied to a conceptual type rainfall-runoff model.

1Most studies dealing with the interpolation of precipitation gauge data have focused in areas where the meteorological network is relatively dense, implying that it is still unknown what interpolation methods are more appropriate in the case of mountain catchments with scarce gauge data. This study evaluates the applicability of Kriging with External Drift (KED) and the Optimal Interpolation Method (OIM) for interpolation of monthly precipitation in these situations. Thiessen Polygons (TP) are used as benchmark. The study area corresponds to the upper subcatchment of Aconcagua River, Central Chile. Cross-validation experiments revealed that all these methods show similar performance in the lower zone of the study area, but OIM outperforms TP and KED at high elevations. Optimal Interpolation Method generally produces the smallest bias in the Andean zone of the study area, with mean errors whose absolute values are smaller than 9% of mean monthly precipitation. From April to September, the root mean squared errors of OIM are between 14% and 33% smaller than those of TP and KED in this zone. Although KED achieves a good agreement to mean monthly values at high elevations (mean errors smaller than 19% in absolute value), its performance is comparable to that of TP in terms of root mean squared errors. Long-term water balances did not provide evidence against the applicability of KED and OIM. Nevertheless, the results of the cross-validation experiments indicate that OIM is a better alternative than KED for the interpolation of monthly precipitation in the study area.

This paper provides a general overview about the use of fuzzy inference systems in the important field of river flow forecasting. It discusses the overall operation of the main two types of fuzzy inference systems, namely Mamdani and Takagi-Sugeno-Kang fuzzy inference systems, and the critical issues related to their application. A literature review of existing studies dealing with the use of fuzzy inference systems in river flow forecasting models is presented, followed by some recommendations for future research areas. This review shows that fuzzy inference systems can be used as effective tools for river flow forecasting, even though their application is rather limited in comparison to the popularity of neural networks models. In addition to this, it was found that there are several unresolved issues requiring further attention before more clear guidelines for the application of fuzzy inference systems can be given.