Approximate Bayesian Computation (ABC) is a statistical tool for handling
parameter inference in a range of challenging statistical problems, mostly
characterized by an intractable likelihood function. In this paper, we focus on the
application of ABC to hydrological models, not as a tool for parametric inference,
but as a mechanism for generating probabilistic forecasts. This mechanism is referred
as Approximate Bayesian Forecasting (ABF). The abcd water balance model
is applied to a case study on Aipe river basin in Columbia to demonstrate the applicability
of ABF. The predictivity of the ABF is compared with the predictivity of the
MCMC algorithm. The results show that the ABF method as similar performance
as the MCMC algorithm in terms of forecasting. Despite the latter is a very flexible
tool and it usually gives better parameter estimates it needs a tractable likelihoo