Scenario-based models combine certain aspects of deterministic and stochastic models. Rather than using a random input variable as a stochastic model does, this approach instead produces multiple simulations that, in turn, are used to determine probabilistic values.
Because a deterministic model is used, model parameter settings or model states such as current soil moisture conditions are saved. These model states serve as a starting point for the multiple runs. The scenarios may be based on either historic records or short-term forecasts.
Examples include Ensemble Streamflow Prediction.