An investigation of the instrumental variable-approximate maximum likelihood method of modeling and forecasting daily flows

The instrumental variable-approximate maximum likelihood (IV0AML) method provides a technique to develop better models for short-time increment hydrologic data. In this method, a recursive input-output model, which consists of a deterministic model and a stochastic noise model are used. These models handle the system and measurement noise separately. The instrumental variable method has been developed to eliminate the bias in parameter estimates.The IV-AML method is investigated in the present study. Parameters of daily rainfall-runoff models are estimated by the IV-AML and by least squares methods and compared. The effects of a rainfall filter on parameter estimates are also investigated. Forecast accuracies of models whose parameters are estimated by IV-AML and least squares methods are compared.The results indicate that the forecast accuracy of models whose parameters are estimated by least squares method is comparable to that of models whose parameters are estimated by IV-AML method. The rainfall filter, on the other hand, reduces the parameter variation and improves forecasts.