Single hydrological model or model calibrated on single objective function often cannot capture all components of a water motion process. One possibility is building several specialized models each of which responsible for a particular sub-process (e.g., high flows or low flows), and combining them using dynamic weights – thus forming a committee model. In this study, we test two different committee models: one uses fuzzy memberships function and another one - weights calculated from hydrological states. Specialized models are calibrated using Adaptive Cluster Covering Algorithm with different objective functions. The performances of the two different committee models are illustrated and compared.@en

In the MLUE method (reported in Shrestha et al. [1, 2]) we run a hydrological model M for multiple realizations of parameters vectors (Monte Carlo simulations), and use this data to build a machine learning model V to predict uncertainty (quantiles) of the model M output. In this paper, for model V, we employ three machine learning techniques, namely, artificial neural networks, model tree, locally weighted regression which leads to several models results. We propose to use the simple averaging method (SA) and the weighted model averaging method (WMA) to form a committee of these models. These approaches are applied to estimate uncertainty of streamflows simulation in Bagmati catchment in Nepal. Tests on the different data sets show that WMA performs a bit better than SA.@en