Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

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,...

Supporting the Constructive Use of Existing Hydrological Models in Participatory Settings: A Set of “Rules of the Game”

When hydrological models are used in support of water management decisions, stakeholders often contest these models because they perceive certain aspects to be inadequately addressed. A strongly contested model may be abandoned completely, even when stakeholders could potentially agree on the validity of part of the information it can produce....

On the calibration of hydrological models in ungauged basins: A framework for integrating hard and soft hydrological information

This paper presents a calibration framework based on the generalized likelihood uncertainty estimation (GLUE) that can be used to condition hydrological model parameter distributions in scarcely gauged river basins, where data is uncertain, intermittent or nonconcomitant. At the heart of this framework is the conditioning of the model parameters...