Different paradigms have emerged in watershed hydrology to deal with the uncertainties
associated with modeling with both similarities and differences in philosophies and …

Abstract Model selection and model averaging have become popular tools to address
conceptual uncertainty in hydro (geo) logical modeling. Within the last two decades, many …

Nonstationary extreme value analysis (NEVA) can improve the statistical representation of
observed flood peak distributions compared to stationary (ST) analysis, but management of …

We conduct a large-scale benchmark experiment aiming to advance the use of machine-
learning quantile regression algorithms for probabilistic hydrological post-processing “at …

This paper is concerned with the formulation of an adequate likelihood function in the
application of Bayesian epistemology to uncertainty quantification of hydrologic models. We …

Bayesian analysis is widely used in science and engineering for real‐time forecasting,
decision making, and to help unravel the processes that explain the observed data. These …

Predictive hydrological uncertainty can be quantified by using ensemble methods. If properly
formulated, these methods can offer improved predictive performance by combining multiple …

Significant Input uncertainty is a major source of error in watershed water quality (WWQ)
modeling. It remains challenging to address the input uncertainty in a rigorous Bayesian …

Variance‐based analysis has emerged as method of choice for quantifying the sensitivity of
the output, y, of a scalar‐valued square‐integrable function, f∈ L2 (), to its d≥ 1 input …

Gaussian process emulators (GPE) are a machine learning approach that replicates
computational demanding models using training runs of that model. Constructing such a …