Treatment of Errors and Uncertainties in Watershed-Scale Hydrological and Hydrogeological Models

Dear Colleagues,

Hydrological models have developed significantly over the past few decades, from simple lumped empirical models to complex physically based integrated surface water–groundwater models. Recently, some studies have even combined hydrological process equations with machine learning (ML) models to form hybrid physics–ML hydrological models. Despite significant developments in model structure, the common errors and significant uncertainties in the modeling results have perplexed model users, and even affect the correctness of our knowledge and understanding of the real-world water cycle system. In terms of uncertainty quantification, early studies have tried a variety of methods, such as parameter sensitivity analysis, stochastic response surface methods, variance decomposition, and Rosenblueth's method. Now, the Bayesian inference-based method is the most popular method to treat modeling errors and quantify uncertainties. Initially, Bayesian inference methods treated all sources of uncertainty (i.e., parametric, input, structural and observational) in a lumped manner. Later, more complex Bayesian frameworks, such as hierarchical Bayesian models, were developed to comprehensively describe, infer and evaluate uncertainties from multiple sources. More recently, the rapid development of ML models has brought opportunities for the interpretation of errors and the quantification of uncertainty in hydrological models. Some influential work has emerged. Despite improvements in methodology, one major challenge facing the hydrological modeling community is to address the high computational costs of these methods, which may require thousands to millions of model runs, especially for computationally expensive models. More efficient methods/algorithms/tools are still highly sought after.

This Special Issue focuses on the treatment of errors and uncertainties in watershed-scale hydrological modeling, considering research on the development and application of new methods, specifically. Application research on the error interpretation and uncertainty quantification of complex hydrological models (such as hydrogeological models), as well as studies on related water systems (such as water quality) are also welcome.

Dr. Feng Han
Dr. Bin Wu
Guest Editors

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• hydrological model
• hydrogeological model
• uncertainty
• errors
• Bayesian inference
• machine learning
• data assimilation
• Monte Carlo simulation
• Markov chain Monte Carlo
• surrogate modeling