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

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (30 October 2023) | Viewed by 1322

Special Issue Editors

School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
Interests: hydrological modeling; ecological modeling; water quality modeling; uncertainty analysis; data assimilation; Bayesian inference; Markov chain Monte Carlo; machine learning; HEIFLOW
Center for Hydrogeological and Environmental Geological Survey, China Geological Survey, Baoding, China
Interests: hydrogeology; integrated surface water-groundwater modelling; uncertainty analysis; optimization; surrogate modeling; climate change

Special Issue Information

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

Published Papers (1 paper)

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14 pages, 2995 KiB  
Nitrate Migration and Transformation in Low Permeability Sediments: Laboratory Experiments and Modeling
by Sai Wang, Bin Wu, Yating Wang and Xiaoyan Wang
Water 2023, 15(14), 2528; - 10 Jul 2023
Cited by 1 | Viewed by 991
The excessive application of fertilizers and the uncontrolled discharge of untreated wastewater on land contribute to the nitrogen pollution of groundwater. Low permeability sediment plays an important role in slowing down the migration of nitrate in groundwater systems. This study focuses on the [...] Read more.
The excessive application of fertilizers and the uncontrolled discharge of untreated wastewater on land contribute to the nitrogen pollution of groundwater. Low permeability sediment plays an important role in slowing down the migration of nitrate in groundwater systems. This study focuses on the mechanisms that affect nitrate transport in the low permeability layer. A soil column experiment was conducted and an MT3D transport model (a modular three-dimensional multispecies transport model) of nitrate was proposed. The results show that a low permeation layer could act as a strong barrier to nitrate migration and transportation, as demonstrated by the column soil experiment. The denitrification rates were controlled by many other factors, including nitrate concentration, flow rate, pH values and sediment particle characteristics. This indicated that denitrification is the most important factor in the attenuation of nitrate in low permeability sediments. This study clarified the law of migration and transformation of nitrate in low permeability soils and the factors that influence these processes. The results not only provide a basis for large-scale nitrate simulations, but also provide a reference for nitrate treatment in low permeability layers. Full article
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