Hydrological Simulation and Uncertainty Analysis Methods Based on Data Assimilation and Deep Learning

Hydrological modeling plays a pivotal role in understanding and predicting water resource dynamics, which are critical for sustainable groundwater management, flood forecasting, and contaminant mitigation. However, hydrological systems are inherently complex, influenced by numerous factors such as aquifer heterogeneity, climate variability, land use changes, and human activities, leading to significant uncertainties in model predictions. Traditional hydrological models often struggle to fully capture these complexities due to limited data availability, imperfect model structures, and the challenges of representing nonlinear processes. Recent advancements in data assimilation techniques have shown promise in integrating observational data with models to improve accuracy and reduce uncertainties. Concurrently, deep learning has emerged as a powerful tool for extracting complex patterns from large datasets, offering new opportunities to enhance hydrological simulations and uncover hidden relationships within the data. The synergistic application of data assimilation and deep learning presents a transformative approach to addressing uncertainties and improving the reliability of hydrological predictions. This Research Topic explores the integration of these methodologies, aiming to advance hydrological modeling and uncertainty analysis for more robust and informed decision-making in water resource management.


The primary goal of this Research Topic is to address the persistent challenges in hydrological modeling, particularly the uncertainties arising from complex system dynamics, data limitations, and imperfect model structures. These uncertainties hinder the reliability of predictions, which are essential for effective water resource management, flood forecasting, and contaminant mitigation. To tackle these issues, this Research Topic aims to explore and advance the integration of data assimilation and deep learning techniques. Specifically, we seek to develop novel data assimilation methods capable of addressing challenges such as nonlinearity, non-Gaussianity, and high-dimensionality, which are common in hydrological systems. By combining data-driven approaches with process understanding, we aim to create more efficient and accurate predictive models that better capture the complexities of hydrological processes. Furthermore, this Research Topic will focus on applying data assimilation and deep learning techniques to quantify errors from different sources within models, such as input data, model parameters, and structural uncertainties. Recent advances in both fields have demonstrated their potential to transform hydrological modeling, yet their combined application remains under-explored. By leveraging the strengths of these methodologies, this Research Topic seeks to develop innovative approaches that enhance the reliability and predictive power of hydrological models. Ultimately, this work aims to provide actionable insights and tools for better decision-making in water resource management, contributing to more sustainable and resilient water systems in the face of growing environmental and anthropogenic pressures.


This Research Topic invites contributions that advance hydrological modeling and uncertainty analysis through the integration of data assimilation and deep learning techniques. We encourage submissions that address key themes, including but not limited to:

• development of novel data assimilation methods to tackle challenges such as nonlinearity, non-Gaussianity, and high-dimensionality in hydrological systems;

• fusion of data-driven and mechanistic modeling approaches to improve the efficiency and accuracy of hydrological predictions;

• quantification of uncertainties from different sources, such as input data, model parameters, and structural errors, using data assimilation or deep learning;

• applications of these methodologies to real-world water resource management, flood forecasting, and contaminant mitigation.


We welcome original research articles and reviews that demonstrate innovative approaches and practical insights. Submissions should highlight the innovative application of data assimilation and deep learning, emphasizing their potential to enhance hydrological modeling and decision-making. Authors are encouraged to present robust methodologies, case studies, and actionable outcomes that contribute to the advancement of the field.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: Hydrological Simulation, Uncertainty Analysis, Data Assimilation, Deep Learning, Parameter Estimation

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.