From Numerical Models to Machine Learning: Evolution in Hydrological Prediction

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Submission deadlines

  1. Manuscript Summary Submission Deadline 9 November 2025 | Manuscript Submission Deadline 27 February 2026

  2. This Research Topic is still accepting articles.

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Background

Hydrological prediction is crucial for water resource management, flood/drought prevention, and climate adaptation. While traditional physics-based numerical models have been foundational, they face limitations in parameter calibration and computational demands, especially in data-scarce regions or for complex processes. These models provide valuable physical interpretability but can be limited by structural uncertainties and data requirements. Advances in computing power, remote sensing data, and artificial intelligence have enabled machine learning to identify complex patterns in big data, offering new approaches to hydrological forecasting. However, purely data-driven ML models often face challenges regarding physical consistency, extrapolation capabilities, and interpretability.



This Research Topic aims to comprehensively explore the historical trajectory, current state, and future potential of methodologies used in hydrological prediction, focusing on the pivotal transition from traditional numerical models to data-driven ML techniques and their synergistic integration. Recent advances highlight the immense potential of hybrid approaches that integrate physical understanding with the pattern recognition strengths of ML, leveraging the best of both paradigms. This represents a paradigm shift in water cycle prediction.



We welcome contributions addressing key questions: How have technological breakthroughs (e.g., increased computing power, satellite remote sensing, big data platforms) and methodological innovations driven this paradigm shift in hydrological prediction? What specific limitations of traditional models have ML and hybrid approaches overcome? How can we effectively integrate physical principles with data-driven methods to enhance prediction accuracy, robustness, and physical realism? This topic will focus on emerging techniques, including deep learning (CNNs, RNNs, Transformers), physics-informed neural networks (PINNs), ML-based parameterization and surrogate modeling, differentiable hydrological modeling, advanced data assimilation, and uncertainty quantification. The goal is to advance predictive capabilities for complex hydrological phenomena across scales, enabling more effective water resource management strategies.



This Research Topic seeks high-quality contributions examining the evolution of hydrological prediction methodologies. We particularly encourage studies focusing on, but not limited to, five key areas:

- Historical evolution and comparative studies assessing paradigm shifts between traditional numerical, pure machine learning, and hybrid modeling approaches, including applications in streamflow forecasting, groundwater modeling, evapotranspiration estimation, and flood/drought prediction.

- Hybrid modeling approaches incorporating physics-informed machine learning (PINNs), ML-enhanced process-based models, surrogate modeling techniques, and ML-based data assimilation methods.

- Methodological challenges including model interpretability, physical consistency, computational efficiency, data sparsity/noise handling, uncertainty quantification, and cross-basin transferability.

- Emerging machine learning techniques including advanced deep learning architectures, graph neural networks, attention mechanisms, transfer learning, and explainable AI approaches.

- Future perspectives on next-generation prediction tools and integrated modeling challenges in hydrological sciences.



We welcome the following manuscript types: Original Research, Review Articles, Perspectives/Opinions, Methodology Articles, and Case Studies from researchers in hydrology, water resources engineering, and related disciplines.

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
  • Data Report
  • Editorial
  • General Commentary
  • Methods
  • Mini Review
  • Opinion
  • Original Research

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Article types

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

  • Brief Research Report
  • Community Case Study
  • Data Report
  • Editorial
  • General Commentary
  • Methods
  • Mini Review
  • Opinion
  • Original Research
  • Perspective
  • Policy and Practice Reviews
  • Policy Brief
  • Review
  • Technology and Code

Keywords: Hydrological prediction, physics-based models, numerical models, climate adaptation, hybrid modeling

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