Robles et al. (2026) A Review of the Advances and Emerging Approaches in Hydrological Forecasting: From Traditional to AI-Powered Models

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Identification

• Journal: Water
• Year: 2026
• Date: 2026-01-04
• Authors: Kevin Paolo V. Robles, Jerose Solmerin, Gerald Christian E. Pugat, Cris Edward F. Monjardin
• DOI: 10.3390/w18010119

Research Groups

Not specified in the provided text.

Short Summary

This review synthesizes developments in hydrological forecasting from 2006 to 2025, examining various modeling approaches and highlighting a shift towards integrated, AI-leveraged systems. It identifies persistent challenges and outlines future directions for more resilient and transparent forecasting frameworks.

Objective

• To synthesize developments in hydrological forecasting from 2006 to 2025 across statistical, physically based, data-driven, and hybrid modeling domains.
• To identify current limitations in hydrological forecasting, including data scarcity, model interpretability, cross-basin generalization, climate non-stationarity, and operational computational demands.
• To outline future directions needed to strengthen hydrological forecasting as a tool for climate adaptation, early warning systems, and long-term water resource planning.

Study Configuration

• Spatial Scale: Global (review of literature across various spatial applications).
• Temporal Scale: Review period: 2006 to 2025. Discusses forecasting horizons from short-term (early warning) to long-term (water resource planning).

Methodology and Data

• Models used: This is a review of models, discussing: statistical approaches, physically based models, data-driven machine learning and deep learning techniques, and hybrid or emerging physics–AI frameworks.
• Data sources: This is a review of data sources used in the literature, including: remote sensing, Internet of Things (IoT) networks, and artificial intelligence applications.

Main Results

• The literature shows a decisive shift toward integrated, data-rich hydrological forecasting systems leveraging remote sensing, IoT networks, and artificial intelligence.
• Hybrid and physics-informed AI models demonstrate notable improvements in forecasting accuracy, lead time, and scalability.
• Persistent challenges in hydrological forecasting include data scarcity, model interpretability, cross-basin generalization, climate non-stationarity, and operational computational demands.
• The review outlines future directions to transition hydrological forecasting towards more resilient, transparent, and decision-oriented frameworks.

Contributions

• Provides a comprehensive synthesis of hydrological forecasting developments from 2006 to 2025 across four major domains.
• Identifies and consolidates the shift towards integrated, data-rich, and AI-leveraged forecasting systems.
• Clearly articulates the persistent limitations and emerging gaps in current hydrological forecasting practices.
• Outlines critical future directions for strengthening hydrological forecasting to enhance climate adaptation, early warning, and water resource planning.

Funding

Not specified in the provided text.

Citation

@article{Robles2026Review,
  author = {Robles, Kevin Paolo V. and Solmerin, Jerose and Pugat, Gerald Christian E. and Monjardin, Cris Edward F.},
  title = {A Review of the Advances and Emerging Approaches in Hydrological Forecasting: From Traditional to AI-Powered Models},
  journal = {Water},
  year = {2026},
  doi = {10.3390/w18010119},
  url = {https://doi.org/10.3390/w18010119}
}