Matta et al. (2026) Bridging the gap: geospatial technology, artificial intelligence, and hydrological models for integrated water resource management

Identification

• Journal: Elsevier eBooks
• Year: 2026
• Date: 2026-01-01
• Authors: Gagan Matta, Gaurav Pant, Pawan Kumar, Rama Pal, Laura Gjyli, Rachida El Morabet, Amit Kumar
• DOI: 10.1016/b978-0-443-36394-8.00008-x

Research Groups

• Hydrology and Science Communication Research Lab., Department of Zoology and Environmental Science, Gurukul Kangri (Deemed to be University), Haridwar, Uttarakhand, India
• Indian Institute of Soil and Water Conservation, Dehradun, Uttarakhand, India
• Department of Applied and Natural Sciences, University "Aleksander Moisiu", Durres, Albania
• Department of Geography, LADES Laboratory, CERES Center, FLSH-M, Hassan II University of Casablanca, Mohammedia, Morocco
• Glaciology Group, Wadia Institute of Himalayan Geology, Dehradun, Uttarakhand, India

Short Summary

This paper outlines the integration of geospatial technology, artificial intelligence (AI), and hydrological models to enhance integrated water resource management (IWRM), addressing complexities and promoting sustainability in a changing world. It reviews their combined potential to combat water scarcity, improve resource efficiency, and strengthen adaptive capacity to hydrological extremes.

Objective

• To outline the integration of geospatial technology, AI, and strong-coupled hydrological models for integrated water resource management (IWRM).
• To review their contributions to managing complexities in water management and making WRM sustainable in a rapidly changing world.
• To review the co-adoption potential of these technologies by outlining a framework for more advanced and integrated applications to combat water scarcity, enhance resource efficiency, and improve adaptive capacity to hydrological extremes through a comprehensive literature and case study survey.

Study Configuration

• Spatial Scale: Conceptual framework applicable to various watershed characteristics and hydrological processes, implying broad, potentially global, applicability for IWRM.
• Temporal Scale: Focuses on improving prediction of hydrological events and long-term sustainability of water resources, implying continuous and future-oriented application.

Methodology and Data

• Models used: Hydrological models (strong-coupled), decision-based risk assessment models, machine learning algorithms (e.g., neural networks, deep learning).
• Data sources: Species-specific spatial data (from geospatial tools), large datasets (for AI pattern recognition), recent literature and case studies (for review and framework development).

Main Results

• Geospatial tools provide essential species-specific spatial data for assessing watershed characteristics, hydrologic processes, and water use.
• Machine learning systems enable pattern recognition in large datasets, improve resource allocation, facilitate precise treatment, and enhance the prediction of hydrological events (e.g., rainfall-runoff, streamflow, flood losses).
• AI principles can be embedded into hydrological modeling to develop more agile, data-driven models that improve with repeated training based on new data.
• The integrated approach of geospatial technology, AI, and hydrological models offers a comprehensive solution to manage complexities in water management, combat water scarcity, enhance resource efficiency, and improve adaptive capacity to hydrological extremes.

Contributions

• Provides a comprehensive conceptual framework for integrating geospatial technology, AI, and strong-coupled hydrological models for IWRM.
• Reviews the collective contributions of these technologies to achieving sustainable water resource management in a rapidly changing global environment.
• Proposes a framework for the co-adoption and advanced integrated application of these capabilities to address critical water challenges, based on a survey of recent literature and case studies.
• Highlights the necessity of blending these technologies to solve complex four-dimensional area problems by connecting vertical and horizontal platforms.

Funding

• Not explicitly mentioned in the provided text.

Citation

@article{Matta2026Bridging,
  author = {Matta, Gagan and Pant, Gaurav and Kumar, Pawan and Pal, Rama and Gjyli, Laura and Morabet, Rachida El and Kumar, Amit},
  title = {Bridging the gap: geospatial technology, artificial intelligence, and hydrological models for integrated water resource management},
  journal = {Elsevier eBooks},
  year = {2026},
  doi = {10.1016/b978-0-443-36394-8.00008-x},
  url = {https://doi.org/10.1016/b978-0-443-36394-8.00008-x}
}