Eslamian et al. (2026) Multimodal data analytics for climate and water resources management

Identification

• Journal: Elsevier eBooks
• Year: 2026
• Date: 2026-01-01
• Authors: Saeid Eslamian, Mousa Maleki, Preethi Nanjundan
• DOI: 10.1016/b978-0-443-27528-9.00021-8

Research Groups

• Department of Water Science and Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, Iran
• Department of Data Science, CHRIST University, Pune, Lavasa Campus, Maharashtra, India

Short Summary

This paper explores the critical role of multimodal data analytics in climate and water resources management, highlighting its ability to provide a comprehensive understanding of complex environmental systems by integrating diverse data sources, despite challenges like data heterogeneity and biases.

Objective

• To demonstrate how integrating multiple data types (multimodal data) can address complex environmental issues in climate and water resources management by providing a more comprehensive understanding, enhancing predictive models, and formulating effective strategies.

Study Configuration

• Spatial Scale: Ranging from localized measurements (ground-based sensors) to broad regional and global coverage (satellite imagery).
• Temporal Scale: From real-time monitoring (water resource management) to historical data analysis, acknowledging the insufficiency of historical data alone for accurate forecasting due to increased climate variability.

Methodology and Data

• Models used: The text discusses the need for "sophisticated computational methods" and "sophisticated computational techniques" to process and integrate diverse data types, but does not specify particular named models (e.g., ISBA, mHM).
• Data sources: Satellite imagery, weather sensor readings, textual reports, social media updates, ground-based sensors.

Main Results

• Multimodal data analytics provides a holistic view of environmental systems, enabling the identification of patterns and trends that are hidden when using single data sources.
• It facilitates tracking both broad environmental shifts (e.g., forest loss, glacier shrinking) and smaller-scale changes (e.g., soil moisture variations, river behavior).
• By integrating diverse information, researchers can achieve a more comprehensive understanding of environmental processes, enhance the precision of predictive models, and formulate more effective strategies for climate adaptation and water resource management.
• Key challenges include data heterogeneity (e.g., raster vs. tabular formats), potential data biases from varying collection methods or resolutions, and the significant computational resources required to handle large data volumes.

Contributions

• Emphasizes the indispensable value of multimodal data analytics for a comprehensive and nuanced understanding of complex climate and water resource systems.
• Articulates the necessity of integrating diverse data types to overcome the limitations of single-source analyses in environmental management.
• Systematically outlines the significant advantages of multimodal data while also addressing the inherent challenges, such as data heterogeneity, biases, and computational demands, thereby guiding future research and implementation efforts.

Funding

• Not specified in the provided text.

Citation

@article{Eslamian2026Multimodal,
  author = {Eslamian, Saeid and Maleki, Mousa and Nanjundan, Preethi},
  title = {Multimodal data analytics for climate and water resources management},
  journal = {Elsevier eBooks},
  year = {2026},
  doi = {10.1016/b978-0-443-27528-9.00021-8},
  url = {https://doi.org/10.1016/b978-0-443-27528-9.00021-8}
}