Valayamkunnath (2026) Advances in land surface models: Enhancing drought monitoring, prediction, and water management

Identification

• Journal: Elsevier eBooks
• Year: 2026
• Date: 2026-01-01
• Authors: Prasanth Valayamkunnath
• DOI: 10.1016/b978-0-443-44625-2.00008-4

Research Groups

School of Earth, Environmental and Sustainability Sciences (SoEESS), Indian Institute of Science Education and Research Thiruvananthapuram, Thiruvananthapuram, Kerala, India

Short Summary

This chapter reviews the evolution of Land Surface Models (LSMs) and their enhanced capabilities for drought monitoring, prediction, and water management. It highlights how integrating advanced data assimilation, satellite observations, and machine learning has improved the detection and forecasting of various drought types.

Objective

• To review the advancements in Land Surface Models (LSMs) and their application in drought monitoring, prediction, and water management, emphasizing the integration of new data sources and machine learning techniques.

Study Configuration

• Spatial Scale: Global (reviewing applications worldwide)
• Temporal Scale: Seasonal to multi-decadal (covering seasonal forecasting, historical trends, and climate change impacts)

Methodology and Data

• Models used: Land Surface Models (LSMs), hydrological schemes, process-rich frameworks (reviewed, not directly applied in a new study)
• Data sources: Satellite observations, data assimilation products, reanalysis (implied for LSM forcing), machine learning techniques

Main Results

• Land Surface Models (LSMs) have evolved from simple hydrological schemes to complex frameworks incorporating vegetation dynamics, plant hydraulics, groundwater interactions, and human water use.
• The integration of data assimilation, satellite observations, and machine learning has significantly improved LSM capabilities for detecting and predicting various drought types, including flash and snow droughts, and for seasonal forecasting.
• LSMs are crucial tools for drought monitoring and water management strategies under climate change scenarios.
• Key challenges remain in addressing uncertainties related to forcing data, model structure, and the representation of human activities.
• Future directions emphasize combining physics-based modeling with machine learning for more accurate drought predictions and enhanced societal resilience.

Contributions

• Provides a comprehensive synthesis of the evolution and current state-of-the-art of Land Surface Models (LSMs) for drought applications.
• Highlights the transformative impact of integrating advanced data sources (e.g., satellite observations) and computational techniques (e.g., data assimilation, machine learning) on drought science.
• Identifies persistent challenges and outlines future research avenues, particularly the synergy between physics-based and machine learning approaches.

Funding

Not specified in the provided text.

Citation

@article{Valayamkunnath2026Advances,
  author = {Valayamkunnath, Prasanth},
  title = {Advances in land surface models: Enhancing drought monitoring, prediction, and water management},
  journal = {Elsevier eBooks},
  year = {2026},
  doi = {10.1016/b978-0-443-44625-2.00008-4},
  url = {https://doi.org/10.1016/b978-0-443-44625-2.00008-4}
}