Extreme Climate Adaptation Strategy eBook

This book presents a systematic framework for risk assessment and predictions of extreme climate impacts using big data analytics, artificial intelligence, and intelligent monitoring technologies. Central to this effort is the concept of a smart water grid, a 'digital water network', capable of real-time monitoring, rapid transmission, accurate forecasting, optimized decision-making, precise allocation, and efficient management especially during floods and droughts.

Building on case studies and original research from the Yangtze Delta — a region increasingly vulnerable to heavy rainfall, prolonged dry spells, sea level rise, and clustered extreme climate events that intensify saltwater intrusion, water shortages, ecological degradation, and disruptions to inland navigation — the book demonstrates how the construction of a high-quality, intelligent water grid advances climate adaptation, supports sustainable water governance, and contributes to national strategies of regional integration. Its interdisciplinary perspective spans hydrology, remote sensing, system dynamics, and machine learning, offering both theoretical foundations and applied methodologies.

This text serves as a reference for researchers, engineers, and policymakers concerned with water resources engineering, environmental management, and disaster risk reduction in deltaic regions confronting climate change.

Contents:

• Preface
• About the Authors
• Acknowledgments
• Introduction
• The Impact of Global Extreme Climate Change and Human Activities on the Water Network of the Yangtze River Delta
• The Construction Strategy for a Smart Water Network in the Yangtze River Delta Region Against the Backdrop of Normalization of Extreme Climate
• Intelligent Water Network Management Platform for the Yangtze River Delta: A Construction Scheme in the Context of Extreme Climate Normalization
• Development of a Risk Exceedance Perception and Analysis System for Smart and High-Quality Water Network
• Estimating Terrain Elevations at 10 m Resolution by Integrating Random Forest Machine Learning Model and Remotely Sensed Data from ICESat-2, Sentinel-1, and Sentinel-2 Satellites
• Dissolved Oxygen Classification and Regression in the Taihu Lake Basin with Bidirectional Long Short-Term Memory Network based on Physical and Chemical Prior Knowledge
• Assessment and Strategies for Water Supply Security Risks in the Estuarine City of Shanghai under Normalized Extreme Climate Conditions
• Suggestions for Future Research
• Bibliography
• Index

Readership: Researchers, engineers, and policymakers concerned with water resources engineering, environmental management, and disaster risk reduction in deltaic regions confronting climate change.