Climate Extremes and Emerging Solutions: Data-Driven Insights and Geospatial Techniques (2026. x, 540 S. X, 540 p. 120 illus., 70 illus. in color. 235 mm)

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Climate Extremes and Emerging Solutions: Data-Driven Insights and Geospatial Techniques (2026. x, 540 S. X, 540 p. 120 illus., 70 illus. in color. 235 mm)

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  • 製本 Hardcover:ハードカバー版
  • 商品コード 9783032144560

Full Description

In this book, the authors discuss the different climate events, extreme events, compound extreme events, use of AI/ML, GIS and remote sensing. This important resource:

Explore the mechanisms, drivers, and impacts of climate extremes and compound events in the context of climate change.
Understand the cascading effects of extreme events across environmental and socio-economic sectors.
Learn how geospatial data, remote sensing, and GIS contribute to hazard detection and risk assessment.
Examine the integration of statistical analysis, machine learning, and AI in predictive modeling and disaster mitigation.
Study emerging climate risks such as flash droughts, compound flooding, and compound drought and heatwave (CDHW) events.
Review case studies highlighting recent research, real-world impacts, and disaster response challenges.
Gain insights to inform policy development, adaptation strategies, and climate resilience planning.

Written for scientists and researchers, academics and students, Climate Extremes and Emerging Solutions: Data-Driven Insights and Geospatial Techniques is a comprehensive book that covers the basic knowledge of the topic combined with practical applications.

Contents

Chapter 1. Forest Fire Risk Zonation in Relation to Environmental and Vegetation Health Indicators.- Chapter 2. Impact of Heatwaves on Human Heat Stress and Thermal Discomfort in the Smart Cities of Madhya Pradesh.- Chapter 3. Performance Assessment and Ranking of CMIP6 Climate Models for Minimum and Maximum Temperatures on the Western Coastline of India.- Chapter 4. Univariate and Multivariate Analysis of Major Drought Types over Mahanadi and Godavari River Basins in Central India.- chapter 5. Optimization of multi reservation operating policy using advanced optimization techniques.- Chapter 6. Comparative Study of Downscaling Methods: Distribution Mapping vs Quantile Mapping for Rainfall and Temperature Data over Central India.- Chapter 7. Integrating satellite data, modeling, and citizen science to derive critical insights into urban dynamics.- Chapter 8. Earth Observation Data for Understanding Historical Hydroclimatic Extremes and Infrastructure Evolution in the Hindu Kush Himalaya.- Chapter 9. Data-Driven Groundwater Flow Modelling Using Advanced Technologies for Sustainable Aquifer Management.- Chapter 10. Linking Climate Extremes and Vegetation Dynamics: A Study of Compound Events in Bhopal District, Central India.- Chapter 11. AI-Driven Approaches to Understanding and Predicting Extreme Climate Events.- Chapter 12. Analysing the Effects of LULC Changes on Land Surface Temperature-A Case Study of Bhopal City.- Chapter 13. Identification of Rainfall Induced Erosion Hotspots Using RUSLE and Geospatial Techniques: Case Study of Upper Krishna River Sub-Basin.- Chapter 14. Assessing Monsoon Wet and Dry Spells under Changing Climate Scenarios: A Case Study of the Upper Krishna River Sub-Basin.- Chapter 15. Drought Projection Assessment based on CMIP5 climate models in Banas River basin.- Chapter 16. Application of GIS & Remote Sensing in Precision Agriculture system.- Chapter 17. Deciphering future flood potential through ensemble climate change modelling in lower Gangetic floodplain region.- Chapter 18. Multi-Temporal Analysis of Coastline Dynamics Using Moderate and High-Resolution Satellite Imagery: A Case Study of Karnataka Coast, India.- Chapter 19. Prediction of Monthly Rainfall Using Different Machine Learning Techniques in Semi-Arid Region of Rajasthan State, India.- Chapter 20. From Satellite to Streams: A Review of River Observability in the SWOT Era.- chapter 21. Climate Change Impact Assessment Using CMIP-6 Data on Hydrological Components of the Upper Tapi Sub-Basin, India.- Chapter 22. Estimation of Reservoir Sedimentation Using Satellite Images.

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