Cover
CONTRIBUTORS
PREFACE
Section I: Introduction
1. 1 Concepts and Methodologies of Environmental Hazards and Disasters
  1. 1.1. INTRODUCTION
  2. 1.2. HYDROMETEOROLOGICAL HAZARDS IN AGRICULTURE
  3. 1.3. BIOPHYSICAL HAZARDS IN AGRICULTURE
  4. 1.4. SUMMARY
  5. ACKNOWLEDGMENTS
  6. REFERENCES
2. 2 Indigenous Knowledge for Disaster Solutions in the Hilly State of Mizoram, Northeast India
  1. 2.1. INTRODUCTION
  2. 2.2. TRADITIONAL PRACTICES FOR LANDSLIDE PREVENTION
  3. 2.3. BAMBOO‐BASED HOUSING IN EARTHQUAKE PRONE ZONE
  4. 2.4. COMMUNITY‐BASED EFFORTS TO COMBAT FOREST FIRES
  5. 2.5. LOCAL WATER HARVESTING SYSTEMS IN MIZORAM
  6. 2.6. CONCLUSIONS
  7. REFERENCES
3. 3 Urban Risk and Resilience to Climate Change and Natural Hazards: A Perspective from Million‐Plus Cities on the Indian Subcontinent
  1. 3.1. BACKGROUND
  2. 3.2. URBAN SYSTEM IN CHANGING CLIMATIC CONDITIONS
  3. 3.3. URBAN HAZARDS AND RISK
  4. 3.4. URBAN RESILIENCE AND ADAPTATION
  5. 3.5. CONCLUSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES
4. 4 The Contribution of Earth Observation in Disaster Prediction, Management, and Mitigation: A Holistic View
  1. 4.1. INTRODUCTION
  2. 4.2. EARTH OBSERVATION (EO) FOR NATURAL DISASTER PREDICTION
  3. 4.3. EARTH OBSERVATION (EO) FOR NATURAL DISASTER MANAGEMENT AND MITIGATION
  4. 4.4. CONCLUSIONS
  5. ACKNOWLEDGMENTS
  6. REFERENCES
Section II: Atmospheric Hazards and Disasters
1. 5 Tropical Cyclones Over the North Indian Ocean in Changing Climate
  1. 5.1. INTRODUCTION
  2. 5.2. DATA AND METHODOLOGY
  3. 5.3. RESULT AND ANALYSIS
  4. 5.4. CONCLUSIONS
  5. ACKNOWLEDGMENTS
  6. REFERENCES
2. 6 Simulation of Intensity and Track of Tropical Cyclones Over the Arabian Sea Using the Weather Research and Forecast (WRF) Modeling System with Different Initial Conditions (ICs)
  1. 6.1 INTRODUCTION
  2. 6.2 SYNOPTIC CONDITIONS ASSOCIATED WITH THE TROPICAL CYCLONES
  3. 6.3 DESCRIPTION OF THE WEATHER RESEARCH AND FORECAST MODEL AND NUMERICAL EXPERIMENTS
  4. 6.4 RESULTS AND DISCUSSION
  5. 6.5 CONCLUSIONS
  6. ACKNOWLEDGMENTS
  7. REFERENCES
3. 7 Development of a Soft Computing Model from the Reanalyzed Atmospheric Data to Detect Severe Weather Conditions
  1. 7.1. INTRODUCTION
  2. 7.2. METHODOLOGY
  3. 7.3. RESULTS AND DISCUSSION
  4. 7.4. CONCLUSIONS
  5. ACKNOWLEDGMENTS
  6. REFERENCES
4. 8 Lightning, the Global Electric Circuit, and Climate
  1. 8.1. INTRODUCTION
  2. 8.2. LIGHTNING DISCHARGES
  3. 8.3. LIGHTNING DISCHARGES AND THE GLOBAL ELECTRIC CIRCUIT
  4. 8.4. ATMOSPHERIC DISCHARGES AND CLIMATE
  5. 8.5. CONCLUSIONS AND RECOMMENDATION
  6. ACKNOWLEDGMENTS
  7. REFERENCES
5. 9 An Exploration of the Panther Mountain Crater Impact Using Spatial Data and GIS Spatial Correlation Analysis Techniques
  1. 9.1. INTRODUCTION
  2. 9.2. MATERIALS
  3. 9.3. METHODOLOGY
  4. 9.4. RESULTS
  5. 9.5. DISCUSSION
  6. 9.6. CONCLUSIONS
  7. ACKNOWLEDGMENTS
  8. REFERENCES
Section III: Land Hazards and Disasters
1. 10 Satellite Radar Interferometry Processing and Elevation Change Analysis for Geoenvironmental Hazard Assessment
  1. 10.1. INTRODUCTION
  2. 10.2. STUDY AREA
  3. 10.3. GEOENVIRONMENTAL MONITORING
  4. 10.4. RADAR INTERFEROMETRY
  5. 10.5. MULTITEMPORAL ELEVATIONS CHANGE ANALYSIS
  6. 10.6 CONCLUSIONS
  7. REFERENCES
2. 11 Assessing the Use of Sentinel‐2 in Burnt Area Cartography: Findings from a Case Study in Spain
  1. 11.1. INTRODUCTION
  2. 11.2. MATERIALS AND METHODS
  3. 11.3. RESULTS
  4. 11.4. DISCUSSION
  5. 11.5. CONCLUSIONS
  6. ACKNOWLEDGMENTS
  7. REFERENCES
3. 12 Assimilating SEVIRI Satellite Observation into the Name‐III Dispersion Model to Improve Volcanic Ash Forecast
  1. 12.1. INTRODUCTION
  2. 12.2. NAME DISPERSION MODEL
  3. 12.3. DATA AND METHODOLOGY
  4. 12.4. RESULTS AND DISCUSSIONS
  5. 12.5. CONCLUSIONS
  6. REFERENCES
4. 13 Geoinformation Technology for Drought Assessment
  1. 13.1. INTRODUCTION
  2. 13.2. STUDY AREA
  3. 13.3. MATERIALS AND METHODS
  4. 13.4. METHODOLOGY
  5. 13.5. RESULTS AND DISCUSSIONS
  6. 13.6. CONCLUSIONS
  7. ACKNOWLEDGMENTS
  8. REFERENCES
5. 14 Introduction to Landslides
  1. 14.1. INTRODUCTION
  2. 14.2. MORPHOLOGY OF LANDSLIDES
  3. 14.3. SLOPE FAILURE
  4. 14.4. CAUSES OF LANDSLIDES
  5. 14.5. TYPES OF LANDSLIDES
  6. 14.6. CLASSIFICATION OF LANDSLIDES
  7. 14.7. IDENTIFICATION OF LANDSLIDES
  8. 14.8. GROUND OBSERVATION
  9. 14.9. MEASUREMENT OF LANDSLIDES
  10. 14.10. LANDSLIDE HAZARD ZONATION MAPPING
  11. 14.11. LANDSLIDE HAZARD MITIGATION
  12. 14.12. SUMMARY
  13. REFERENCES
6. 15 Probabilistic Landslide Hazard Assessment using Statistical Information Value (SIV) and GIS Techniques: A Case Study of Himachal Pradesh, India
  1. 15.1. INTRODUCTION
  2. 15.2. STATISTICAL INFORMATION VALUE (SIV) MODEL
  3. 15.3. LANDSLIDE CONDITIONING FACTORS
  4. 15.4. METHODOLOGY
  5. 15.5. RESULTS AND DISCUSSION
  6. 15.6. CONCLUSION
  7. REFERENCES
7. 16 One‐Dimensional Hydrodynamic Modeling of the River Tapi: The 2006 Flood, Surat, India
  1. 16.1. INTRODUCTION
  2. 16.2. STUDY AREA AND DATA USED
  3. 16.3. METHODOLOGIES
  4. 16.4. RESULTS
  5. 16.5. VALIDATION OF SIMULATED FLOW
  6. 16.6. SENSITIVITY OF STAGE RELATION WITH TIDAL CONDITION
  7. 16.7. RECOMMENDATIONS AND SUGGESTION FOR FLOOD PREVENTION
  8. 16.8. DISCUSSIONS
  9. 16.9. CONCLUSIONS
  10. ACKNOWLEDGMENTS
  11. REFERENCES
Section IV: Ocean Hazards and Disasters
1. 17 Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal
  1. 17.1. INTRODUCTION
  2. 17.2. METHODOLOGY
  3. 17.3. TROPICAL CYCLONE ACTIVITY OVER THE NORTH INDIAN OCEAN
  4. 17.4. STUDIES ON TROPICAL CYCLONE–INDUCED STORM SURGES FOR THE BAY OF BENGAL
  5. 17.5. CHARACTERISTICS OF OCEAN WIND WAVES AND THEIR ROLE DURING EXTREME WEATHER EVENTS
  6. 17.6. COUPLED WAVE‐HYDRODYNAMIC MODELS
  7. 17.7 STORM SURGE AND INUNDATION MODELING FOR CYCLONE THANE
  8. 17.8. STORM SURGE AND INUNDATION MODELING FOR CYCLONE AILA
  9. 17.9. COUPLED MODELING SYSTEM FOR CYCLONE PHAILIN
  10. 17.10. COUPLED MODELING SYSTEM FOR CYCLONE HUDHUD
  11. 17.11. SUMMARY AND CONCLUSIONS
  12. REFERENCES
2. 18 Space‐Based Measurement of Rainfall Over India and Nearby Oceans Using Remote Sensing Application
  1. 18.1. INTRODUCTION
  2. 18.2. RAINFALL ESTIMATION USING INFRARED OBSERVATIONS
  3. 18.3. RAINFALL ESTIMATION USING MICROWAVE OBSERVATIONS
  4. 18.4. RAINFALL ESTIMATION USING MERGED IR AND MICROWAVE OBSERVATIONS
  5. 18.5. CONCLUSION
  6. ACKNOWLEDGMENT
  7. REFERENCES
3. 19 Modeling Tsunami Attenuation and Impacts on Coastal Communities
  1. 19.1. INTRODUCTION
  2. 19.2. MODELING TSUNAMIS
  3. 19.3. TSUNAMI DEFENSE STRUCTURES
  4. 19.4. TSUNAMI‐BORNE DEBRIS
  5. 19.5. INFRASTRUCTURE
  6. 19.6. SUMMARY
  7. REFERENCES
INDEX
End User License Agreement

List of Tables

Chapter 1
1. Table 1.1 Categories of Aridity Climatic Conditions.
2. Table 1.2 Typical Small Water Requirement Crops in Dry Conditions.
3. Table 1.3 Temperatures of Frost Damage to Deciduous Fruit Trees.
Chapter 5
1. Table 5.1 Decadal Frequencies of Tropical Cyclones in Different Seasons Over ...
2. Table 5.2 Decadal Frequencies of Tropical Cyclones in Different Season Over t...
3. Table 5.3 Decadal Frequencies of Cyclonic Storms and Severe Cyclonic Storms D...
4. Table 5.4 Decadal frequencies of Cyclonic Storms and Severe Cyclonic Storms D...
5. Table 5.5 Tricadal Variations in Frequencies of Tropical Cyclones.
6. Table 5.6 Tricadal Frequencies of Tropical Cyclones (CS + SCS).
7. Table 5.7 The Frequency of Cyclonic Storms (CS) and Severe Cyclonic Storms (S...
8. Table 5.8 The Direction of Movement of Tropical Cyclones in the Bay of Bengal...
9. Table 5.9 Direction of Movement of Tropical Cyclones in the Arabian Sea.
Chapter 6
1. Table 6.1 WRF Model Configuration.
2. Table 6.2 Mean Absolute Errors of MSLP (hPa) and 10 m Surface Wind (m/s) for ...
Chapter 7
1. Table 7.1 The Minimum, Maximum, Mean, and Standard Deviation Values of Atmosp...
2. Table 7.2 Correlation Coefficient Matrix Among the Parameters.
Chapter 9
1. Table 9.1 Description of the Surficial Geology Classifications.
2. Table 9.2 Description of the Bedrock Geology Classifications.
3. Table 9.3 The Results of the Correlation Analysis in Spreadsheet Software.
4. Table 9.4 Difference Between Scenario Correlations in Spreadsheet Software.
5. Table 9.5 The Results of the Correlation Analysis in GIS.
6. Table 9.6 Difference Between Scenario Correlations in GIS.
7. Table 9.7 The Results of the Error Assessment for All Scenarios (18 Total).
8. Table 9.8 GIS Analysis Correlation Results for the Six Scenarios That Did Not...
9. Table 9.9 The Difference Between the Results From Each Method, Shown for Both...
Chapter 10
1. Table 10.1 The Percentage Contribution of Each Class.
2. Table 10.2 Source Code of Software Module for Elevation Change Estimation.
3. Table 10.3 Expert Priorities Comparison Matrix.
Chapter 11
1. Table 11.1 Sentinel‐2 Bands with Resolution, Central Wavelengths, and Bandwid...
2. Table 11.2 General CORINE Land Cover Classes of the Study Site.
3. Table 11.3 Details of Spectral Indices Used.
4. Table 11.4 Results from Accuracy Assessment.
Chapter 12
1. Table 12.1 NAME Size Distribution of Ash Particles in Terms of Cumulative Fra...
2. Table 12.2
3. Table 12.3
4. Table 12.4
5. Table 12.5
6. Table 12.6
7. Table 12.7
8. Table 12.8
9. Table 12.9
10. Table 12.10
11. Table 12.11 Plume Height for the Eyjafjallajökull Volcano Observed by Weather...
12. Table 12.12
Chapter 13
1. Table 13.1 Characteristics of Rainfall Data.
2. Table 13.2 Classification of SPI.
3. Table 13.3 District‐wise Crop Yield (Jowar and Rice) for Drought and Normal Y...
Chapter 14
1. Table 14.1 Type of Landslides With Respect to Depth of Slide.
2. Table 14.2 Classification of Landslides
3. Table 14.3 Causative Factors and Landslide Hazard Evaluation Factor.
4. Table 14.4 Landslide Zones on the Basis of LHEF.
5. Table 14.5 Landslide Zonation on the Basis of Landslide Significance.
6. Table 14.6 Major Landslides in India.
Chapter 15
1. Table 15.1 Key Data Sets Used.
2. Table 15.2 Results Derived From the Analysis of the SIV Model.
Chapter 16
1. Table 16.1 Bridge ID and Other Details.
2. Table 16.2 Manning’sn Values and Root Mean Square Error (RMSE) (m) for the Fl...
3. Table 16.3 Q Less Than 8,500 m³/s (>3.0 lakh cusecs).
4. Table 16.4 Q Between 8,500 and 12,740 m³/s (3.0–4.5 lakh cusecs).
5. Table 16.5 Q Between 12,740 and 17,000 m³/s (4.5–6.0 lakh cusecs).
6. Table 16.6 Q Between 17,000 and 21,230 m³/s (6.0–7.5 lakh cusecs).
7. Table 16.7 Q Between 21,230 and 25,485 m³/s (7.5–9.0 lakh cusecs).
8. Table 16.8 Q More Than 25,485 m³/s (>9.0 lakh cusecs ).
Chapter 17
1. Table 17.1 Research Advances in the Field of Ocean Surface Waves During the P...
2. Table 17.2 Model Computed Inland Inundation Along the Tamil Nadu Coast From C...
3. Table 17.3 Beach Slope and Shoreline Characteristics for the Tamil Nadu Coast...
Chapter 19
1. Table 19.1 Percentage Reduction of the Run‐Up for the Rectilinear Layout.
2. Table 19.2 Percentage Reduction of the Run‐Up for the Staggered Layout.

List of Illustrations

Chapter 1
1. Figure 1.1 Heavy rainfall, leading to floods, could also cause soil erosion ...
2. Figure 1.2 Precipitation procedure chain.
3. Figure 1.3 Number of observed hail days for different climatic zones of Gree...
4. Figure 1.4 Map of Greece showing the three protected agricultural areas of t...
5. Figure 1.5 (a) Cloud seeding operation; (b) operational framework of hail su...
6. Figure 1.6 Simplified sketch of processes and drivers relevant for meteorolo...
7. Figure 1.7 Cumulative large areal extent (number of pixels, 8 x 8 km²) of ex...
8. Figure 1.8 Cumulative small areal extent (number of pixels 8 x 8 km²) of ext...
9. Figure 1.9 Formation of a frost pocket in a valley
10. Figure 1.10 Architectural components of the AEGIS platform showing the linka...
Chapter 2
1. Figure 2.1 Traditional practices used for landslides management in mountaino...
2. Figure 2.2 Multistorey buildings in Aizawl city are vulnerable to earthquake...
3. Figure 2.3 Traditional water harvesting practices used local people to cope ...
Chapter 3
1. Figure 3.1 Spatiotemporal distribution of cities with million‐plus populatio...
2. Figure 3.2 Influence of meteorological and geological hazards on major citie...
3. Figure 3.3 Frequency of major disaster events affecting million‐plus cities ...
Chapter 4
1. Figure 4.1 Number of recorded all natural disaster events (drought, floods, ...
2. Figure 4.2 Floods in the river system of the central United States Midwest o...
3. Figure 4.3 The image was created by the NOAA‐15 POES based Advanced Very Hig...
4. Figure 4.4 This topographical image was created using the Shuttle Radar Topo...
5. Figure 4.5 The Klyuchevskoy is one of the world's most restless active volca...
6. Figure 4.6 This image was captured by the Visible Infrared Imaging Radiomete...
7. Figure 4.7 These maps represent the modeled maximum wave height (a) and trav...
Chapter 5
1. Figure 5.1 Monthly variation in frequency of tropical cyclones over the Bay ...
2. Figure 5.2 Monthly frequency of tropical cyclones over the Arabian Sea, 1931...
3. Figure 5.3 Variation in seasonal frequency of tropical cyclones over the Bay...
4. Figure 5.4 Variation in seasonal frequency of tropical cyclones over the Ara...
5. Figure 5.5 Annual variation in frequencies of tropical cyclones over the Bay...
6. Figure 5.6 Annual variation in frequencies of tropical cyclones over the Bay...
7. Figure 5.7 Annual variation in frequencies of tropical cyclones over the Bay...
8. Figure 5.8 Annual variation in frequency of tropical cyclones during 1931–20...
9. Figure 5.9 Annual variation in frequency of tropical cyclones during 1931–20...
10. Figure 5.10 Annual frequency of tropical cyclones during 1931–2012 over the ...
11. Figure 5.11 Decadal frequencies of tropical cyclones over the Bay of Bengal....
12. Figure 5.12 Decadal frequency of tropical cyclones over the Arabian Sea.
13. Figure 5.13 The frequencies of severe cyclonic storms formed over South Bay(...
14. Figure 5.14 The frequency of tropical cyclones in the Bay of Bengal moving t...
15. Figure 5.15 The direction of movement of tropical cyclones over Arabian Sea....
Chapter 6
1. Figure 6.1 Time evolution of (a) MSLP, (b) 10 m wind, and (c) RMSE of MSLP a...
2. Figure 6.2 Time evolution of (a) MSLP, (b) 10 m wind, and (c) RMSE of MSLP a...
3. Figure 6.3 Wind field at 850 hPa for (a) Megh in the IC 07 case at 00 UTC, (...
4. Figure 6.4 Pressure level for (a) Megh in the IC 07 case at 00 UTC, (b) Megh...
5. Figure 6.5 Track prediction of (a) Chapala for all six cases and (b) Megh fo...
Chapter 7
1. Figure 7.1 Annual average rainfall (mm) for the period 1998 to 2013 as shown...
2. Figure 7.2 Spatial plots for (a) PR reflectivity and (b) DWR reflectivity, a...
3. Figure 7.3 (a) Storm height and (b) vertical profiles of reflectivity on 25 ...
4. Figure 7.4 Storm to storm variation of CAPE, CIN, Sh1, Sh2, PW, and ETH.
5. Figure 7.5 MLP architecture for rain height estimation.
6. Figure 7.6 Scatter plot and ETH_20dBZ. observed versus estimated for (a) trai...
7. Figure 7.7 Temporal variation of ETH_20dBZ observed and estimated from storm ...
Chapter 8
1. Figure 8.1 Schematic of processes relevant to the GEC, extending from the Ea...
2. Figure 8.2 Transient luminous events (sprites, blue jets, gigantic jets, and...
3. Figure 8.3 Gigantic jets records on 2 August 2013 (GJ1, GJ2) and 7 September...
4. Figure 8.4 The pulsating blue jet from the top of the northern cloud. Frame ...
5. Figure 8.5 (a) Yearly variations of cosmic ray flux, F10.7 cm (solar radio f...
6. Figure 8.6 Schematic diagram of various cloud microphysical processes, which...
Chapter 9
1. Figure 9.1 Location of the Catskill Mountains and the greater Allegheny Plat...
2. Figure 9.2 Schematic summary of data processing methodology.
3. Figure 9.3 Maps of the study area for (a) soil type classification (52 class...
4. Figure 9.4 Graph of the correlation analysis in spreadsheet software for eac...
5. Figure 9.5 Graph of the correlation analysis in GIS for each of the scenario...
Chapter 10
1. Figure 10.1 SAR image interferometric processing flow diagram.
2. Figure 10.2 Sentinel‐1 (SLC) image acquisition timeline.
3. Figure 10.3 Satellite radar interferometry study area.
4. Figure 10.4 Elevation change classes maps within the study area: (a) for 6.2...
5. Figure 10.5 Interpolated weights of elevation change values δh.
6. Figure 10.6 Spatial distribution of geoenvironmental hazard values (a) in le...
Chapter 11
1. Figure 11.1 Location of the area of interest (Sierra de Gata region, Spain)....
2. Figure 11.2 True color (left) and CORINE (right) land cover map of the study...
3. Figure 11.3 Thematic maps for burnt area detection, using (a) NBR_b12, (b) NB...
4. Figure 11.4 The total amount of burnt area created by each differentiated in...
Chapter 12
1. Figure 12.1 (a) Ash plume and cloud on 17 April 2010; (b) MODIS satellite im...
2. Figure 12.2 (a) Volcanic ash from real RGB satellite observation; (b) volcan...
3. Figure 12.3 Far‐field ash cloud locations interpreted from SEVIRI ash column...
4. Figure 12.4 Ash column loading (in g/m²) simulated for run model 1 on 6 May ...
5. Figure 12.5 Ash column loading (in g/m²) simulated for run model 2 on 6 May ...
6. Figure 12.6 Ash column loading (in g/m²) simulated for run model 3 on 6 May ...
7. Figure 12.7 The comparison results between run models 1, 2, and 3 versus Met...
8. Figure 12.8 Comparison between run model 1 and run model 3, which elucidates...
Chapter 13
1. Figure 13.1 Study area.
2. Figure 13.2 Meteorological drought scenario for the months of August and Sep...
3. Figure 13.3 Spatial pattern of SPI for drought and normal years.
4. Figure 13.4 SPI characteristics for drought and normal years (August).
5. Figure 13.5 SPI‐VCI distinctiveness for drought and normal years (August).
6. Figure 13.6 Spatial pattern of VCI for drought (2002, 2008) and normal years...
7. Figure 13.7 Correlation of VCI and crop yield (jowar)
8. Figure 13.8 Comparison of VCI and crop yield (jowar) for drought and normal ...
9. Figure 13.9 Comparison of VCI and crop yield (rice) for drought and normal y...
Chapter 14
1. Figure 14.1 Morphology of landslides.
2. Figure 14.2 Different types of slope failure.
3. Figure 14.3 Different types of landslides.
4. Figure 14.4 Cracks on the ground surface indicating the initial process of c...
5. Figure 14.5 Borehole extensometer.
6. Figure 14.6 Landslide affected states in India.
7. Figure 14.7 Safe and unsafe excavation of outcrop.
8. Figure 14.8 Slope modification and stone pitching.
9. Figure 14.9 Retaining wall.
10. Figure 14.10 Gabion structure.
11. Figure 14.11 Rockfall protection with wire net.
Chapter 15
1. Figure 15.1 Location of the study area.
2. Figure 15.2 Geoenvironmental factors considered in this study: (a) slope, (b...
3. Figure 15.3 Methodology followed for landslide susceptibility mapping.
4. Figure 15.4 Landslide susceptibility map generated using the SIV model.
5. Figure 15.5 Validation of landslide susceptibility map through ROC curve.
Chapter 16
1. Figure 16.1 Location of the Tapi basin, lower Tapi basin, and lower Tapi Riv...
2. Figure 16.2 Lower Tapi River with inline structure, gauge‐discharge station,...
3. Figure 16.3 Tidal cycle during the flood in 2006.
4. Figure 16.4 (a) Observed and simulated flood hydrograph at Kakrapar weir (19...
5. Figure 16.5 Velocity‐slope curve lower Tapi River.
6. Figure 16.6 Simulated maximum discharge carrying capacity of sections from t...
7. Figure 16.7 The maximum discharge carrying capacity of the Tapi River in and...
8. Figure 16.8 (a) Observed and simulated flood hydrograph at Kakrapar weir (20...
9. Figure 16.9 (a) Observed and simulated stage at Nehru (Hope) bridge (2006), ...
10. Figure 16.10 Simulated Nehru bridge stage with tidal level, without tidal le...
11. Figure 16.11 Scatter plot with and without tidal level at Nehru bridge.
12. Figure 16.12 (a) River bed line, right bank line, and water surface at 2006 ...
13. Figure 16.13 (a) Toposheet overlay on Google maps, river banks and illegal s...
14. Figure 16.14 Simulated stage and discharge at different bridges.
15. Figure 16.15 Rating curves of (a) Sardar Vallabhai Bridge, (b)Swami Vivekana...
16. Figure 16.16 (a) Nana Varaccha to Mota Varaccha Bridge, (b) Dabholi‐Jhagirpu...
Chapter 17
1. Figure 17.1 Annual frequency of (a) cyclones and depressions, and (b) very s...
2. Figure 17.2 (a) Bathymetry for the west coast of India and (b) idealized bat...
3. Figure 17.3 Grid structure along with the synthetic cyclone tracks.
4. Figure 17.4 Storm surge evolution along various locations on the west coast ...
5. Figure 17.5 Time variation of nonlinear term (NLT) for tracks 1, 5, 9, and 1...
6. Figure 17.6 Flow chart for the stand‐alone modeling system.
7. Figure 17.7 Satellite imagery of cyclone Thane.
8. Figure 17.8 Track of cyclone Thane in the Bay of Bengal (left) and cyclone a...
9. Figure 17.9 Blended topography of the study domain.
10. Figure 17.10 (a) The study domain and the finite element grid structure, and...
11. Figure 17.11 The Holland wind field over the study domain at time of landfal...
12. Figure 17.12 The comparison of observed, predicted, and model‐simulated wate...
13. Figure 17.13 The residual at (a) Ennore and (b) Nagapattinam.
14. Figure 17.14 Maximum water surface elevation computed by ADCIRC and the magn...
15. Figure 17.15 Estimated inundation along the selected 40 coastal locations.
16. Figure 17.16 (a) Beach slope along the Tamil Nadu coast, and (b) validation ...
17. Figure 17.17 Track of cyclone Aila (IMD report).
18. Figure 17.18 The topography of the study region (offshore from GEBCO and ons...
19. Figure 17.19 The enlarged view of mesh near to Sagar Island.
20. Figure 17.20 The actual and modified track of cyclone Aila.
21. Figure 17.21 The peak storm surge observed along the Sundarbans region.
22. Figure 17.22 Representative locations along the Bangladesh coast used for th...
23. Figure 17.23 The comparison of tide and storm tide at selected locations.
24. Figure 17.24 Model computed amplitude and phase difference of the storm tide...
25. Figure 17.25 The surge alone component at different coastal locations from t...
26. Figure 17.26 (a) Model computed onshore inundation for the entire head Bay, ...
27. Figure 17.27 Comparison of inundation scenario (a) MODIS imagery of onshore ...
28. Figure 17.28 Storm‐surge affected areas and associated onshore inundation ra...
29. Figure 17.29 Flow chart of the modeling system.
30. Figure 17.30 The satellite imagery of cyclone Phailin.
31. Figure 17.31 The finite element mesh of the study region.
32. Figure 17.32 Phailin wind field generated using the Jelesnianski formulation...
33. Figure 17.33 Comparison of model computed significant wave height against bu...
34. Figure 17.34 The water level at Ganjam for coupled and uncoupled run.
35. Figure 17.35 Model‐computed storm surge for the Paradeep location.
36. Figure 17.36 Peak storm surge computed at Ganjam from (a) uncoupled and (b) ...
37. Figure 17.37 Alongshore time evolution of wave induced setup between Gopalpu...
38. Figure 17.38 Spatial distribution of the wave induced setup along the coast....
39. Figure 17.39 Satellite image of cyclone Hudhud at the time of landfall.
40. Figure 17.40 The track of cyclone Hudhud based on the IMD report.
41. Figure 17.41 The bathymetry of the study region.
42. Figure 17.42 The finite element mesh of the study region.
43. Figure 17.43 The zoomed image of the coastal region.
44. Figure 17.44 Comparison of the radial profile of the cyclonic wind speeds wi...
45. Figure 17.45 (a) Time series plot of the wind envelope from original Jelesni...
46. Figure 17.46 Model‐computed significant wave height.
47. Figure 17.47 Validation of significant wave height with buoy observations of...
48. Figure 17.48 Time series plot of the surge residual (in meters) at Bheemunip...
49. Figure 17.49 Comparison of wave‐induced setup along Visakhapatnam and Bheemu...
50. Figure 17.50 Coastal vulnerability rank based on (a) coastal slopes and (b) ...
Chapter 18
1. Figure 18.1 ISRO AWS rain gauge network (in 2009) and DWR location. The open...
2. Figure 18.2 Validation of rainfall estimated from the MGPI technique with th...
3. Figure 18.3 Scatter plot between the scattering index from the SSM/I and rai...
4. Figure 18.4 Rainfall rate using the scattering index scheme on 23 July 2008 ...
5. Figure 18.5 Scatter plot between rainfall from regional scattering index sch...
6. Figure 18.6 Scatter plot between rainfall from regional scattering index sch...
7. Figure 18.7 Root mean square error at different rainfall ranges from linear ...
8. Figure 18.8 Scatter plot between Meteosat brightness temperature and PR rain...
9. Figure 18.9 (a) Brightness temperature in Kelvin and (b) rainfall in mm/3 h,...
10. Figure 18.10 Scatter plot between the rainfall from Mishra et al. (2010) and...
11. Figure 18.11 Rainfall rate (mm/h) during 22 October 2008 at 0000 UTC from (a...
12. Figure 18.12 Scatter plot between the rainfall from the present technique an...
13. Figure 18.13 Line and scatter plot between binned rainfall from rain gauge a...
Chapter 19
1. Figure 19.1 Dimensions of tree spacing for (a) rectilinear layouts and (b) s...
2. Figure 19.2 Bore elevation for (a) baseline reference model, and (b) rectili...
3. Figure 19.3 Vertical run‐up for a water depth of 2.25 m (C = 1.5m) for (a) r...
4. Figure 19.4 Vertical run‐up for a water depth of 2.4 m (C = 0 m) for (a) rec...
5. Figure 19.5 Vertical run‐up for a water depth of 3 m (C = –6 m) for (a) rect...
6. Figure 19.6 Velocity profiles for the rectilinear and staggered layouts (a) ...
7. Figure 19.7 Comparison of numerical and physical debris impact locations.
8. Figure 19.8 Comparison of physical, numerical, and analytically calculated i...
9. Figure 19.9 Shielding effect of the coastal forest for (a) rectilinear fores...
10. Figure 19.10 Building plan for overturned health supplement building in Onag...
11. Figure 19.11 Visual representation of flow past the health supplement buildi...
12. Figure 19.12 Modeled run‐up and force acting on the upstream face of the hea...

CONTRIBUTORS

Craig Amos
Department of Geography and Earth Sciences
University of Aberystwyth
Wales, United Kingdom

Prasad K. Bhaskaran
Department of Ocean Engineering and Naval Architecture
Indian Institute of Technology Kharagpur
West Bengal, India

R. Bhatla
Department of Geophysics
Banaras Hindu University
Varanasi, Uttar Pradesh, India;
DST‐Mahamana Centre of Excellence in Climate Change Research
Institute of Environment and Sustainable Development Banaras Hindu University
Varanasi, Uttar Pradesh, India

Sagarika Chandra
Indian Institute of Tropical Meteorology
Pune, India

Prabhjot Singh Chawla
Gautam Buddha University
Greater Noida, India

Nicolas R. Dalezios
University of Thessaly
Department of Civil Engineering
Pedion Areos
Volos, Greece

D. M. Denis
Department of Irrigation and Drainage Engineering
Sam Higginbottom University of Agriculture,
Technology and Sciences
Uttar Pradesh, India

Diksha
Department of Geoinformatics
Central University of Jharkhand
Ranchi, India

Devajyoti Dutta
National Centre for Medium Range Weather Forecasting (NCMRWF)
Ministry of Earth Sciences
Uttar Pradesh, India

Dipanwita Dutta
Department of Remote Sensing and GIS
Vidyasagar University
West Bengal, India

Ioannis N. Faraslis
University of Thessaly
Department of Planning and Regional Development
Pedion Areos
Volos, Greece

Konstantinos P. Ferentinos
Hellenic Agricultural Organization “Demeter” Soil & Water Resources Institute
Department of Agricultural Engineering
Athens, Greece

Dawei Han
Department of Civil Engineering
University of Bristol
Bristol, UK

Tanvir Islam
NASA Jet Propulsion Laboratory
Pasadena, California, USA

Shilpi Kalra
Gautam Buddha University
Greater Noida, India

M. L. Khan
Department of Botany
Dr. Harisingh Gour Vishwavidyalaya Sagar
Madhya Pradesh, India

Vinod Prasad Khanduri
Department of Forestry
Uttarakhand University of Horticulture and Forestry
Ranichauri, Uttarakhand, India

Anna Kozlova
Scientific Centre for Aerospace Research of the Earth
National Academy of Sciences of Ukraine
Kiev, Ukraine

Amit Kumar
Department of Geoinformatics
Central University of Jharkhand
Ranchi, India

Awadhesh Kumar
Department of HAMP
Mizoram University
Mizoram, India

Kewat Sanjay Kumar
Department of Forestry
Mizoram University
Mizoram, India

Sushil Kumar
Gautam Buddha University
Greater Noida, India

Arnab Kundu
DST‐Mahamana Centre of Excellence in Climate Change Research
Banaras Hindu University
Varanasi, Uttar Pradesh, India;
Institute of Environment and Sustainable Development
Banaras Hindu University
Varanasi, Uttar Pradesh, India

Shona Mackie
Department of Earth Sciences
University of Bristol
Bristol, United Kingdom

R. K. Mall
DST‐Mahamana Centre of Excellence in Climate Change Research
Banaras Hindu University
Varanasi, Uttar Pradesh, India;
Institute of Environment and Sustainable Development
Banaras Hindu University
Varanasi, Uttar Pradesh, India

Anoop Kumar Mishra
Center for Remote Sensing and Geoinformatics
Sathyabama University
Chennai, Tamil Nadu, India

Tad Murty
Department of Civil Engineering
University of Ottawa
Ontario, Canada

I. Nistor
Department of Civil Engineering
University of Ottawa
Ontario, Canada

A. C. Pandey
Department of Geoinformatics
Central University of Jharkhand
Ranchi, India

H. K. Pandey
Department of Civil Engineering
Motilal Nehru National Institute of Technology
Allahabad, India

Varsha Pandey
Institute of Environment and Sustainable
Development and DST‐Mahamana Center for Excellence in Climate Change Research
Banaras Hindu University
Uttar Pradesh, India

Dhruvesh P. Patel
Department of Civil Engineering
School of Technology
PDPU, Gujarat, India

N. R. Patel
Department of Agriculture and Soil
Indian Institute of Remote Sensing (ISRO)
Uttarakhand, India

Prajakta Patil
Department of Earth Sciences
University of Bristol
Bristol, United Kingdom

George P. Petropoulos
School of Mineral & Resources Engineering Technical University of Crete Kounoupidiana Campus Crete, Greece;
Department of Soil & Water Resources Institute of Industrial & Forage Crops Hellenic Agricultural Organization “Demeter” (former NAGREF)
Directorate General of Agricultural Research, Larisa, Greece

S. Piché
Department of Civil Engineering
University of Ottawa
Ontario, Canada

Iryna Piestova
Scientific Centre for Aerospace Research of the Earth
National Academy of Sciences of Ukraine
Kiev, Ukraine

Cristina Prieto
Environmental Hydraulics Institute Universidad de Cantabria
Parque Científico y Tecnológico de Cantabria Santander, Spain

Praveen Kumar Rai
Amity Institute of Geo‐Informatics and Remote Sensing
Amity University
Noida, India;
Department of Geography
Institute of Science
Banaras Hindu University
Uttar Pradesh, India

Raveena Raj
Environmental Science
Department of Botany
Banaras Hindu University
Varanasi, Uttar Pradesh, India

A. D. Rao
Centre for Atmospheric Sciences
Indian Institute of Technology Delhi
New Delhi, India

Kishan Singh Rawat
Center for Remote Sensing and Geoinformatics Sathyabama University
Chennai, Tamil Nadu, India

Ashish Routray
National Centre for Medium Range Weather Forecasting (NCMRWF)
Ministry of Earth Sciences
Uttar Pradesh, India

Sourabh Sakhare
Indian Institute of Surveying & Mapping Survey of India Training Institute
Hyderabad, India

Ankit Sharma
Amity Institute of Geo‐Informatics and Remote Sensing
Amity University
Noida, India

Shivani
Environmental Science
Department of Botany
Banaras Hindu University
Varanasi, Uttar Pradesh, India

Devendraa Siingh
Indian Institute of Tropical Meteorology
Pune, India

Prafull Singh
Amity Institute of Geo‐Informatics and Remote Sensing Amity University
Noida, India

Sudhir Kumar Singh
K. Banerjee Centre of Atmospheric and Ocean Studies
University of Allahabad
Allahabad, India

Prashant K. Srivastava
Institute of Environment and Sustainable Development and DST‐Mahamana Center for Excellence in Climate Change Research
Banaras Hindu University
Uttar Pradesh, India

Sergey Stankevich
Scientific Centre for Aerospace Research of the Earth
National Academy of Sciences of Ukraine
Kiev, Ukraine

Sawyer Reid Stippa
Department of Geography and Earth Sciences
University of Aberystwyth
Wales, United Kingdom

Ujjwal Sur
Amity Institute of Geo‐Informatics and Remote Sensing
Amity University
Noida, India

Olga Titarenko
Scientific Centre for Aerospace Research of the Earth
National Academy of Sciences of Ukraine
Kiev, Ukraine

N. Jeni Victor
Indian Institute of Tropical Meteorology
Pune, India

I. M. Watson
Department of Earth Sciences
University of Bristol
Bristol, United Kingdom

PREFACE

We are often told our Universe began with a Big Bang, a disaster that made the stars and galaxies we see today. And, ever since, life on earth has evolved and flourished, surviving a series of unexpected bangs and calamities of one or another type. Nowadays, every place on earth is vulnerable to some kind of disaster, whether natural or human induced. Not only are developing and third world countries with less infrastructure and facilities in danger, but leading developed economies too face severe negative impacts due to disasters in terms of human and capital losses, mostly due to our lack of understanding of the processes involved in contributing to the severity of a disaster. With the most stated reason for the increase in the number and frequency of these disasters being the recent episodes of climate change and variability in earth’s history, many researchers have directed their studies towards developing more advanced and sophisticated early warning systems and techniques for precise prediction and forecasting of disaster.

In this context, this book highlights state‐of‐the‐art new approaches, various modelling aspects, the role of field observations and management strategies, and efficient use of infrastructure in combating disasters. It addresses the interests of a wide spectrum of readers with a common interest in geospatial science, geology, water resource management, database management, planning and policy making, and resource management. The chapters in book focus mostly on emphasizing the investigation and identification of disasters through advanced computational techniques in conjunction with Geographic Information Systems (GIS) and Earth observation data sets for better management, adaptation, and mitigation of natural disasters.

The book is divided into four sections. Section I focuses on a general introduction to the disaster management and mitigation, with an overview on the different types of disaster and the importance of the existing traditional technologies mostly widely used for natural disasters, emphasizing the relevance of indigenous approaches in disaster management. The section also underlines the importance of community‐based techniques in disaster management, postdisaster management, and developing mitigation plans. Section II contains chapters presenting detailed studies on atmospheric hazards and disasters, with some studies focusing on extreme weather events such clouds burst and tropical cyclones. To highlight the advancement in modern technologies for disaster management on land surfaces, Section III presents the role of modern technologies for disaster management and mitigation in cases such as drought and landslides. The section contains articles focusing on the role of earth observing techniques, database management through cloud management, and emergency preparedness using Global Positioning Systems (GPS). The next section, Section IV, illustrates the application and capability of satellite and mesoscale modelling for better understanding and management of oceanic disasters disasters and hazards.

Section I, opens with an introductory chapter (Dalezios et al.) on concepts and methodologies of environmental hazards and disasters, providing the basics concepts of disasters in different fields. Chapter 2 (Kumar et al.), on indigenous knowledge for disasters solution in hilly states, discusses the role of local or indigenous people’s knowledge towards understanding and developing mitigation plans for disasters in hilly areas. Chapter 3 (Diksha et al.) presents an overview of the risk of disasters in urban areas and the relationship with climate change. The chapter provides a perspective from those cities on the Indian subcontinent with more than million inhabitants. The last chapter of this section (Pandey et al.), on the role of earth observing techniques in disaster prediction, management, and mitigation, provides a brief description of remote sensing and GIS techniques in disaster monitoring.

Section II of the book focuses on atmospheric hazards and related disasters. Chapter 5 (Bhatla and team) provides detailed accounts of tropical cyclones over the North Indian Ocean in changing climate, while Chapter 6 (Kumar and team) provides a detailed analysis of the simulation of the intensity and track of tropical cyclones over the Arabian Sea using the WRF modelling system. In Chapter 7 (Dutta et al.) a soft computing model developed using reanalyzed atmospheric data to detect severe weather conditions is described. Chapter 8 (Victor et al.) covers lightning, the global electric circuit, and the relationship with the climate. Chapter 9 (Stippa et al.) provides an exploration of the Panther Mountain crater impact using spatial data and GIS spatial correlation analysis techniques.

Section III of the book focuses on land hazards and disasters; it highlights disasters on land such as drought, landslides, volcanic eruption, and forest fires. The section starts with Chapter 10 (Stankevich and team) exploring satellite radar interferometry processing and elevation change analysis for geo‐environmental hazard assessment and continues with Chapter 11 (Amos et al.) documenting the use of Sentinel‐2 in burnt area cartography and the findings from a case study in Spain. Chapter 12 (Patil and team) provides an assessment of the Name‐III dispersion model after assimilating the SEVIRI satellite observation for volcanic ash forecast. Chapter 13 (Kundu et al.) describes geo‐information technology for drought assessment using satellite and geospatial techniques. Chapter 14 (Pandey et al.) provides an introduction to the causes and control of landslides, while Chapter 15 (Sharma and team) reviews probabilistic landslide hazard assessment using Statistical Information Value (SIV) and GIS techniques. Chapter 16 (Patel et al.) introduces 1D hydrodynamic modelling for flood risk assessment, to simulate and understand flooding risk in coastal areas.

The last section of the book contains chapters discussing oceanic hazards and disasters, with Chapter 17 (Bhaskaran et al.) covering tropical cyclone induced storm surges and wind‐waves in the Bay of Bengal, Chapter 18 (Mishra and Rawat) discussing space‐based measurement of rainfall over India and nearby oceans using remote sensing applications, and Chapter 19 (Piche et al.) detailing the modelling of tsunami attenuation and the impact on coastal communities.

We believe that this book will be beneficial for people with a common interest in disaster management and mitigation. The variety of techniques outlined in this book, such as geospatial techniques, remote sensing and applications, emergency preparedness, policy making, and other diverse topics, in the earth, environmental, and hydrological sciences fields will provide readers with updated knowledge. We hope that this book will be beneficial for academics, scientists, environmentalists, meteorologists, environmental consultants, and computing experts working in the area of disaster risk management and mitigation.

Prashant K. Srivastava

Sudhir Kumar Singh

U. C. Mohanty

Tad Murty

Techniques for Disaster Risk Management and Mitigation

CONTRIBUTORS

PREFACE

Section I
Introduction