List of Contributors xii Series Preface xvi 1 The ANYWHERE Paradigm Shift in Responding to Weather and Climate Emergencies: Impact Forecasting, Dynamic Vulnerability and the Need for Citizen''s Involvement 1 Daniel Sempere- Torres and Marc Berenguer 1.1 Disaster Risk Management in Times of Climate Change: The Need of a Proactive Approach 1 1.2 Adapting Risk Management to the ''New Normality'': The Case of Flood Risk Management 2 1.3 Changing the Paradigm: Impact- Based Multi- Hazard Early Warning Systems to Move from Reactive to Pro- Active Emergency Response Strategies 4 1.3.1 From Reactive to Proactive Emergency Response Strategies 5 1.3.2 The ANYWHERE MH- IEWS 9 1.

4 The New Paradigm: Dynamic Vulnerability 13 1.5 Future Work: From Multi- Hazards to Multi- Risk IEWS 16 Notes 17 References 18 2 Hydrometeorological Drought Forecasts: Lessons Learned from ANYWHERE and Next Steps to Improve Drought Management 23 Samuel J. Sutanto and Henny A.J. Van Lanen 2.1 Introduction 23 2.2 Method for Forecasting Hydrometeorological Droughts 25 2.2.

1 The Climate (ECMWF SEAS5) and Hydrological (LISFLOOD) Models 25 2.2.2 The Drought Indices 26 2.2.3 The Drought Forecast Algorithms 28 2.3 Hydrometeorological Drought Forecasts 30 2.3.1 Meteorological Drought Forecasts 30 2.

3.2 Hydrological Drought Forecasts 31 2.4 Drought Forecast Performance 33 2.4.1 The Origin of Seasonal Drought Forecast Skill 33 2.4.2 Examples of Assessment of Seasonal Drought Forecast Performance 34 2.5 Importance of Catchment Memory 38 2.

6 Outlook and Future Improvements 40 2.6.1 Drought Impact Forecasts 41 2.6.2 Compound and Cascading (CC) Dry Hazards 43 References 44 3 Experiences and Lessons Learnt in Wildfire Management with PROPAGATOR, an Operational Cellular- Automata- Based Wildfire Simulator 49 Andrea Trucchia, Mirko D''Andrea, Francesco Baghino, Nicolò Perello, Nicola Rebora, and Paolo Fiorucci 3.1 Introduction 49 3.1.1 Mathematical Models for Wildfire Management 50 3.

2 Synopsis of Propagator Development: More than a Decade of Wildfire Simulations 52 3.3 Propagator Model 55 3.4 Case Studies 62 3.4.1 Data Retrieval 62 3.5 Results and Discussion 65 3.5.1 Performance Indicators 65 3.

5.2 Performances of Test Cases 70 3.5.3 An Example of Continuous Improvement and Operational Deployment: Implementation in Ireland 71 3.6 Conclusions 71 References 73 4 Building an Operational Decision Support System for Multiple Weather- Induced Health Hazards: ANYWHERE Developments and Future Applications 77 Claudia Di Napoli 4.1 Introduction 77 4.2 Heatwave Prediction in ANYWHERE 79 4.2.

1 The Universal Thermal Climate Index 80 4.2.2 Forecasting Algorithms 80 4.2.3 Heatwave Products 81 4.2.4 Integration in the MH- EWS 81 4.2.

5 Temperature Products 81 4.3 Air Pollution Prediction in ANYWHERE 83 4.3.1 Air Quality 83 4.3.2 Forecasting Algorithms 85 4.3.3 Air Quality Products 85 4.

3.4 Integration in the MH- EWS 85 4.4 ANYWHERE MH- EWS in Action: The European 2017 Heatwave 86 4.5 Implementation at Pilot Sites 87 4.5.1 Integration of Local Heatwave and Air Pollution Products 90 4.5.2 Evaluation at Pilot Sites 92 4.

6 Future Applications 93 4.6.1 Impact- Based Warnings 93 4.6.2 Multi- Hazard Forecasting 95 4.6.3 Cold Spells as a Health Hazard 97 4.6.

4 Social Sensing 97 4.6.5 Protecting the Vulnerable 98 4.7 Conclusions 98 Funding 99 Acknowledgements 99 Notes 99 References 99 5 The EUMETNET OPERA Radar Network - European- Wide Precipitation Composites Supporting Rainfall- Induced Flash Flood Emergency Management 105 Shinju Park, Marc Berenguer, Daniel Sempere- Torres, and Annakaisa Von Lerber 5.1 Introduction 105 5.2 The EUMETNET OPERA Radar Precipitation Composites 106 5.3 Monitoring the Quality of the Opera Precipitation Composites 108 5.4 Application of Opera Precipitation Composites for Flash Flood Hazard Nowcasting 110 5.

5 Conclusions and Outlook 113 References 116 6 Towards Impact- Based Communication During Climate Emergencies: A Community- Based Approach to Improve Flood Early Warning Systems 119 Erika Meléndez- Landaverde, Daniel Sempere- Torres, and Shinju Park 6.1 Introduction 119 6.2 Impact- Based Early Warning Systems (IB- EWS) for Actionable Decisions: Key Aspects 121 6.2.1 Partnerships for an Effective Co- Design IB- EWS 122 6.2.2 End Users: Identifying Needs for Emergency Response 123 6.2.

3 Risk Identification and Impact Data Collection 124 6.2.4 Evaluation of IB- EWSs 125 6.3 The Next Step for Community- Based EWS: The Site- Specific EWS Framework (SS- EWS) 125 6.3.1 The Site- Specific Early Warning System Framework (SS- EWS) 126 6.4 The SS- EWS in Catalonia, NE Spain: Experiences and Lessons Learned 128 6.4.

1 Community- Based Sessions in Terrassa: The Co- Design Process and Experiences 129 6.4.2 Community- Based Emergency Response: SS- EWS Real- Time Application in Terrassa 132 6.4.3 The Site- Specific Warnings (SSWs): Their Influence on the Risk Perception and Understanding of Users in Blanes 132 6.4.4 A4alerts: Mobile Application for Emergency Communication 134 6.5 An Outlook on Future Community and Impact- Based Communication Tools for Floods 135 Notes 137 References 137 7 Challenges for a Better Use of Crowdsourcing Information in Climate Emergency Situational Awareness and Early Warning Systems 141 Milan Kalas, Joy Ommer, Amin Shakya, Sasa Vraníc, Denys Kolokol, and Tommaso Sabattini 7.

1 Introduction 141 7.2 Crowd- Generated Content to Support Emergency Management and Early Warning 143 7.2.1 Examples of the Citizen Science in Disaster Risk Management 143 7.2.2 Tools 144 7.2.3 Challenges in the Integration and Application of Citizen- Generated Content in DRM 145 7.

3 ANYWHERE Applications and Their Lessons Learnt 146 7.3.1 Crowd Mapping to Support Real- Time Risk Assessment 147 7.3.2 Social Media Streaming to Increase Emergency Situational Awareness 147 7.3.3 A Crowdsourcing Solution for Collecting Information on the Magnitude and Impact of Disasters 153 7.3.

4 Towards a Holistic System 155 7.3.5 Facilitating Communication Between Actors in Emergency Management 157 7.4 Conclusion 158 Note 159 References 159 8 Co- Evaluation: How to Measure Achievements in Complex Co- Production Projects? ANYWHERE''s Contribution to Enhance Emergency Management of Weather and Climate Events 163 Oliver Gebhardt and Christian Kuhlicke 8.1 Introduction 163 8.2 Application of the ANYWHERE Co- Evaluation Framework 165 8.2.1 Step 1: Context Analysis 166 8.

2.2 Step 2: Description of Baseline Scenario and ANYWHERE Scenario 166 8.2.3 Step 3: Selection of Suitable and Feasible Criteria 166 8.2.4 Step 4: Selection of Appropriate Co- Evaluation Method 167 8.2.5 Step 5: Data Collection 167 8.

2.6 Step 6: Data Aggregation and Analysis 168 8.3 Discussion of Co- Evaluation Results 168 8.4 Discussion 176 8.5 Conclusion 177 Notes 177 References 178 9 Using Artificial Intelligence to Manage Extreme Weather Events: The Impact of the beAWARE Solution 181 Anastasios Karakostas, Stefanos Vrochidis, and Ioannis Kompatsiaris 9.1 Introduction 181 9.2 Overall Objectives of the Project 182 9.3 The Impact of beAWARE 188 9.

3.1 Scientific and Innovation Impact 188 9.3.2 Economic Impact 191 9.3.3 Safety Impact 191 9.3.4 Training Impact 191 9.

3.5 Policymakers 193 9.3.6 First Responders 194 9.3.7 General Public (Citizens) 195 9.4 Conclusion 196 Acknowledgement 197 References 197 10 Innovative Visual Analysis Solutions to Support Disaster Management 199 Emmanouil Michail, Panagiotis Giannakeris, Ilias Koulalis, Stefanos Vrochidis, and Ioannis Kompatsiaris 10.1 Introduction 199 10.

2 Related Work 200 10.3 Methodology 203 10.3.1 Disaster Detection 204 10.3.2 Object Detection 205 10.3.3 River Level Monitoring 206 10.

3.4 Drone Analysis 206 10.3.5 Traffic Analysis and Management 209 10.4 System Evaluation 211 10.4.1 Disaster Detection 212 10.4.

2 Object Detection and Tracking 213 10.4.3 River Level Monitoring 215 10.4.4 Drone Analysis 217 10.4.5 Traffic Analysis and Management 219 10.5 Conclusions 221 References 221 11 Social Media Monitoring for Disaster Management 224 Stelios Andreadis, Ilias Gialampoukidis, Stefanos Vrochidis, and Ioannis Kompatsiaris 11.

1 Introduction 224 11.2 Social Media Analysis 225 11.2.1 Framework Overview 225 11.2.2 Data Collection from Twitter 226 11.2.3 Analysis of Social Media Data 227 11.

2.4 Data Representation 232 11.3 Social Media Clustering 234 11.3.1 Evaluation of Spatial Clustering Techniques 234 11.3.2 The Proposed Spatiotemporal Clustering 236 11.4 Visualizations 237 11.

4.1 Annotation Tool 237 11.4.2 Demonstration Tool 239 11.5 Conclusion 240 Notes 241 References 241 12 Human- Centred Public Warnings 24.