Responding to Extreme Weather Events E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Hydrometeorological Extreme Events
• Sprache: Englisch

Seitenzahl: 763

Veröffentlichungsjahr: 2024

Beschreibung

RESPONDING TO EXTREME WEATHER EVENTS An up-to-date discussion of the latest in weather-related event forecasting and management In Responding to Extreme Weather Events, a team of distinguished researchers delivers a timely and authoritative exploration of three international extreme weather projects: ANYWHERE, I-REACT, and BeAWARE. The key contributions from policymaking, science, and industry in each project are discussed, as are the resulting improved measures and technologies for forecasting and managing weather-related extreme events. The authors cover the entire crisis management cycle, from awareness and early warning to effective responses to extreme weather events. Readers will also find: * A thorough introduction to the science and policy background of managing extreme weather events * Comprehensive explorations of impact forecasting for extreme weather events, including discussion of the ANYWHERE project * Practical discussions of how to improve resilience to weather-related emergencies with advanced cyber technologies, including discussion of the I-REACT project * A novel framework for crisis management during extreme weather events, including discussion of the BeAWARE project Essential for disaster management professionals, Responding to Extreme Weather Events will also benefit academic staff and researchers with an interest in extreme weather events and their consequences.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

List of Contributors

Series Preface

1 The ANYWHERE Paradigm Shift in Responding to Weather and Climate Emergencies

1.1 Disaster Risk Management in Times of Climate Change: The Need of a Proactive Approach

1.2 Adapting Risk Management to the ‘New Normality’: The Case of Flood Risk Management

1.3 Changing the Paradigm: Impact‐Based Multi‐Hazard Early Warning Systems to Move from Reactive to Pro‐Active Emergency Response Strategies

1.4 The New Paradigm: Dynamic Vulnerability

1.5 Future Work: From Multi‐Hazards to Multi‐Risk IEWS

References

Notes

2 Hydrometeorological Drought Forecasts

2.1 Introduction

2.2 Method for Forecasting Hydrometeorological Droughts

2.3 Hydrometeorological Drought Forecasts

2.4 Drought Forecast Performance

2.5 Importance of Catchment Memory

2.6 Outlook and Future Improvements

References

3 Experiences and Lessons Learnt in Wildfire Management with PROPAGATOR, an Operational Cellular‐Automata‐Based Wildfire Simulator

3.1 Introduction

3.2 Synopsis of Propagator Development: More than a Decade of Wildfire Simulations

3.3 Propagator Model

3.4 Case Studies

3.5 Results and Discussion

3.6 Conclusions

References

4 Building an Operational Decision Support System for Multiple Weather‐Induced Health Hazards

4.1 Introduction

4.2 Heatwave Prediction in ANYWHERE

4.3 Air Pollution Prediction in ANYWHERE

4.4 ANYWHERE MH‐EWS in Action: The European 2017 Heatwave

4.5 Implementation at Pilot Sites

4.6 Future Applications

4.7 Conclusions

Funding

Acknowledgements

References

Notes

5 The EUMETNET OPERA Radar Network – European‐Wide Precipitation Composites Supporting Rainfall‐Induced Flash Flood Emergency Management

5.1 Introduction

5.2 The EUMETNET OPERA Radar Precipitation Composites

5.3 Monitoring the Quality of the Opera Precipitation Composites

5.4 Application of Opera Precipitation Composites for Flash Flood Hazard Nowcasting

5.5 Conclusions and Outlook

References

6 Towards Impact‐Based Communication During Climate Emergencies

6.1 Introduction

6.2 Impact‐Based Early Warning Systems (IB‐EWS) for Actionable Decisions: Key Aspects

6.3 The Next Step for Community‐Based EWS: The Site‐Specific EWS Framework (SS‐EWS)

6.4 The SS‐EWS in Catalonia, NE Spain: Experiences and Lessons Learned

6.5 An Outlook on Future Community and Impact‐Based Communication Tools for Floods

References

Notes

7 Challenges for a Better Use of Crowdsourcing Information in Climate Emergency Situational Awareness and Early Warning Systems

7.1 Introduction

7.2 Crowd‐Generated Content to Support Emergency Management and Early Warning

7.3 ANYWHERE Applications and Their Lessons Learnt

7.4 Conclusion

References

Note

8 Co‐Evaluation: How to Measure Achievements in Complex Co‐Production Projects? ANYWHERE’s Contribution to Enhance Emergency Management of Weather and Climate Events

8.1 Introduction

8.2 Application of the ANYWHERE Co‐Evaluation Framework

8.3 Discussion of Co‐Evaluation Results

8.4 Discussion

8.5 Conclusion

References

Notes

9 Using Artificial Intelligence to Manage Extreme Weather Events

9.1 Introduction

9.2 Overall Objectives of the Project

9.3 The Impact of beAWARE

9.4 Conclusion

Acknowledgement

References

10 Innovative Visual Analysis Solutions to Support Disaster Management

10.1 Introduction

10.2 Related Work

10.3 Methodology

10.4 System Evaluation

10.5 Conclusions

References

11 Social Media Monitoring for Disaster Management

11.1 Introduction

11.2 Social Media Analysis

11.3 Social Media Clustering

11.4 Visualizations

11.5 Conclusion

References

Notes

12 Human‐Centred Public Warnings

12.1 Introduction

12.2 Risk Communication

12.3 Technical Standards and Recommendations

12.4 Future Outlooks in Public Warning and Risk Communication

References

Note

13 A DRM Solution for Professionals and Citizens

13.1 A Novel Mobile Application for DRR

13.2 The I‐REACT Co‐Design Approach

13.3 The Development and Implementation of the I‐REACT Mobile Solution

13.4 Gamified Crowdsourcing for Disaster Risk Management

13.5 The I‐REACT Wearable Solution for First Responders

13.6 Improved Positioning of First Responders Using EGNSS Technologies

References

14 Transforming Data Coming from Social Media Streams into Disaster‐Related Information

14.1 Introduction

14.2 Natural Language Processing Methods for Emergency‐Related Text Processing

14.3 Model Architecture

14.4 Classification Results

14.5 Image Filtering and Classification for Contextual Awareness

14.6 Event Detection

14.7 Impact Extraction

14.8 Annex 1: Definition of Yara Rules for Impact Estimation

Funding

References

Notes

15 Conclusions and Perspectives

15.1 Introduction

15.2 Policy Background

15.3 Actor’s Interactions and Community Building

15.4 Research Trends Related to Disaster Risks (Including Climate Extremes) in the Security Research Area

15.5 Conclusions, Gaps and Recommendations

References

Notes

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Differences between riverine and coastal floods compared with plu...

Table 1.2 Some examples of recent heavy rainfall events giving us what can ...

Chapter 3

Table 3.1 The first six rows show the nominal fire spread probability

p

n

be...

Table 3.2 Description of the several case studies simulated in this work wi...

Table 3.3 Performance indicators computed for the case studies of PROPAGATO...

Chapter 4

Table 4.1 Category scales of the UTCI and corresponding physiological respo...

Table 4.2 Air pollutants included in the ANYWHERE MH‐EWS, their impacts and...

Table 4.3 Air quality index classification and corresponding protection mea...

Table 4.4 Threshold definitions to determine the impact levels in the NUTS ...

Chapter 5

Table 5.1 A summary table of the evaluation of OPERA daily rainfall accumul...

Chapter 6

Table 6.1 Comparison between traditional hazard‐orientated and impact‐based...

Table 6.2 The impact‐based rainfall thresholds, potential impacts, the warn...

Table 6.3 Statements to evaluate the variables influencing the decision‐mak...

Table 6.4 Results from the influence of the site‐specific (SSWs) and the of...

Chapter 10

Table 10.1 Gathered datasets for flood, fire, smoke detection.

Table 10.2 Object detection performance during the 1st and 2nd pilot tests....

Table 10.3 Average Precision results of the object detection classifier.

Chapter 11

Table 11.1 Search keywords per language and pilot.

Table 11.2 Number of collected tweets per use case and language.

Table 11.3 List of emoticons and emojis that are considered irrelevant.

Table 11.4 Number of tweets annotated within beAWARE.

Table 11.5 Results of the evaluation of the visual classifier.

Table 11.6 Best parameters from TF, TFIDF, word2vec text classification met...

Table 11.7 Estimated number of clusters and NMI score of each clustering in...

Chapter 12

Table 12.1 Requirements vs technologies – mobile application, social media ...

Table 12.2 Public warning channel overview.

Table 12.3 Functions of social media in disasters.

Table 12.4 Recommended best practices for people‐centred PWS design and ma ...

Chapter 13

Table 13.1 Instance types specifications.

Table 13.2 Delay contributions.

Table 13.3 GNSS receivers' performance comparison.

Table 13.4 Data with ublox 8T.

Chapter 14

Table 14.1 Results using a BoW with an SVM classifier.

Table 14.2 Results using a CNN classifier with multilingual FastText word e...

Table 14.3 Results using a CNN with multilingual XML‐T contextual word embe...

Table 14.4 Label distribution in the NudeNet dataset.

Table 14.5 Experimental results of multilabel model and comparison with ref...

Table 14.6 Quality metrics concerning content type (A), Coherency (B), Haza...

Table 14.7 Detection rates computed over the EM‐DAT dataset.

Table 14.8 Impact estimation taxonomy proposed. Natural disasters typically...

Table 14.9 Impact estimation classification results.

Table 14.10 Impact estimation regression results.

Table 14.11 Example of Yara rules for the population category.

Table 14.12 Example of Yara rule for infrastructures and damages.

Table 14.13 Example of Yara rule to detect damaged roads.

List of Illustrations

Chapter 1

Figure 1.1 Evolution of the warning systems to support decision‐making durin...

Figure 1.2 (Above)

ERIC flash flood indicator

announcing 74% probability of ...

Figure 1.3 Change on the management model of weather‐induced emergencies tha...

Figure 1.4 ANYWHERE multi‐hazard IEWS forecasting platform: products and too...

Figure 1.5 A4EU platform impact forecasting scheme: artificial intelligence ...

Figure 1.6 Impact forecasting implemented on the A4EU platform, showing crit...

Figure 1.7 (Above) Scheme of the automatic activation of prioritary actions ...

Figure 1.8 Dynamic vulnerability approach: impact forecasting IEWS are used ...

Figure 1.9 Scheme of the methodological approach to transform MH‐IEWS into

m

...

Chapter 2

Figure 2.1 Drought characteristics derived from (a) the standardized drought...

Figure 2.2 Flowchart of drought algorithms, where

α

x‐i

,

β

x‐i

...

Figure 2.3 Schematic presentation of the SPI‐12 calculation using a fusion o...

Figure 2.4 Forecasted drought severity class (median of the 51 ensemble memb...

Figure 2.5 Forecasted drought severity class (median of the 51 ensemble memb...

Figure 2.6 Meteorological drought for the pan‐European region in August 2003...

Figure 2.7 Brier Skill Scores (BSS) for the Ripoll and Guardiola (Ter and Ll...

Figure 2.8 Correlation between forecast performance (BS), BFI, and gRC for 1...

Figure 2.9 Examples of drought impact forecasting: (a) forecasted LIO and re...

Figure 2.10 Multi‐hazard hotspot forecast maps at pan‐European scale issued ...

Chapter 3

Figure 3.1 Countries where operational (dark grey, orange for online product...

Figure 3.2 The state diagram of the CA. States

−

1, 1, 0 represent, res...

Figure 3.3 Averaging procedure used by PROPAGATOR to furnish probabilistic o...

Figure 3.4 Moore neighbourhood implemented in PROPAGATOR.

Figure 3.5 Topographic correction factor

α

h

.

Figure 3.6 Wind influencing factor

α

w

.

Figure 3.7 Influence of the combined slope‐wind factor on the Fire Spread Pr...

Figure 3.8 Effect of the fuel moisture ratio (dead fuel moisture over dead m...

Figure 3.9 Comparison between the actual burnt areas and the simulated ones ...

Figure 3.10 Comparison between the actual burnt areas and the simulated ones...

Figure 3.11 In this Figure, a wildfire in Loughglinn, County Roscommon, Irel...

Chapter 4

Figure 4.1 Products of weather‐induced health hazards in the ANYWHERE multi‐...

Figure 4.2 Heat stress as expressed by the UTCI (12UTC) during the June 2017...

Figure 4.3 Ozone concentrations (μg m

−3

) during the June 2017 heatwave...

Figure 4.4 A4CENEM as an example of ANYWHERE prototype at a pilot site. (a) ...

Figure 4.5 Predictability of the ANYWHERE heatwave index product for Catalon...

Figure 4.6 A framework for impact‐based forecasting of air pollution health ...

Chapter 5

Figure 5.1 Current status of the OPERA data production chain plotted togethe...

Figure 5.2 Time series of daily R/G in (a) 2013 and (b) 2021, based on the c...

Figure 5.3 Median values of R/G ratio obtained from daily OPERA accumulation...

Figure 5.4 Gauge‐adjusted OPERA hourly accumulation observed on 8 Jan 2021 a...

Figure 5.5 Processing chain of the ERICHA FF hazard nowcasting system implem...

Figure 5.6 Daily rainfall accumulation and flash flood hazard summary obtain...

Figure 5.7 ERICHA 24‐hour precipitation accumulation computed from the gauge...

Figure 5.8 Examples of monthly maximum basin‐aggregated hourly accumulations...

Chapter 6

Figure 6.1 The next step in the evolution of early warning systems: the site...

Figure 6.2 The A4alerts mobile app home screen.

Chapter 7

Figure 7.1 Example of emergency detection with the ANYWHERE social media too...

Figure 7.2 Example of the SMAT integration in the ANYWHERE A4EU Pan‐European...

Figure 7.3 Example of integration of SMAT multi‐hazard stream in the predict...

Figure 7.4 Language distribution of flood related tweets between 1 July 2022...

Figure 7.5 Language distribution of flood‐related tweets geolocated in Germa...

Figure 7.6 Examples of ANYWHERE crowdsourcing platform.

Figure 7.7 Schematic view of the ANYWHERE use of CS information.

Chapter 8

Figure 8.1 Comparative assessment of usefulness of relevant features of the ...

Figure 8.2 A4COR – comparison of emergency management systems based on knowl...

Figure 8.3 Comparison of emergency management systems based on working routine ...

Figure 8.4 Comparison of emergency management systems based on technical cri...

Figure 8.5 Relative importance of all co‐evaluation criteria at the pilot si...

Figure 8.6 Performance comparison of A4COR and legacy system by criterion at...

Figure 8.7 Performance comparison of ANYWHERE systems and legacy systems: al...

Figure 8.8 Performance comparison of ANYWHERE systems and legacy systems: me...

Chapter 9

Figure 9.1 The beAWARE concept.

Figure 9.2 Sensor and social media data.

Figure 9.3 Visual context analysis in emergency events.

Figure 9.4 Main public safety answering point of beAWARE.

Figure 9.5 Participants performing tasks during the Vicenza pilot.

Figure 9.6 Command centre in Vicenza and in Valencia pilot.

Chapter 10

Figure 10.1 A captured frame from Bacchiglione river (on the left) and an im...

Figure 10.2 Visualization of detected vehicles and their trajectories.

Figure 10.3 Non‐normalized (left) and normalized (right) confusion matrix of...

Figure 10.4 False positive flood cases.

Figure 10.5 False positive fire cases.

Figure 10.6 False positive smoke cases.

Figure 10.7 Pedestrians and vehicles detected in flooded areas and near a fi...

Figure 10.8 Comparison of water level values measured from the sensor (green...

Figure 10.9 Left images: example of an accurate estimation of water level du...

Figure 10.10 Normalized confusion matrix of image classification on a drone ...

Figure 10.11 Example of a ‘smoke’ image mis‐classified as ‘other’.

Figure 10.12 Examples of object detection results on drone footage.

Chapter 11

Figure 11.1 Complete flow of the SMA framework.

Figure 11.2 Clustered tweets and outliers in Valencia.

Figure 11.3 NMI score for different values of eps, for minPts 2, 3 and 4.

Figure 11.4 The flow of SMC in the beAWARE system.

Figure 11.5 beAWARE annotation tool – homepage.

Figure 11.6 beAWARE annotation tool – tweets presentation and buttons to ann...

Figure 11.7 beAWARE demonstration tool.

Chapter 12

Figure 12.1 DRM ecosystem.

Figure 12.2 The four elements of systematic people‐centred DRM.

Figure 12.3 Crisis and emergency risk communication (CERC) cycle.

Figure 12.4 Comparison between (a) the CAPEX model and (b) the OPEX model.

Figure 12.5 Suggested Public Warning System architecture.

Figure 12.6 Suggested public warning subsystem for sirens.

Figure 12.7 An SMS alert structure derived from CAP. Note that temporal info...

Figure 12.8 CAP warning message showed on a web platform. This example refer...

Figure 12.9 DRR messages via Twitter in comparison.

Figure 12.10 I‐REACT reporting system.

Chapter 13

Figure 13.1 I‐REACT co‐design strategy, from user research to conceptual des...

Figure 13.2 I‐REACT Co‐design workshop overview.

Figure 13.3 Initial stakeholders' needs and wants.

Figure 13.4 Acceptable crowdsourced data (by priority).

Figure 13.5 Informative needs per DRR phase.

Figure 13.6 Visual analysis of the collaborative sketches.

Figure 13.7 Log in (left) and Home (right) of the app.

Figure 13.8 Map & Forecast view: location‐based contents visualized on the m...

Figure 13.9 Map view of a forecast layer (temperature). Layer selection (lef...

Figure 13.10 'Warning' (left) and 'Report Request' (right): card (bottom) an...

Figure 13.11 Social media information: tweet card (left), List (centre) and ...

Figure 13.12 Report creation procedure made of five different steps.

Figure 13.13 Reports to be reviewed: report card (left), list (centre), and ...

Figure 13.14 I‐REACT gamification strategy: competencies, levels and require...

Figure 13.15 I‐REACT mechanics: statuses, competencies, Achievements and bar...

Figure 13.16 Wearable device data flow.

Figure 13.17 Example of wearable device mounting and operational scenario.

Figure 13.18 Device modes – internal logic.

Figure 13.19 Communication flow.

Figure 13.20 Proof of concept prototype – version 2 (PCB, enclosure, and ele...

Figure 13.21 Final device version – assembled electronic device, the final v...

Figure 13.22 Test setup and configuration.

Figure 13.23 ‘Corner test’: REF‐GNSS (up), right‐zoomed, wearable GNSS (down...

Figure 13.24 EDAS data provisioning scheme.

Figure 13.25 Augmentation module scheme.

Figure 13.26 Response time with 20 P1 instances.

Figure 13.27 Response time with 200 users and P1 instances.

Figure 13.28 Response time with 20 instances and 200 users.

Figure 13.29 Protection levels.

Figure 13.30 Results of data collections for the algorithm tuning. (a) and (...

Figure 13.31 A 25 km long track zoomed in on the most critical point (i.e. w...

Figure 13.32 A 10 km long urban track zoomed in on the most critical point. ...

Figure 13.33 Protection levels computed without the use of Galileo.

Figure 13.34 Protection levels computed with the use of Galileo.

Figure 13.35 Protection levels computed after further optimization.

Chapter 14

Figure 14.1 Google searches compared to tweets and news coverage regarding H...

Figure 14.2 Weekly interest trends for the most popular social news platform...

Figure 14.3 CNNfor text categorization from embeddings.

Figure 14.4 Performances of the NSFW models on the validation and test set. ...

Figure 14.5 Distribution of incidents over class‐positive and class‐negative...

Figure 14.6 Distribution of places over class‐positive and class‐negative im...

Figure 14.7 Detail of multilabel model. It exploits a single CNN architectur...

Figure 14.8 Relevant media for a flood event. The media ranking system assig...

Figure 14.9 Relevant media for the storm event. The media ranking system ass...

Figure 14.10 Event detection pipeline, highlighting the major processing ste...

Figure 14.11 Impact extraction building blocks. The yellow blocks represent ...

Chapter 15

Figure 15.1 Policies related to Disaster Resilient Societies. Acronyms stand...

Guide

Cover Page

Table of Contents

Series Page

Title Page

Copyright Page

List of Contributors

Series Preface

Begin Reading

Index

WILEY END USER LICENSE AGREEMENT

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Other Hydrometeorological Extreme Events Titles

Hydrometeorological Hazards: Interfacing Science and PolicyEdited by Philippe Quevauviller

Coastal Storms: Processes and ImpactsEdited by Paolo Ciavola and Giovanni Coco

Drought: Science and PolicyEdited by Ana Iglesias, Dionysis Assimacopoulos, and Henny A.J. Van Lanen

Facing Hydrometeorological Extreme Events: A Governance IssueEdited by Isabelle La Jeunesse and Corinne Larrue

Hydrometeorological Extreme Events and Public HealthEdited by Franziska Matthies‐Wiesler and Philippe Quevauviller

Responding to Extreme Weather EventsEdited by Daniel Sempere‐Torres, Anastasios Karakostas, Claudio Rossi, and Philippe Quevauviller

Responding to Extreme Weather Events

Edited by

Daniel Sempere-Torres

Center of Applied Research in HydrometeorologyUniversitat Politècnica de CatalunyaBarcelona, Spain

Anastasios Karakostas

DRAXIS Environmental S.A., Thessaloniki, Greece

Claudio Rossi

LINKS FoundationTurin, Italy

Philippe Quevauviller

European CommissionVrije Universiteit BrusselBrussels, Belgium

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List of Contributors

Stelios AndreadisCentre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI), Thessaloniki, Greece

Edoardo ArnaudoDipartimento di Automatica e Informatica (DAUIN), Politecnico di Torino, Turin, Italy

Francesco BaghinoCima Research Foundation, Savona, ItalyDIBRIS – Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy

Marc BerenguerCenter of Applied Research in Hydrometeorology, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain

Giacomo BlancoLINKS Foundation, Turin, Italy

Lorenzo BongiovanniLINKS Foundation, Turin, Italy

Mirko D’AndreaCima Research Foundation, Savona, Italy

Paolo FiorucciCima Research Foundation, Savona, Italy

Antonella FrisielloLINKS Foundation, Turin, Italy

Oliver GebhardtDepartment of Economics, Helmholtz Center for Environmental Research GmbH – UFZ, Leipzig, Germany

Ilias GialampoukidisCentre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI), Thessaloniki, Greece

Panagiotis GiannakerisCentre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI), Thessaloniki, Greece

Milan KalasKAJO s.r.o., Bytca, Slovakia

Anastasios KarakostasDRAXIS Environmental S.A., Thessaloniki, Greece

Denys KolokolKAJO s.r.o., Bytca, Slovakia

Ioannis KompatsiarisCentre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI), Thessaloniki, Greece

Ilias KoulalisCentre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI), Thessaloniki, Greece

Christian KuhlickeDepartment of Urban & Environmental Sociology, Helmholtz Center for Environmental Research GmbH – UFZ, Leipzig, GermanyInstitute for Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany

Henny A.J. Van LanenHydrology and Quantitative Water Management, Department of Environmental Sciences, Wageningen University and Research, Wageningen, the Netherlands

Annakaisa Von LerberFinnish Meteorological Institute, Helsinki, Finland

Gianluca MaruccoLINKS Foundation, Turin, Italy

Erika Meléndez‐LandaverdeCenter of Applied Research in Hydrometeorology, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain

Emmanouil MichailCentre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI), Thessaloniki, Greece

Claudia Di NapoliSchool of Agriculture, Policy and Development, University of Reading, Reading, UKDepartment of Geography and Environmental Science, University of Reading, Reading, UK

Joy OmmerKAJO s.r.o., Bytca, SlovakiaDepartment of Geography and Environmental Science, University of Reading, Reading, UK

Shinju ParkCenter of Applied Research in Hydrometeorology, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain

Nicolò PerelloCima Research Foundation, Savona, ItalyDIBRIS – Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy

Marco PiniLINKS Foundation, Turin, Italy

Philippe QuevauvillerEuropean Commission, Vrije Universiteit Brussel, Brussels, Belgium

Nicola ReboraCima Research Foundation, Savona, Italy

Claudio RossiLINKS Foundation, Turin, Italy

Tommaso SabattiniKAJO s.r.o., Bytca, Slovakia

Dario SalzaLINKS Foundation, Turin, Italy

Daniel Sempere‐TorresCenter of Applied Research in Hydrometeorology, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain

Amin ShakyaFaculty of Geo‐information and Earth Observation, University of Twente, WRS, Enschede, Netherlands

Samuel J. SutantoWater System and Global Change, Department of Environmental Sciences, Wageningen University and Research, Wageningen, the Netherlands

Andrea TrucchiaCima Research Foundation, Savona, Italy

Saša VranícKAJO s.r.o., Bytca, Slovakia

Stefanos VrochidisCentre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI), Thessaloniki, Greece

Series Preface

The increasing frequency and severity of hydrometeorological extreme events are reported in many studies and surveys, including the 5th IPCC Assessment Report. This report and other sources highlight the increasing probability that these events are partly driven by climate change, while other causes are linked to the increased exposure and vulnerability of societies in exposed areas (which are not only due to climate change but also to mismanagement of risks and ‘lost memories’ about them). Efforts are on‐going to enhance today's forecasting, prediction and early warning capabilities in order to improve the assessment of vulnerability and risks and develop adequate prevention, mitigation and preparedness measures.

The Book Series on ‘Hydrometeorological Extreme Events’ has the ambition to gather available knowledge in this area, taking stock of research and policy developments at international level. While individual publications exist on specific hazards, the proposed series is the first of its kind to propose an enlarged coverage of various extreme events that are generally studied by different (not necessarily interconnected) research teams.

The Series encompasses several volumes dealing with various aspects of hydrometeorological extreme events, primarily discussing science‐policy interfacing issues, and developing specific discussions about floods, coastal storms (including storm surges), droughts, resilience and adaptation, governance, and public health impacts. While the books are looking at the crisis management cycle as a whole, the focus of the discussions is generally oriented toward the knowledge base of the different events, prevention and preparedness, early warning and improved prediction systems.

The involvement of internationally renowned scientists (from different horizons and disciplines) behind the knowledge base of hydrometeorological events makes this series unique in this respect. The overall series will provide a multidisciplinary description of various scientific and policy features concerning hydrometeorological extreme events, as written by authors from different countries, making it a truly international book series.

The Series so far is made of five volumes, an introductory one on ‘Hydrometeorological Hazards: Interfacing Science and Policy’ (2015, Ed. Ph. Quevauviller), a second volume dealing with ‘Coastal storms: Processes and Impacts’ (2017, Ed. P. Ciavola and G. Coco), a third volume on ‘Droughts: Science and Policy’ (2019, Ed. A. Iglesias, D. Assimacopoulos and H.A.J. Van Lanen), a fourth volume intitled ‘Facing Hydrometeorological Extreme Events: A Governance issue’ (2020, Ed. I. La Jeunesse and C. Larrue), and a fifth one on ‘Hydrometeorological Extreme Events and Public Health’ (2022, Ed. F. Matthies‐Wiesler and Ph. Quevauviller). The volume ‘Responding to Extreme Weather Events’ is the sixth book of this Series; it has been written by experts in the field, covering various horizons and (policy and scientific) views gather from three major international research projects funded by the European Union Horizon2020 Framework Programme. It offers the reader an overview of scientific knowledge about challenges related to responses to weather extreme events, in particular impact forecasting, use of artificial intelligence and cybertechnologies for extreme weather event’s management, and communication and public warnings.

Ph. Quevauviller

Series Editor

1The ANYWHERE Paradigm Shift in Responding to Weather and Climate Emergencies: Impact Forecasting, Dynamic Vulnerability and the Need for Citizen’s Involvement

Daniel Sempere‐Torres and Marc Berenguer

Center of Applied Research in Hydrometeorology, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain

1.1 Disaster Risk Management in Times of Climate Change: The Need of a Proactive Approach

The world has just seen the hottest decade on record during which the title for the hottest year was beaten eight times (WMO 2023). This tendency will continue for decades, even if global and European efforts to cut greenhouse gas emissions prove effective. We also know today that ‘There is no definitive way to limit global temperature rise to 1.5°C above pre‐industrial levels’(IPCC SR 1.5). Even a drastic temporary decrease in emissions (the 2008 financial crisis or during COVID‐19 pandemic) has proved to have little effect on the overall trajectory of global warming. Therefore, and especially after the extreme events observed worldwide during 2021 and 2022, it is widely recognized that the effects of climate change (CC) are already happening today.

Moreover, the analysis of the impacts of natural hazards in the last 50 years (see WMO 2021) shows that the frequency and severity of these extreme climate and weather events are increasing and exacerbating climate‐related economic and social losses. And the urgency to react to their consequences is a social priority with significant political and economic implications, as proven by the climate emergency declaration of the EU parliament (November2019),1 and several other national and regional parliaments2 and leading cities3.

As stated by the EU Strategy on Adaptation to Climate Change (EC 2021a, b),4 the EU and the global community are underprepared for the increasing intensity, frequency and pervasiveness of climate change impacts, especially as emissions continue to rise. We must rapidly build our resilience to CC by moving from raising public awareness and concern to mass action on adaptation. Accordingly, the ‘Adaptation to CC, including Societal Transformation’, has become one of the five Horizon Europe Missions to push this significant societal challenge5.

In this regard, Early Warning Systems (EWSs) have become a crucial instrument for disaster risk management (DRM). Now promoted by the United Nations (UN) through the ‘Early Warnings for All initiative’6, EWS can be especially critical during weather/climate emergencies. However, to be effective, they must be able to trigger the intended actions for damage reduction to be undertaken by authorities, first and second responders and citizens (i.e. the earliest responders in place, also seen as the zero‐order responders, Briones et al. 2019).

Nonetheless, triggering the full chain of emergency management starting with the hazard forecasts up to the emergency management actions is not a simple objective, as the catastrophic floods of July 2021 in Germany and Belgium7 exemplified (over 180 deaths in just a 200 mm daily rainfall event, see Table 1.2). Currently, the available scientific and technical advancements enabling us to anticipate extreme events are not well integrated into the real‐life protocols of authorities and first responders. Hence it is critical to develop and implement EWSs adapted to the local needs of authorities, first responders and the population. And be able to connect them to local/community risk management plans able to ensure that the warnings can trigger the required local actions that can effectively reduce damages and loss.

This chapter, and some of the following ones, summarizes the paradigm shift in responding to weather and climate emergencies based in the project results and lessons learnt during the ANYWHERE innovation action.

1.2 Adapting Risk Management to the ‘New Normality’: The Case of Flood Risk Management

Before describing the details of the ANYWHERE proposed tools and results, it is important to illustrate the challenge of what it means to consider the effects induced by the CC through a particular well‐known hazard, such as floods.

Floods are the most significant natural hazards in Europe in terms of the number of events, people affected and economic losses. But it is also, together with storms, the most relevant natural hazard worldwide (CRED 2020). Hydrological hazards (floods, and heavy‐rain‐induced disasters) are also the natural hazard that has most increased in frequency in the last 30 years (Kron et al. 2019).

Table 1.1 Differences between riverine and coastal floods compared with pluvial and flash floods under climate change effects.

Riverine and coastal floods

Pluvial and flash floods

Time response

Long: days

Short: several 1/4 hours

Location

We know where:

Mapping of risk can be done

Defence and structural measures are possible

PLANNING

is CRUCIAL

Can be ANYWHERE:

The probability increases with climate change (an increase in heavy rains)

The probability increases with an increase in wildfires

Structural measures are out of the question

REAL‐TIME MANAGEMENT

of the response is crucial

What to do

We know

what to do

River restoration

Floodplain recuperation

EVACUATION is possible

At present we DO NOT know

what to do:

Need of a NEW PARADIGM

CITIZENS'

involvement is crucial

SELF‐PROTECTION

Flood Risk Management Plans

Subsidiarity principle

In this context and as seen in Table 1.1, it is important to recognize the differences between what are considered ‘classical or typical floods’ (e.g. riverine and coastal floods) and the ‘new intensified floods’, episodes that are not only increasing in their frequencies but also in their intensities and amount (and level) of seen socio‐economic impacts due to CC (e.g. pluvial and flash floods).

On one hand, riverine and coastal flood events have long response times (that can go from several hours to several days) and thus the time between the event starts and the main consequences is of the order of several hours or days. However, pluvial and flash floods are directly related to heavy‐rains and the associated torrential phenomena have extremely short response times (usually a few quarters of an hour). Consequently, these types of events trigger emergencies that develop too quick for a reactive response based on direct observations. Thus, the only appropriate emergency response must be based on the timely anticipation of the event (at least a few hours in advance, Alfieri et al. 2016). This implies that decision‐making needs to rely into trusted, but uncertain, high‐resolution forecasts instead of waiting to receive direct observations (only available when the impacts are already occurring), what it is by itself a significant operational challenge.

On the other hand, for the first category we can anticipate where these kinds of events will happen (around the river flood‐prone areas, or in particular areas of the coastal line). Therefore, risk cartographies can be pre‐established, making possible to plan defences and structural measures, as well as evacuation plans. Thus, planning is crucial to cope with these types of floods.

Contrarily, heavy‐rainfall‐induced floods can happen anywhere. Moreover, given the effects of CC on the increase of the frequency and severity of heavy rains, as well as on other factors amplifying the torrential character of flash floods (such as the increase of the number and severity of forest fires, which worsens the magnitude of flash floods due to the loss of vegetation; see Lavabre et al. 1993; Versini et al. 2013; Wine and Cadol, 2016; Wagenbrenner et al. 2021), pluvial and flash floods have multiplied by 3 in the last 30 years8 and they are at present the climate‐induced hazard that has increased the most. In this context, emergency management cannot only be based on planning, and the real‐time management of the response becomes crucial.

Furthermore, whereas in riverine and coastal floods we have enough experience with effective measures to reduce and manage the associated risks (through river restoration works, floodplain recuperation actions and evacuation plans etc …), in the case of pluvial and flash floods, we need to recognize that essentially the current established knowledge in flood risk planning and management turns out to be useless (as in the case of July 2021 in Germany and Belgium).

In these floods, as in any other hazards where climate change is making knowledge based on past experience irrelevant, we need to acknowledge that a change of paradigm is required. Change of paradigm that implies accepting to move from planning‐based strategies towards real‐time management strategies, essentially based on EWSs adapted to the local needs, providing actionable information able to trigger the response, not just of the local authorities, but also of the citizens. This requires a disruptive societal transformation in emergency management through the implementation of flood risk management plans, which should include as a major component the concept of self‐preparedness and self‐protection actions, previously identified and adapted to the most vulnerable points and communities, a transformation that should be supported by advanced and adapted technological tools (Gräßler et al. 2020).

To understand the urgency of such a societal transformation, Table 1.2 shows the main characteristics of recent heavy‐rainfall events recorded in Europe in 2020 and 2021. Whereas during the catastrophic floods in Germany in July 2021, the quantities recorded represented the equivalent of 2 months of accumulated rainfall registered in 24 hours, we can see that this event is not extraordinary in our ‘new’ CC times. Thus, we urgently need to start being prepared to face events delivering these 2‐month accumulated rainfall in less than 24 hours or more, such as the event on the 4 October 2021 in Rossiglione, Liguria (IT), where the European rainfall‐accumulated record in 12 hours has been beaten: 740 mm in 12 hours, representing one year mean rainfall accumulated in 12 hours.

These and the other events in Table 1.2 can help us to understand the magnitude of the new scenarios we need to be prepared to, and the urgency with which we need to start initiating the adaptation to the consequences of climate change.

1.3 Changing the Paradigm: Impact‐Based Multi‐Hazard Early Warning Systems to Move from Reactive to Pro‐Active Emergency Response Strategies

In this context, adapted DRM will require an update of the tools and methodologies to evolve our present risk assessment capacities, crisis management and preparedness strategies for the natural hazards under CC. Thus, an enhanced DRM cycle will require tools using different types of information and forecasts that can enable the anticipation of disasters, providing Early Warnings supporting the situational awareness and rapid deployment of responders in vulnerable areas.

Table 1.2 Some examples of recent heavy rainfall events giving us what can characterize the ‘new normality’ under climate change times.

Total accumulated rainfall

Maximal intensity

In terms of average monthly rainfall

14 July 2021 Germany

200 mm in 24 h

>40 mm in 1 h

2‐month rainfall in 24 h

1 September 2021 @ Alcanar (ES)

220 mm in

3 h

260 in 24 h

77 mm in 30 minutes

6

‐

month rainfall in 24 h

8 September 2021 @ Agen (FR)

130 mm in

3 h

80 mm in 1 h

2‐month rainfall in 3 h

18 December 2020 @ Cerdanyola (ES)

300 mm

in 24 h

>100 mm in 1 h

6

‐

month rainfall in 24 h

4 October 2021 @ Rossiglione, Liguria (IT)

900 mm

in 24 h

740 mm in 12 h

>180 mm in 1 h

12‐month rainfall in 12 h

To that end, impact‐based EWS (IEWS) (WMO 2021) and particularly multi‐hazard impact‐based EWS (MH‐IEWS) for weather emergencies have been promoted by the WMO and the Sendai Framework (Target G),9 (Murray 2021) see Figure 1.1,10,11, as the next step to translate forecasts into information supporting actionable decisions during emergencies and triggering site‐specific actions based on early risk forecasts.

Although many current initiatives are trying to develop the concept of IEWS for weather and climate‐induced disasters, there are very few successful experiences in implementing and demonstrating MH‐IEWS in an operational environment (Merz et al. 2020). The successful H2020 Innovation Action ANYWHERE (www.anywhere‐h2020.eu), winner of the EC Security Innovation Resilience Award in 2022,12 is one of them.

1.3.1 From Reactive to Proactive Emergency Response Strategies

This innovative pathway can be clarified by taking the example of the case of the floods in Germany in July 2021. For this event, a clear warning for the river Ahr13 (one of the most affected areas) was available through the European Flood Awareness System (EFAS,14 part of the Copernicus Emergency Management Services, CEMS15), was available more than 24 hours in advance of the floods, see Figure 1.2. Moreover, the ANYWHERE A4EU system16 provided a high‐resolution warning based on the OPERA network radar data17 for the portion of the river most affected 5 hours in advance (with enough time to take self‐protection actions and reduce the number of fatalities). Consequently, the technology to activate actionable solutions through a risk management self‐protection protocol was fully available and working correctly. However, the EU society has not yet the capacity to react effectively to these new climate‐induced emergencies, even if we have already the technology to anticipate their occurrence and impacts,18 as the declarations in front of the Commission of Inquiry about these floods in the Walloon Parliament have shown.19 Thus, the main challenge is how to use this technology to empower Emergency Management Centres to transform advanced meteorological forecasts into high‐resolution hazard and impact forecasting products providing information about the magnitude of the event and the expected consequences, allowing them to trigger the required actions to minimize damages and losses.

Figure 1.1 Evolution of the warning systems to support decision‐making during weather and climate emergency. The initial general weather forecast has been transformed in different families of weather warnings issued by the National Meteorological Services. The advancements of the last years include the integration of probabilistic approaches using ensemble forecasting, as in the European Flood Awareness System (EFAS), or the impact‐based multi‐hazard early warning systems (MH‐IEWS), among which ANYWHERE is one of the first real‐time systems tested in operational environment in several Emergency Management Centres in Europe. In the next years, it is foreseen that these MH‐IEWS could evolve towards new Multi‐Risk Early Warning Services able to be massively adopted to support international initiatives such as the Sendai Framework for Disaster Risk Reduction, to promote the EWS4ALL initiative of the WMO as well as the international initiatives supporting climate change adaptation (CCA).

In this strategy, an important step is to understand that nowadays, the usual practices in most emergency management centres (EMCs) are still mainly reactive (first the emergency is detected, usually through 112 calls, then the reaction follows pre‐established protocols, see Figure 1.3‐above). There are very few exceptions of EMCs able to act in proactive mode, i.e. integrating forecasting capabilities or initiating the response based on the early detection of weak signals (before the emergency becomes evident). In the last years, several H2020 projects (EMERGENT, ANYWHERE, I‐REACT, BEAWARE) have shown that technological developments can be of precious help for an anticipated response of first responders.

Figure 1.2 (Above) ERIC flash flood indicator announcing 74% probability of exceeding the highest warning level for the Ahr river (Germany) using the meteorological model forecast run on the 13 July at 12 : 00 UTC (>24 hours before the flood peak).

Source: EFAS.

(Below) Forecasted ERICHA flash flood indicator showing the maximum warning level on the Ahr river issued the 14 July at 14 hours UTC (5 hours in advance) using the rainfall nowcasts from the OPERA radars composites.

Source: ANYWHERE.

Figure 1.3 Change on the management model of weather‐induced emergencies thanks to the ANYWHERE project developments: (above) Instead of detecting the impacts with delay time 1, and start the emergency actions with delay 2; (below) the ANYWHERE platform allow to anticipate the detection of the event and advance the response before the occurrence of the impact.

Source: Courtesy of Sergio Delgado, Department of Civil Protection of the Generalitat de Catalunya.

In particular the ANYWHERE project has developed an operational multi‐hazard EWS for extreme weather and climate events, able to translate the most advanced meteorological forecasts into impact forecasting products to support emergency management (Abily et al. 2020, see Section 1.3.2). The system was verified, tested and operationally demonstrated in 7 Emergency Management Centres covering the entire climatic range in the EU for 18 months,20 demonstrating in real time that the generalization of the proactive way of working in EMCs is now possible (see Figure 1.3‐below).

These ANYWHERE innovations translate meteorological forecasts into anticipated impacts and automatically connect them to critical points to trigger a set of pre‐defined actions (for instance, those of the self‐protection plans), allowing civil protections and EMCs to start the response phase before the occurrence of the impacts, reducing the damages through the concept of dynamic vulnerability (Sempere‐Torres 2019), see Section 1.4.

This capacity was tested operationally during the 50‐year return period Storm Gloria (19–23 January 2020), which severely affected the east coast of Spain, and in particular Catalonia in a severe way. During this event the Civil Protection of Catalonia triggered several response actions (including the management of the river Ter dams, and the confinement of tens of thousands of affected inhabitants of different cities) based on impact forecasting early warnings for the first time in Europe, before observations were available (saving over six hours for the operations).

1.3.2 The ANYWHERE MH‐IEWS

The impact forecasting concept implemented by the ANYWHERE project consists of running state‐of‐the‐art hazard‐forecasting algorithms and models (driven by advanced meteorological forecasts) and combining them with the available exposure and vulnerability information to translate them into impact forecasts (see Figure 1.4).

In ANYWHERE, these algorithms and models are connected or encapsulated in a joint real‐time MH‐IEWS running in parallel to generate hazard forecasts for floods, flash floods, landslides and debris flows, storm surges, forest wildfires, droughts, heatwaves and weather‐induced health impact, convective storms, severe winds and snowfall. The outputs of these algorithms were compiled in a catalogue of products describing the hydro‐meteorological situation and forecasting the hazard level and expected impacts21 that were served in real time by the ANYWHERE MH‐IEWS to support emergency management and self‐protection actions in the pilot sites of the project.

Given the differences in the characteristic scales of the different weather and climate hazards considered, the driving meteorological inputs are adapted to each hazard. These included the use of observations and radar‐based precipitation nowcasts for the most local and fast‐evolving hazards, such as convective storms or local flash floods and landslides (Palau 2021; Palau et al. 2020, 2023); limited‐area Numerical Weather Prediction (NWP) models (driving the forecasting systems for floods, flash floods) and medium‐range and seasonal forecasts (for the drought impact forecasting algorithms).

The ANYWHERE MH‐IEWS is connected to the Continental‐scale hazard and impact forecasts generated by the Copernicus Emergency Services (CEMS)22: mainly, the hydrological forecasts of the European Flood Awareness System (EFAS); the fire of the European Forest Fire Information System (EFFIS) and the European Drought Observatory (EDO).

Figure 1.4 ANYWHERE multi‐hazard IEWS forecasting platform: products and tools/algorithms to forecast weather‐induced natural hazards and associated impacts.

The EFAS flood products were complemented with flash flood hazard and impact nowcasts at Continental scale (Park et al. 2019; Ritter et al. 2021) and regional scale (Corral et al. 2019; Poletti et al. 2019; Ritter et al. 2020, 2022; Láng‐Ritter et al. 2022), combining the hazard forecasts with the flood hazard and risk maps developed in the framework of the EU Floods Directive (2007); the vulnerability layers at the relevant scale to assess the expected losses and the expected impacts on population and critical points.

The storm surge forecasting models relied on a nested approach covering the European coasts at coarse resolution (EFAS‐COAST, Fernández‐Montblanc et al. 2019) to regional scale to forecast surge levels and waves parameters, feeding a high‐resolution flood/erosion model at local scale providing flow velocity, maximum inundation depth and expected shoreline retreat (Armaroli et al. 2019; Duo et al. 2020).

The ANYWHERE MH‐IEWS also integrates weather‐induced health impact forecasts at the European scale by including two different types hazards: (i) those related to Air quality from the Copernicus Atmosphere Monitoring Service using a regional multi‐model approach (Marécal et al. 2015), and (ii) those due to heat waves based on forecasts of the Universal Thermal Climate Index (UTCI, Di Napoli et al. 2018, 2021a, b) assessing the heat strain on the human body by combining the weather forecasts with a physiological model and an adaptive model for clothing insulation.

Complementing the products from EFFIS (based on computing fire danger indices based on medium‐range weather forecasts, Di Giuseppe et al. 2016, 2017, 2018; Vitolo et al. 2018, 2019), the RISICO model (Fiorucci et al. 2008; Perello et al. 2022) is used to forecast the forest fire hazard both at European and regional scales, in the latter using higher‐resolution and more accurate exposure datasets. After ignition, PROPAGATOR (Trucchia et al. 2020) is used to estimate the trajectory and extent of the forest fire given the weather conditions and identify the vulnerable areas potentially at risk.

Forecasts of drought impacts are based on transforming hazard forecasts (typically characterized with drought indices computed from medium‐range and seasonal weather and hydrological forecasts; Diaz et al. 2020a, b; Sutanto et al. 2020a, b; Sutanto and Van Lanen 2021) into impact forecasts using machine‐learning to train the algorithm from a database of historical reported impacts (Sutanto et al. 2019, 2020c, d).

And finally, the impacts caused by precipitation included the analysis of the impacts caused by convective cells (based on radar cell tracking; Rossi et al. 2014) and on forecasting the type of precipitation (Fehlman et al. 2018a, b, 2020; Gascón et al. 2018), particularly focusing on the impacts of snowfall on road conditions and road traffic (Cerreta et al. 2019).

Running the algorithms in parallel in the MH‐IEWS allows to explore the compound and cascading effects of weather and climate events (Schauewecker et al. 2019; Sutanto et al. 2019; Láng‐Ritter et al. 2022).

These real‐time hazard and impact forecasting products are integrated with additional high‐resolution local information about vulnerability and exposure with artificial intelligence and served by the MH‐IEWS together with regional layers of exposure and vulnerability in the emergency command centres (see Figure 1.5).

All this information, usually available but not interconnected, is now processed in the single platform ANYWHERE for EU (A4EU)23 to automatically identify the affected most vulnerable points, including their characteristics and location, and other advanced services, with the capacity to convert the warnings into actionable decisions supporting the response in emergency management centres (see Figure 1.6).

This is an innovation disruption in the field of Civil Protection and Emergency Management because the system allows the emergency response specialist to focus on local impacts, without the necessity to look in detail on the meteorological forecasts and triggers, and on a narrow set of vulnerable locations (i.e. Schools, Train Stations, Hospitals, Seveso facilities, among others) instead of vast regions, supporting them to magnify their response capabilities (see Figure 1.7).

Figure 1.5 A4EU platform impact forecasting scheme: artificial intelligence is used to integrate the hazard impact forecasting products served by the MH‐EWS with high‐resolution local impact models and local layers of exposure and vulnerability in the emergency command centres.

Figure 1.6 Impact forecasting implemented on the A4EU platform, showing critical points that are at risk, by automatic integration of the MHEWS forecasts with high‐resolution impact models and the local vulnerability and exposure cartographies. The figure shows how the flash‐flood early warning product ERIC (from EFAS) can be used to trigger an automatic warn to the predefined critical points potentially affected using the risk flood maps prepared under the Flood Directive.

1.4 The New Paradigm: Dynamic Vulnerability

The resilience of societies heavily depends on how their citizens behave individually or collectively. Therefore resilience, and emergency management in general, is primarily based on the capacity to coordinate many human actions; to share situation assessment; to make, implement and control coordinated actions; and to adapt the response to changing situations. In addition, the human factor is essentially critical when looking at communications between first‐responder authorities and citizens, obtaining trust and confidence, avoiding false rumours and managing the psycho‐social elements during the crisis and the recovery period afterwards.

In this framework, the traditional concepts of the emergency management cycle are facing a critical paradigm shift due to the rapid change of society by the disruption of mobile devices, IoT and technologies transforming the world into an interconnected society. However, the usual emergency management often disregards that both first responders and, around eighty per cent of European citizens are connected through mobile networks, carrying in our pockets what 15 years ago would have been an unimaginably sophisticated and miniaturized equipment, wasting what should be seen as an outstanding opportunity.

On the other hand, the increase of frequency and magnitude of extreme climate events induced by CC requires a paradigm change in risk governance and policies at European and global scales. The ‘new normality’ situation in which we are reaching new records more frequently requires acknowledging that the capacities of public response services might be exceeded and the key role of the citizens and communities and their engagement throughout the phases of climate DRM and CCA (Hügel and Davies 2020), as well as providing a methodological approach to empower citizens as ‘assets’ in terms of self‐protection response (Sempere‐Torres 2019).

Moreover, during an emergency situation, citizens and communities will in the first instance depend on themselves. The capacity of professional emergency services is limited and therefore prioritizes the weak and endangered persons who need them most. Thus, it is important that citizens and communities protect themselves during emergencies and this can be more efficiently achieved if self‐preparedness and protection plans are pre‐defined and incorporated into the routine of the communities. The key to the success of these self‐protection plans and protocols is a multiple effort, including information (first knowing the risk in your environment, Terti et al. 2019, 2020; Weyrich et al. 2021), preparedness (taking the necessary precautionary measures to cope with emergency situations) and solidarity (reaching out to those in need before, during and after an emergency).

However, involving the citizens in DRM is a challenging societal transformation, especially in such a complex field which involves many stakeholders and organizations; with different objectives, procedures, reporting structures and definitions. And that involves citizens in various roles: as disaster victims, as potential sensors providing relevant information, as well as participating in generating and distributing rumours and fake news (Simon et al. 2015; Venier 2020). Therefore, citizens can be both an important ‘asset’ to help first responders but may also create a lack of trust or confidence (Díaz et al. 2016) in the emergency management since they ‘use personal information and communication technology to respond to disasters in creative ways to cope with uncertainty’ (Palen and Anderson 2016; Venier and Capone 2019).

Figure 1.7 (Above) Scheme of the automatic activation of prioritary actions on the most vulnerable points for the A4EU flash flood impact forecasting. The pre‐identified vulnerable points are labelled (Courtesy of HYDS). (Below) Application to the event of 9 October 2018 in Mallorca Island (ES). The orange pixel (1 × 1 km) is the trigger for a flash flood warning over 10‐year return period The Flood Directive risk map allows the system to identify the associated area to be flooded and the vulnerable buildings in which the self‐protection protocols should be activated.

At present, citizens involved in an emergency are mainly seen by first responders as victims or as a potential nuisance or liability, but not as a potential source of help ignoring their capacities as an asset in crisis management. However, in the next future, given the rising disaster risk due to population growth and CC, citizens as ‘informal’ volunteers (Whittaker et al. 2015) can effectively assist first/second responders by providing the much‐needed additional support to react during emergencies. Changing this perception is not just a technological challenge but a societal challenge that requires understanding the present barriers and enablers and making a considerable effort to show the benefits of embracing such a change.

In this context, ANYWHERE has proposed the innovative concept of dynamic vulnerability (see Figure 1.8). First high‐resolution local information, from predefined priority or most vulnerable points, is added to the impact‐based early warnings, to convert them into site‐specific warnings (SSWs, Melendez‐Landaverde et al. 2020; Melendez‐Landaverde and Sempere‐Torres 2022