The speech will mainly concentrate on three parts: the introduction of the situation of disaster emergency mapping, the current research hotspots and the development trends of cartography for emergency and disaster management.

The overall situation part: Firstly, starting from the impact of disasters and their management needs, based on the current research ”the body of knowledge for cartography”, it will introduce the classification of disaster and emergency management mapping based on disaster types, disaster management processes, spatial dimensions and spatial temporal scales.

The current research hotspots will mainly involve the following aspects: big data-driven disaster emergency mapping, dynamic simulation of disaster processes, user-centred map design and implementation and disaster scenarios and digital twins.

Facing the development trends, it will explore new methods and ideas driven by new technologies such as artificial intelligence, as well as the design and implementation of disaster narrative maps based on the coupling of nature, humanity, and memory for long-term disaster prevention and disaster education.

Issuing warnings, be it hazard-based or impact-oriented, requires a data processing pipeline to generate a reliable warning product which can be distributed to the end-user. Each step along the chain, including both manual and automatic ones, offers its own challenges to refine the data.

Traditionally, hazard-based warnings are derived from initially complex, gridded weather forecast data and have to be simplified for easy understanding by the public. Yet, hazard warnings do not provide specific information regarding their consequences as, for example, physical damage to infrastructure, disruption of societal activities, or economic losses. Under the umbrella initiative Early Warnings for All (EW4A) the World Meteorological Organization advocates for the advancement of early warning systems, increasingly tailoring them to the needs of specific users, with a focus on impacts, informing actions to mitigate damage. Developing accurate and useful impact-based forecasts is challenged by limited data and information, lack of standardized technical protocols, issues sharing impact data and little knowledge on the needs of various user groups.

To ensure the quality of any warning system, verification is crucial. A dense network of measurements. Yet even if this is given, as simplifications are made to issue a pleasing product to the user, verifying warnings poses a number of challenges. Impact warnings are even more challenging to verify and guidelines are needed to do so.

This session aims to unite scientists, natural catastrophe modelers, weather forecasters, tool developers, stakeholders, and policy professionals, and discuss advancements and challenges related to the entire warning chain. We welcome inputs on the identification of extreme weather and impacts, the generation of hazard or impact warnings and forecasts, their verification, visualization, uncertainty, and user needs. The session features expert presentations and a panel discussion to allow the community to collaborate on developing storylines, marking a significant step forward in weather and impact modeling.

We present the development and application of the GVZ (cantonal building insurance Zurich) windstorm risk model to assess building damages in the Canton of Zurich, Switzerland. This model offers two key applications that support both rapid damage estimation immediately after a windstorm event and probabilistic risk modelling.

The first application uses high-resolution meteorological data from MeteoSwiss's ICON model to provide impact forecasting and post-event analysis. The model estimates the number of buildings affected and the potential damages at the municipal level, visualized through an interactive dashboard. This tool supports resource allocation and informed decision-making during and immediately after windstorm events.

The second application involves a probabilistic risk assessment based on a winterstorm hazard event set generated using the CLIMADA platform (ETH Zurich, Aznar-Siguan and Bresch, 2019) and the method described by Schwierz et al. (2010) and Welker et al. (2021). We estimate return periods for extreme events like Winterstorm Lothar. Our findings indicate that a storm of Lothar's magnitude today would result in approximately CHF 80 million in damages, corresponding to a 130-year return period.

Hail is a significant contributor to weather-related damages to buildings, cars, and agriculture in Switzerland, demanding actionable information on hail risks and forecasts across sectors. The research project scClim (https://scclim.ethz.ch/) addresses this demand by establishing a seamless model chain from observing and modeling hail events to the quantification of hail impacts, including simulations to compare hail occurrence in current and future climate. Within the project, we have developed a hail impact forecast prototype that has been co-designed with relevant stakeholders from public and private institutions and is running in a pre-operational fashion during the 4-year research project. Combining ensemble weather forecasts with exposure and vulnerability information, we use the open-source risk assessment platform CLIMADA to provide impact-based forecasts for buildings and different crop types in Switzerland. Furthermore, the platform provides post-event assessments of hail impacts based on operational radar data and/or crowdsourced hail reports. Finally, by means of high-resolution convection-resolving climate simulations using a pseudo global warming approach for a 3 K global warming level that have been conducted within the project, we discuss hail impact distributions in current climate and hail impact projections to future climate.

Increasingly intense rainfall events are causing serious damages to infrastructures and endangering human lives. To better protect them, early warning systems can be set up to evacuate people, move and protect cars or protect infrastructure by installing flood barriers. However, warnings must be issued with sufficient lead times (tens of minutes) before the flood event occurs in order to be useful.

Deterministic forecasts based on the advection of precipitation radar measurements can anticipate flash flood precipitation. However, these systems are subject to considerable uncertainties, especially for extreme convective events that tend to cause flash floods. These uncertainties include the growth and decay of the storm cell, as well as the estimation of the cell's displacement. The use of these deterministic forecasts leads to low detection probabilities for localized intense precipitation events. The generation of ensemble precipitation forecasts improves on deterministic forecasts by proposing several precipitation scenarios, some of which may lead to higher discharge forecasts.

As part of the Radar4Infra project, a flash flood forecasting and alert system based on NowPrecip1.0 radar forecast (Sideris et al., 2020) is being developed for several small catchments. This system builds on recent improvements of the weather radar network and data processing (Germann et al., 2022), sophisticated nowcasting algorithms (Sideris et al., 2020) and a state-of-the-art rainfall-funoff model adapted for Alpine catchments (Jordan, 2007). More precisely, it consists of a radar precipitation forecast, with a spatial resolution of 1 km², a temporal resolution of 10 min, a forecast horizon of 6 hours and a forecast update rate of 10 min. This forecast is then introduced into the Routing System rainfall-runoff simulation model (Schäfli et al., 2005; Jordan, 2007), also with a 10 min temporal resolution. The rainfall-runoff simulation model is calibrated on flow measurements, with input data from rain gauges or precipitation fields (CPCH from MeteoSwiss, Sideris et al., 2014).

The methodology followed consists of evaluating the quality of deterministic flow forecasts using the "probability of detection" and "false alarm ratio" indicators, calculated at several flow thresholds (Cosson, 2023). These benchmark forecasts are then compared with ensemble forecasts. The latter are derived from preliminary tests (NowPrecip2.0) carried out by MeteoSwiss for a few selected flood events.

Two examples of hindcasts showed promising results : the Anniviers event (catchment area 88 km² in the Swiss Alps), and the Cressier event (catchment area 4.5 km² in the Jura region). In the Anniviers example, the system predicted a flood peak almost two hours ahead of time, while the watershed response time was only one hour in this situation. In the Cressier example, the "NowPrecip2.0 RZC-based" ensemble forecast was not able to predict the peak discharge, but the system predicted a smaller flood one hour ahead, although the response time of the catchment area is only 20min. Moreover, in both cases, the deterministic NowPrecip1.0 RZC-based forecast was not able to predict any flood discharge at all.

Ensemble radar-based hydro-meteorological cascade allowed to predict flash floods better than deterministic radar advectionpredictions.

Accurate and precise weather forecasting is crucial for issuing timely weather hazard warnings. However, current numerical weather prediction (NWP) models often struggle to accurately represent extreme weather events due to limitations in spatial and temporal resolutions. Additionally, with only a few model runs typically initialized per day, the effectiveness of the data assimilation process in capturing rapidly changing environments and improving initial conditions is limited. These constraints prevent NWP models from capturing small-scale weather features, such as severe convective thunderstorms. Furthermore, standard fixed thresholds used in issuing weather warnings may not adequately account for the varying levels of risk associated with different locations and use cases. This uniform approach can lead to either underestimation or overestimation of the actual risk.

To address these challenges, Meteomatics has developed the operational high-resolution NWP model EURO1k. Characterized by a 1 km horizontal grid spacing, a 72-hour forecast horizon, and an hourly refresh rate across the pan-European domain, the EURO1k model strongly enhances forecast accuracy. This high resolution allows EURO1k to accurately represent small-scale weather patterns, resulting in precise forecasts of extreme weather events. Additionally, thanks to its hourly refresh rate and data assimilation capabilities, the EURO1k model can be utilized for nowcasting. In addition to assimilating standard data sources like radar, satellite data, weather stations, and radiosondes, the EURO1k model also integrates data from a network of Meteodrones—small unmanned aircraft systems (UAS) developed by Meteomatics that collect vertical atmospheric profiles up to 6000m in altitude.

Moreover, Meteomatics has developed a highly customizable weather warning system, where multiple weather variables can be combined, and specific thresholds selected to enable targeted warnings for specific locations. The integration of the high-resolution EURO1k model with this customizable alert system allows for more accurate and use-case-specific warnings, optimally addressing relevant local risks. By employing this advanced approach, Meteomatics substantially enhances the reliability and precision of weather hazard warnings, ultimately improving preparedness and response measures.

Advancements in web development enable the transfer of large amounts of data via the web, making it feasible to integrate insights gained from flood modelling into early-warning systems and even near real-time applications. The proposed web tool leverages hydrological forecasts, such as those issued by the Federal Office for the Environment in Switzerland (FOEN 2024), to map predicted floods. It offers an interactive map that visualizes potential flooding areas based on different members of a probabilistic forecast, enabling users to explore a range of flood scenarios.

The key features displayed include flood depths, temporal information, hazard classes indicating flood severity for human life and infrastructure, and estimations of potential damage, including the number of potentially exposed buildings, population, and workplaces. This comprehensive visualization enhances awareness and understanding of anticipated flood events for target users, such as emergency responders, governmental authorities, and insurance companies.

The tool facilitates proactive decision-making by providing near-real-time information on probable flood threats, thereby supporting early warning and strategic planning in flood risk management. It utilizes GeoServer as an interface to transfer requested flood information from a PostgreSQL database, where results from precomputed flood scenarios are stored, directly to the client side.

Developed using insights from Mosimann et al. (2023) and Mosimann et al. (2024), this tool also addresses the challenges of providing probabilistic flood hazard and impact information. It serves as a proof of concept for implementing the surrogate flood model approach in near-real-time flood warning systems and illustrates the potential for forecasting systems to meet the diverse needs of stakeholders. For example, insurance companies might focus on potential damages to plan resource allocation, while emergency responders prioritize information on population and the areas likely to experience severe flood intensities.

Publication bibliography

FOEN (2024): Forecasts and flood alerts. Stations with forecasts. Federal Office for the Environment FOEN. Available online at https://www.hydrodaten.admin.ch/en/messstationen-vorhersage, checked on 8/28/2024.

Mosimann, Markus; Kauzlaric, Martina; Schick, Simon; Martius, Olivia; Zischg, Andreas Paul (2023): Evaluation of surrogate flood models for the use in impact-based flood warning systems at national scale. In Environmental Modelling & Software, p. 105936. DOI: 10.1016/j.envsoft.2023.105936.

Mosimann, Markus; Martius, Olivia; Zischg, Andreas Paul (2024): Two Sides of the Same Coin? Hydrometeorological Uncertainties in Impact-Based Flood Warning Systems and Climate Change Sensitivity of Floodplains. DOI: 10.2139/ssrn.4893831.

This presentation will explore the development and implementation of Germany's Naturgefahrenportal (NGP), a centralized platform providing the general public with authoritative information on natural hazards. The NGP aims to consolidate various sources of hazard-related data into a single accessible platform, enhancing public understanding and disaster response.

Designed for clarity and ease of use, the NGP offers real-time warnings and guidance on preventive measures and emergency actions. It serves as an introductory resource, linking to regional or single-hazard portals for deeper insights, and does not compete with existing warning apps, as it does not provide active alert features, such as push notifications.

Key Aspects of the Presentation:

1. Challenges in Standardizing Data: A significant challenge in developing the NGP is the standardization of data sourced from various stakeholders. The presentation will delve into the strategies employed to harmonize these diverse data sets. This standardization is vital for presenting a unified and coherent picture of risks, which facilitates quicker and more effective public response.
2. Harmonization of Maps: The presentation will also address the efforts to harmonize map data, a critical component of the NGP. Different institutions often use varying map scales, symbols, and legends, leading to potential confusion among users. By standardizing these elements, the NGP ensures that risk information is presented clearly and consistently. This harmonization is essential for providing users with an intuitive and accurate understanding of geographic risks, thus enabling more informed decision-making.
3. Incorporation of Socio-Economic Research: A key innovation of the NGP is the integration of the latest socio-economic research to enhance public comprehension of natural hazards. The presentation will discuss how insights from socio-economic studies are being applied to tailor the portal’s communication strategies to the diverse needs of the population. By understanding how different demographic groups perceive risk and respond to warnings, the NGP can present information in a manner that maximizes its effectiveness, ultimately leading to better public preparedness and response.

Added Value and Impact:

The integration of all relevant information into a single, unified platform significantly enhances the value of the data provided, offering a holistic view rather than isolated fragments of information.

The NGP represents a major step forward in the communication of natural hazards in Germany. By addressing the challenges of data standardization, map harmonization, and the incorporation of socio-economic research, the NGP sets a new benchmark for how risk and threat information is conveyed to the public. With its barrier-free design, it is a valuable resource for users with various impairments or disabilities, while recognizing that individual needs may vary. This presentation will offer useful insights into the processes and strategies behind the development of this key public resource, illustrating how it can serve as a model for effective public communication in disaster preparedness and response.

In conclusion, the NGP is more than just a portal—it is a critical instrument for enhancing public safety and resilience.

Traditionally, most natural hazard warnings are verified based on whether they exceeded the predefined physical threshold, or resulted in the expected impact, in a specific region during the respective time period. If these boxes can be ticked, the warning is generally considered a “hit” - the job is done. In recent years, however, initiatives such as the ‘Early Warnings for All (EW4A)’ and the ‘HIWeather Project’ by the World Meteorological Organization (WMO) have shifted the focus towards the “last mile of the warning value chain”, calling to involve the recipients in the warnings production process. Thus, a natural hazard warning is only a real “hit” if it helps recipients make informed decisions to protect themselves and minimize the damage caused by the hazard. Furthermore, only if the hazard warning levels and thresholds match the perception of the recipients will the warnings have the desired effect and prevent reduced risk perception and negligence of the necessary measures. To this end, the recipients must be given a voice in
the warning verification, completing the traditional verification with a “social verification”.

For this purpose, MeteoSwiss has launched an on-going pilot project in 2022 to survey the affected population in the warning area directly after a natural hazard using so-called flash-polls, following the example of other European weather services (e.g., UK Met Office and KNMI). Specifically, people in the affected area are asked whether they received, understood and acted upon the hazard warning, and whether they found the warning useful and helpful in assessing the risk and deciding what action to take. Finally, they are asked whether they consider the warning level to be appropriate given the impact and intensity of the natural hazard event, which serves as a social verification. Taken together, this information provides valuable insights into people's risk perception and behavior and can indicate opportunities for improvement in the current warning process.

The presentation will introduce the preliminary findings of the ongoing pilot project and the flash-polls conducted to date, including insights on the perception of natural hazards by the population and the social verification of the warnings, as well as indications of possible further improvements of the warning system at MeteoSwiss. Finally, the main challenges and limitations of the pilot project will be discussed and an outlook will be given on how the social verification will be continued after the pilot project is completed.

From an end-to-end perspective, distribution is integral to achieving a high reach in disseminating natural hazards. Which channels suit this, and what does the trend look like? What needs to be considered to make an app attractive?

Warning communication is effective if it reaches people with the information that they need, at the right time and in a format that they find useful and usable. This task appears to be particularly difficult when decisions by stakeholders and citizens have to be made within contexts where uncertainty is high, multiple sources of information are available for the receiver, and decisions are urgent. This poses several challenges for the development of two-way and people-centred communication for risk information management. In this presentation, we discuss some of these challenges. By focusing on natural hazards, we look at the evidence of how information sources, social and environmental cues, channel access/preferences, and receiver’s characteristics influence behavioural responses to warnings. Moreover, we present research findings of how people respond to different types of warnings (standard vs. impact based) and to inconsistent warning information provided by public and private weather offices in Switzerland. We focus also on evaluating the results of warning communication efforts and on the role of new technologies that increasingly allow to evaluate communication effectiveness, sometimes even in real time through smartphone applications. We conclude with some reflections about the key aspects of the warning that need to be considered to improve the relationship between warner and receiver, when designing or upgrading communication strategies for risk information management.

The climate crisis enhances early warning systems' relevance and impact on preparedness. By tailoring their messages to the receiver, they become more effective. However, this requires personal data that is not always openly accessible. Employing a representative study, we explored whether the Swiss population

• i) likes to receive tailored warnings in the event of a severe heatwave
• ii) whether these would help them to better prepare for the situation
• iii) And whether they would be willing to share the necessary data.

The results are consistent with previous findings: Tailored warnings are attractive and improve preparedness, but there are concerns about data protection. As a result, warnings can currently only be tailored to a limited extent. We would like to discuss our findings and share further implications in our presentation.

Risk communication is critical to build resilient communities, raise awareness and increase preparedness. Moreover it contributes to advance knowledge and to infleunce the adoption of protective behaviours before, during and after a disaster. Over the last two decades, seismic risk communication has evolved significantly, as shown also by trends in scientific publications. This evolution has led to a reorientation from a predominantly “one-way”, top-down communication model to promoting new models that focus on people, their needs and their participation in disaster risk management. The recommendations of the Sendai Framework 2015-2030, recent disaster experiences and research have made it clear that new models of risk communication can improve its effectiveness. In this contribution, we critically address this transition by conducting a scoping review (n=109 publications) on seismic risk communication in Europe. We analyze the approaches, messages, tools and channels used for seismic risk communication and how they have changed over time. The results show that the stated objectives of seismic risk communication are, in decreasing order, to share information, raise awareness, change behaviors/beliefs and increase preparedness. Pupils, students and citizens are the main addressees of communication activities. Over the years, one trend has emerged quite clearly: communication has been progressively aimed more at encouraging proactive behaviors than simply informing the public. To do so, face-to-face conversations, hands-on activities, serious games and videos have been increasingly used as risk communication tools. The results also show the growing importance of social media for reaching different target groups. A striking result is that only a fifth of the analysed publications explicitly build on or test risk communication theories. Future research could focus on comparing practices in different countries and for different risks (e.g. earthquakes and floods) and on innovating communication theories and methods, particularly by incorporating the role of information technologies and social media.

Informing the public about natural hazards enables informed decision-making and enhances resilience. Achieving this requires translating scientific information into accessible and understandable formats. This presentation demonstrates how incorporating evaluation into the design process can improve these efforts. Our findings reveal that neither professionals nor the public consistently make optimal decisions.

At MeteoSwiss, most hazard warnings are currently verified manually by a team of forecasters. While this approach has proven to be very successful and makes it possible to incorporate the extensive knowledge of the forecasters into the warning verification, it also has its limitations. Manual verification is labor-intensive and somewhat subjective, which can lead to inconsistencies in the assessment of warnings. Furthermore, manual verification only allows for limited granularity in the verification process and long-term statistics, making it difficult to identify systematic biases or potential areas of improvement in the warning system.

To address these challenges, we are developing an objective and automated verification system to improve the efficiency, consistency, and detail of the warning evaluation. Such an approach will lead to a more standardized verification of warnings, reduce human bias, capture a richer set of observation data, and be able to generate a more extensive set of key figures and scores. Automated verification also enables a more straightforward evaluation of past warnings and, therefore, the possibility to identify trends, patterns, and potential biases that might have been overlooked with case-by-case manual verification. In this regard, an automatic verification system will also be beneficial for developing and testing automated warning proposals.

This presentation will share preliminary results from our ongoing work, including verification results from case studies and a comparison to the traditional manual approach. Moreover, we will present a statistical analysis based on the objective verification of past warnings and some first-identified shortcomings of the warning system at MeteoSwiss. We will also highlight some of the key challenges we have encountered so far.

Our goal is to demonstrate the potential benefits of an objective warning verification system, particularly in terms of its ability to gain deeper insights into the quality of issued hazard warnings and to drive the continuous improvement of the warning system at MeteoSwiss.

The Snow Avalanche Forecast Editor SAFE was introduced in the Swiss avalanche warning system in December 2023. This is based on European standards, but for the first time also permits the integration of machine-learning models by including the model data as an additional virtual forecaster in the decision-making process. It is available as open source to all interested parties.

Severe weather poses significant threats to society, necessitating the development of effective forecasting and warning systems to mitigate their impacts. In the framework of the renewal of the warning system at MeteoSwiss, we use the possibility to redesign the software as well as the scientific approach of generating warnings. The production chain is designed such that the data is guided and refined along the pathway. Here, we present the operational production process currently in development for the next generation warning system. The production chain can be divided into four steps. The first step is the combination of all available data into one data stream (seamless weather). The second step identifies extreme weather events in this data stream and prepares warning proposals for the forecasters. The third step consists of the forecasters evaluating the proposals, changing them if necessary, and issuing the official warnings. In this step, the only human interaction with the otherwise automatic warning system takes place. In the fourth step, the warning products are customized and distributed to our customers. In the fifth and last step the warnings are verified automatically to monitor the quality of the system.

MeteoSwiss furthermore aims to integrate impact-based forecasts into warnings and the warning chain introduced above. The premise of reducing people’s harm by introducing possible impacts in extreme weather warnings is a paradigm change for many national weather services (Geiger et al. 2024). Being at the intersection of probabilistic forecasts, uncertainty analysis and state-of-the art risk modelling, this new paradigm also entails significant challenges. Through collaboration with stakeholders including first responders and local authorities, MeteoSwiss draws on pilot regions to find specific use cases from professionals impacted by severe weather to learn about the specific needs and enhancement potentials for early warning. Co-developing these use cases, conducting impact studies, and eventually providing stable products serves as a learning for further development and scaling up of impact-based warnings.

This requires an operational impact model for Switzerland, which flows back into the warning production chain, posing various scientific and technical challenges on the way; we will discuss a solution developed at MeteoSwiss, and highlight the need for seamless conceptual and technical integration within the current warning production processes, with the ultimate aim to better cater to the diverse needs of society in the face of severe weather events while maintaining a reliable warning chain.

The increasing risks due to hydro-meteorological events necessitate innovative and flexible forecasting tools for effective management of flood risks. FloodWaive addresses this challenge with DeepWaive, a groundbreaking generalized Deep Learning (DL) model for probabilistic 2D flood forecasting. Our approach overcomes the limitations of traditional hydrodynamic models, offering rapid, accurate, and scalable impact-based flood forecasting across diverse geographical domains.

DeepWaive integrates in-house developed DL architectures with hydrodynamic 2D models, enabling ad-hoc simulations of pluvial and fluvial events of varying intensities and durations over extensive areas. Unlike conventional AI models that require retraining for each new domain, DeepWaive generalizes across different topographies and regional characteristics, eliminating the need for domain-specific retraining and enhancing scalability.

With a speed-up factor of up to 10^6, DeepWaive can translate precipitation or discharge values into spatial hydraulic flooding processes within seconds. This capability facilitates the processing of several ensemble rainfall forecasts into impact and probability-based forecasts and warnings. The model's applications extend to dynamic risk analyses, real-time evaluation of flood protection measures, and dam break simulations.

While still in development, DeepWaive represents a significant leap in flood forecasting technology. Our goal is to offer a universally deployable and comprehensible, real-time flood prediction tool, empowering crisis and flood risk management to make informed decisions quickly, potentially saving lives and reducing economic losses.

In the real world, people must decide how to respond to hazardous weather or climate events. Forecasts are crucial for timely decision-making, yet relevant services may be lacking or not tailored to user needs and often fail to prompt action. The I-CISK project (Innovating Climate Services through Integrating Scientific and Local Knowledge) aims to develop a new generation of climate services that directly address users' requirements.

Evaluating the performance of these services is key to their success and guides their development. Understanding the strengths and weaknesses of a service helps users decide whether to incorporate it into their operational activities. However, standard statistical verification is often complex and not easily applicable in real-world, action-driven scenarios. A 'user-centred' evaluation approach considers the specific context in which forecast data is used, making the results more relevant and understandable.

We present a flexible process for user-centred evaluation and demonstrate its application to cold wave forecasting for disaster risk reduction in Lesotho, one of the I-CISK' Living Laboratories'. Lesotho's mountainous terrain makes its population vulnerable to cold waves, which have caused fatalities, livestock losses, and severe transport disruptions. We evaluate ECMWF temperature and snow forecasts within the Lesotho Red Cross Society's draft Early Action Protocol framework for cold wave disaster risk reduction.

The importance of early warning for flood disaster preparedness cannot be less stressed on. Techniques of visualization and communication have evolved from desktop application of dash boards to mobile friendly application alerts. The dovetailing of real time data and 72-hour early warning forecasted data from the Meteorological Department has helped save lives and conduct safe evacuation of livestock and persons with disabilities, from hospitals and jails, amongst others.R2R framework has also stressed on the importance of early warning systems for preparedness and response to disasters.

This paper will include case studies from India demonstrating the preparedness and responsiveness to flood disasters. Their early warning system architecture and alert protocols will be presented.

The use of real time data and integration of numerically forecasted data towards creating an early warning system for South East Asia, and various parts of India will be showcased in this study including the use of hydrological models. The endeavour of the author is to imbibe the sharing of best practices, as well as providing room for discussion of methodological problems in risk modeling and visualization, including resilience analytics and improvements towards disaster management.

As climate-related disasters and humanitarian crises become more frequent, the need for faster and more effective responses is crucial. Anticipatory Action (AA) shifts from reacting to disasters after they happen to taking action before they strike, based on scientific forecasts.

This keynote will explore how scientific data, like weather and climate forecasts, can trigger early humanitarian interventions. We'll look at real-world examples where UN OCHA has used these triggers in Africa and Asia, discussing how reliable science can help decide when and where to act. We'll also address challenges in scaling this approach, such as the need for strong partnerships and accurate data.

The goal is to show how science can improve humanitarian efforts, making them faster and more effective, ultimately saving lives and reducing suffering. This talk will encourage collaboration between scientists and humanitarian workers to refine these methods, ensuring that future actions are not just reactive but also anticipatory.