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ITS1.4/HS4.8

The Sendai Framework for disaster risk reduction (SFDRR) and its seventh global target recognizes that increased efforts are required to develop risk-informed and impact-based multi-hazard early warning systems. Despite significant advances in disaster forecasting and warning technology, it remains challenging to produce useful forecasts and warnings that are understood and used to trigger early actions. Overcoming these challenges requires understanding of the reliability of forecast tools and implementation barriers in combination with the development of new risk-informed processes. It also requires a commitment to create and share risk and impact data and to co-produce impact-based forecasting models and services. To deal with the problem of coming into action in response to imperfect forecasts, novel science-based concepts have recently emerged. As an example, Forecast-based Financing and Impact-based Multi-Hazard Early Warning Systems are currently being implemented operationally by both governmental and non-governmental organisations in several countries as a result of increasing international effort by several organizations such as the WMO, World Bank, IFRC and UNDRR to reduce disaster losses and ensuring reaching the objectives of SFDRR. This session aims to showcase lessons learnt and best practices on impact-based multi-hazards early warning system from the perspective of both the knowledge producers and users. It presents novel methods to translate forecast of various climate-related and geohazards into an impact-based forecast. The session addresses the role of humanitarian agencies, scientists and communities at risk in creating standard operating procedures for economically feasible actions and reflects on the influence of forecast uncertainty across different time scales in decision-making. Moreover, it provides an overview of state-of-the-art methods, such as using Artificial Intelligence, big data and space applications, and presents innovative ways of addressing the difficulties in implementing forecast-based actions. We invite submissions on the development and use of operational impact-based forecast systems for early action; developing cost-efficient portfolios of early actions for climate/geo-related impact preparedness such as cash-transfer for droughts, weather-based insurance for floods; assessments on the types and costs of possible forecast-based disaster risk management actions; practical applications of impact forecasts.

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Co-organized by AS4/NH9/SM3
Convener: Marc van den Homberg | Co-conveners: Bapon Fakhruddin, Andrea FicchiECSECS, Gabriela Guimarães NobreECSECS, Annegien Tijssen, David MacLeodECSECS, Maurine Ambani, Alison SneddonECSECS
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| Attendance Thu, 07 May, 08:30–10:15 (CEST)

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Chat time: Thursday, 7 May 2020, 08:30–10:15

D2336 |
EGU2020-11979
Hyojin Han, YoungYoung Park, Ji-Hyeon Kim, Yongjun Ahn, KyongJun Lee, Ji Ae Song, Yeongseon Kim, and Wonho Kim

The Korea Meteorological Administration (KMA) set a main policy goal as “Impact-based forecasting (IBF) for mitigation of meteorological disaster risks” in 2016. As a first step toward the goal, each regional office of the KMA operated a prototype of impact-based forecast service tailored to major severe weather conditions in each region from 2016 to 2018. As a result, the prototype service was found to contribute to reducing meteorological disasters in those regions. In order to determine quantitative impacts caused by meteorological disasters, a multi-ministerial R&D project was began in 2018 which is aiming to develop the Hazard Impact Models (HIM) for heavy rainfall and heatwave/coldwave. The project will be completed by the end of 2020, and the developed HIM will be operated for the KMA operational IBF. 
The KMA officially launched heatwave IBF service from June to September 2019 in order to support effective reduction of heatwave impacts. The KMA provided risk levels in different colors (attention-green, caution-yellow, warning-orange, danger-red), impact information and response tips for seven sectors—health, industry, livestock, aquaculture, agriculture, transportation and electric power—considering the regional characteristics. This information was disseminated to the public on the KMA's website. It was also provided to disaster response related agencies through the Meteorological Information Portal Service System for Disaster Prevention, as well as to local governments’ disaster response managers and officials managing the socially vulnerable people through mobile text messages. According to user satisfaction survey, a great number of users showed positive responses to the KMA heatwave IBF. Based on the success of heatwave IBF, coldwave IBF trial service was offered from December 2019 to March 2020. In addition, KMA plans to expand IBF to other high-impact weathers such as typhoon, heavy snow, heavy rainfall, and so on.

How to cite: Han, H., Park, Y., Kim, J.-H., Ahn, Y., Lee, K., Song, J. A., Kim, Y., and Kim, W.: The Progress of KMA’s Impact-based Forecasting, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11979, https://doi.org/10.5194/egusphere-egu2020-11979, 2020

D2337 |
EGU2020-6003
Jonathan Lala, Juan Bazo, and Paul Block

The last few years have seen a major innovation within disaster management and financing through the emergence of standardized forecast-based action protocols. Given sufficient forecasting skill and lead time, financial resources can be shifted from disaster response to disaster preparedness, potentially saving both lives and property. Short-term (hours to days) early warning systems are common worldwide; however, longer-term (months to seasons) early actions are still relatively under-studied. Seeking to address both, the Peruvian Red Cross has developed an Early Action Protocol (EAP) for El Niño-related extreme precipitation and floods. The EAP has well-defined risk metrics, forecast triggers, and early actions ranging from 5 days to 3 months before a forecasted disaster. Changes in climate regimes, forecast technology, or institutional and financial constraints, however, may significantly alter expected impacts of these early actions. A robust sensitivity analysis of situational and technological constraints is thus conducted to identify benefits and tradeoffs of various actions given various future scenarios, ensuring an adaptive and effective protocol that can be used for a wide range of changing circumstances.

How to cite: Lala, J., Bazo, J., and Block, P.: Designing a multi-objective framework for forecast-based action of extreme rains in Peru, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6003, https://doi.org/10.5194/egusphere-egu2020-6003, 2020

D2338 |
EGU2020-1367
Sazzad Hossain, Hannah Cloke, Andrea Ficchì, and Elisabeth Stephens

There is high temporal variability in the occurrence of the monsoon floods in Bangladesh during the South Asian summer monsoon. Detailed flood forecast information about flood timing and duration can play a vital role in flood preparedness decisions. The objective of this study is to understand different stakeholder perceptions about existing forecasting tools and data, and how these can support preparedness and response activities. Forecast users can be divided into three broad categories-national, sub-national and community level. The stakeholders working at national level are involved in policy making while the sub-national level involved in implementation of policies.  In order to identify the appropriate lead-time for better flood preparedness and the challenges in communicating probabilistic forecasts to users, semi-structured interviews with key stakeholders involved in various sectors of flood disaster management at national and sub-national level, community level household surveys, focus group discussions and a national consultation workshop were undertaken during the 2019 monsoon.

It was found all major stakeholders working at national and sub-national levels are aware of the availability of forecasts and receive flood forecasts from the Flood Forecasting and Warning Centre (FFWC). However, about 40% of the respondents at the community do not receive forecast information. Before the flood event, policy level stakeholders need to know the availability of resources and preparedness at the sub-national level for better response activities. On the other hand, sub-national level stakeholders of different government agencies act as a bridge between policy level and the local community. Existing short-range forecasts cannot provide information about the potential flood duration which is essential for resources assessment, mobilization and preparedness activities.

People living in the floodplain are aware about the flood seasons as it is an annual phenomenon. However, they can anticipate floods events only 2 to 3 days beforehand based on the available early warning and their risk knowledge. This short-range forecast can be used for some basic household level response activities such as protecting household equipment or moving their livestock to a safer place. It is essential to know the actual duration and flood extent for their agricultural decisions such as understanding when to transplant young crops into the field. The study found that all stakeholders need forecast information with a lead-time between 15 to 20 days for better flood preparedness decisions. People are likely to have seen deterministic forecasts so far and are not used to probabilistic forecasts with multiple scenarios for a same event. However, national forecast bulletins may include probability of flooding events based on a threshold known as flood danger level. Capacity development of the local community is necessary to improve understanding of the probabilistic forecast and overcome communication challenges.

 

 

How to cite: Hossain, S., Cloke, H., Ficchì, A., and Stephens, E.: Flood preparedness decisions and stakeholders' perspectives on flood early warning in Bangladesh, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1367, https://doi.org/10.5194/egusphere-egu2020-1367, 2019

D2339 |
EGU2020-507
Thirza Teule, Anaïs Couasnon, Kostas Bischiniotis, Julia Blasch, and Marc van den Homberg

Flood risk, a function of hazard, exposure, and vulnerability, is increasing globally and has led to more and more disastrous flood events. Previous research has shown that taking early action is much more cost-effective than responding once the flood occurs. Such an anticipatory approach requires flood early warning systems (EWS) that provide ample lead time and that have sufficient spatial resolution. However, in developing countries, often the skill of available forecasts is insufficient to create a more effective triggering mechanism as part of a flood EWS.

This research presents an assessment of two methods to improve an existing flood EWS using a case study of the most flood-prone area of Malawi, i.e. the Lower Shire Valley. First, the forecast skill and trigger levels of the medium-term Global Flood Awareness System (GloFAS) model are determined for four gauge locations to assess how they can improve the national EWS. Secondly, an assessment is done on how the process of integrating flood forecasts based on local knowledge with official forecasts, can help to improve the EWS. This is done by semi-structured interviews at the national level and focus group discussions at the community level. The study shows that GloFAS does not predict absolute discharge values precisely, but can be used to predict floods if the correct trigger levels are set per location. The integration of multiple forecast sources is found to be useful at both national and community levels. An integration process is proposed where village stakeholders should take the leading role by using existing disaster management and civil protection coordination mechanisms. Overall, both methods can contribute to improving the flood EWS and decreasing the flood risk in the Lower Shire Valley in Malawi.

How to cite: Teule, T., Couasnon, A., Bischiniotis, K., Blasch, J., and van den Homberg, M.: Towards improving a national flood early warning system with global ensemble flood predictions and local knowledge; a case study on the Lower Shire Valley in Malawi., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-507, https://doi.org/10.5194/egusphere-egu2020-507, 2019

D2340 |
EGU2020-19559
Stefania Giodini, Aklilu Teklesadik, Jannis Visser, Orla Canavan, Innocent Bwalya, Irene Amuron, and Marc van den Homberg
Flooding in Zambia occurs on almost an annual basis greatly affecting the livelihoods of communities. Early action is crucial to mitigate the impact of flooding but needs to be guided by an early warning that is credible and actionable, linked to situational awareness based on data.  The 510 data team at the Netherlands Red Cross has been working together with the Red Cross Red Crescent Climate Centre, Zambia Red Cross Society, Water Resources Management Authority (WARMA) and Zambia Disaster Management and Mitigation Unit (DMMU) to develop a data driven early warning system to support impact based early action implementation. The system has been co-designed with the relevant local stakeholders and  integrates a hydrological model with a vulnerability capacity assessment based on secondary data for the whole country at the highest level of possible granularity (district level). A threshold based trigger model has been developed together with local decision makers to activate the system with a lead time up to 7 days. The system is being integrated in the Emergency Operation Centre operated by Zambia's DMMU as a part of the country standard early action protocol. This paper describes the system design, results from the first activations and lessons learned. 

How to cite: Giodini, S., Teklesadik, A., Visser, J., Canavan, O., Bwalya, I., Amuron, I., and van den Homberg, M.: On the development and operationalization of an impact-based forecasting system to support early action for river floods in Zambia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19559, https://doi.org/10.5194/egusphere-egu2020-19559, 2020

D2341 |
EGU2020-20150
Hans de Moel, Lucas Wouters, Marleen de Ruiter, Anais Couasnon, Marc van den Homberg, Aklilu Teklesadik, and Jacopo Margutti

Reliable information on building stock and its vulnerability is important for understanding societal exposure to flooding and other natural hazards. Unfortunately, this often lacks in developing countries, resulting in flood damage assessments that use aggregated information collected on a national- or district level. In many instances, this information does not provide a representation of the built environment, nor its characteristics. This study aims to improve current assessments of flood damage by extracting structural characteristics on an individual building level and estimating flood damage based on its related susceptibility. An Object-Based Image Analysis (OBIA) of high-resolution drone imagery is carried out, after which a machine learning algorithm is used to classify building types and outline building shapes. This is applied to local stage-dependent damage curves. To estimate damage, the flood impact is based on the flood extent of the 2019 mid-January floods in Malawi, derived from satellite remote sensing. Corresponding water depth is extracted from this inundation map and taken as the damaging hydrological parameter in the model. The approach is applied to three villages in a flood-prone area in the Southern Shire basin in Malawi. By comparing the estimated damage from the individual object approach with an aggregated land-use approach, we highlight the potential for very detailed and local damage assessments using drone imagery in low accessible and dynamic environments. The results show that the different approaches on exposed elements make a significant difference in damage estimation and we make recommendations for future assessments in similar areas and scales.

How to cite: de Moel, H., Wouters, L., de Ruiter, M., Couasnon, A., van den Homberg, M., Teklesadik, A., and Margutti, J.: Improving flood damage assessments in data-scarce areas by retrieving building characteristics through automated UAV image processing, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20150, https://doi.org/10.5194/egusphere-egu2020-20150, 2020

D2342 |
EGU2020-16179
Tanja Portele, Christof Lorenz, Patrick Laux, and Harald Kunstmann

Semi-arid regions are the regions mostly affected by drought. In these climatically sensitive regions, the frequency and intensity of drought and hot extremes is projected to increase. With increasing precipitation variability in semi-arid regions, sustainable water management is required. Proactive drought and extreme event preparedness, as well as damage mitigation could be provided by the use of seasonal climate forecasts. However, their probabilistic nature, the lack of clear action derivations and institutional conservatism impedes their application in decision making of the water management sector. Using the latest global seasonal climate forecast product (SEAS5) at 35 km resolution and 7 months forecast horizon of the European Centre for Medium-Range Weather Forecasts, we show that seasonal-forecast-based actions offer potential economic benefit and allow for climate proofing in semi-arid regions in the case of drought and extreme events. Our analysis includes 7 semi-arid, in parts highly managed river basins with extents from tens of thousands to millions of square kilometers in Africa, Asia and South America. The value of the forecast-based action is derived from the skill measures of hit (worthy action) and false alarm (action in vain) rate and is related to economic expenses through ratios of associated costs and losses of an early action. For water management policies, forecast probability triggers for early action plans can be offered based on expense minimization and event maximization criteria. Our results show that even high lead times and long accumulation periods attain value for a range of users and cost-loss situations. For example, in the case of extreme wet conditions (monthly precipitation above 90th percentile), seasonal-forecast-based action in 5 out of 7 regions can still achieve more than 50 % of saved expenses of a perfect forecast at 6 months in advance. The utility of seasonal forecasts strongly depends on the user, the cost-loss situation, the region and the concrete application. In general, seasonal forecasts allow decision makers to save expenses, and to adapt to and mitigate damages of extreme events related to climate change.

How to cite: Portele, T., Lorenz, C., Laux, P., and Kunstmann, H.: Proactive Drought and Extreme Event Preparedness: Seasonal Climate Forecasts offer Benefit for Decision Making in Water Management in Semi-arid Regions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16179, https://doi.org/10.5194/egusphere-egu2020-16179, 2020

D2343 |
EGU2020-6647
Pedram Rowhani, Adam Barrett, Seb Oliver, James Muthoka, Edward Salakpi, and Steven Duivenvoorden

Droughts are a major threat globally as they can cause substantial damage to society, especially in regions that depend on rain-fed agriculture. Acting early based on alerts provided by early warning systems (EWS) can potentially provide substantial mitigation, reducing the financial and human cost. However, existing EWS tend only to monitor current, rather than forecast future, environmental and socioeconomic indicators of drought, and hence are not always sufficiently timely to be effective in practice. In Kenya, the National Drought Management Authority (NDMA) provides monthly bulletins assessing food security in the 23 arid and semiarid regions using current biophysical (e.g., rainfall, vegetation condition) and socio-economic (production, access, and utilisation) factors. One key biophysical indicator used by the NDMA drought phase classification is based on the Vegetation Condition Index (VCI).

In this study we explore machine-learning techniques to forecast (up to six weeks ahead) the 3-month VCI, commonly used in the pastoral areas of Kenya to monitor droughts. we specifically focus on Gaussian Process modelling and linear autoregressive modelling to forecast this indicator, which are derived from both Landsat (every 16 days at 30m resolution) and the MODerate resolution Imaging Spectroradiometer (MODIS - daily data at 500m resolution).

Our methods provide highly skillful forecasting several weeks ahead. As a benchmark we predicted the drought alert marker used by NDMA (VCI3M< 35). Both of our models were able to predict this alert marker four weeks ahead with a hit rate of around 89% and a false alarm rate of around 4%, or 81% and 6% respectively six weeks ahead.

The forecasts developed here could, for example, help establish a new drought phase classification (`Early Alert') which, along with adequate preparedness actions developed by the disaster risk managers, would minimise the risk of a worsening drought condition.

How to cite: Rowhani, P., Barrett, A., Oliver, S., Muthoka, J., Salakpi, E., and Duivenvoorden, S.: Forecasting vegetation condition to mitigate the impacts of drought in Kenya., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6647, https://doi.org/10.5194/egusphere-egu2020-6647, 2020

D2344 |
EGU2020-16114
| Highlight
Ileen Streefkerk, Hessel Winsemius, Marc van den Homberg, Micha Werner, Tina Comes, Maurits Ertsen, Neha Mittal, and Gumbi Gumbi

Most people of Malawi are dependent on rainfed agriculture for their livelihoods. This leaves them vulnerable to drought and changing rainfall patterns due to climate change. Farmers have adopted local strategies and knowledge which have evolved over time to help in reducing the overall vulnerability to climate variability shocks. One other option to increase the resilience of rainfed farmers to drought, is providing forecast information on the upcoming rainfall season. Forecast information has the potential to inform farmers in their decisions surrounding agricultural strategies. However, significant challenges remain in the provision of forecast information. Often, the forecast information is not tailored to farmers, resulting in limited uptake of forecast information into their agricultural decision-making.

Therefore, this study explores how drought forecast information can be linked to existing farmers strategies and local knowledge on predicting future rainfall patterns. By using participatory approaches, an understanding is created of what requirements drought forecast information should meet to effectively inform farmers in their decision-making. Based on these requirements a sequential threshold model, using meteorological indicators based on farmer’s local knowledge was developed to predict drought indicators (e.g. late onset of rains and dry spells). Additionally, using interviews among stakeholders and a visualisation of the current information flow, further insights on the current drought information system was developed.

The results of this research show that local knowledge has a predictive value for forecasting drought indicators. The skill of the forecast differs per location with an increased skill for Southern locations. In addition, the results suggest that local knowledge indicators have an increased predictive value in forecasting the locally relevant drought indicators in comparison the currently used ENSO-related indicators. This research argues that the inclusion of local knowledge could potentially improve the current forecast information by tailoring it to farmer's forecast requirements and context. Therefore, the findings of this research could be insightful and relevant for actors or research fields involved in drought forecasting in relation to user-specific needs. 

How to cite: Streefkerk, I., Winsemius, H., van den Homberg, M., Werner, M., Comes, T., Ertsen, M., Mittal, N., and Gumbi, G.: Linking Drought Forecast Information to Smallholder Farmer’s Strategies and Local Knowledge in Southern Malawi, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16114, https://doi.org/10.5194/egusphere-egu2020-16114, 2020

D2345 |
EGU2020-19652
Alexia Calvel, Micha Werner, Marc van den Homberg, Hans van der Kwast, Andrés Cabrera Flamini, and Neha Mittal

Droughts constitute one of the major and complex natural hazards that may lead to food insecurity due to its long-term and cumulative impact, compounded by the difficulty of drought being predicted. Efforts to improve early warning systems are being conducted to help reduce the impacts caused by drought events, and although significant advances have been made in the forecasting of drought, provision of actionable warning that leads to effective response is challenging due to a range of factors.  In this study we aim to improve our understanding of how people-centred warning communication and dissemination is being carried out for drought warning in Malawi.  Our methodology is based on five focus group discussions with farmers and 25 semi-structured interviews with various government officials, as well as with representatives from UNDP, WFP and NGOs. The analysis of these interviews and discussions is based on a qualitative approach using the concept of grounded theory and content analysis; to better understand the organisational structure, communication processes and the ability of warnings triggering actions by farmers and NGOs.

Our results identified that both within the farming communities as with the NGO’s and working at the local level there is a different perception than expected of what constitutes drought. Droughts are considered to be events that cause the failure of crops, which relates primarily to the occurrence of prolonged dry spells following the planting season, fall army worms and even the occurrence of floods. Consistently, drought warnings that are disseminated at the local level have been found to focus on these aspects. Moreover, it was found that although these warnings do trigger actions, they do so only to a certain extent. Daily weather forecast are not being used by farmers due to the high uncertainty associated with these predictions. For NGOs, drought early warnings are used in combination with the famine early warnings to trigger early actions.

Communication channels and processes were found to be well adapted to local conditions and to disseminate the consistent drought warnings and guidance to end-users. This has led to improved trust towards drought early warnings received. However, the high level of illiteracy and lack of understanding of the link between impacts and weather information render the seasonal forecast and text-messages unusable to farmers, with agricultural extension officers and the community-radios the preferred channels of communication. Organisational structures and processes appear to be relatively clear. Nevertheless, feedback mechanisms were found to be only scantily implemented due to the lack documentation on local perceptions and indigenous knowledge. Overall our results show that progress has been made in meeting the requirements for a people-centred early warning. However, external challenges such as a lack of local funds which has led to a high dependency on donors or the frequent changes of government officials affect the well-development of such an approach.  

How to cite: Calvel, A., Werner, M., van den Homberg, M., van der Kwast, H., Cabrera Flamini, A., and Mittal, N.: A review of the effectiveness of drought warning communication and dissemination in Malawi , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19652, https://doi.org/10.5194/egusphere-egu2020-19652, 2020

D2346 |
EGU2020-19891
Marc van den Homberg, Gabriela Guimarães Nobre, and Edward Bolton

The project “Forecast based Financing for Food Security” (F4S) was initiated in July 2019 with the aim to provide a deeper understanding of how forecast information could be routinely used as a basis for financing early action for preventing food insecurity in pilot areas in Ethiopia, Kenya, and Uganda. The F4S project is linked to the existing Innovative Approaches in Response Preparedness Project and is in response to the growing interest and attention placed in recent years by academic institutions, development and humanitarian agencies on creating evidence that can leverage risk prevention and disaster risk reduction.

To ensure adequate forecast-based actions one needs to have the right information and evidence to guide fast decision-making. Key enabling aspects are an understanding of the impact of food insecurity, the resources needed to address it and an insight in the associated costs, beneficiaries’ preferences and lead times. In response to that, the F4S is currently:

  • Developing an impact-based probabilistic food insecurity forecasting model using Machine Learning algorithms and datasets of food insecurity drivers;
  • Collecting local evidence on food insecurity triggers and information on individual preferences on key design elements of cash transfer mechanisms through surveys and choice experiments;
  • Evaluating the cost-effectiveness of different cash transfer mechanisms.

This PICO presentation seeks to share lessons learnt and preliminary results on the development of triggers for enabling early action against the first signs of food insecurity in Eastern Africa. It presents key results obtained through surveys and choice experiments regarding local knowledge in association with food insecurity and aid design. Furthermore, it presents the potential cost-effectiveness and advantages of acting based on forecasts.

How to cite: van den Homberg, M., Guimarães Nobre, G., and Bolton, E.: Forecast based action: developing triggers for preventing food insecurity in Eastern Africa, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19891, https://doi.org/10.5194/egusphere-egu2020-19891, 2020

D2347 |
EGU2020-15190
Steffen Lohrey, Melissa Chua, Jerôme Faucet, and Jason Lee

Purpose: Extreme heat threatens poor urban populations, and particularly those who are economically forced to work in the outdoors and hot environments. Thus, the Vietnamese Red Cross, with technical support from the German Red Cross, is implementing a Forecast-based Financing project to assist vulnerable population groups in urban areas before and during heatwaves. In order to inform this humanitarian project on choosing appropriate early actions, this research investigates empirical evidence on heat vulnerability using data from a “Knowledge Attitudes Practices” (KAP) survey conducted in 2018 among outdoor workers in Hanoi, Vietnam.

Methods: We analyze the outcome of the KAP survey, which comprised 1027 respondents classified into four different occupation groups. Key questions comprised respondents’ self-reported economic and health situation, impacts from past heatwaves, as well as on knowledge about measures reducing health impacts from extreme heat. We first use descriptive statistics to assess the basic properties of the surveyed population groups. We then use a principal component analysis to identify properties that best captured the variability of responses and to identify sub-groups.

Results: The different occupation groups surveyed (builders, vendors, bikers) showed distinctively different properties, not only in mean age (28 year, 45 years and 43 years respectively), but also in their knowledge about heat-health symptoms and their access to night-time air-conditioning (builders: only 14% compared to 42% for bikers). Air-conditioning access did not correlate with reported income.  Builders knew considerably less about heat risk than other groups, but also reported fewer perceived symptoms. The three most common health symptoms reported were tiredness, sweating and thirst, with 22% of respondents having sought medical advice because of heat-related symptoms. Income reduction during heat events was reported by 48% of respondents. The vast majority of respondents have reported to increase drinking (89%) or to remain in shaded areas (87%). Most respondents (76%) could access and understand weather forecasts and early warnings.

Conclusion: Our data and analysis highlight how different occupation groups of outdoor workers in Hanoi vary in their socio-economic properties and their vulnerability to extreme heat. These insights into different groups can be used to direct the implementation of early actions for anticipatory humanitarian assistance before and during heatwaves.

How to cite: Lohrey, S., Chua, M., Faucet, J., and Lee, J.: Knowledge, Attitudes and Practices on extreme heat: Insights from outdoor workers in Hanoi, Vietnam, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15190, https://doi.org/10.5194/egusphere-egu2020-15190, 2020

D2348 |
EGU2020-12337
| Highlight
Jian Li

Tropical cyclones could cause large casualties and economic loss in coastal area of China. It is of great importance to develop a method that can provide pre-event rapid loss assessment in a timely manner prior to the landing of a tropical cyclone. In this study, a pre-event tropical cyclone disaster loss assessment method based on similar tropical cyclone retrieval with multiple hazard indicators is proposed. Multiple indicators include tropical cyclone location, maximum wind speed, central pressure, radius of maximum wind, forward speed, Integrated Kinetic Energy (IKE), maximum storm surge, and maximum significant wave height. Firstly, the track similarity is measured by similarity deviation considering only the locations of tropical cyclone tracks. Secondly, the intensity similarity is measured by best similarity coefficient using central pressure, radius of maximum wind, maximum wind speed, moving speed, wind, storm surge, and wave intensity indices. Then, the potential loss of current tropical cyclone is assessed based on the retrieved similar tropical cyclones loss. Taking tropical cyclone Utor (2013) that affected China as an example, the potential loss is predicted according to the five most similar historical tropical cyclones which are retrieved from all the historical tropical cyclones. The method is flexible for rapid disaster loss assessment since it provides a relatively satisfactory result based on two scenarios of input dataset availability.

How to cite: Li, J.: Potential Tropical Cyclone Disaster Loss Assessment based on Multiple Hazard Indicators, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12337, https://doi.org/10.5194/egusphere-egu2020-12337, 2020

D2349 |
EGU2020-11104
| Highlight
Cecilia Valbonesi

How Earthquake Early Warning Systems can affect scientist’s liability? International perspective for domestic questions.

Early Warning Systems (EWS) represent a technical-scientific challenge aimed at improving the chance of surviving of the population exposed to the effects of dangerous natural events. This improvement must necessarily face great difficulties in the application fields, because EWS may turn into serious responsibilities for people involved as scientists and engineers.

In this complex scenario is necessary to consider the differences among EWS (e.g. meteo, tsunami, earthquake) and their capability of predicting and avoiding the consequences of damaging events.

The development of EWS in Italy is not homogenous.

Some of these systems, such as Earthquake EWS (EEWS), are in a testing phase and we really need to learn a lot from the comparison with other Countries that have been adopting these solutions for years.

This recognition is very important, because the tragic and deadly events of the L'Aquila earthquake, the landslide in Sarno, and the recent eruption of Stromboli volcano have taught us that the relationship between science and law in Italy is really difficult.

So, before entering in the operative phase of the EEWS is necessary to start from a recognition of the international and national legislative and jurisprudential frameworks that supports the assessment of criminal and civil liability in the event of a “wrong” technical-scientific response, unable to decrease the consequences for people and infrastructures.

The future application of EEWS in our Country must be supported by a study and research of solutions that allow scientists and engineers to operate with more awareness and less fear of the consequences of this not renounceable progress.

In this framework, the different roles of those involved in the development and dissemination of EEWS are also relevant: the responsibilities of scientists developing the tools are not the same as those of technical operators who are called upon to disseminate the alert.

In all these cases, however, the offer of an EEW service represents a promise to the population to face the harmful consequences of certain natural and disastrous events.

This promise certainly creates a legitimate expectation that, where betrayed, can give rise to criminal and civil liability for adverse events (manslaughter, negligence, unintentional disaster etc.).

Population, however, should not only expect to receive a correct alarm but must be put in the condition to understand the uncertainties involved in rapid estimates, to be prepared to face the risk, and to react in the right ways.

How to cite: Valbonesi, C.: How Earthquake Early Warning Systems can affect scientist’s liability? International perspective for domestic questions., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11104, https://doi.org/10.5194/egusphere-egu2020-11104, 2020