Displays

Extreme weather of increasing intensity and frequency is the sharp edge of climate change. Greater understanding of exactly how the risks to people and property from such events are changing is therefore of considerable value to society; it enables the effective allocation of resources for adaption planning and provides a foundation for cost-benefit analysis of mitigation policy. Moreover, the first global stocktake following the Paris Agreement aims to comprehensively detail climate change-related loss and countries’ adaption ambition. Thus there is a clear imperative for greater understanding of the drivers of extreme weather risks.

To this end, the emerging field of Extreme Event Attribution (EEA) is becoming increasingly able to attribute the specific meteorological conditions (or even the impacts) of an event to human-induced climate change. This provides a tangible, evidence-based bridge between the global phenomenon of climate change and the scales at which people live and decisions are made. However, EEA studies are currently undertaken on an ad-hoc basis, in part due to discrepancies in data availability in different regions but also the lack of comprehensive, coordinated efforts. To provide greater utility to vital policy questions, insights from EEA need to be integrated into a wider system for documenting past events and understanding drivers of change.

In accordance with this, we propose a standardised framework for recording historical extreme weather events in an inventory structure. In our method, existing hazard-loss databases such as EMDAT provide a basis for event selection and give some basic impact details. Then, additional impact information, as well as detail about the process chain leading from antecedent conditions to impacts (the ‘event narrative’), is researched from a range of academic, government and NGO sources. Finally, existing attribution literature provides the link, or lack thereof, to human climate change. The comprehensive nature of such an inventory will align with the remit of the global stocktaking process, and offers a new and valuable perspective for understanding and adapting to changing risks at both national and sub-national scales.

To demonstrate the framework, we will here present inventories of past extreme weather events for the UK and the Caribbean in the period 2000-2019. Specifically, we will explore the logic and methodology behind the inventory framework, and use these examples to consider potential applications as well as foreseen drawbacks to the concept.

How to cite: Clarke, B., Otto, F., and Jones, R.: An inventory of extreme events and their impacts: implications for changing risks and climate adaption, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10068, https://doi.org/10.5194/egusphere-egu2020-10068, 2020.

Mountain regions are an important hotspot of vulnerability to climate change. These ecosystems are experiencing a higher warming rate than other areas in the world, with severe consequences on the environment, the economy and society. This is particularly relevant for Azerbaijan’s mountain regions, where the climate change impacts on water management could lead to severe consequences on the main local socio-economic activities such as agriculture and livestock farming.

For these reasons, the Impact Chains (ICs) methodology has been applied within two regions of Azerbaijan to understand and investigate cause-effect chains of current and future risk from different type of climate hazards following the approach proposed in the Fifth Assessment Report (AR5) of the International Panel on Climate Change (IPCC). ICs provide a consolidated scheme which helps to better understand, systemize and prioritize the factors driving climate impact related risks in a specific system and to perform climate risk assessments. It includes the underlying root-causes of climate risk, hazard, exposure and vulnerability factors and their interactions coming from quantitative and qualitative information.

Here we present the ICs study for Azerbaijan’s mountain regions accounting for flood, drought, erosion, heat stress and forest fires identified as the most relevant hazards in the country.

Climate conditions and future hazard components were assessed looking at future daily temperature and precipitation data until 2099 from two RCP (Representative Concentration Pathways) scenarios provided by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). The spatialized dataset is an ensemble of four global climate model simulations at a resolution of 0.5°x0.5°. In particular, the ISIMIP projections were exploited to extract the future evolution and spatial distribution over the region of relevant indicators for climate and climate hazards, including weather extremes and droughts.

The different levels of exposure and vulnerability were evaluated combining quantitative and qualitative information coming from spatial analysis, workshop discussion and questionnaires with local stakeholders and experts.

To finalize the risk assessment, the hazard, exposure and vulnerability components were combined through aggregation and normalisation techniques and risk indicators and hotspot maps for Azerbaijan’s mountain regions were developed.

The information provided by the ICs will be available to further analyse the risk processes and local dynamics, and to support local stakeholders in decision-making process and future investments on risk reduction and climate adaptation plans.

How to cite: Zebisch, M., Terzi, S., Crespi, A., Sonnenschein, R., and Steger, S.: Climate Risk and Vulnerability Assessment in Azerbaijan’s mountain regions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6531, https://doi.org/10.5194/egusphere-egu2020-6531, 2020.

Our ecosystems are facing increasingly extensive and complex natural disasters originating from natural or man-made hazards. Examples include the wild fires in Portugal 2017, Chile 2017, California 2018 and most recently Australia 2019/2020 as well as widespread flood events in Austria and the Czech Republic in 2013 and in Serbia and Croatia in 2014. These complex crisis situations highlight the increasing demands of stakeholders to monitor, anticipate, prepare for and learn from disasters. Research and innovation in this area needs to revolve around the expertise and guidance from practitioners in order to find solutions that are accepted and to benefit from their domain knowledge. In the European Commission (EC) H2020-funded project HEIMDALL on a Multi-Hazard Cooperative Management Tool for Data Exchange, Response Planning and Scenario Building we address the challenge of co-designing technological solutions for an improved adaptive emergency management at local, regional, national and European level with a multi-disciplinary group of experts including firefighters, police, emergency medical services, command and control and civil protection.

In order to find the most practical scenario-based solutions we follow a three-step approach: 1) Identification of immediate and long-term prevention and response planning activities that involve complex multi-hazard scenarios and information that needs to be represented in a conceptual scenario model to improve these activities; 2) Extension of that scenario data model by a harmonized lessons learnt data structure which allows stakeholders to capture experience of the emergency management in complex disasters; 3) Development and implementation of a scenario matching tool which allows users to find situations with a similar context, environmental conditions, hazard behaviour and stressed capabilities, from local storage as well as shared by other organizations. We believe that the combination of recording and matching scenarios including lessons learnt from prior incidents can improve the ability of stakeholders to learn and evolve from complex situations and thereby allow them to respond more effectively and operate more efficiently during disasters. Results of successive user exercises and evaluations of the implemented products and tools throughout the project underpin this assumption and at the same time indicate future research needs.

The HEIMDALL project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 740689.

How to cite: Friedemann, M., Henkel, F., Barth, B., Vendrell, J., Martin, D., Nolde, M., and Riedlinger, T.: Cross-domain scenario data model for the matching of comparable disaster situations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4662, https://doi.org/10.5194/egusphere-egu2020-4662, 2020.

Wildfire frequency and severity is dramatically increasing. Wildfires cause loss of life and destroy human and natural assets; smoke chokes cities; large fires release significant amounts of carbon; and fires can permanently change ecosystems so they are less effective carbon sinks. Countries with a history of wildfire, such as Australia, are facing unprecedented fires that outstrip response capacity.

Yet even in the worst catastrophes, scant attention is being paid to the massive potential for community-based initiatives to reduce risk and enhance resilience. The vast majority of wildfire reviews focus on suppression operations, and there is a clear need for these to be complemented with broader learning that provides holistic insights about how wildfire risk is generated, and how resilience might be increased.

This presentation will report on the findings from one such comprehensive and holistic review of a wildfire disaster in southwest Tasmania, Australia, in January 2019. The event resulted in a locally unprecedented human and animal evacuation, and burnt through large swathes of precious wilderness world heritage area. Utilising the Post-Event Review Capability (PERC) methodology, this study investigated the causes, successes and failures of this disaster. This presentation will present findings and recommendations that are locally actionable yet provide a number of generalised lessons pertinent across multiple risk contexts. Findings demonstrate the significance of community-based actions for wildfire risk management.

How to cite: Keating, A. and Handmer, J.: Post-Event Reviews for building Wildfire Resilience: The case of Tasmania, Australia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13758, https://doi.org/10.5194/egusphere-egu2020-13758, 2020.

We present results from state-of-the-art kilometre scale numerical models of tropical cyclones over Bangladesh.  We demonstrate how the latest generation of numerical models are filling the data gap in regions of the world with sparse observational networks, and compare our results to the latest generation global reanalyses.  We show how an ensemble of simulations expands our understanding of plausible events beyond our limited observations record.  Utilising this ensemble information in a Bayesian data analysis framework, we can robustly estimate prediction intervals for various parameters, such as peak wind speed or extreme rainfall, which when combined with Decision Theory and a loss function offer a coherent data-to-decision framework supporting disaster risk assessment and management strategies. We show how this decision making could be integrated into current global weather and climate forecast ensembles to provide forecasting of hazards and impacts up to 5 days ahead of an event, and in a future climate context.  We end with some thoughts on the ways this could influence the future of risk management and insurance underwriting and the challenges of working with big numerical model datasets.

How to cite: Steptoe, H., Economou, T., and Becker, B.: Improving our understanding of Bangladesh tropical cyclone risk: decision making insights using kilometre scale numerical modelling and Bayesian data analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7721, https://doi.org/10.5194/egusphere-egu2020-7721, 2020.

With coastlines becoming increasingly urbanised worldwide, the economic risk posed by storm surges to coastal communities has never been greater. Given the financial and ecological costs of manmade coastal defences, the past few years have seen growing interest in the effectiveness of natural coastal “defences” in reducing the risk of flooding to coastal properties, but estimating their actual economic value in reducing storm surge risk remains a challenging subject.

In this study, we estimate the value of mangroves in reducing annual losses to property from storm surges along a large stretch of coastline in Florida (USA), by employing a catastrophe modelling approach widely used in the insurance industry. We use a hydrodynamic coastal flood model coupled to a property loss model and a large property exposure dataset to estimate annual economic losses from hurricane-driven storm surges in Collier County, a hurricane-prone part of Florida. We then estimate the impact that removing mangroves in the region would have on average annual losses (AAL) caused by coastal flooding. We find that mangroves reduce AAL to properties behind them by over 25%, and that these benefits are distributed very heterogeneously along the coastline. Mangrove presence can also act to enhance the storm surge risk in areas where development has occurred seaward of mangroves.

In addition to looking at annual losses, we also focus on the storm surge caused by a specific severe event in Florida, based on Hurricane Irma (2017), and we estimate that existing mangroves reduced economic property damage by hundreds of millions of USD, and reduced coastal flooding for hundreds of thousands of people.

Together these studies aim to financially quantify some of the risk reduction services provided by natural defences in terms of reducing the cost of coastal flooding, and show that these services can be included in a catastrophe modelling framework commonly used in the insurance industry.

How to cite: Thomas, C., Narayan, S., Matthewman, J., Shepard, C., Geselbracht, L., Nzerem, K., and Beck, M.: What value do mangroves have in reducing the cost of storm surges?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1385, https://doi.org/10.5194/egusphere-egu2020-1385, 2020.

Pluvial floods in urban areas are caused by local, fast storm events with very high rainfall rates, which lead to inundation of streets and buildings before the storm water reaches a watercourse.  An increase in frequency and intensity of heavy rainfall events and an on-going urbanization may further increase the risk of pluvial flooding in many urban areas.  Current early warning systems for pluvial floods are limited to rainfall predictions with fixed thresholds for rainfall duration and intensity and often do not provide the necessary information to effectively protect people and goods.  We present a proof-of-concept for an impact-based early warning system for pluvial floods. 

Using a model chain consisting of a rainfall forecast, an inundation, a contaminant transport and a damage model, we are able to provide predictions for the expected rainfall, the inundated areas, spreading of potential contamination and the expected damage to residential buildings. We use a neural network-based inundation model, which significantly reduces the computation time of the model chain.  To demonstrate the feasibility, we perform a hindcast of a recent pluvial flood event in an urban area in Germany.  The required spatio-temporal accuracy of rainfall forecasts is still a major challenge, but our results show that reliable impact-based warnings can be issued up to 5 minutes before the peak of an extreme rainfall event.  To effectively disseminate the warnings issued by the model chain we propose a two-way mobile warning application that allows for the collection of real-time validation data.

How to cite: Rözer, V., Peche, A., Berkhahn, S., Feng, Y., Fuchs, L., Graf, T., Haberlandt, U., Kreibich, H., Sämann, R., Sester, M., Shehu, B., Wahl, J., and Neuweiler, I.: Impact-based early warning for pluvial floods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10507, https://doi.org/10.5194/egusphere-egu2020-10507, 2020.

It is gradually increasing that the scale of economical and social damage caused by recent risk meteorological phenomena. The aspect of damage due to heat wave and cold wave as well as heavy rain, typoon, heavy snow are getting complicated. So the demand for customized information to protect the lives and property of the people has also been increasing constantly. Daegu Metropolitan City and Gyeongsangbuk-do Province are especially the area where heat wave occurs most frequently in south Korea. In addittion, as global warming has been accelerated recently, there are growing concerns that heat-related patients and livestock mortality increase. Therefore, Daegu Regional Office of KMA(Korea Meteorological Administration) developed heat wave impact forecast services from 2016 to prevent and minimize damages from heat wave in our area and has been improving and operating the service as of 2019.

Daegu Regional Office of KMA’s Impact heat wave impact forecast is for reducing the damage caused by heat wave in our area in 2019. It is carried in 4 steps: Attention, Caution, Warning, Danger and in 7 field: Health, Stock Raising, Fish culture, Agriculture, Industry, Traffic and Electricity. We used the objective value such as the highest temperature and the number of days to last as the criteria for each stage of the health sector. Other sectors except health, we considered the risk level in the health sector basically and the level of risk was determined taking into account the vulnerability and exposure of each region. We analysed them of each region in detail in order to provide the correct contents. Finally, we were able to obtain a number of analysis results that linked to heat wave and various types of damage. Our services have been conveying to public by facsimile, E-mail as a document and is provided by KMA website(http://www.weather.go.kr). And we announced the information that we made about heat wave on YouTube.

As a result, our services seem to have positive impacts. The number of heat-related patients decreased by 47% from the previous year. After the service was provided, a questionnaire survey was conducted for recipient, and 84% of respondent gave positive outcome about our services.

Based on the studies and services, Daegu Regional Office of KMA is going to calculate a more objective risk level for other sector except health in order to play a role in future regional customization services. And we are going to reform the document of heat wave impact forecast to contain more information focused on our region.

How to cite: Kim, S. and Kim, B.: Heat Wave Impact Forecast by Analysis of Vulnerability and Exposure over Daegu Metropolitan City and Gyeongsangbuk-do Province, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4285, https://doi.org/10.5194/egusphere-egu2020-4285, 2020.

The frequency of extreme weather phenomena such as heat wave and cold wave has increased recently, and the intensity of weather has been strengthened, resulting in human and physical damage. The Republic of Korea has been working to reduce damage since 2018 by including heat and cold waves in natural disasters. The Korea Meteorological Administration (KMA) also provides impact-based forecasts, which requires research that suits local characteristics. In this study, weather observation data related to the summer heat wave in Busan, Ulsan and South Gyeongsang Province was analyzed to determine the weather conditions for the heat wave. In addition, in relation to the heat wave impact-based forecast that was provided regularly in 2019, the heat threshold was applied by comparing the current status of the heat-related patients with the maximum temperature, the number of consecutive days of the heat wave and the current status of the heat-related patients. The impacts of heat waves in different fields were analyzed, including livestock waste, fisheries food damage, and heat damage by crops. The cold wave also analyzed the number of days of cold wave in Busan, Ulsan, and South Gyeongsang Province by comparing the lowest temperature with the current status of cold-related patients. The impacts of cold weather conditions such as wind direction, wind speed and the number of consecutive days of the cold wave were also analyzed. Further, for regular provision of cold wave impact-based forecast to be implemented in 2020, the impacts of each cold wave vulnerable areas suitable for Busan, Ulsan, and South Gyeongsang Province were analyzed and referred to when applying cold wave thresholds.

How to cite: Jo, M., Ye, J., Yun, J., You, J., Kim, J., Jang, J., and Noh, H.: Study on the Influence of Heat Wave and Cold Wave Characteristics and Vulnerable Areas in Busan, Ulsan and Gyeongsangnam-do, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7045, https://doi.org/10.5194/egusphere-egu2020-7045, 2020.

In Korea, severe heat waves are frequent in summer, and the number of people who affected by them increases year by year. This study analyzes the correlation between excess mortality and the daily maximum temperature(T_max) in August for the last decade(2009-2018). In addition, it analyzes T_max when the patients by heat illness occur. The analysis shows a positive correlation(R=0.524, P=0.02) between the number of excess mortality and T_max. In terms of patients by heat waves, the patients occur variously from 26℃ to 39℃, and the maximum number of patients appears in 34~35℃. In case of the duration of T_max ≥ 33℃, the number of patients shows a peak at entrance of the period, and it drops after the 4th day and no patients showing after the 9th day. But, in case of T_max < 33℃, the heat illness in the 4th day occurs more than any other days, and it decreases slowly. In addition, it seems that it is not enough for the public to recognize accurately and respond risks appropriately with current temperature forecasts, so the Korea Meteorological Administration provides HIBFWS which includes countermeasures along with regional risk levels for the heatwave. Also, it analyzes socio-economic-environmental vulnerability for production of the information in Jeju province.

How to cite: Kim, S.: Analysis on the damage, vulnerability and correlation with temperature caused by heat waves in Jeju province(Korea), and Heatwave Impact Based Forecast and Warning Service(HIBFWS), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6292, https://doi.org/10.5194/egusphere-egu2020-6292, 2020.

The relationship between natural hazard-induced disasters and macroeconomic growth has been examined widely on global and national scales, but little research has been focused on the subnational level, especially in China. We examined the impacts of natural hazard-induced disasters on the regional growth in China based on subnational panel data for the period from 1990 to 2016. First, we used the number of people affected and the direct economic losses as the measures of the scale of disasters. Then, we used the direct damages of meteorological disasters and earthquakes as disaster measures separately to examine the impacts of different disaster types. Finally, we performed intraregional effects regressions to observe the spatial heterogeneity within the regions. The results show that the adverse short-term effects of disasters is most pronounced in the central region, while the direct damage of disasters is a positive stimulus of growth in the whole of China. However, this stimulus is observed in a lagged way and is reflected differently meteorological disasters in central and eastern China and earthquakes in western China are related to regional growth. The results demonstrate that the short-term macroeconomic impacts of these disasters in the three geographical regions of China largely depend on regional economic development levels and the disaster types.

How to cite: Tang, R., Wu, J., Ye, M., and Liu, W.: Impact of Economic Development Levels and Disaster Types on the Short-Term Macroeconomic Consequences of Natural Hazard-Induced Disasters in China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2266, https://doi.org/10.5194/egusphere-egu2020-2266, 2020.

The forced displacement of individuals and communities as a result of extreme weather events and the impact of anthropogenic climate change has been described as one of the greatest humanitarian challenges of the 21st Century. A multi-sectoral approach is required to address the humanitarian dimensions of climate displacement. Approaches span initiatives to prevent or reduce the conditions that lead to displacement (for example, resilience and adaptation strategies); response to displacement (including access to essential humanitarian assistance); recovery initiatives that increase resilience and support for the attainment of sustainable solutions (return, local integration and resettlement). Within the discussions on the humanitarian dimension of climate displacement, there has been increasing recognition of the specific importance of preparedness initiatives. Practitioners like the Red Cross Red Crescent Climate Centre (RCCC), for instance, have been starting to apply tests of forecast-based financing (FbF) to inform short-term humanitarian assistance based on disaster warnings from scientific forecasts. This paper serves as an innovative contribution towards understanding how FbF can be used as an effective approach to prepare for or prevent climate-related forced displacement. Using a panel econometric analysis, this paper models climate-related forced migration movements and humanitarian needs in Somalia during recent compound drought events. The model results support the improvement of early warning systems in the region and more broadly, the inclusive development and provision of time-effective humanitarian aid to those displaced globally.

How to cite: Thalheimer, L., Li, S., Simperingham, E., Jjemba, E., and Otto, F.: Climate displacement, humanitarian needs and Forecast-based financing, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20916, https://doi.org/10.5194/egusphere-egu2020-20916, 2020.

Models designed to estimate financial impact of earthquake for France are usually poorly constrained and mostly consist of sub-models of either pan-European or Caribbean models for respectively French mainland and Lesser Antilles territories. Even if those turnkey models produce first order estimation for quantifying the impact of an earthquake, they lack of in-situ studies to take into account the specificities of French territories on the overall workflow of modeling especially on hazard, vulnerability and loss estimation. Consequently, these models can’t be used with a high confidence in order to estimate the overall exposure of France in relation to not yet occurred but plausible earthquakes.

BRGM, as the French geological survey institute, and CCR, as the French State owned public reinsurance company, are both deeply concerned in a better understanding of the consequences of natural disasters occurring in France. Thus, since 2014, BRGM and CCR have been collaborating, amongst other projects, to develop a new consistent and reliable earthquake impact model for the French mainland and overseas territories covered by the specific French Natural Disasters Compensation Scheme.

This model encompasses a complete modeling chain from hazard to loss estimation. It consists in performing damage scenarios in order to evaluate the financial consequences for compensable insured property on buildings for a given seismic source, defined deterministically or probabilistically. To date, the model evaluates the consequences of seismic events for almost all kind of buildings in France: dwellings (houses and apartments), retail trade, professional and technical business services and industrial facilities. The seismic hazard is estimated deterministically for reference events by region but also probabilistically by generating stochastic earthquake dataset calibrated on the French seismic historical activity. Specific vulnerability assessments have been performed providing hazard to damage relationships specifically calibrated on French buildings.

The model can been used to estimate the consequences of real event such as the unusual M5.2 shallow earthquake occurred in November 2019 in France, providing fast estimation of its impact. The model, using the stochastic earthquake generator, allows us to estimate the exposure of French territories to earthquake providing indicators to support prevention actions led by the French government in the most exposed areas. Some of these indicators are already available throughout dedicated platform to insurances companies and public authorities and should be supporting State decision-makers and local authorities for prevention action such as retrofitting of buildings or adapting building codes.

How to cite: Tinard, P., Rey, J., Monfort-Climent, D., Imtiaz, A., Hoste-Colomer, R., Negulescu, C., and Gehl, P.: New insights into the Evaluation of Financial Impact of Earthquakes in France: Benefits for Compensation and Prevention, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5128, https://doi.org/10.5194/egusphere-egu2020-5128, 2020.

The increasing loss of human life and property due to earthquakes in past years have increased the demand for seismic risk analysis for people to be better prepared for a potential threat. With the centralization and increase of population near urban centres and megacities, earthquakes occur in these places will cause much more damage than in the past. Therefore, the quantification of seismic risk is extremely important. Seismic risk modelling results provide the spatial distribution of expected damage and loss to exposed elements in an earthquake of different magnitudes. Therefore, seismic risk model can play a key role in the following aspects: (i) to assess the potential seismic hazard and loss for a target area from both deterministic and probabilistic view; (ii) to support the long-term plan for seismic risk mitigation and preparedness; (iii) to prioritize decision making in emergency response and disaster management; and (iv) to optimize retrofitting strategies.

The modelling of seismic risk is typically composed of three modules, namely hazard, exposure and vulnerability. Different researchers have applied different assumptions in modelled the seismic hazard, exposed stock value and their vulnerability. Therefore, uncertainty exists in every step of the loss modelling chain. Thus, it is quite essential to evaluate the reasonability of the loss modelling results. One way to check the reasonability of modelled seismic loss is by comparison with real losses derived from post-earthquake surveys. China has a long history of recording historical devastating natural disasters including major losses during earthquakes and associated secondary events, which can be dating back to 1831 B.C. (Gu, 1989). Based on this bunch of damage information, Daniell (2014) developed an empirical loss function for mainland China during his PhD study. The advantage of this loss function compared with others is its normalization of historical loss with the socio-economic indicator (e.g. Human Development Index) and its calibration of damage functions of previous events to relate to the present conditions. Therefore, the loss estimated based on the empirical loss function developed in Daniell (2014) (tagged as “empirical loss”) will be used to evaluate losses estimated purely from modelled parameters (tagged as “analytical loss”).

Our results indicate that for both deterministic and probabilistic hazard scenarios, the empirical loss and analytical loss are within two times’ difference (i.e. the empirical loss is generally larger than analytical loss, but it is lower than two times of the analytical loss). When the building vulnerability change is scaled in the empirical loss function of Daniell (2014) by using HDI and the soil amplification effect is integrated into the analytical loss modelling process, the difference between “empirical loss” and “analytical loss” will further decrease. This congruence verifies the reliability of the parameters we use in modelling seismic loss.

How to cite: Xin, D., Daniell, J., and Wenzel, F.: Comparison of modelled seismic loss against historical damage information, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4526, https://doi.org/10.5194/egusphere-egu2020-4526, 2020.

A rapid evaluation of flood damage is strategic for the good success of emergency management activities after a natural disaster. A method for the estimation of economic damage is developed considering the impact of hydrogeological phenomena with meteoclimatic forcing over settlements, industrial and rural areas and commercial activities.

Damage estimation is a very current research field, but the available methods are far from being effective in the period immediately following the event. This is due particularly to the intrinsic complexity and variability of the damage process and the lack of reliable and consistent damage measures across areas at least at the regional scale.

This work proposes a national scale first approximation correlation between vulnerated area and expected damage. The relationship, expressed in terms of power law, is calibrated on a huge number of single damage records collected by the Italian government all through the country during flood and landslide events in the last 6 years. Data have been grouped following the type of flood. Records come from official data provided by government commissioners in charge of emergency management, according to the national law. Validation, carried out on an independent data set, is quite encouraging and provides indications for further developments.

How to cite: Boschini, I., Zambrini, F., Menduni, G., Molinari, D., and Bignami, D.: Analysis, estimation and prediction criteria for damage from geo-hydrological events: a top-down approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22502, https://doi.org/10.5194/egusphere-egu2020-22502, 2020.

The Valle d'Aosta Region and the Autonomous Province of Bolzano territories include the highest mountain areas of Italy, where most of the communication infrastructures or strategic activities are totally or in part partially exposed to the rockfall hazards.  

For this reason, the two administrations have established an operational cooperation in order to compare their procedures and to define the criteria and best practices to prioritize and project the mitigation the rockfall mitigation measures. The result achieved by the work group have inspired a new incoming version of the Italian technical standard UNI 11211 “Rockfall protective measures”.   

As a part of the rockfall risk assessment of the designing the mitigation measures, it is necessary to assess the actual effectiveness of the alternative mitigation options which have been identified.  

The choice whether to mitigate the event intensity or the expected damage, with either structural or non-structural measures, will usually achieve a risk mitigation level, associated to a complimentary residual risk. 

Therefore, the project management has to evaluate the degree of hazard and risk mitigation for any given solution. The acceptability of the residual risk and its possible mitigation through organizational measures are to be evaluated as well. A long-term cost/benefit analysis has to be performed, taking also into account the tolerability over time of the handling costs. 

The first milestone in the decisional process the definition of the acceptable risk level. As a matter of fact, which is the key criterion supporting the decision to undertake cost-effective investments in mitigation works. For that reason, a preliminary analysis of the in-situ geological conditions should be as complete and detailed as possible. Project managers have to be aware that the zero-option has to be taken in to account as well, in the case the risk level would not be acceptable. 

Moreover, it has to be taken into account that the risk evaluation is always site-specific, because the rockfall mitigation projects have to be based on a detailed geological reference model. Local changes in geological, hydrogeological, morphological and structural conditions, vegetation, vulnerability and exposure of the objects at risk may lead to different hazard and risk conditions even at a local scale. Therefore, a risk assessment analysis is consistent to a single project and can’t be directly upscaled to implement, for instance, a municipal land management plan.   

Another key point in the decision-making process is the expected damage assessment, which has to include not only the direct damages (e.g.: loss of human lives) but also the indirect damages and their economic and social impacts. As a consequence, in assessing the acceptable risk both the probability of direct and indirect damage and the economic and social benefits derived from its acceptance have to be weighted. 

The final result has led to guidelines based on QRA (Quantitative Risk Assessment) method and defining three risk levels: Acceptable, ALARP (As Low As Reasonably Practicable) and Unacceptable, providing to the project managers a rational and objective framework to manage rockfall hazards in Italy. 

How to cite: Strada, C., Bertolo, D., Mair, V., and Paganone, M.: Key elements to assess proposals for rockfall risk mitigation in the context of a technical and economic feasibility project – the experience of two alpine italian regions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5753, https://doi.org/10.5194/egusphere-egu2020-5753, 2020.

The management of complex crisis situations, whether natural, accidental or intentional origin, generally requires the participation and coordination of multiple first response organizations, including, but not limited to: firefighting units, police departments, medical emergency services, civil protection units and command and control centers. Considering this multi-disciplinary context, there is the need to provide integrated tools which can address the requirements of the different first responders involved in disaster risk management and enhance cooperation and inter-organizational coordination.

In this sense HEIMDALL (Multi-Hazard Cooperative Management Tool for Data Exchange, Response Planning and Scenario Building), a H2020 granted project (project number 606982), aims at improving preparedness of societies to cope with complex crisis situations by providing a flexible platform for multi-hazard (wildfires, floods and landslides) emergency planning and management, which makes use of innovative technologies for the definition of multi-disciplinary scenarios and response plans, providing integrated assets to support emergency management, such as monitoring, modeling, situation and risk assessment, decision support and communication tools.

On one hand, HEIMDALL platform allows impact assessment and risk management through merging geo-spatial information (inhabited areas, industrial facilities, transport infrastructure …), hazard modeling and the data generated during the on-going crisis, such as in situ information generated by the first responders, satellite images, meteorological data and monitoring sensors. All in real- or near real-time. On the other hand, includes a catalogue of past events where one can see the impact the hazard had, which decisions and actions were taken to manage the disaster and the lessons learnt. This approach provides an overall perspective of the situation, helping the disaster risk management decision-making and enhancing the preparedness and training of first-responders units by creating fictional situations or replicating historical scenarios, as it can be used before, during and after a disaster.

To support landslides management HEIMDALL platform includes two modules developed by the Institut Cartografic i Geologic de Catalunya (ICGC): Landslides and in situ sensors for terrain monitoring. Landslides module performs simulations of terrain movements in order to enhance the emergency response and identify safe areas for the deployment of advanced command & control post. The module integrates and automates the mapping of landslide susceptibility through two open source software (Scoops3Di and FLOW-R). Also included a tool that process pre-and post-event meteorological data in order to record the triggering rain’s intensity and foresee whether the hazard will increase or not during the next days. This tool helps establishing regional thresholds for landslide triggering rain. The in situ sensors module integrates data from monitoring sensors (tiltmeters, crackmeters, …) installed on slow moving landslides, allowing the raising of warnings in case of any acceleration that could represent any risk.

How to cite: Marturià, J., Becerra, J., Buxò, P., Abancó, C., and Rodríguez, X.: HEIMDALL platform for Landslide emergency/risk management , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21459, https://doi.org/10.5194/egusphere-egu2020-21459, 2020.

An accurate understanding of physical and economic effects of landslides is fundamental to develop more refined risk management, mitigation strategies and land use policies. We develop a measure to consider the interconnection between physical and economic exposure, e.g. what we call the economic landslide susceptibility, namely the probability of landslide occurrence in an area weighted for its socio-economic exposure. The economic landslide susceptibility is estimated trough a pixel-based method designed for large areas. The method makes use of landslide susceptibility maps and a real estate market value maps for any given areas under analysis. We apply this methodology to the Umbria Region (Central Italy). The innovative concept of economic landslide susceptibility (that is de facto an ex ante landslide cost assessment) may be interpreted as the potential loss that an area might suffer in terms of its propensity for landslides. Useful applications of the proposed method lie in a better territorial management and in the land use planning.

How to cite: Donnini, M., Modica, M., Salvati, P., Marchesini, I., Rossi, M., Guzzetti, F., and Zoboli, R.: Economic Landslide Susceptibility under a socio-economic perspective: an application to Umbria Region (Central Italy), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8766, https://doi.org/10.5194/egusphere-egu2020-8766, 2020.

The Netherlands is a low-lying country: a large, densely populated and urbanised part lies below mean sea-level. The risk of flooding is therefore omnipresent and flood protection measures, such as dikes along rivers, are vital for the safety of the population and their economy. The regional water authorities apply high safety standards when monitoring and maintaining the dikes.

The water authority Hoogheemraadschap Stichtse Rijnlanden (HDSR) has launched a maintenance program to investigate and reinforce the northern Lek River dike between Schoonhoven and Amerongen. The geology of this area has been shaped by fluvial activity during the Holocene, resulting in a heterogeneous composition of the shallow subsurface. The strength and stability of the dike depend on both its design and the geology in its subsurface. A sandy channel deposit may lead to piping and undercutting of the dike while weak (e.g. peat) or layered strata under certain hydraulic pressures could potentially lead to collapse and catastrophic failure of the dike. Detailed knowledge of the subsurface in the area is therefore essential to design fit-for-purpose reinforcement measures.

The national GeoTOP model, built and maintained by TNO - Geological Survey Netherlands, is a 3D stochastic geological voxel model that provides insight in the lithostratigraphy and lithology up to a depth of 50 meters below MSL with voxels (3D cells) measuring 100x100x0.5 m. However, to estimate the risk of piping and other forms of instability, HDSR needs a higher level of detail. In this study we therefore constructed a high-resolution voxel model for three sections along the Lek River dike.

To model the lithology of each voxel we used borehole descriptions, cone penetration test (CPT) data and paleogeographic maps of the Rhine-Meuse Delta. Using CPT data as well as borehole descriptions allowed for higher-resolution modelling. To use the CPT’s for calculation of the lithology, the CPT measurements were translated into lithological classes using an Artificial Neural Network. Special attention was paid to the shape and position of the buried paleo channels, as their presence is a potential risk for piping, and to the mapping of man-made features in the landscape. The resulting 3D geological model has a voxel cell size of 25x25x0.25 m, a resolution that is 32x higher than the GeoTOP model.

The new high-resolution model is now used by HDSR for:

• identification of dike segments that need further investigation
• designing location-specific and fit-for-purpose dike reinforcement measures
• explaining proposed measures to local stakeholders.

The first two applications potentially reduce costs significantly; whereas the third application aids creating social foundation for reinforcement measures. Most importantly, the new high resolution model helps HDSR to enhance safety behind the dikes.

How to cite: Kars, R. H., de Bruijn, R., Dabekaussen, W., Meijninger, B. M. L., and Stafleu, J.: High-resolution 3D geological modelling of the Lek River dike for enhanced flood protection, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9132, https://doi.org/10.5194/egusphere-egu2020-9132, 2020.

The paper focuses on the impact of riverbank erosion on the island of Majuli. Majuli is a large and populous river island in the India state of Assam. However, the island suffers from the erosional work of Rivers Brahmaputra in the south and Luhit in the north and this has led to the loss of land and the resultant displacement of population in 110 out of 243 villages of Majuli. The most significant impact of riverbank erosion has been on the livelihood pattern of the island, as erosion has affected both agriculture and fishing activities. However, the impact of erosion is not felt equally by the entire population of Majuli. Those who live near the banks of the river are disproportionately affected by erosion, while those living in the more central parts of the island have benefitted from it by using the changing economic structure of the island. Riverbank erosion has thus, had a profound impact on the society, economy and livelihood structure of the island and has created a more unequal society. The paper tries to count this intangible cost of riverbank erosion by analyzing the disparity in the economic impact of riverbank erosion from the perspective of political ecology with the help of survey and personal interviews carried out in Majuli.

Keywords: Majuli, Brahmaputra, Riverbank Erosion, Displacement, Economic Impact, Political Ecology

How to cite: Sahay, A.: Displacement, marginalization and changing economic structure: counting the intangible costs of riverbank erosion in Majuli island of India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20602, https://doi.org/10.5194/egusphere-egu2020-20602, 2020.

Tailings dams are earth embankments used to store toxic mine waste and effluent. Their failure, as already seen in January 2019 with the fatal failure of Brumadinho dam in Brazil, can cause loss of life, irreversible damage to ecosystems and large economic damages. In countries with limited resources, it is challenging for the authorities to be able to assess the risk and effectively monitor this type of infrastructure, especially when located in remote areas.

We are developing DAMSAT (Dam Monitoring from SATellites), a web-based system for a sustainable and cost effective way of remotely monitor tailings and water retention dams to support early decision making and reduce the social, economic and environmental impacts downstream of potential failures.

DAMSAT monitors the displacement of the structures using earth observation technologies such as Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) technologies, combined with real-time in-situ devices. These observations combined with weather forecasting tools allow the issue of alerts for unusual behaviour or weather conditions that could lead to dam failure. These alerts are part of the Disaster Risk Management cycle to trigger the implementation of mitigation measures to reduce the likelihood of failure of the dam or the potential consequences downstream.  

In order to have a better understanding of these potential consequences and provide all the information necessary for asset managers to take decisions, DAMSAT also assesses the hazard component of disaster risk due to dam failure using a set of modelling tools. A dam breach simulation model (EMBREA) is combined with a mud flow model to spread the flood hazard downstream of the dam if a failure occurs.  The consequences of the flood are assessed in terms of loss of life using an evacuation model, the Life Safety Model. Different flood warning scenarios and evacuation strategies are mapped to inform emergency planning.

DAMSAT is currently being piloted in two mining regions in Peru with the involvement of government organisations and other relevant stakeholders. 

How to cite: Davison, M., Roca, M., and Petkovsek, G.: Supporting reduction of risks of tailings dams using earth observation data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7658, https://doi.org/10.5194/egusphere-egu2020-7658, 2020.

Compound natural hazards, like El Niño events, which trigger torrential rain, mudslides, riverine and flash floods, cause high damage to society. An improved risk management based on reliable risk assessments are urgently needed. However, knowledge about the complex processes leading to El Niño damage is lacking, and so are loss models.  We explore a large dataset of building damage from the coastal El Niño event 2017 in Peru. We use data-mining techniques to analyse data of damage grades of about 180.000 affected houses together with satellite observations and open geo-information. In a first step, we use unsupervised clustering (t-SNE + OPTICS) to separate regions of different dominant processes. Secondly, we train various supervised classification algorithms and create feature importance rankings per cluster, to identify drivers of observed damage for each of these regions. A comparison of different algorithms provides further insights about the potential and limitations of these methods and datasets. Results indicate that topographic wetness is the most important indicator, as selected by the algorithms, when using the entire dataset. Rainfall sum and maximum from TRMM satellite measurements are identified as damage driver despite the coarse spatial resolution. Also urbanity, based on a focal window around the global urban footprint, appears to play a role for the amount of damage. At least a coarse separation of processes is possible: the slope length and steepness, bare soil index, stream power index, and maximum rainfall are dominating the damage processes in lower mountain ranges and canyons, indicating rapid processes. Damage in upper mountain areas seem more influenced by the rainfall sum, local topographic position, and vegetation cover. In the lowlands, topographic wetness is very dominant, indicating ponding water or riverine floods. As opposed to previous work, this study constructs importance rankings based entirely on real observed damage to buildings. It is therefore a step towards data-driven damage assessments for El Niño events.

How to cite: Brill, F. and Kreibich, H.: A data-mining approach to investigate El Niño damage in Peru, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10025, https://doi.org/10.5194/egusphere-egu2020-10025, 2020.

In 2019, the typhoon Lekima hit China, bringing strong winds and heavy rainfall to the nine provinces and municipalities on the northeastern coast of China. According to the Ministry of Emergency Management of the People’s Republic of China, Lekima caused 66 direct fatalities, 14 million affected people and is responsible for a direct economic loss in excess of 50 billion yuan. The current observation technologies include remote sensing and meteorological observation. But they have a long time cycle of data collection and a low interaction with disaster victims. Social media big data is a new data source for natural disaster research, which can provide technical reference for natural hazard analysis, risk assessment and emergency rescue information management.

We propose an assessment framework of social media data-based typhoon-induced flood assessment, which includes five parts: (1) Data acquisition. Obtain Sina Weibo text and some tag attributes based on keywords, time and location. (2) Spatiotemporal quantitative analysis. Collect the public concerns and trends from the perspective of words, time and space of different scales to judge the impact range of typhoon-induced flood. (3) Text classification and multi-source heterogeneous data fusion analysis. Build a hazard intensity and disaster text classification model by CNN (Convolutional Neural Networks), then integrate multi-source data including meteorological monitoring, population economy and disaster report for secondary evaluation and correction. (4) Text clustering and sub event mining. Extract subevents by BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) text clustering algorithms for automatic recognition of emergencies. (5) Emotional analysis and crisis management. Use time-space sequence model and four-quadrant analysis method to track the public negative emotions and find the potential crisis for emergency management.

This framework is validated with the case study of typhoon Lekima. The results show that social media big data makes up for the gap of data efficiency and spatial coverage. Our framework can assess the influence coverage, hazard intensity, disaster information and emergency needs, and it can reverse the disaster propagation process based on the spatiotemporal sequence. The assessment results after the secondary correction of multi-source data can be used in the actual system.

The proposed framework can be applied on a wide spatial scope and even full coverage; it is spatially efficient and can obtain feedback from affected areas and people almost immediately at the same time as a disaster occurs. Hence, it has a promising potential in large-scale and real-time disaster assessment.

How to cite: Tang, J., Yang, S., and Wang, W.: A Social Media Big Data-Based Disaster Assessment Framework for Typhoon-induced Flood: Case Study of Typhoon Lekima, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6389, https://doi.org/10.5194/egusphere-egu2020-6389, 2020.

How to cite: Young, R.: Coastal Hazards & Targeted Acquisitions: A Reasonable Shoreline Management Alternative , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10960, https://doi.org/10.5194/egusphere-egu2020-10960, 2020.

On the 12^th of November of 2019, flood levels in the Venice Lagoon have reached the mark of 1.87 metres, the second-highest level since records began in 1923. Although a recurrent problem in Venice, the significance of this event have raise awareness of the issue of coastal inundation hazard in Italy, particularly at the highly vulnerable territory of the regions facing the North Adriatic Sea. Several are the processes that contribute to a costal inundation event. On the short term, processes such as high tide and storm surge events can result in sea levels, potentially triggering devastating impacts on human settlements and activities. On the long term, the land subsidence and mean sea level (MSL) changes are important factors; in fact, in some regions such as Jakarta and Bangkok the land is expected to subside by more than 1 meter, while MSL is expected to rise during the next decades, reaching global mean absolute values ranging from 0.3–0.6m (RCP 2.6) to 0.5–1.1m (RCP 8.5) by the end of the century. The combined effect of global sea level rise, local subsidence, and short term phenomena can potentially increase the frequency and intensity of extreme sea levels (ESL), posing a major threat to coastal areas. Currently, almost 700 million people live in low-lying coastal areas, and about 13% of them are exposed to a 100-year flood. In Italy, a territory that is highly vulnerable to coastal flooding are the Regions facing the North Adriatic Sea, mainly due to two factors: the morphological characteristic of this territory, characterised by low-lying areas, and the bathymetry and shape of the Adriatic basin, which cause water level to accumulate and increase rapidly during storm surge events, especially during winter. In this paper, we evaluate two different coastal inundation modelling techniques, one hydrostatic (as part of the EIT Climate-KIC SaferPLACES project) and another hydrodynamic (the ANUGA model), by stressing the models with different ESL, both for the historical mean sea level and for MSL projections at 2050 and 2100. The two different inundation models are tested on three pilot sites particularly vulnerable to coastal flooding located in the North Adriatic Sea: Venice, Cesenatico, and Rimini. We compare our modelling results with existing hazard records and previous hazard and risk assessments. Finally, we apply a flood damage model developed for Italy to estimate the potential economic damages linked to the different flood scenarios, and we calculate the change in expected annual damages according to the relative extreme sea levels.

How to cite: Hrast Essenfelder, A., Amadio, M., Bagli, S., and Mazzoli, P.: Coastal inundation hazard in the North Adriatic Sea under Climate Change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8981, https://doi.org/10.5194/egusphere-egu2020-8981, 2020.

        Heavy rainfall is one of the most frequent and severe weather hazards in the world which becomeone of the hugest natural risks.  It has been found that during the flood season in South China, high intensive precipitation occurs very frequently due to the impact of east Asian monsoon.  An unexpected and unusual extreme precipitation event could lead to millions or billions worth of damage, wash out vehicles and houses, destroy agricultural fields, and threat people’s lives.  Determining the linkage between heavy rainfall causes, critical meteorological condition, and impacts can make it easier to classify risk level.  However, due to the insufficiencies of quantitative heavy rainfall related property damages, and low efficient precipitation forecast, the risk evaluation could not be well determined.  Therefore, we employed an improved short-term precipitation forecast based on ensemble deep learning algorithms that can provide more accurate prediction, and apply  25 years of insurance data to aid as proxy for the evaluation of short-term heavy rainfall risks, aiming to trigger in-time precautions and reduce losses. 

       The improved short-term precipitation forecast is built based on combination of scale-invariant feature transform (SIFT) algorithm and ensemble model including convolutional neural network (CNN), gradient boosting decision tree (GBDT), and neural network.  The main dataset used includes radar images and station observed precipitation.  The past 1.5 hour radar reflectivity images are measured at 15 times with an interval of 6 minutes, and in 4 different heights from 0.5 km to 3.5 km with an interval of 1 km.  The hourly site precipitation is obtained from ground meteorology stations.  The SIFT is used to calculate cloud trajectory velocity, and the CNN is implemented with features including pinpoint local radar images, spatial-temporal descriptions of the cloud movement and the global description of the cloud pattern.  Weights are assigned to the ensemble model to compute the following 2-3 hours forecasting results.  Additionally, the insurance data include more than 50 thousand records provided on a geography coordinate level for the last 25 years. 

       Result shows that the insurance data have a strong correlation with short-term precipitation.  It also indicates that our proposed model of short-term precipitation forecast outperforms only-deep learning-based and traditional optical flow-based methods.  The insurance data could provide a good proxy for describing heavy rainfall damage and to aid to explore the causes and impacts.  This study would greatly assist policy makers, civil protection agencies, and insurance companies to improve emergency systems and response mechanisms.

How to cite: Zheng, Y., Zhou, Z., Fang, C., Liang, J., Ren, B., Lin, J., Cheng, C., and Gu, Q.: Study on the relationship between improved short-term precipitation forecast and insurance data for risk evaluation in Southern China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19346, https://doi.org/10.5194/egusphere-egu2020-19346, 2020.