NH9.3

The management of risks arising from natural hazards requires a reliable estimation of the hazards’ impact on exposed objects. The data sets used for this estimation have improved during the recent years reflecting an increasing amount of detail with regard to spatial, temporal or process information. Yet, the influence of the choice of data and the degree of detail on the estimated risk is rarely assessed.

We estimated flood damage to private households and companies for a flood event in 2013 in Germany using two different approaches to describe the hazard, the exposed objects and their vulnerability towards the hazard with varying levels of detail. One flood map is based on local flood maps computed by the European Joint Research Center not including embankments, while the other flood map was derived especially for this particular flood event. Exposed elements are mapped using the land use based data set BEAM (Basic European Asset Map) and with an object-based approach using OpenStreetMap data. The vulnerability is described by ordinary Stage-Damage-Functions and by tree-based models including additional damage-driving variables. The estimations are validated with reported damage numbers per federal state and compared to each other to quantify the influence of the different data sets at various spatial scales.

The results suggest that a stronger focus on exposed elements could improve the reliability of impact estimations considerably. The individual assessment of the influence of the different components on the overall risk points out promising next steps for further investigations.

How to cite: Sieg, T. and Thieken, A.: The Influence of Input Data on Flood Risk Estimates, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8396, https://doi.org/10.5194/egusphere-egu21-8396, 2021.

A large part of Europe is at risk of earthquake disaster events. In the last decade, earthquakes in Europe led to direct economic losses of approximately €29 billion as well as close to 19,000 fatalities. As a result of public awareness campaigns, business organisations are increasingly cognisant of earthquake risks. However, to date there is limited support available to help them systematically manage these risks. Our research supports decision makers based at European business organizations in their efforts to prepare for and respond to earthquakes. We look at how earthquake early warning (EEW) systems and earthquake operational forecasting (OEF) systems can best support business disaster risk management (DRM). We focus hereby on EEW and OEF based decision support systems for preparedness and rapid response, and also their integration with business continuity planning. Our article is based on an extensive literature review of the state of the art in OEF and EEW systems. We have validated and built on these insights through participatory action research (PAR) with potential business users of EEW and OEF systems in Europe. There is great variability in the ways in which different European businesses currently manage earthquake risk. Our research has given us insights into business users’ needs and expectations of EEW and OEF systems. We have harmonized and integrated these insights towards the development of a common earthquake decision support protocol for business organisations. This protocol covers business considerations that extend beyond the prevention of fatalities and direct economic loss to long-term organisational resilience. Combining insights from facilities management, organization science and engineering, we present various considerations for the development of business centric EEW and OEF decision support systems. We outline barriers to the development and uptake of such systems and describe what opportunities they present for different stakeholders.

How to cite: Mulder, F., Morga, M., and Jones, K.: Insights for Business Centric Earthquake Early Warning and Operational Forecasting Systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12856, https://doi.org/10.5194/egusphere-egu21-12856, 2021.

Floods are a global hazard that may have adverse impacts on a wide-range of social, economic, and environmental processes. Nowadays our cities are flooding with increased occurrence due to more severe weather events but also due to anthropogenic pressures like soil sealing, urban growth and, in some areas,  land subsidence. Frequency and intensity of extreme floods are expected to further increase in the future in many places due to climate change. 

The characterisation of flood events and of their multi-hazard nature is a fundamental step in order to maximise the resilience of cities to potential flood losses and damages. 

SaferPLACES employs innovative climate, hydrological and raster-based flood hazard and economic modelling techniques to assess pluvial, fluvial and coastal flood hazards and risks in urban environments under current and future climate scenarios.

SaferPLACES platform provides a cost-effective and user-friendly cloud-based solution for flood hazard and risk mapping. Moreover SaferPLACES supports multiple stakeholders in designing and assessing multiple mitigation measures such as flood barriers, water tanks, green-blue based solutions and building specific damage mitigation actions.

The intelligence behind the SaferPLACES platform integrates innovative fast DEM-based flood hazard assessment methods and Bayesian damage models, which are able to provide results in short computation times by exploiting the power of cloud computing.

A beta version of the platform is available at platform.saferplaces.co and active for four pilot cities: Rimini and Milan in Italy, Pamplona in Spain and Cologne in Germany.

SaferPLACES (saferplaces.co) is a research project founded by EIT Climate-KIC (www.climate-kic.org).

How to cite: Bagli, S., Mazzoli, P., Renzi, F., Luzzi, V., Persiano, S., Castellarin, A., Mysiak, J., Essenfelder, A., Larosa, F., Pasetti, S., Folegani, M., Schröter, K., Ullrich, S., and Mediero, L.: SaferPLACES platform: a cloud-based climate service addressing urban flooding hazard and risk., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8435, https://doi.org/10.5194/egusphere-egu21-8435, 2021.

Gathering, analyzing and disseminating up-to-date information related to incidents and disasters is key to disaster management and relief. Satellite imagery, geo-information, and in-situ data are the mainly used information sources to support decision making. However, limitations in data timeliness as well as in spatial and temporal resolution lead to systematic information gaps in current well-established satellite-based workflows. Citizen observations spread through social media channels, like Twitter, as well as freely available webdata, like WikiData or the GDELT database, are promising complementary sources of relevant information that might be utilized to fill these information gaps and to support in-situ data acquisition. Practical examples for this are impact assessments based on social media eyewitness reports, and the utilization of this information for the early tasking of satellite or drone-based image acquisitions.

The great potential, for instance of social media data analysis in crisis response, was investigated and demonstrated in various related research works. However, the barriers of utilizing webdata and appropriate information extraction methods for decision support in real-world scenarios are still high, for instance due to information overload, varying surrounding conditions, or issues related to limited field work infrastructures, trustworthiness, and legal aspects.

Within the current DLR research project "Data4Human", demand driven data services for humanitarian aid are developed. Among others, one project goal is to investigate the practical benefit of augmenting existing workflows of the involved partners (German Red Cross, World Food Programme, and Humanitarian Open Street Map) with social media (Twitter) and real-time global event database (GDELT) data. In this contribution, the general concepts, ideas and corresponding methods for webdata analysis are presented. State-of-the-art deep learning models are utilized to filter, classify and cluster the data to automatically identify potentially crisis-related data, to assess impacts, and to summarize and characterize the course of events, respectively. We present first practical findings and analysis results for the 2019 cyclones Idai and Kenneth.

How to cite: Kersten, J., Kopitzsch, M., Bongard, J., and Klan, F.: Combining Remote Sensing with Webdata and Machine Learning to Support Humanitarian Relief Work, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8621, https://doi.org/10.5194/egusphere-egu21-8621, 2021.

Severe air pollution is hazardous to human health and long-term exposure to air pollution degrades not only human health but also the quality of life. In the recent years, public concern and awareness of air quality have been greatly raised in South Korea, and this is somewhat contradictory to the level of particulate matter with diameters less than 10 μm (PM10). The observed PM10 levels cannot explain the elevated levels of public concern specifically after 2013–2014 because the average PM10 was much higher in the past (prior to 2013) and shows a decreasing tendency during the recent decades over South Korea. This study utilizes big data from internet search engines (internet search volume data from Google and NAVER) to understand how people perceive air quality differently from the level of observed PM10 and what influences public perception of air quality. An index, air quality perception index (AQPI), is newly proposed in this study and it is assumed that the internet search volume data with a keyword of “air quality” are representative of this index. An empirical model that simulates AQPI is developed by employing the decay theory of forgetting and is trained by PM10, visibility, and internet search volume data. The results show that the memory decay exponent and the accumulation of past memory traces, which represent the weighted sum of past perceived air quality, play key roles in explaining the public's perception of air quality. A severe haze event with an extremely long duration that occurred in the year 2013–2014 is found to trigger the increase in public awareness of air quality, acting as a turning point. Before the turning point, AQPI is more influenced by sensory information (visibility) due to the low awareness level, but after the turning point it is more influenced by PM10 and people slowly forget about air quality. The retrospective AQPI analysis assuming a low level of awareness confirms that perceived air quality is indeed worst in the year 2013–2014. In other words, the high level of awareness after experiencing the record-long severe haze event in 2013–2014 makes people remember longer and more sensitive to the level of pollutants, thus explaining the increased public concerns in the recent years. Our results suggest the promising potential of social data for a better understanding of public perception and awareness of other natural and/or man-made hazards.

How to cite: Ryu, Y.-H. and Min, S.-K.: Innovative utilization of internet search volume data to understand public awareness and perception of air quality, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3606, https://doi.org/10.5194/egusphere-egu21-3606, 2021.

The Twitter Stream API offers the possibility to develop (near) real-time methods and applications to detect and monitor impacts of crisis events and their changes over time. As demonstrated by various related research, the content of individual tweets or even entire thematic trends can be utilized to support disaster management, fill information gaps and augment results of satellite-based workflows as well as to extend and improve disaster management databases. Considering the sheer volume of incoming tweets, it is necessary to automatically identify the small number of crisis-relevant tweets and present them in a manageable way.

Current approaches for identifying crisis-related content focus on the use of supervised models that decide on the relevance of each tweet individually. Although supervised models can efficiently process the high number of incoming tweets, they have to be extensively pre-trained. Furthermore, the models do not capture the history of already processed messages. During a crisis, various and unique sub-events can occur that are likely to be not covered by the respective supervised model and its training data. Unsupervised learning offers both, to take into account tweets from the past, and a higher adaptive capability, which in turn allows a customization to the specific needs of different disasters. From a practical point of view, drawbacks of unsupervised methods are the higher computational costs and the potential need of user interaction for result interpretation.

In order to enhance the limited generalization capabilities of pre-trained models as well as to speed up and guide unsupervised learning, we propose a combination of both concepts. A successive clustering of incoming tweets allows to semantically aggregate the stream data, whereas pre-trained models allow to identify potentially crisis-relevant clusters. Besides the identification of potentially crisis-related content based on semantically aggregated clusters, this approach offers a sound foundation for visualizations, and further related tasks, like event detection as well as the extraction of detailed information about the temporal or spatial development of events.

Our work focuses on analyzing the entire freely available Twitter stream by combining an interval-based semantic clustering with an supervised machine learning model for identifying crisis-related messages. The stream is divided into intervals, e.g. of one hour, and each tweet is projected into a numerical vector by using state-of-the-art sentence embeddings. The embeddings are then grouped by a parametric Chinese Restaurant Process clustering. At the end of each interval, a pre-trained feed-forward neural network decides whether a cluster contains crisis-related tweets. With a further developed concept of cluster chains and central centroids, crisis-related clusters of different intervals can be linked in a topic- and even subtopic-related manner.

Initial results show that the hybrid approach can significantly improve the results of pre-trained supervised methods. This is especially true for categories in which the supervised model could not be sufficiently pre-trained due to missing labels. In addition, the semantic clustering of tweets offers a flexible and customizable procedure, resulting in a practical summary of topic-specific stream content.

How to cite: Bongard, J. and Kersten, J.: Combining Supervised and Unsupervised Learning to Detect and Semantically Aggregate Crisis-Related Twitter Content, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8637, https://doi.org/10.5194/egusphere-egu21-8637, 2021.

Extreme weather causes substantial damage to livelihoods of smallholder farmers globally and are projected to become more frequent in the coming decades as a result of climate change. Index insurance can theoretically help farmers to adapt and mitigate the risks posed by extreme weather events, providing a financial safety net in the event of crop damage or harvest failure. However, uptake of index insurance in practice has lagged far behind expectations. A key reason is that many existing index insurance products suffer from high levels of basis risk, where insurance payouts correlate poorly with actual crop losses due to deficiencies in the underlying index relationship, contract structure or data used to trigger insurance payouts to farmers. 

In this study, we analyse to what extent the use of crop simulation models and crop phenology monitoring from satellite remote sensing can reduce basis risk in index insurance. Our approach uses a calibrated biophysical process-based crop model (APSIM) to generate a large synthetic crop yield training dataset in order to overcome lack of detailed in-situ observational yield datasets – a common limitation and source of uncertainty in traditional index insurance product design. We use this synthetic yield dataset to train a simple statistical model of crop yields as a function of meteorological and crop growth conditions that can be quantified using open-access earth observation imagery, radiative transfer models, and gridded weather products. Our approach thus provides a scalable tool for yield estimation in smallholder environments, which leverages multiple complementary sources of data that to date have largely been used in isolation in the design and implementation of index insurance

We apply our yield estimation framework to a case study of rice production in Odisha state in eastern India, an area where agriculture is exposed to significant production risks from monsoonal rainfall variability. Our results demonstrate that yield estimation accuracy improves when using meteorological and crop growth data in combination as predictors, and when accounting for the timing of critical crop development stages using satellite phenological monitoring. Validating against observed yield data from crop cutting experiments, our framework is able to explain around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level that is the key spatial unit for area-yield index insurance products covering millions of smallholder farmers in India. Crucially, our modelling approach significantly outperforms vegetation index-based models that were trained directly on the observed yield data, highlighting the added value obtained from use of crop simulation models in combination with other data sources commonly used in index design.

How to cite: H. Afshar, M., Foster, T., Higginbottom, T. P., Parkes, B., Hufkens, K., Mansabdar, S., Ceballos, F., and Kramer, B.: Overcoming basis risk in agricultural index insurance using crop simulation modeling and satellite crop phenology, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9534, https://doi.org/10.5194/egusphere-egu21-9534, 2021.

A steady increase in the frequency and severity of extreme climate events has been observed in recent years, causing losses amounting to billions of dollars. Floods and droughts are responsible for almost half of those losses, severely affecting people’s livelihoods in the form of damaged property, goods and even loss of life. Weather index insurance is an innovative tool in risk transfer for disasters induced by natural hazards. In this type of insurance, payouts are triggered when an index calculated from one or multiple environmental variables exceeds a predefined threshold. Thus, contrary to traditional insurance, it does not require costly and time-consuming post-event loss assessments. Its ease of application makes it an ideal solution for developing countries, where fast payouts in light of a catastrophic event would guarantee the survival of an economic sector, for example, providing the monetary resources necessary for farmers to sustain a prolonged period of extreme temperatures. The main obstacle to a wider application of this type of insurance mechanism stems from the so-called basis risk, which arises when a loss event takes place but a payout is not issued, or vice-versa.

This study proposes and tests the application of machine learning algorithms for the identification of extreme flood and drought events in the context of weather index insurance, with the aim of reducing basis risk. Neural networks and support vector machines, widely adopted for classification problems, are employed exploring thousands of possible configurations based on the combination of different model parameters. The models were developed and tested in the Dominican Republic context, leveraging datasets from multiple sources with low latency, covering a time period between 2000 and 2019. Using rainfall (GSMaP, CMORPH, CHIRPS, CCS, PERSIANN and IMERG) and soil moisture (ERA5) data, the machine learning algorithms provided a strong improvement when compared to logistic regression models, used as a baseline for both hazards. Furthermore, increasing the number of information provided during model training proved to be beneficial to the performances, improving their classification accuracy and confirming the ability of these algorithms to exploit big data. Results highlight the potential of machine learning for application within index insurance products.

How to cite: Cesarini, L., Figueiredo, R., Monteleone, B., and Martina, M.: Near real-time identification of extreme events for weather index insurance using machine learning algorithms, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12930, https://doi.org/10.5194/egusphere-egu21-12930, 2021.

Several drought risk financing projects have been developed to strengthen the disaster resilience of the world’s vulnerable communities, countries and regions. Satellite-derived information plays a vital role to characterize historical and current drought impacts. Various independent earth observation datasets can be used to cross-validate each other, strengthening the disaster narrative and reduce basis risk. However, satellite data require additional socioeconomic information, which often shows critical gaps, to close the gap between hazards, vulnerabilities and impacts. While satellite-derived information is considered to be objective there are various projects with payout trigger mechanisms that rely on subjective assessments, for instance expressed as a declaration of emergency. The next generation of risk financing solutions for extreme weather and climate events will have to merge these two perspectives. The World Bank’s Next Generation Drought Index (NGDI) project might be the first attempt to link a convergence of evidence approach applied to satellite-derived insurance triggers with a guided integration of local expertise. The project aims to 1) avoid the perception of more complex technical methods as analytical black boxes 2) benchmark different datasets, model outputs and index parameters, and 3) lower the entry barrier for novel risk financing solutions by establishing local risk ownership. This study focuses on the first results of the NGDI project for Senegal. 

How to cite: Enenkel, M., Osgood, D., and Diro, R.: The importance of co-design in satellite-derived drought risk financing, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7613, https://doi.org/10.5194/egusphere-egu21-7613, 2021.