ITS3.2/BG7

As has been made acutely clear in recent years, many natural and human systems are particularly prone to the co-occurrence of extremes like severe heat, heavy rainfall, storm-surge flooding, severe drought, and extreme wildfire conditions. The co-occurrence of these conditions, both simultaneously (or in rapid succession) in a given location or in different parts of the world, is critical for a broad suite of climate-sensitive concerns, including agricultural markets, food security, poverty vulnerability, supply chains, weather-related insurance and reinsurance, and disaster preparedness and recovery - particularly when those conditions are sufficiently extreme to fall outside of historical experience. This seminar will summarize recent work quantifying changes in the frequency of unprecedented events without consideration for joint probability probability, and then present a framework for quantifying the spatial and temporal co-occurrence of climate stresses in a nonstationary climate. This framework shows that, globally, anthropogenic climate forcing has doubled the joint probability of years that are both warm and dry in the same location (relative to the 1961–1990 baseline). In addition, the joint probability that key crop and pasture regions simultaneously experience severely warm conditions in conjunction with dry years has also increased, including high statistical confidence that human influence has increased the probability of previously unprecedented co-occurring combinations. The potential for this methodology to lend insight for other sectors that are accustomed to deploying resources based on historical probabilities, such as wildfire risk management, will also be discussed.

How to cite: Diffenbaugh, N.: Multidimensional risk in a nonstationary climate: changes in joint probability of extreme conditions in space and time, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1836, https://doi.org/10.5194/egusphere-egu21-1836, 2021.

Currently, the global food system is the single largest threat to people and planet. Food is the leading cause behind transgressing five of the nine planetary boundaries. It is a major source of carbon emissions, as well as the single largest contributor to global deforestation, overuse of fresh water and eutrophication of our aquatic ecosystems. And while agriculture has been a major engine of poverty reduction, agricultural activities are unable to deliver a decent livelihood for an estimated 80 percent of those living in extreme poverty. The projected increase in frequency and severity of climate extreme events is posing additional threats to the global food system.

A transformation towards a more inclusive, sustainable and health-promoting food system is urgently needed. This presentation will introduce the newly established Food Systems Economics Commission (FSEC) that provides detailed and robust evidence assessing the implications of the policy and investment decisions needed to foster a food system transformation. It integrates global modelling tools such as integrated assessment modelling and innovative applications of agent-based modelling with political economy considerations.  It investigates the hidden costs of our current food system, explores transitions pathways towards a new food and land use economy and suggests key policy instruments to foster the transformation towards a sustainable, inclusive, healthy and resilient food system.

How to cite: Gaupp, F.: Pathways to a sustainable, inclusive and healthy food system, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5115, https://doi.org/10.5194/egusphere-egu21-5115, 2021.

Future climate projections show a strengthening of the hydrological cycle with more droughts and floods expected in many regions of the world. This means a higher likelihood of cascading drought-to-flood disasters such as the Millennium Drought – Brisbane flooding in Australia or the California drought – Oroville spillway collapse in the US. Droughts allow ample time for impacts and adaptation, which influence hazard, exposure, and vulnerability of a subsequent flood. When we treat the flood risk as independent from the drought this might lead to large underestimations of future risk.

Here, we present the PerfectSTORM project (‘STOrylines of futuRe extreMes’). In this project we will study drought-to-flood events to provide the understanding needed to prevent major disasters in the future. We will use a mixed-methods approach based on a combination of qualitative and quantitative storylines of past and future drought-to-flood risk in case studies and extrapolation of this rich case study information to the global scale. Qualitative storylines will be collected with narrative interviews and mental simulation workshops and will be analysed to develop timelines and causal loop diagrams. Quantitative storylines will be developed from timeseries of hydrological and social data that will be analysed to distinguish interrelated drivers and modelled with system dynamics modelling. These storylines will then be combined in an iterative way using innovative data visualisation as a basis for co-creating management solutions.

To generalise our case study understanding, a range of global datasets will be analysed to find global types and hotspots of drought-to-flood events. This information will be combined with the system dynamics model developed in the case studies and a global multi-dimensional possibility space will be developed. This will allow us to explore positive pathways for future management of drought-to-flood events in different parts of the world. The PerfectSTORM project will provide in-depth understanding of the hydrosocial feedbacks and dynamic vulnerability of cascading hazards.

How to cite: Van Loon, A., Matanó, A., di Baldassarre, G., Day, R., Garcia, M., Rohse, M., de Ruiter, M., Koehler, J., and Ward, P.: Unravelling socio-hydrological processes behind cascading drought-to-flood disasters, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10715, https://doi.org/10.5194/egusphere-egu21-10715, 2021.

Floodplain forests are very complex, productive ecosystems, capable of storing huge amounts of soil carbon. With the increasing occurrence of extreme events, they are today among the most threatened ecosystems. Our study’s main goal was to assess the productivity of a floodplain forest located at Lanžhot in the Czech Republic from two perspectives: carbon uptake (using an eddy covariance method) and stem radius variations (using dendrometers). We aimed to determine which conditions allow for high ecosystem production and what role drought plays in reducing such production potential. Additionally, we were interested to determine the relative soil water content threshold indicating the onset and duration of this event. We hypothesized that summer drought in 2018 had the most significant negative effects on the overall annual carbon and water budgets. In contrast with our original hypothesis, we found that an exceptionally warm spring in 2018 caused a positive gross primary production (GPP) and evapotranspiration (ET) anomaly that consequently led in 2018 to the highest seasonal total GPP and ET from all of the investigated years (2015–2018). The results showed ring-porous species to be the most drought resistant. Relative soil water content threshold of approximately 0.45 was determined as indicating the onset of drought stress.

How to cite: Kowalska, N., Šigut, L., Stojanović, M., Fischer, M., Kyselova, I., and Pavelka, M.: Analysis of floodplain forest sensitivity to drought, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-711, https://doi.org/10.5194/egusphere-egu21-711, 2021.

The consequences of climate change in South and Central America are already widespread and take on many forms. Albeit there is an increasing number of studies focusing on specific climate change-related risks in the region, a synthesis of risks for the 21^st century, together with current and future adaptation options is lacking. This study synthesizes major climate related risks in South and Central America, while also looking at implications for adaptation measures and (un)avoidable loss and damage. A review of over 100 peer-reviewed articles published since 2013 was completed to examine the current and projected state of the risks. We identify eight key risks in South and Central America that have the potential to become severe with climate change during the 21^st century. The criteria for a severe risk relate to the number of people potentially affected, the severity of the negative effects of the risk, the importance of the affected systems, and the irreversibility versus potential to reduce the risk. The risks are analysed in relation to different climate scenarios, and changes in associated hazards, exposure, and/or vulnerability. The risks include 1: risk of food insecurity due to repeated and/or extreme drought conditions; 2: risk to life and infrastructure due to floods and landslides; 3: risk of water insecurity in Central America and the Andes region; 4: systemic risks of surpassing infrastructure and public service system capacities due to cascading impacts of storms, floods and epidemics; 5: risk of severe health effects due to increasing epidemics (in particular vector-borne diseases); 6: risk of large-scale ecological transformation of the Amazon forest; 7: risk to coral reef ecosystems due to coral bleaching in Central America; 8: risk to coastal socio-ecological systems due to sea level rise, intensification of upwelling and ocean acidification. In addition, we focus on already implemented and possible adaptation measures for each of the risks. Subsequently, we draw conclusions of the potential losses and damages caused by each risk. Our assessment of risks in the Central and South America region show that several risks have the potential to become severe already in the near future. The extent of the severity is driven by the specific region’s exposure, vulnerability and adaptation capacity. Adaptation capacity is in turn dependent on physical as well as socio-economic systems. Inequalities, corruption, and poor communication between decision makers, stakeholders and the scientific community together with a lack of available data can critically limit adaptation options. Still, many adaptation options are available, and efforts to thoroughly research further adaptation measures should be of highest priority. This will undoubtedly save both lives and severe economic damage as South and Central America face the consequences of climate change.

How to cite: Hagen, I., Huggel, C., Ramajo Gallardo, L., Ometto, J. P., Chacón, N., and Castellanos, E. J.: Climate change-related risks and adaptation measures in South and Central America during the 21st century, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2166, https://doi.org/10.5194/egusphere-egu21-2166, 2021.

The SDGs recognize the importance of ensuring conservation, restoration and sustainable use of terrestrial ecosystems and their services and strengthening the resilience and adaptive capacity to climate-related hazards. Vegetation primary production is the main function of terrestrial ecosystems providing food and other services to society. Agricultural production is a main source of employment, livelihood and income for a large portion of population, especially in developing countries.

Anticipating the changes in vegetation primary production resilience – the plant capacity to cope with disturbances and shocks including such as those related to climate variability and extremes – is therefore critical to understand and project ecosystems’ responses to global change and the impacts on the related ecosystem services, to support mitigation actions, and to define proper adaptation plans. However, the estimation of resilience is not straightforward.

Here, we applied a recently proposed resilience metrics – the annual production resilience indicator (Zampieri et al. 2019, 2020) – to quantify the changes in vegetation gross primary production (GPP) resilience computed from a large ensemble of state-of-the-art CMIP6 Earth System Model (ESM) simulations.

Our results indicate that climate change mitigation is necessary to significantly reduce the risk of losing terrestrial ecosystems production resilience. In the ‘Sustainability (Taking the Green Road)’ and ‘Middle of the Road’ scenarios considered here (ssp126 and ssp245), the areas where vegetation shows increasing GPP resilience (mainly boreal, African and Indian monsoon regions) are wider than the areas with decreasing resilience. The situation drastically reverses in the ’Fossil-fuel Development (Taking the Highway)’ scenario (ssp585), mostly because the increase of GPP interannual variability outbalances the mean GPP increase due to the CO₂ fertilization effect in this high greenhouse gases’ emission scenario. 

To raise social awareness, identify adaptation plans, but especially to stimulate mitigation efforts, we analyse and discuss the gains and losses of vegetation GPP resilience for each World country. Among the larger countries, Brazil is exposed to the highest risk of losing vegetation GPP resilience, especially in the higher emission scenario.

This study explores the linkages between future climate, associated changes in resilience of global vegetation gross primary production, and the mitigation pathways that society can undertake to conserve and restore ecosystems and their services, on which human well-being depends.

How to cite: Zampieri, M., Grizzetti, B., and Toreti, A.: Rise and fall of projected vegetation primary production resilience to climate variability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2208, https://doi.org/10.5194/egusphere-egu21-2208, 2021.

Climatic characteristics and weather events have always conditioned the success of a harvest. Climate change and the associated increase in intense weather phenomena in recent years are making it clearer than ever that agriculture is among the sectors most at risk. Although problems in agriculture are found all over the world, the most vulnerable contexts are those where agriculture is low-tech and rainfed. Here, adaptation strategies are even more urgent to secure the food production. Assuming that the awareness of climate change is the basis for the adoption of adaptation and mitigation strategies, it is interesting to correlate the degree of perception of local inhabitants with their willingness to adopt bottom-up initiatives.

The current study focuses on banana producers’ perceptions of climate change in a tropical valley, and the initiatives that farmers adopt to cope with recent intense weather events. The banana plant (Musa Musacae) grows in tropical climates with annual rainfall around 2000 mm and average temperatures around 27°C. The species’ threadlike root system and the weak pseudostem make it particularly vulnerable to wind gusts, which, at speeds higher than 15 m/s, can bend and knock over entire plantations. The increased frequency of convective thunderstorms observed in connection with climate change has made downburst phenomena more frequent and caused greater crop loss.

The aim of the present work is to estimate the correlation between banana producers’ perceptions of climate change and their bottom-up initiatives for adaptation. To achieve this goal, the case study of the Upper Huallaga valley, which is located in the Peruvian Amazon region as shown in Figure 1, is analysed. The work was carried out at two levels: (i) we interviewed 73 banana producers in the valley, (ii) we estimated the alterations and trends in temperature and precipitation recorded by the only three available meteorological stations within the valley. Finally, we compared the two databases to evaluate if the perception of the population was confirmed by the data. Most of the surveyed population observed an increase in temperature, consistent with the results of the data analysis, and an increase in precipitation, which was not consistent with observations as these showed a cyclic variation without a clear trend. With regards to the adaptation measures, it was observed that, although a clear majority of the sample surveyed (around 82%) agreed with the existence of climate change, only 46% of them had taken any initiative to counteract adverse events in some way. However, it is important to note that the strategies implemented were all devised and implemented by the farmers themselves. Funding and coordinating the dissemination of these adaptation practices by the local authority through a rural development plan could certainly strengthen the population’s effort.

Figure 1, On the left side: the Upper Huallaga basin. On the right side: the study area

How to cite: Serrao, L., Giovannini, L., Balcazar Terrones, L. E., Huamaní Yupanqui, H. A., and Zardi, D.: From climate change perception to bottom-up adaptation initiatives: a case study from banana producers of Upper Huallaga basin, Peru, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2341, https://doi.org/10.5194/egusphere-egu21-2341, 2021.

Extreme hydro-meteorological events often affect the economy, social life, health, and well-being. One indicator for the impact of extreme events on society is the concurrently increased societal attention. Such increases can help to measure and understand the vulnerability of the society to extreme events, and to evaluate the relevance of an event, which is important for disaster research and risk management. In this study, we analyzed and characterized hydro-meteorological extreme events from a societal impact perspective. In particular, we investigated the impact of heat waves on societal attention in European countries with contrasting climate (Germany, Spain, and Sweden) using Google trends data during 2010–2019. Thus, we seek to answer two general research questions: (i) how and when do extreme events trigger societal attention, (ii) are there temperature thresholds at which societal attention increases? 

To describe heat waves, we used maximum, minimum, average, and apparent temperature, aggregated to a weekly time scale. We analyzed the relationship between temperature and societal attention using piecewise regression to identify potential temperature-related thresholds in societal attention. The threshold is determined as the breaking point between two linear models fitted to data. We determined the corresponding goodness of fit by computing R² for each temperature variable. The variable with the highest R² is considered as the most influential one.

The overall relationship between temperature and Google attention to heat waves is significant in all countries and reveals clear temperature thresholds. The variable with the highest explanatory power is the weekly average of the daily maximum temperatures, which accounts for 71% of google attention in Germany, 51 % in Sweden, and 38 % in Spain. For Germany, similar results are found with media attention. In Sweden, with its colder climate, a lower temperature threshold is identified, indicating higher heat vulnerability. No significant impact of temperatures from the previous weeks is found. While further work is needed to improve the understanding of the attention-heat coupling, the demonstrated significant societal attention response to heat waves offers the opportunity to characterize heat waves from an impact perspective using the identified temperature variables, time scales, and thresholds.

How to cite: Bogdanovich, E., Guenther, L., Reichstein, M., Ruhrmann, G., and Orth, R.: Characterizing hydro-meteorological extremes from a societal perspective, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5127, https://doi.org/10.5194/egusphere-egu21-5127, 2021.

Droughts and heatwaves have large impacts on the terrestrial carbon cycle. They lead to reductions in gross and net carbon uptake or anomalous carbon emissions by the vegetation to the atmosphere because of responses such as stomatal closure, hydraulic failure and vegetation mortality. The impacts are particularly strong when drought and heat occur at the same time. Climate model simulations diverge in their occurrence frequency of compound hot and dry events, and so far it is unclear how these differences affect carbon dynamics. Furthermore, it is unknown whether a higher frequency of droughts and heatwaves leads to long-term changes in carbon dynamics, and how such an increase might affect vegetation composition.

To study the immediate and long-term effects of varying signatures of droughts and heatwaves on carbon dynamics and vegetation composition, we employ the state-of-the-art dynamic global vegetation model LPX-Bern (v1.4) under different drought-heat scenarios. We constructed six 100-yr long scenarios with different drought-heat signatures: a “control”, “no extremes”, “no compound extremes”, “heat only”, “drought only”, and “compound drought and heat” scenario. This was done by sampling daily climate variables from a 2000-year stationary simulation of a General Circulation Model (EC-Earth) for present-day climate conditions. Such a sampling ensures physically-consistent co-variability between climate variables in the climate forcing.

The scenarios differ little in their mean climate conditions (global mean land temperature differences of around 0.3°C and global mean land precipitation differences smaller than 7%), but vary strongly in the occurrence frequency of extremes such as droughts, heatwaves, and compound drought and heatwaves (up to five times more compound extremes in the “hotdry “scenario than in the “control”), allowing us to study the effects of the extremes on vegetation. Combined hot and dry extremes reduce all tree types and promotes grassland, while only hot extremes favours trees, especially in higher latitudes. No extremes are preferred by all tree types in LPX. Net Ecosystem Production (NEP) is expected to increase in most regions for the “noextremes” scenario, while the “hotdry” scenario is likely to reduce NEP.

Our results provide a better understanding of the links between hot and dry conditions and vegetation dynamics as well as carbon dynamics. These analyses may help to reduce uncertainties in carbon cycle projections, which is important for constraining carbon cycle-climate feedbacks. The presented scenarios can be used for a variety of purposes such as studying the effects of differing drought-heat signatures on crop yield or the occurrence of fire besides others.

How to cite: Tschumi, E., Lienert, S., van der Wiel, K., Joos, F., and Zscheischler, J.: The effect of differing drought-heat signatures on terrestrial carbon dynamics and vegetation composition, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5414, https://doi.org/10.5194/egusphere-egu21-5414, 2021.

A detailed analysis of extreme heatwave events in Serbia from the biometeorological point of view is presented in this study.  For this purpose, the newly developed Heat Wave Magnitude Index daily (HWMId), was used on Physiologically equivalent temperature (PET) for Serbia. A series of daily maximum air temperature, relative humidity, the wind was used to calculate PET for the investigated period 1979–2019. HWMId is defined as the maximum magnitude of the heatwaves in a year. Here, the heatwave is characterized as 3 consecutive days with maximum PET above the daily threshold for the reference period 1981–2010. The analysis revealed that during the investigated period the most intensive heat waves occurred in 2007, 2012 and 2015. HWMId values for 2007 were in the range of 8 to 23 indicating extreme heat stress, while for the other two events the values were not as high. Hourly temperatures revealed that the PET values during the day were as high as 55°C. Thus, the mitigation and adaptation to extreme temperature events are of vital importance for humans and their everyday activities. Future investigation should be oriented towards a way to deal with the oppressive heat. Additionally, more research is needed in order to explain and predict these catastrophic events. The main focus of future activities will be on determining the physical causes which lead to the occurrence of extreme heatwaves.

Keywords: Heat Wave Magnitude Index daily, Physiologically equivalent temperature, Serbia, heat waves

Acknowledgment: This research is supported by EXtremeClimTwin project funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952384

How to cite: Basarin, B., Lukić, T., and Micić Ponjiger, T.: Detailed analysis of extreme heatwaves in Serbia, South-East Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7481, https://doi.org/10.5194/egusphere-egu21-7481, 2021.

Understanding how agricultural societies were impacted and adapted to past climate variations is critical to face to contemporary climate change and guaranty the food security (#SDG2 Zero Hunger). However, linking climate and change in the behaviour of a population are difficult to evidence. Here, we studied the climate variations of the period between 200 and 1300 CE and its impact on the pre-Viking and Viking societies in Southeastern Norway, including the adaptation and resilience of the agricultural management. This period includes, between 300 and 800 CE, one of the coldest period of the last 2000 years. We used a retrospective approach combining a multi-proxy analysis of lake sediments, including geochemical and palynological analyses, to reconstruct past changes in temperature and agricultural practices during the period 200-1300 CE. We associated variations in Ca/Ti ratio as a result of change in lake productivity with the temperature. The periods 200-300 and 800-1300 CE were warmer than the period between 300 and 800 CE, which is known as the “Dark Ages Cold Period” in the Northern Hemisphere. During this colder period, phases dominated by grazing activities (280-420 CE, 480-580 CE, 700-780 CE) alternated with phases dominated by the cultivation of cereals and hemp (before 280 CE, 420-480 CE, 580-700 CE, and after 800 CE). The alternation of these phases is synchronous of temperature changes. Cold periods are associated to livestock farming, and warmer periods to crop farming. This result suggests that when temperature no longer allowed crop farming, the food production specialized in animal breeding. The result of a Principal Component Analysis show a succession of phases of crisis, adaptation and resilience of the socio-environmental system. The Viking Age (800-1000 CE) started with an increase in temperature and corresponds to the warmest period between 200 and 1300 CE, allowing a larger development of the agriculture practices and society. Our results prove that the pre-Viking society adapted their agricultural practices to the climate variability of the Late Antiquity and that the Vikings expanded with climate warming.

How to cite: Bajard, M., Ballo, E., Høeg, H. I., Bakke, J., Støren, E., Loftsgarden, K., Iversen, F., Hagopian, W. M., Jahren, A. H., Svensen, H. H., and Krüger, K.: Climate variability controlled the development of the pre-Viking society during the Late Antiquity in Southeastern Norway, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8427, https://doi.org/10.5194/egusphere-egu21-8427, 2021.

Temperature has been identified as a potential cause for human conflict. Conflict poses a fundamental obstacle to Sustainable Development Goal 16 which acknowledges the importance of building peace, justice and strong institutions for people around the world. Today, conflict is no longer limited to the physical space. The increasing digitalization of all areas of everyday life reinforces the impact of cyber racism, cyber discrimination and online hate. It disproportionally affects groups with an already increased risk of marginalization such as women, lgbtq+ youth or people of color, causing affected persons to feel unsafe in digital spaces and limiting their access to online services. Twitter is one of the biggest social media platforms with more than 300 million active users around the world. We provide evidence that the amount of racist content posted to Twitter is non-linearly influenced by temperature. Exploiting the linguistic plurality of Europe, we investigate the relationship between daily maximum temperature and racist or xenophobic content online using a fixed-effects panel-regression approach for countries spanning multiple European climatic zones. Racist tweets are lowest between daily temperatures of 8°C to 17°C whereas ambient temperatures warmer or colder are associated with steep, non-linear increases. Within the next 30 years, temperatures are projected to shift with new heat extremes being reached. To quantify the potential impact on cyber hate, the number of days outside this range, weighted by the identified temperature-racist-tweet response curve is projected to increase across Europe. Results suggest, that future warming and more extreme temperatures could aggravate xenophobia and racism online, further hindering the achievement of SDG 16 and posing a challenge for future human well-being.  

How to cite: Stechemesser, A., Wenz, L., Kotz, M., and Levermann, A.: The relationship between temperature and digital hate – strong increase of racist tweets outside of climate comfort zone in Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16489, https://doi.org/10.5194/egusphere-egu21-16489, 2021.

Recent IPCC focused their assessment of changes in climate extremes primarily on the likely range and on mapping them as multi-model means. Recently, it has been argued that focusing primarily on the likely range potentially ignores changes in the physical climate system that are unlikely to occur but are associated with the highest risks for human and ecological systems. This is particularly the case for extremes where impacts often non-linearly depend on changes in hazards and where uncertainties are typically large both due to model response uncertainty and internal variability. Low-likelihood high-warming storylines have been proposed as a powerful tool to assess and communicate the risk associated with such future climates. However, storylines that are consistent across variables and spatial patterns is challenging.

Here, we introduce and compare different approaches for creating low-likelihood high-warming storylines for extremes based on CMIP6 models, and discuss their strengths and limitations for temperature extremes, heavy rainfall and droughts. We demonstrate that all approaches yield storylines in which changes in hot extremes, extreme rainfall and droughts strongly exceed the multi-model mean over large parts of the globe. This suggests that a focus on the likely range may indeed substantially underestimate the risk associated with changes in extremes.

We further demonstrate that the choice of the storyline approach needs to be informed by the purpose of the assessment. Pattern-scaling based storyline approaches are simple and easy to communicate and provide a reasonable first guess for extremes that are closely related to temperature changes. However, they often lead to implausible global patterns and violate physical consistency across regions and different variables. Particularly for wet and dry extremes, the models showing the largest global warming often do not show the greatest changes in extremes. Other more complex approaches have the advantage of generating storylines of globally coherent patterns of changes in extremes. Such approaches allow assessing physically consistent and spatially coherent global low-likelihood high-warming storylines of regional extremes that are suited for global risk assessment and resilience building across different sectors.

How to cite: Fischer, E., Schwingshackl, C., and Sillmann, J.: Low-likelihood high-warming storylines for extremes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8736, https://doi.org/10.5194/egusphere-egu21-8736, 2021.

Abstract:

Introduction and theoretical background: The increase in extreme events as a result of climate change has serious consequences for the world (Bevacqua, Yu, & Zhang, 2018; Clark et al., 1998), with higher impacts on Andean communities, which are more vulnerable to its effects due to the scarce resources they have to cope with its effects. The study on local risk perception, as a strategy that allows people to be more aware of the hazard and therefore be more willing to deal with the eventuality of the hazard (Lopez and Marvan, 2018). Our study analyses experience with extreme events: severe storms, avalanches, droughts and floods. Furthermore, we analyze how experiences with extreme weather can be related to risk perception, communication, and adaptive behaviours.

Methods: After a thorough pilot. We selected two interviewers, from the same community. To comply with COVID-19 health protocols, the questionnaire was implemented online. All questions were presented in a closed format. The total number of participants (N=200) belonged to the Phinaya community located at the bottom of the Quelccaya glacier (5650 mamsl). All gave their consent to participate voluntarily in the study.

Results: 86% indicated having experienced drought or water shortage in the last 5 years between 1 and more than 3 times, 14% did not. Then 59% indicated that they had experienced storms between 1 and more than 3 times in the last 5 years, 41% indicated that they had not experienced any. Regarding floods, 21.5% indicated that they had experienced them, while 78.5% had not. 34.9 % indicated that they had experienced avalanches. 97.5% said they were very concerned about climate change. 82% received information on storms, 90% received information on droughts, 82% received information on floods, 51% received information on avalanches. There is a relationship between people who have had experiences with severe storms and those who have experienced landslides and avalanches. Regarding the perception of risk, we found differences between men and women. No clear relationship was identified between risk perception and extreme events. It is observed that communications about droughts influenced negatively on risk perception, the other extreme events did not show significant relations. Finally, with respect to adaptation behaviours, we found a positive relationship between experiences with storms, and perceptions of risk of climate change, greater perception of risk, greater willingness to develop adaptive behaviours.

Conclusions: Most people have been exposed to more than one type of extreme events such as droughts and storms. This study contributes to a better understanding of the relationships between public perception of climate change in Andean communities and corroborates the important role of communication and adaptive behaviors in the context of risk perceptions.

How to cite: Monge-Rodríguez, F. S. and Alvarado- Yepez, A.: Extreme events, risk perception, communication, and adaptation in the context of climate change: the case of an Andean community in Peru, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9380, https://doi.org/10.5194/egusphere-egu21-9380, 2021.

Terrestrial carbon storage is largely driven by prevailing climate conditions. However, ecosystems are not only affected by mean climate conditions but also by day-to-day climate variability, which is projected to increase in the future. Here we explore the effects of low vs. high climate variability on global terrestrial carbon storage in the dynamic global vegetation model LPJ-GUESS. Low variability corresponds to linear interpolation between monthly means while high variability corresponds to daily means. We conduct three factorial simulations: one driven by low variability for temperature, radiation, and precipitation; one with low temperature and radiation variability but high precipitation variability; and one with high variability for all climatic drivers. All three options are commonly used in existing LPJ-GUESS studies but have so far not been compared to each other in terms of carbon cycle impacts. Surprisingly, the low variability simulation results in the smallest terrestrial carbon stocks globally (1963 Gt C), while low temperature/radiation variability but high precipitation variability simulates the largest carbon storage (2171 Gt C). Differences are most pronounced in high latitudes and deviations from the global trend also occur in some regions. Exploring the underlying processes, we find that differences in carbon stocks are largely driven by differences in ecosystem productivity. In LPJ-GUESS, high precipitation variability increases nitrogen availability via enhanced nitrogen mineralisation and reduced leaching, thereby promoting plant growth. In contrast, high temperature variability decreases productivity as the optimum temperature range for photosynthesis is often exceeded in temperate and boreal regions. Differences in fire mortality and soil water availability across simulations seem to be less important. Our results suggest that future changes in climate variability could impact ecosystem carbon storage via subtle effects on photosynthesis and coupled carbon-nutrient cycling. They also imply that ecosystem modellers need to be aware that changing the temporal resolution of the input climate (e.g. from monthly to daily means) may substantially affect their simulation results.

How to cite: Krause, A., Küpfer, K., and Rammig, A.: Forcing climate variability has large impacts on terrestrial carbon storage in a dynamic global vegetation model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10242, https://doi.org/10.5194/egusphere-egu21-10242, 2021.

This keynote presentation introduces the sources, methods, and major findings of the History of Climate and Society (HCS), a recently-coined field that uncovers the past influences of climate change on human history. It begins by offering a brief history of the field, from the eighteenth century through the present. It then describes how HCS scholars “reconstruct” past climate changes by combining what they call the “archives of nature” – paleoclimatic proxy sources such as tree rings, ice cores, or marine sediments – with the texts, stories, and ruins that constitute the “archives of society.” Next, it explains how HCS scholars in different disciplines have used distinct statistical and qualitative methods, and distinct causal frameworks, to identify the influence of climate change in the archives of society. It explores how HCS scholars conceptualize the vulnerability and resilience of past societies by introducing some telling case studies, and explaining how those case studies have grown more complex as HCS matured as a field. It then emphasizes the enduring challenges faced by HCS scholars and how, in recent months, they have been identified and are beginning to be addressed. Finally, it describes how HCS has informed climate change policy and public discourse, before offering some key lessons that policymakers can learn from the field.

How to cite: Degroot, D.: The Impacts of Climate Change on Societies: What Can We Learn from the Past?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-99, https://doi.org/10.5194/egusphere-egu21-99, 2021.

Weather extremes such as heat waves, tropical cyclones, and river floods are likely to intensify with increasing global mean temperature. In a globally connected supply and trade network such extreme weather events cause economic shocks that may interfere with each other potentially amplifying their overall economic impact.

Here we analyze the economic resonance of consecutive extreme events, that is the overlapping of economic response dynamics of more than one extreme event category both spatially and temporally. In our analysis we focus on the event categories heat stress, river floods, and tropical cyclones. We simulate, via an agent-based anomaly model with more than 7,000 economic agents and 1.8 million connections, the regional (direct) and inter-regional (indirect via supply chains) economic losses and gains for each extreme event category individually as well as for their concurrent occurrence for the next two decades (2020-2039). Thus we compare the outcome of the sum of the three single simulations to the outcome of the concurrent simulation. We show that the global welfare losses due to concurrent weather extremes are increased by more than 18% due to market effects compared to the summation of the losses of each single event category. Overall, this economic resonance yields a non-linearly enhanced price effect, which leads to a stronger economic impact. As well as a highly heterogeneous distribution of the amplification of regional welfare losses among countries.

Our analysis is based on the climate models of the CMIP5 ensemble which have been bias-corrected within the ISIMIP2b project towards an observation-based data set using a trend-preserving method. From these we use RCP2.6 and 6.0 for future climate projections. Thus we compute for each of the three extreme weather event category regional, and sectoral production failure on a daily time scale. Our agent-based dynamic economic loss-propagation model Acclimate then uses these local production failures to compute the immediate response dynamics within the global supply chain as well as the subsequent trade adjustments. The Acclimate model thereby depicts a highly interconnected network of firms and consumers, which maximize their profits by choosing the optimal production level and corresponding upstream demand as well as the optimal distribution of this demand among its suppliers; transport and storage inventories act as buffers for supply shocks. The model accounts for local price changes; supply and demand mismatches are resolved explicitly over time.

Our results suggest that economic impacts of weather extremes are larger than can be derived from conventional single event analysis. Consequently the societal cost of climate change are likely to be underestimated in studies focusing on single extreme categories.

How to cite: Kuhla, K., Willner, S. N., Otto, C., Geiger, T., and Levemann, A.: Economic ripple resonance from consecutive weather extremes amplifies consumption losses, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12529, https://doi.org/10.5194/egusphere-egu21-12529, 2021.

The main goal of Climate Services for eXtremes (which in is an integral part of the ClimXtreme framework) is to advance our understanding of the intensity as well as the spatio-temporal distribution of extreme weather and climate events, but tailored to the needs of stakeholders in the agricultural and forestry sector. The project is designed to optimise the communication between scientists and decision makers and thus to maximise the mutual benefit with regard to climate adaption. The scientists involved learn from the interview partners what climate information is actually required on the ground to facilitate the development of adaptation strategies, whereas the sector experts gain insights into the capabilities and limits of state-of-the-art climate information.

In order to increase the efficiency of the knowledge transfer between scientists and stakeholders, we introduce a process-chain based approach: (i) the sector-specific identification of the characteristics of extreme weather conditions in close cooperation with partners from forestry and agriculture, (ii) the analysis of past and future weather and climate extremes with various statistical techniques, (iii) the investigation of the effects of these extremes by means of forest and agricultural case studies, and (iv) the development of possible needs-based adaptation strategies to future climatic conditions and extreme events based on this information.

The extended summer drought in Germany during the warm seasons 2018 to 2020 is the perfect testbed for the approach, given the wide-ranging consequences this multi-year event had especially on the forestry sector. The event will be analysed from a probabilistic point of view, i.e. what is the return time and what were the causal factors from an atmospheric dynamic and teleconnection point of view. There is also potential to investigate the role of climate change in terms of altered risks. With this information, we can offer initial guidance for the project partners as to what they have to prepare for. But crucially, the interview feedback will help guide our ultimate research strategy. It will be a function of spatial scale, indices of interest as well as scope and complexity of the data and services our partners require. The new insights will serve as a basis to investigate such extreme drought events under potential future climate conditions. 

How to cite: Haustein, K., Rechid, D., Knutzen, F., Groth, M., Averbeck, P., Froer, O., Hey, L., and Jungkunst, H.: Climate Services for eXtremes: Bi-directional knowledge transfer for developing adaptation strategies in agriculture and forestry on the example of the 2018-2020 summer drought in Germany., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13157, https://doi.org/10.5194/egusphere-egu21-13157, 2021.

Most studies of climate change impacts, adaptation and vulnerability confine their attention to impacts and responses within the same geographical region. However, cross-border climate change impacts that occur remotely from the location of their initial impact can severely disrupt societies and livelihoods (Benzie et al., 2019; Carter et al., under review). In this paper we present a conceptual framework and accompanying terminology for describing and analysing such cross-border impacts. The conceptual framework distinguishes an initial impact that is caused by a climate trigger within a specific region. Downstream consequences of that impact propagate through an impact transmission system while adaptation responses to deal with the impact are propagated through a response transmission system.

The framework recognises and classifies differences in the types of climate trigger, categories of cross-border impacts, scales and dynamics of impact transmission, targets and dynamics of responses and the socio-economic and environmental context. We will demonstrate how the framework can be applied using  historical examples of cross-border impacts (e.g. the severe 2011 floods that affected industrial production in Thailand, propagating through the global economy) as well as prospective cases (e.g. multiple cross-border risks and opportunities presented by Arctic sea ice decline).

We argue that the framework provides a simple, but flexible, structure to describe and analyse cross-border climate impacts and their consequences. It offers a foundation for consistent comparisons of different patterns of cross-border impacts in different sectors and geographies. It also aids understanding of adaptation strategies and their potential consequences. In particular, with systematic application of the framework it is possible to highlight gaps in our existing understanding of system dynamics, or gain new insights into particular leverage points within the system. These can be targeted in order to find ways of building resilience to climate change in the region of origin, along the impact transmission system and in the recipient region exposed to the propagated risk.

Acknowledgement

This work is being undertaken as part of the European Commission Horizon 2020-funded project CASCADES (Cascading climate risks: Towards adaptive and resilient European Societies).

References

Benzie M, Carter TR, Carlsen H, Taylor R (2019) Cross-border climate change impacts: implications for the European Union. Regional Environmental Change 19: 763-776, https://doi.org/10.1007/s10113-018-1436-1.

Carter TR, Benzie M, Campiglio E, Carlsen H, Fronzek S, Hildén M, Reyer CPO, West C (in review) A conceptual framework for cross-border impacts of climate change.

How to cite: Carter, T. R., Benzie, M., Campiglio, E., Carlsen, H., Fronzek, S., Hildén, M., Reyer, C., and West, C.: A framework for analysing cross-border climate change impacts, responses and their propagation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13263, https://doi.org/10.5194/egusphere-egu21-13263, 2021.

Accurate and complete inventory of natural hazard occurrence and their level of impact is a key first step to risk assessment, but it remains a challenge, especially for high frequency low impact events that rarely makes it to the news media. This challenge is even greater in rural areas of developing countries such as Uganda, where limited IT facilities prevent dissemination of information through social media. Here we report on a citizen-science initiative to monitor small-scale disasters (landslides and floods) occurring in the Rwenzori Mountains. A network of citizen (geo-)observers was established in February 2017 to collect temporally explicit geo-referenced information on eight different hazards and their impact using smartphone technology. Since then, over 500 hazard occurrences have been reported. However, such dataset needs to be assessed for its accuracy and potential biases before being used for scientific analysis. In this study, we evaluate the accuracy and completeness of the geo-observer-based disaster reports. First, systematic errors are reduced by peer reviewing the reports and implementing automatic tests to assess potential errors in detection and biases. Then, we compare the geo-observer-based records with two independent inventories collected through systematic field mapping and  satellite imagery mapping, focusing on landslide and flood events for the period between May 2019 and May 2020.  Results show over 95% of the geo-observer reports validated in the field were correctly identified and recorded less than 5 days after the occurrence (60% true positives, 1% false positives and 39% false negatives). For the satellite imagery mapping, 29% were true positives, 43% false positives and 28% false negatives. Geo-observers provide near real time disaster information on the location and level of impact, something difficult to achieve with systematic field and satellite imagery mapping. Depending on the topography of the area and the weather conditions, it can take several days to weeks before a cloud-free satellite image of a place can be obtained. The false negatives in the Geo-observer data are due to the tendency to report mainly occurrences along roads and rural foot paths since such occurrences are easily seen and accessed. Isolated small and inaccessible landslides are often not seen or reported to the Geo-observers. While satellite imagery mapping provides an opportunity to record disaster occurrences even in extremely inaccessible places, small landslides are often missed while shallow ones can easily be confused with freshly cleared vegetation for crop planting. Citizen science-based disaster reporting therefore not only provide the spatial occurrence of disasters but also the temporal and weather-related information, necessary for disaster risk analysis.

How to cite: Sekajugo, J., Kagoro, G. R., Jacobs, L., Kabaseke, C., Namara, E., Dewitte, O., and Kervyn, M.: Accuracy and completeness of a near real-time citizen science-based multi-disaster inventory in the Rwenzori Mountains, Uganda, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14282, https://doi.org/10.5194/egusphere-egu21-14282, 2021.

The current observed global warming is projected to intensify by the end of the 21^st century. According to simulations of the climate system and its impacts on populations, previous studies show significant projected impacts on four main sectors: water, health, energy and agriculture. Concurrent analyses have also focused on the time of emergence (ToE) of future climate modifications to assess when new climate regimes will emerge from a prior reference. Here we propose to investigate the timing and the emergence of global warming impacts on populations over three main vulnerable regions: Western Africa (WAF), Eastern Africa (EAF) and South-eastern Asia (SEA). We propose to analyse multi-sectoral impacts that may affect human being by accounting for (but not limited to) 6 fields: crop failure, water scarcity, health, droughts, floods, and heatwaves. The ISIMIP2b protocol (phase 2b of the Intersectoral Impact Model Intercomparison Project), which provides simulated impacts from 1 to 8 sectoral impact models and four CMIP5 (5^th phase of the Coupled Model Intercomparison Project) climate models, is used in this study.

              Preliminary results under the RCP8.5 future climate scenario show a strong acceleration of the decrease of the annual maize yields before 2048 in WAF and EAF according to the CLM45 impact model, suggesting a significant emergence at this time. No particular fluctuation from the long-term trend is shown in SEA. CMIP5 climate forcing (i.e. GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR and MIROC5) responses in maize yields exhibit larger uncertainties over EAF than over WAF and SEA. Drought metrics such as the annual number of consecutive dry days (i.e. daily precipitations < 1mm) and the annual number of periods with more than 5 consecutive dry days show an acceleration of their increases around 2052 in WAF with large climate forcing uncertainties, but no significant emergence over EAF and SEA. Flood metrics from the ORCHIDEE impact model simulations do not exhibit particular fluctuation nor acceleration of the change during the 21^st century in the three regions. The next step of our study is to quantify the ToE of the significant fluctuations compared to the long-term trends of the different metrics that cover every impact sectors. The Kolmogorov-Smirnov test (‘KS-test’) method will be applied as the statistical approach to quantify the ToE independently from the signal shape. Impact models uncertainties will also be quantified compared to the climate model uncertainties, in order to assess whether impact or climate modelings is the main driver of the total uncertainties when studying the emergence of the impacts of global warming.

How to cite: Brouillet, A. and Sultan, B.: Emergence of multisectoral impacts of the global warming during the 21st century., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16466, https://doi.org/10.5194/egusphere-egu21-16466, 2021.

Social media has been described as a form of distributed cognition, a mechanism for understanding a situation using information spread across many minds. The interactions among people in social media are a form of collective intelligence, as they allow people to make sense of a developing event collectively. Social media users can contribute to creating a "sensor" for citizen-generated data that modelling or monitoring systems can assimilate during a crisis. Gaining situational awareness in a disaster is critical and time-sensitive. Social media presents the possibilities of a growing data source to help improve response in the early hours and days of a crisis. However, social media platforms may not provide the functionality of summarising the information that is useful for crisis responders.
SMDRM is a software platform that streamlines the processing of text and images extracted from Twitter in near real-time during a specific event. The data is collected using a combination of keywords and locations based on daily forecasts from the early warnings systems of the Copernicus Emergency Management Service such as EFAS, GloFAS and EFFIS (emergency.copernicus.eu) or triggered manually in case of earthquakes or not-forecasted events. Text is automatically "annotated" using a binary multilingual classifier trained on 12 languages and extended with multilingual embeddings. Simultaneously, a multi-class convolutional neural network labels relevant images for floods, storms, earthquakes and fires. The information that doesn't embed coordinates is geolocated in a two-step algorithm where location candidates are first selected using a multilingual named-entity recognition tool and then searched on available gazetteers. The last step of the SMDRM data processing is the aggregation of relevant information in spatial (administrative areas) and temporal (daily) units. Social media activity about an event can finally be distributed as a data map and visualised on a map server and made available to users.
SMDRM could offer timely information useful for reducing the hazard models' uncertainty and providing added-value information such as reports or descriptions of the situation on the ground or in the vicinity. Other stakeholders, such as research groups could access new data to complement the ones extracted from traditional sensors or earth observation. 
The platform can adapt to cope with the varying workload as it uses scalable software containers. If the number of tweets is higher during an impactful event, the platform can use more containers to annotate them. SMDR code, together with the tens of thousands of annotated social media messages used for training its models, will be released as an open-source platform whose modules can be adapted to serve other research projects. We describe the platform's architecture and implementation details, and two use cases where images and text were used as a use-case to test the system's modules.

How to cite: Lorini, V., Salamon, P., and Castillo, C.: SMDRM - Social Media for Disaster Risk Management, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15012, https://doi.org/10.5194/egusphere-egu21-15012, 2021.

The ubiquitousness of social media has created a valuable and massive amount of data relating to real-live events which are being explored to investigate a wide range of phenomena including, disaster monitoring, health surveillance, user sentiments, etc. For events such as landslide, which mostly occur in remote and localized areas, social media provide an opportunity to expand landslide mapping beyond the current approach.

Traditional sources of landslide events are via media reports, scientific articles, or aerial photography, thus, limiting landslide mapping to only areas where such resources exist. This is followed by repeated in-situ measurements with equipment such as LIDAR to create a 3D geomorphological model of the landslide. At locations with poor accessibility or hazardous conditions, mobilizing personnel and equipment to the site is often impossible. The financial implication of repeated field operations also means landslide monitoring programs are prioritized. These deficiencies have hampered the generation of a robust landslide inventory which is the crucial tool for understanding past landslides and developing an effective system for managing future landslides.

This study demonstrates the application of social media analytics for the identification and modeling of landslides along the South West coast of the UK. From the analysis of over 100,000 tweets, 23 landslide events reported by Twitter users are identified. Five (5) of these events have not been previously reported. Also, drone videos obtained from Twitter and YouTube were processed using Structure from Motion-Multiview Stereo (SFM-MVS) photogrammetry techniques to create a 3D model of landslides at five (5) selected locations.

Analysis of the 3D model created at one of the locations shows that an estimated 1480 m³ of earth material was removed from the landslide due to the impact of Storm Dennis and Storm Ciara events of the 8^th – 9^th and 15^th -16^th of February 2020 respectively, while an estimated 295 m³ was retained at the base of the landslide, possibly an effect of the landslide control/stabilization installation.

The result from this study shows the potential of social media to expand landslide coverage in the UK and to provide a high-resolution 3D model at minimum cost. These data can be used to monitor landslide evolution and to assess their hazard.

How to cite: Todowede, S. and Manzella, I.: 3D modeling of UK Coastline using social media data for landslide mapping and monitoring, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6211, https://doi.org/10.5194/egusphere-egu21-6211, 2021.

Very few research studies have been dedicated to understanding the role of social media, diversity and vulnerability during a highly impacting event for a society. Social media are very important nowadays as a way to be in "connection to" and "link between" individuals. Thanks to technological support it is possible to create new virtual and real social relationships and networks and to be always up to date about what happen in the world. The role that virtual space plays "reducing distances", connecting people and places and facilitating the provision of support to people in need, has been receiving increasing interest in disaster studies in last years. In particular, connectivity has assumed an increasing role in relation to the diffusion of means to reach people and places in virtual mode. Furthermore, the use of social media as a means of providing information on disasters and risks could help to reduce exposure in disasters. However, several knowledge gaps are still opened, and in particular which are the potential repercussions of a high connected disaster management process on vulnerability? How can the weight of diversity change into the virtual space? The premise is that not everyone has the same possibility of accessing social media (e.g. to be informed, to know what is happening and to link with rescuers). The difficulty of accessing social media can make people invisible into the disaster management process with the risk that someone could be left behind. Thus, this presentation aims to discuss the challenges that derive from an increasing use of social platform in providing and receiving information during disasters. A second relevant point, that this presentation aims to discuss, is linked to the way citizens perceive communication platforms and how the flow of information significantly impacts on the interpretation and on the management of risk. Conclusions of this work suggest that communication should take into account the risk perception models by the public and therefore the peculiarities of each vulnerable group, to provide "targeted" communications in relation to the cultural context with the aim of reducing vulnerability growing up citizens’ awareness and knowledge. This presentation is the result of the work provided as part of the EU H2020 founded project LINKS (http://links-project.eu). 

How to cite: Nardini, O., Bonati, S., Morelli, S., and Pazzi, V.: Social media, diversity and vulnerability: their role in a disaster, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12717, https://doi.org/10.5194/egusphere-egu21-12717, 2021.

Sakhalin Island is a region with high rate of seismic activity. Tens of felt earthquakes occur within the studied area every year. Rapid macroseismic observation through the web questionaries, social networks etc. gives reliable information about ground shaking intensities and today is processed by major seismological agencies (Bossu et al., 2018; Quitoriano and Wald, 2020).

The recent development of the methodology began with the web-based macroseismic observations following Dengler and Deweey (1998) and Wald et al. (1999). Widespread global use of Community Internet Intensity (CII) was routinely applied by the U.S. Geological Survey (USGS) through the USGS DYFI questionaries. Over 5 millions felt reports were collected during last 15 years (Quitoriano and Wald, 2020).

During last 5 years the methodology was tested in Sakhalin Island (Konovalov et al., 2018). For the collection of felt reports we used regional internet resource (https://eqalert.ru/#/). The DYFI USGS questionnaires translated into Russian were used for processing the macroseismic information. The felt reports of the respondents from each settlement were transformed to the Community Weighted Sum (CWS) which takes into account various indicators of ground shaking: human sensations, position of objects, visible damages of the building. The CII was calculated using the equation (Wald et al., 1999):

CII = 3.4 ln (CWS) – 4.38.

The obtained values were rounded to the first number after the comma. In general CII should be similar to the MM intensity.

During the period from 2016 to 2020 we have got about 400 felt reports. Most of the responses came in the first minutes after the origin time of seismic event. Data with only one report or incorrectly submitted questionnaires were excluded in further calculations. The small number of the felt reports may be explained by low population density of the central and northern districts of Sakhalin Island. Finally we have found correlation between the CII and PGA (cm/s/s) which is given by the equation:

CII = 2.5 log (PGA) + 2.32.

It is suggested that given approach can be used as a robust tool for express analysis of ground shaking. It is also a good way to involve the population and bring them closer to understanding the scientific process in the era of the growth of computer technology and social networks.

How to cite: Konovalov, A., Stepnova, Y., and Stepnov, A.: Assessment of Community Internet Intensity (CII) in Sakhalin Island, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1873, https://doi.org/10.5194/egusphere-egu21-1873, 2021.

Introduction

Flood early warning systems (FEWS) can reduce casualties and economic losses (UNEP, 2012). The EC Horizon 2020 project FANFAR (www.fanfar.eu) aims to co-develop a FEWS in West Africa together with stakeholders, predicting streamflow and return period threshold exceedance (Andersson et al., 2020). A Multi-Criteria Decision Analysis (MCDA) indicated, that stakeholders find information accuracy especially important, among a broad set of fundamental objectives (Lienert et al., 2020). Social media have the potential to support accuracy assessment by detecting flood events (Lorini et al., 2019; de Bruijn et al., 2019) due to their large spatial coverage (Restrepo-Estrada et al., 2018). We investigated the potential of social media to assess FANFAR forecast accuracy.

Research Approach

FANFAR forecasts are based on HYPE, which is a semi-distributed land-cover and sub-catchment based hydrological model (Arheimer et al., 2020). We lumped the forecasted flood risk (FFR) on a country scale and compared it to flood events detected on Twitter, using an algorithm (FEDA) developed by de Bruijn et al. (2019). FEDA detects flood-related tweet bursts based on regionally and temporally adjusted thresholds (de Bruijn et al., 2019). We compared FEDA detected events with floods from the disaster database EM-DAT (https://www.emdat.be/), to find if tweets indicate flooding. We also compared FEDA to the lumped FFR to identify false positives (FP), false negatives (FN), and true positives (TP), from which we deduced the probability of detection (POD) and false alarm rate (FAR). We further calculated the correlation of single flood-related tweets with the lumped FFR and investigated seasonality, lag, and the influence of rainfall.

Findings

The detailed findings are described in Hohmann (2021). FEDA (i.e., tweets) and EM-DAT events (i.e., floods) mostly occurred in the same period. However, FEDA detected shorter and more frequent events than EM-DAT. In the Upper Niger, POD_FEDA and FAR_FEDA (deduced from FEDA) were of similar order of magnitude as the POD_S and FAR_S (deduced from streamflow) but were different in the Lower Niger region. This suggests that tweets can be employed additionally to e.g. streamflow timeseries as a complementary way to evaluate accuracy. Correlation analysis between single flood-related tweets and the lumped FFR showed no relationship. We also did not find a systematic influence of seasonality or a lagged response between tweets and FFR. The correlation coefficients between tweets and rainfall ranged from 0.1-0.9, but were mostly non-significant. This suggests that a performance assessment based on single tweets is not (yet) adequate. Also, since FEDA does not differentiate between pluvial and fluvial floods, it is less suited to assess the accuracy of FANFAR. Our findings suggest the need for inclusion of other factors into the performance assessment of FEWSs, such as regional thresholds to identify TP, FP, and FN. Also, rainfall causing pluvial flooding must be considered. Finally, our approach is limited to Twitter. Further research should assess the potential of e.g. Facebook to be included in FEWS performance assessment. The question whether social media, FEWSs, or EM-DAT are correct remains, and is in our opinion best addressed by employing multiple data sources.

How to cite: Hohmann, T., Lienert, J., Andersson, J., Molnar, D., Molnar, P., and Kuller, M.: Assessment of Flood Early Warning Systems with Social Media, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8816, https://doi.org/10.5194/egusphere-egu21-8816, 2021.

Increased availability of social media and crowdsourced data is becoming a precious source of information in Disaster Risk Management, heralding a new era where the policy makers adapt their strategies to the potential of these new technologies.  This is also happening in the field of Flood Risk Management, where the aid of new technologies can provide important support for disaster risk reduction. On the one hand, they play an important role in the collection, monitoring and data analysis of physical flood processes. On the other hand, they foster the involvement of citizens threatened by the flood risk situation, creating shared knowledge and collaboration and becoming tools to educate and empower citizens' behavior, increasing community resilience.

Evidence shows that community response to flood risk is associated with the social context in which a specific flood occurs. A wide range of sociodemographic characteristics, but also the psychological factor of risk perception, have been identified as factors influencing citizens’ response, contributing in increasing or decreasing the effects of flooding on the environment.

In this study a coupled approach that combine Crowdsourced retrieved data and information from newspaper media is proposed and applied to the urban territory of the city of Brindisi (southern Italy), subject to multiple sources of flood risk, in order to demonstrate potential advantages arising from the implementation of such built analysis.

Crowdsourcing data based on e-survey allowed the collection of social flood data in order to explore how citizens living in the urban area of Brindisi perceive flood risk and assess their preparedness for protective measures. Specifically, the degree of citizen risk perception has been investigated through factors influencing risk perception subdivided into three categories: world view, media influence and social value and trust; the degree of citizens’ preparedness knowledge has been investigated asking citizens to select the recognized Flood Protection Strategies from the set of alternatives in the Civil Protection Behavioral Guide.

Integration of available data about previous floods with a newspaper-based research of historical floods allowed to detect a tendence of Brindisi urban territory to be subject to floods that can be reconducted mainly to pluvial and fluvial type. Journal reports provided precious details not only on affected streets and neighborhoods, but also on type and dynamics of damages. Results of surveys showed how this flood phenomenology is perceived by population, providing an important integration of the information available from current flood maps. Measurement of emergency measures knowledge revealed to be an effective source of information for an a priori modelling of reliable flooding scenarios.Results emerging from proposed approach can constitute a precious support for emergency managers and local Authorities, because of its ability in capture heterogeneities in flood phenomenology and population preparedness. Emergency planning phase can be therefore enriched with elements that contribute to the definition of risk potential situations and therefore make the response and recovery phase more effective.

How to cite: Santoro, S., Totaro, V., Lovreglio, R., Camarda, D., Iacobellis, V., and Fratino, U.: An integrated approach for investigating flood risk perception in urban areas: some hints from the city of Brindisi (southern Italy), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12935, https://doi.org/10.5194/egusphere-egu21-12935, 2021.