NH9.1

Wildfire risk around the world is rapidly increasing, leading to dramatic impacts on ecosystems and society. Economic damages of the past seasons threaten individual households, insurance companies, brokers and governmental authorities alike. Here, we present a probabilistic wildfire risk model to assess fire and economic risk. The model creates synthetic fire seasons through probabilistic ignition and dynamic random-walk spreading of fires.

The risk of natural catastrophes is commonly modeled using the three components hazard, exposure and vulnerability. This approach is used in the well-established open-source platform CLIMADA (CLIMate ADAptation). Here we show its extension for a globally consistent wildfire risk model. The model allows for the evaluation of economic damages of past and current wildfire events as well as a probabilistic risk assessment for any exposure on a seasonal basis. It is built on open and global data to ensure consistent modelling, including in data-sparse regions.

The hazard component uses Fire Information for Resource Management System (FIRMS) data acquired by the MODIS and VIIRS satellite missions and provided by Earthdata. We aggregate point information of fire activity using clustering algorithms over space and time to identify separate events while allowing for different resolutions (minimum of 375 m). For the exposure component, CLIMADA’s LitPop model is used, which geographically distributes assets using data on night-light intensity and population density. To assess the vulnerability, the model has been calibrated using reported damage data. Although uncertainties remain large, error scores after calibration resemble those of well-established hazards, such as tropical cyclones. To allow for probabilistic risk assessment, synthetic fire seasons are generated using a random-walk-type stochastic fire generator, which hinges on grid-point specific fire spread probabilities combined with an overall fire propagation probability. The framework further allows for a simple integration of additional data in order to reflect climate trends.

How to cite: Lüthi, S. and Bresch, D.: A globally consistent probabilistic wildfire risk model to assess economic damages, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2241, https://doi.org/10.5194/egusphere-egu21-2241, 2021.

Fire following earthquake (FFE) can pose considerable threat in densely populated urban area with significant earthquake hazard and presence of non-fire-resistant buildings typology. Severe building damage and consequently broken pipelines can lead to release of flammable gasses and liquid, which increase possibility of fire occurrence when they come into contact with ignition sources, like short circuits or open flames. Numerous simultaneous ignitions followed by uncontrolled fire spread to adjacent buildings can lead to major fires and conflagrations, whose damage can substantially exceed the earthquake shaking damage. Well-known example of such high financial losses due to FFE is Mw 7.9 San Francisco 1906, where Great Fire losses were 10 times higher than due to earthquake shaking itself. Thus, the quantification of FFE losses has particularly important role for the current underwriting products and the industry requires their further detailed consideration in the catastrophe models and pricing approaches. Impact Forecasting, Aon’s catastrophe model development centre of excellence, has been committed to help (re)insurers on that matter.

This paper presents quantification of FFE contribution to mean losses for case study of the Vancouver region, Canada for specific scenario Mw 7.5 Strait of Georgia crustal earthquake. FFE methodology encompasses 3 phases: ignitions, fire spread and suppression and loss estimation. Number of ignitions (fires that require fire department response) and their location were calculated using HAZUS empirical equation with input variables earthquake shaking intensity and estimated total building floor area. An urban fire spread is a complicated phenomenon that includes numerous uncertainties. An advanced cellular automata (CA) engine is used for simulation of the fire spread and suppression based on Zhao 2011. The CA engine represents collection of grid-arranged cells, where each grid cell changes state as a function of time according to a defined set of rules that includes the states of adjacent cells. The CA simulations include only matrix mathematical operations that allow us to take into account building construction types and their damage due to earthquake shaking, meteorological and environmental data and fire suppression modifiers. Unlike in older empirical approach, the fire spread CA engine enable to consider fire spread not only from initially ignited building as well as fire developing within a single building, building-to-building fire spread, and fire extinguishing works at the same time. An output of CA engine is the building fire-state grades based on which damage functions are created with PGA as input parameter at the level of 3-digit postal codes. For the chosen scenario potential contribution to mean loss due to FFE could be up to 75% depending on typical buildings setting within 3-digit postal codes.

How to cite: Stripajova, S., Vodicka, J., Pazak, P., and Trendafiloski, G.: Fire Following Earthquake in the catastrophe models: Case study – Vancouver region, Canada, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4499, https://doi.org/10.5194/egusphere-egu21-4499, 2021.

Land subsidence associated with groundwater withdrawal is often an underestimated geological hazard that may produce important damage to buildings and infrastructure, change flood risk in some areas, and cause loss of groundwater storage capacity. In the current framework of global climate change, the increasing agricultural and urban use of groundwater resources is a growing problem, especially in arid and semiarid areas. Because monitoring subsidence in these areas is important for management, but early detection is difficult due to slow displacement rates, we developed global groundwater induced land subsidence probability maps.  Global land subsidence probability was calculated by applying statistical methods to a set of susceptible geographical, environmental and geological properties based on known, documented subsidence affected areas. Highest values of subsidence probability are concentrated over flat areas composed of unconsolidated sediments, and in agricultural or urban areas subject to prolonged dry periods. Including water scarcity and groundwater use data resulted in an estimation of a proxy land subsidence hazard. Calculated probability does not imply that all the high value areas are currently incurring land subsidence, but it can alert policymakers and groundwater managers to areas that have potential exposure to subsidence hazards and warrant monitoring. The complete results of this work are published in Science Policy Forum section under the title “Mapping the global threat of land subsidence” DOI: 10.1126/science.abb8549

How to cite: Ezquerro, P., Herrera-García, G., Tomás, R., Béjar-Pizarro, M., López-Vinielles, J., Rossi, M., Mateos, R. M., Carreón-Freyre, D., Lambert, J., Teatini, P., Cabral-Cano, E., Erkens, G., Galloway, D., Hung, W.-C., Kakar, N., Sneed, M., Tosi, L., Wang, H., and Ye, S.: Improving global land subsidence analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16000, https://doi.org/10.5194/egusphere-egu21-16000, 2021.

Tropical cyclones (TC) cause devastating damage to infrastructure and are a concerning threat to human life. Records of historical TCs are very limited and therefore the data to study impacts of this natural hazard remain sparse. The generation of synthetic storm tracks is an important tool to overcome this spatial and temporal limitation.

We perform the first global model intercomparisons of different synthetic TC track sets. We use the CLIMADA (CLIMate ADAptation, Aznar-Siguan and Bresch, 2019) platform, which integrates hazard, exposure, and vulnerability data, to compute TC risk and to quantify socio-economic impacts for different storm track sets. Our comparison shows how the selection of a TC track set might affect the estimated damage and which dataset is suitable to answer what type of research question. Specifically, we provide a qualitative overview of the different TC model types, we compare damage by return period and perform a global sensitivity analysis for selected TC damage model parameters.

We contrast the following sources of tropical cyclone tracks: i) observed storms from IBTrACS (Knapp et al., 2010), ii) probabilistic events obtained from historical ones by a direct random-walk process (Kleppek et al., 2008), synthetic tracks from coupled statistical-dynamical models iii) from Emanuel et al. (2006, 2008), and iv) CHAZ (Lee et al., 2018), and v) synthetic tracks from a fully statistical model, STORM (Bloemendaal et al., 2020). We find that the choice of event set becomes more important when studying tail events, basins with smaller historical event sets or small areas. In these cases we discover modelled losses to vary by more than an order of magnitude. This variance can partly be explained by the varying distribution of hazard intensities at landfall between event sets.

How to cite: Meiler, S., Emanuel, K., and Bresch, D. N.: How much do modeled tropical cyclone impacts depend on the hazard set choice?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1829, https://doi.org/10.5194/egusphere-egu21-1829, 2021.

With increasing global economic damages due to weather-related events, insurance has even more become a valuable measure to share risks and increase resilience. Insurance solutions can be designed and implemented in various forms. Among these, cross-country insurance schemes emerged in the last years.

Natural catastrophe risk pools have the potential benefit of diversifying losses (thus lowering premiums) and of reducing administrative costs (as they are shared among countries). Currently, there are three catastrophe risk pools globally in place: the Caribbean Catastrophe Risk Insurance Facility (CCRIF), the African Risk Capacity (ARC), and the Pacific Catastrophe Risk Assessment and Financing Initiative (PCRAFI).

In the present work we aim to study the feasibility of establishing a global risk pool and, in particular, how countries might best be grouped together to achieve the greatest diversification. As a first step, this requires an assessment of global damages. We do this using the CLIMADA impact modeling platform and estimate worldwide damages from tropical cyclones. Then, we apply extreme value analysis and assess the diversification potential of various hypothetical pools based on measures from the systemic risk literature.

How to cite: Ciullo, A., Strobl, E., Martius, O., and Bresch, D. N.: A feasibility study of a global risk pool scheme against tropical cyclones, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10662, https://doi.org/10.5194/egusphere-egu21-10662, 2021.

Global flood models (GFM) are increasingly being used to estimate societal and economic risks of river flooding. A recent collective validation of several GFMs highlighted substantial differences in performance between models and between validation sites. However, it has not been systematically quantified to what extent the choice of the underlying climate forcing and global hydrological model (GHM) influence flood model performance. Here, we investigate this sensitivity by comparing simulated flood extent with satellite imagery of past flood events, for an ensemble of three climate reanalyses and 11 GHMs. We study eight historical flood events covering four continents and various climate zones. Our results show that model performance varies greatly among the events. For most regions, the simulated inundation extent is relatively insensitive to the choice of GHM. For some events, however, individual GHMs lead to much lower agreement with observations than the others, mostly resulting from an overestimation of inundated areas. Two of the climate forcings show very similar results, while with the third, differences between GHMs become more pronounced. We further show that neither a previously used flood-volume adjustment procedure, nor the application of a global flood protection database, substantially improves model performance. Our study guides future applications of these models, and highlights regions and models where targeted improvements might yield the largest performance gains.

How to cite: Mester, B., Willner, S., Frieler, K., and Schewe, J.: Sensitivity of global river flood simulations to the choice of climate forcing and hydrological model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2297, https://doi.org/10.5194/egusphere-egu21-2297, 2021.

Disaster displacements create hardships, particularly for socio-economically vulnerable groups. Displaced people face heightened risks to their well-being, including their physical and mental health and personal security. Assisting displaced people is an important part of any humanitarian response to disasters.

Among weather-related disasters, river flooding is responsible for a large part of population displacement. River flood risk is expected to increase due to climate change and its effects on the hydrological cycle. At the same time, socioeconomic development scenarios indicate substantial increases of population in many regions that experience flood-induced displacement.

We have modelled projected changes to flood-driven population displacement in the 21^st Century with the CLIMADA (CLIMate ADAptation) platform, in collaboration with the Internal Displacement Monitoring Centre.

We show that both climate and population change are projected to lead to an increase of relative global flood displacement risk by roughly 350% by the end of the century. If we keep the population fixed at present levels, we find a roughly 150% increase in relative global flood displacement risk by the end of the century, or a 50% increase of risk per degree of global warming. We model displacement probabilities as a function of population density, flood depth and flood fraction.

Although the resolution of the global model is limited, the effect of climate change is robust across greenhouse gas concentration scenarios, climate models and hydrological models. Our work potentially enables the creation of a displacement early warning system.

How to cite: Kam, P. M.: Modelling population displacement: both climate change and population growth heighten displacement risk due to river floods., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2449, https://doi.org/10.5194/egusphere-egu21-2449, 2021.

Floods are extreme hydro-meteorological hazards that pose significant risks to the economy and society. Reducing the risk associated with floods and better adapting to them is a daunting task because flood risk dynamics are influenced by different factors. Flood risk is usually defined as the product of three components: hazard, exposure and vulnerability. Global Flood Risk Models (GFRM) represent the underlying physical hazard, the exposure of people, properties or other assets to the hazard, and the losses that may occur following a flood event.  Consequently, they are used by governmental agencies, risk reduction organisations, global investors and the (re)insurance industry to help manage the societal and financial risks associated with floods. GFRMs are subject to many sources of uncertainty, including uncertainty in processes representation, model parameters and input data; however, the relative importance of these different sources is poorly understood. Currently, no evidence exists on which uncertain input factor mostly control the final uncertainty in predicted losses in different places and circumstances. In this project, we use JBA’s (a leading flood risk modelling company) Global Flood Model and Open Exposure Data (OED) to develop an appropriate methodological approach to analyse the sensitivity of loss predictions in a structured way. This is particularly challenging as input uncertainties exhibit complex spatially distributed and spatially-structured (correlated) patterns. We apply the methodology to the Rhine river basin, covering regions with different physical and socio-economic characteristics. We pursue the following objectives; (1) Identify and quantify the various sources of uncertainty e.g. associated to rainfall data, extraction of flood events sets, defence database, vulnerability curves, exposure portfolios (2) Analyse their relative importance on flood losses predictions across places along the river (3) Understand which of them are most important at each place. We aim to produce scientifically robust evidence about the importance of different sources of uncertainty across places with different climate, hydrology and socio-economic characteristics and try to address questions related to exposure and vulnerability dynamics, flood losses modelling and adaptation strategies. Such evidence base will help prioritise efforts for uncertainty reduction of the case study model, as well as other flood risk models used by (re)insurers and government agencies, ultimately contributing to more informed decisions for flood risk mitigation.

How to cite: Sarailidis, G., Pianosi, F., Wagener, T., Styles, K., Hutchings, S., and Lamb, R.: Uncertainty quantification and attribution in flood risk assessment using Global Flood Models: an application to the river Rhine basin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3488, https://doi.org/10.5194/egusphere-egu21-3488, 2021.

Datasets supporting the study of natural disasters and allowing spatial/temporal analyses of phenomena and their interactions with human societies is rapidly growing, due to the efforts of insurance companies, universities and humanitarian organizations. At the global scale, several disasters catalogues are available, even if some are only partially accessible. Generally, the focus is on the complete impact of disasters, in terms of areas affected and economic damage. Each record is a natural disaster, while database fields contain parameters assessing disaster magnitude. One of this parameter is the number of fatalities.

In Australia and USA, databases of fatalities caused by specific kinds of natural disasters are available, while, for Europe, natural disasters mortality is often investigated using global databases.

The present research focus on floods and their effects on people mortality. We named “flood fatalities” (FFs) people killed by direct impact of flood events due to the following short-term clinical causes: 1) Drowning; 2) Collapse/Heart attack; 3) Poly-trauma; 4) Poly-trauma and Suffocation; 5) Hypothermia; 6) Suffocation; 7) Electrocution.

For a 40-years study period and for 9 European study areas, we performed a survey of FFs reported in four of the widely known global databases. Then we compared figures with the results of a very specific research carried out for the same study areas and study period at a country scale, and focusing on a very restricted field: fatalities caused by floods.

The comparison highlights as the use of global databases can supply figures of FFs not correctly estimated, either underestimated or overestimated.

Underestimation depends on the fact that collecting data at the global scale needs some severity threshold of floods to be included in the database. Thus, local events causing a few FFs, as i.e. flash flooding, are systematically excluded, even if the majority of floods that occur in developed countries kill less than 10 people. This results in an underestimation of FFs, which is going to increase due to the increasing frequency of localized floods or flash floods related to climate change. Overestimation, instead, can happen due to the classification of fatalities occurred at the same time of the flood, even if they are caused by other phenomena (i.e., landslides, debris flows and wind).

This work aims to demonstrate how a database of flood fatalities realized at a country scale can supply realistic figures of fatalities in European countries, providing information that can reduce flood fatalities in the future. Our database is available for the period 1980-2018 (Petrucci et al., 2019). We encourage researchers working in European countries to collaborate with us to increase spatial coverage of the database and promote its common use in studies on flood mortality.

Petrucci O., Aceto, L., Bianchi, C., Bigot, V., Brázdil, R., Pereira, S., Kahraman, A., Kılıç, O., Kotroni, V., Llasat, M.C., Llasat-Botija, M., Papagiannaki, K., Pasqua. A.A., Řehoř J., Rossello Geli, J. Salvati, P., Vinet, F., Zêzere, J.L. (2019). Flood Fatalities in Europe, 1980–2018: Variability, Features, and Lessons to Learn. Water, 11(8), 1682.

How to cite: Mercuri, M. and Petrucci, O.: Fatalities caused by floods: a comparison between global databases and country scale historical research, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2688, https://doi.org/10.5194/egusphere-egu21-2688, 2021.

The execution of large-scale (i.e., continental or global) hydraulic modeling is nowadays a reality thanks to the increasing computational capacity, data availability, as well as understanding of essential physical dynamics. Such achievements are typically associated to a compromise in terms of model resolutions (the finer being of few tens of meters, with a coarsened representation of the terrain) and, thus, accuracy on representing the topographic peculiarities of the flood-prone areas. Nevertheless, the experience gained observing the dynamics of past inundations highlights the role of small-scale topographic features (e.g., minor embankments, road deck, railways, etc.) in driving the flow paths. Recent advances on automated identification of flood defense from high resolution digital elevation model paved the way to include hydraulically relevant features (e.g., main levees) while preserving the model resolution suitable for large-scale applications (Wing et al, 2020).
The present study extends this approach to flood-prone areas by investigating how the automatic detection of minor topographic discontinuities can enhance the estimation of flood dynamics of large-scale models. Taking advantage of high-resolution topographic data (i.e., 1-2 m) the approach automatically detects hydraulically relevant features and preserves their height while coarsening the resolution of the terrain used into the hydraulic model. The impact of such approach on the inundation dynamic is tested referring to three different case-studies that recently experienced riverine flooding: Secchia and Enza rivers (2014, 2017, respectively; Italy), Des Moines (Iowa, USA). The results confirm the relevance of small-scale topographic features, which, when considered, ensure a high correspondence to observations and local models. The element of strength of the presented approach is that such performances are ensured without requiring the adoption of high grid resolutions, and thus, not affecting the overall computational costs.

How to cite: Domeneghetti, A., Leonardi, A., Wing, O. E. J., Carisi, F., and Brath, A.: The role of small-scale topographic features on inundation dynamics: potential impacts on large-scale investigations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9093, https://doi.org/10.5194/egusphere-egu21-9093, 2021.

There are currently several large-scale gridded archives available for the study of flood exposure, and the results will inevitably depend on the datasets included in the analysis. The purpose of this work is to demonstrate how country flood exposure, here represented as the presence of population within floodplains, is influenced by dataset choices.

We conduct this geographical analysis in two parts. First, we conduct a global analysis showing how different flood exposure metrics influence comparisons between countries. Second, we overlay five commonly used gridded archives (three population archives and two floodplain archives) for 32 countries. The purpose is to quantify the influence of data choices, while also giving an overview of the various dataset methodologies. We finally zoom in on areas where the five datasets yield very dissimilar results, to exemplify typical differences among the datasets.

How to cite: Lindersson, S., Mård, J., Brandimarte, L., and Di Baldassarre, G.: What dataset should I choose? The influence of data choices on flood exposure estimations at national scales, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12962, https://doi.org/10.5194/egusphere-egu21-12962, 2021.

Temporal clustering of extreme precipitation events on subseasonal time scales is a type of compound event, which can cause large precipitation accumulations and lead to floods. We present a novel count-based procedure to identify subseasonal clustering of extreme precipitation events. Furthermore, we introduce two metrics to characterise the frequency of subseasonal clustering episodes and their relevance for large precipitation accumulations. The advantage of this approach is that it does not require the investigated variable (here precipitation) to satisfy any specific statistical properties. Applying this methodology to the ERA5 reanalysis data set, we identify regions where subseasonal clustering of annual high precipitation percentiles occurs frequently and contributes substantially to large precipitation accumulations. Those regions are the east and northeast of the Asian continent (north of Yellow Sea, in the Chinese provinces of Hebei, Jilin and Liaoning; North and South Korea; Siberia and east of Mongolia), central Canada and south of California, Afghanistan, Pakistan, the southeast of the Iberian Peninsula, and the north of Argentina and south of Bolivia. Our method is robust with respect to the parameters used to define the extreme events (the percentile threshold and the run length) and the length of the subseasonal time window (here 2 – 4 weeks). The procedure could also be used to identify temporal clustering of other variables (e.g. heat waves) and can be applied on different time scales (e.g. for drought years). For a complementary study on the subseasonal clustering of European extreme precipitation events and its relationship to large-scale atmospheric drivers, please refer to Barton et al.

How to cite: Kopp, J., Rivoire, P., Ali, S. M., Barton, Y., and Martius, O.: A novel method to identify subseasonal clustering episodes of extreme precipitation events and their contribution to large accumulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-836, https://doi.org/10.5194/egusphere-egu21-836, 2021.

Extreme precipitation events that occur in close succession can have important societal and economic repercussions. Few studies have investigated the link between large-scale atmospheric drivers and temporal clustering of extreme precipitation events on a subseasonal scale, i.e. 20-day time scale. Here we use 40 years of reanalysis data (ERA-5) to investigate the link between possibly influential atmospheric variables and the temporal clustering of catchment-averaged extreme rainfall events in Europe. We define extreme events as exceedances above the 99th percentile and runs of consecutive days are declustered. We then explicitly model the seasonal rate of extreme occurrences using penalized cubic splines. The smoothed seasonal rate of extremes is then used to (i) infer the significance of subseasonal clustering and (ii) serves as the baseline rate for the subsequent modelling step. We use a Poisson generalized linear model with the baseline rate set as an offset to model the relationship between the temporal clustering and predictor variables. These variables are the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), atmospheric blocks, and a measure of the recurrence of synoptic-scale Rossby wave packets (RRWPs).

Initial results from four carefully selected catchments reveal the following patterns: for south-western Spain, the NAO, and AO indices tend to be notably lower on significantly clustered extreme rainfall days, whereas for northern Scotland the opposite effect is observed. Also, for south-western Spain, the Greenland atmospheric blocking frequency is significantly enhanced on clustering days. Last, on clustering days in north-western France, Scandinavian blocks are significantly more frequent.

For a complementary study on a methodology to identify subseasonal clustering episodes of extreme precipitation events and their contribution to large accumulations please refer to Kopp et al.

How to cite: Barton, Y., Rivoire, P., Kopp, J., Ali, S. M., and Martius, O.: On the subseasonal clustering of European extreme precipitation events and its relationship to large-scale atmospheric drivers, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2946, https://doi.org/10.5194/egusphere-egu21-2946, 2021.

There are many types and degrees of uncertainty associated with spatial data and processes. 

There are many factors and attributes associated with debris flow analyses which are prone to uncertainty.  For simplicity, in this presentation, only two attributes of debris flow events are investigated along with the impact of their uncertainty on the determination of environmental predisposing factors.    These two attributes, critical to debris flow susceptibility analyses, are landslide classification and event location.  The associated predisposing factors studied herein are lithology, soils, climate, ecophysiographic units, topography, hydrology, and tectonics.

In a landslide susceptibility analysis, landslide event location accuracy is paramount yet often inaccurately known.  Landslide inventories are often constructed based on mapping from aerial imagery, media reports, and field work by third party sources; and in a data-driven approach to debris flow susceptibility analysis the landslide type is important in modeling the relevant predisposing factors distinctive to each landslide type. 

In a study of global debris flow susceptibility an analysis of the impact between known location and a location accuracy offset, and landslide categorization uncertainty demonstrates the impact of uncertainty in defining the appropriate predisposing factors associated with debris flows.

This analysis is part of a larger debris flow global susceptibility determination which trains on known debris flow events and the predisposing factors associated with them to identify potential areas that may be susceptible to debris flows.  This study looks at the impact/differences that mis-categorization or location uncertainty have on the determination of predisposing factors, along with methods of conveying uncertainty information. 

How to cite: Kurilla, L. and Fubelli, G.: Impact of spatial data uncertainty in global debris flow susceptibility analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-628, https://doi.org/10.5194/egusphere-egu21-628, 2021.

Biological invasions can be compared to natural hazards as besides their environmental effect can also produce rapid and damaging socioeconomic impacts. Additionally, their causes and consequences are generally well understood yet difficult to predict, and their incidence is almost unfeasible to control. For both phenomena, it is their random and uncontrollable nature that demand planning for the worst. Therefore, biological invasions and natural hazards require similar management strategies and commitments.

The aim of this study was to support decision makers and stakeholders in Lesvos Island Greece in prioritizing high impact alien plant species. We applied an integrated framework that combined a literature review and a systematic roadside survey of alien plants presence, along with their distribution, abundances, habitat preferences and impacts. Relied on this solid base we structured a prioritization scheme that would identify and rank aliens according to their invasiveness and produce alert lists of the most invasive ones. Two Risk Assessment protocols were implemented: the European and Mediterranean Plant Protection Organization (EPPO) prioritization scheme, and the Australian Weed Risk Assessment (A-WRA). Each screening method delivered assessment lists that classified aliens as invasive, possibly invasive, and non-invasive. With the aim of benchmarking the performances of the two methods we compared their results with a third invasiveness estimation performed by a panel of experts at national level.

In total, 151 alien plants from 53 different families were found. The most abundant families were Asteraceae (10%), Amaranthaceae and Poaceae (9%), and Fabaceae (8%). A subset of 87 species, which excluded urban, ornamental, or cultivated plants with rare occurrences and no documented impacts, was assessed. According to the EPPO scheme, 8% of species categorized as invasive, 57% as possibly invasive and 34% as non-invasive. The A-WRA method was stricter, classifying 80% of species as invasive, 14% as possibly invasive and only 6% as non-invasive. Compared to expert’s opinion, EPPO scheme indicated a 10% match for invasive and a 43% for non-invasive species, whereas A-WRA an 83% and 14% respectively.

Main ranking differences between the two methods are due to the diverse input information each one requires, and to differences in the relevant importance of that information to the final ranking. A-WRA is a precautionary method that rejects even minor invaders, whereas EPPO method is a rapid prioritization tool that provides information for a subsequent appropriate Pest Risk Analysis. Our framework delivers critical information and can improve the development of early-warning systems that would promote successful preventative management strategies for biological invasions.

Acknowledgements: This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme “Human Resources Development, Education and Lifelong Learning 2014-2020” in the context of the project “An Integrative Framework for the Study of Alien Flora” (MIS 5049419).

How to cite: Galanidis, A., Michelaki, C., and Dimitrakopoulos, P.: Biological invasions as natural hazards: towards building a strategy to cope with invasive alien plants, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11167, https://doi.org/10.5194/egusphere-egu21-11167, 2021.

This study aims at exploring whether changes in the spatial distribution of the human population and the built-up areas within floodplains can be associated with extreme flood events generating severe economic losses and fatalities. We relate economic losses and fatalities caused by floods during 1990‐2000, with changes in human population and built‐up areas in floodplains during 2000‐2015 by exploiting global archives as the Global Human Settlement, GFPLAIN250m, and the EM-DAT datasets. Despite the frequent flood losses in the previous period 1990‐2000, we found that population and built‐up areas in floodplains increased in the period 2000‐2015 for the majority of the analyzed countries. On the other hand, we observed a reduction in floodplains population after more severe flood losses that occurred in the period 1975‐2000. Finally, floodplains population increased after a period of high flood fatalities in low‐income countries, while built‐up areas increased after a period of frequent economic losses in upper‐middle and high‐income countries. This study can be used as a general framework for advancing knowledge of human‐flood interactions and support the development of sustainable policies and measures for flood risk management and disaster risk reduction.

How to cite: Mazzoleni, M., Mård, J., Rusca, M., Odongo, V., Lindersson, S., and Di Baldassarre, G.: A global analysis of the interplay between flood severity ad human dynamics in floodplains, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-670, https://doi.org/10.5194/egusphere-egu21-670, 2021.

Colombia is a country with a recent history of an armed conflict from 1960. In 2012, a negotiation process started between the Colombian National Government and the largest left-wing guerrilla group (FARC - Fuerzas Armadas Revolucionarias de Colombia in Spanish). Finally, in 2016 a final peace agreement was signed, where several compromises were taken by both sides. For starting, one of the most important element of the agreement was to define concentration areas into the Colombia territory, where the entire FARC members should be located transitory, and posteriorly those areas must change to permanent settlements following the current regulation related to land use planning in Colombia. This study shows the hazard, vulnerability and risk assessment for 5 concentration areas, which were prioritized, of 24 total areas established. The multi-hazard assessment was analysed from a regional (10-m resolution) and detailed (0.5-m resolution) approach.

For the regional approach, landslide susceptibility was assessed through analytic hierarchy process and weight of evidence methodologies compared to logistic regression and landslide hazard was evaluated with SHALSTAB and Newmark's models for rainfall and seismic triggers. Floods hazard was analysed through a combined methodology using unit hydrograph and the morphometric descriptor HAND. Meanwhile, torrential flows hazard was analysed from a morphometric evaluation and sediment availability from SHALSTAB unstable areas joined with the flood methodology using sediment and water volumes to establish the corresponding area of impact.

For the detailed approach, through field samples and local geotechnical parameters and using TRIGRS and SCOOPS 3D models, the hazard evaluation was carried out for a deterministic result and using FOSM model results can be processed to obtain a probabilistic hazard map. Flood hazard was estimated using the bidimensional hydrodynamic model IBER and the discharge was enhanced simulating the sediment volumes from unstable areas to assess torrential flows hazard but also the mass flow simulation model r.avaflow was employed for a better simulation of the rheology of the flow using the same discharge rates.

This study shows the role of multi-hazard studies as a fundamental element in a peace process, to establish new settlements in the rural area according to the Colombian land-use planning regulation, and under very complex and mountainous terrains conditions. One of the critical points in the short and long term for the sustainability of this peace process is to provide safe areas where FARC members may start a new life.

How to cite: Aristizábal Giraldo, E. V. and the Universidad Nacional de Colombia and Ministerio de Vivienda, Ciudad y Territorio: Hydro-meteorological hazard analysis for new settlements framed in the Colombian peace process, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13833, https://doi.org/10.5194/egusphere-egu21-13833, 2021.

In the recent years, flood situations have been experienced in some regions of Albania, but those that constitute a greater risk are those in the sub localities of Shkodra region. Fortunately, natural disaster are events that occur rarely, but the truth is that they have a high impact on the lives of many people, especially those belonging to developing countries such as Albania. They are associated with the loss of many lives, causing major impact on economic performance and social aspects as well as breaking, the chain of macro and microeconomic balances.

The purpose of this study is to identify the socio-economic impact of floods in the Shkodra region. Describing also the different categories of cost imposed by natural hazard as flood and the most effective way of economic recovery in the conditions of a small developing countries such as Albanian economy. This valuation will be carried out using secondary data such as; the macro and micro economic impact of these natural disasters, climate change, floods over the years in the Shkodra region, damage caused and management of these natural disasters. While primary data are provided through the qualitative method of structured interviews, designed to highlight the socio-economic impact of the flood on individuals and families in these areas, during 2018 flood.

Many catastrophes cannot be avoided, especially natural disasters, however their effects can be mitigated through good management. Over the last 30 years, investments in rehabilitating flood protection infrastructure have been minimal. Flood damage has been assessed mainly after events and detailed flood protection models have been prepared mainly based on emergency responses. Residents affected by flood experienced damage and loss, and while seasonal rains begin, they are worry and fear for loss property and livestock.

Key word; Flood, Climate Change, Economic Impact, Social Impact, Shkodra Region.

How to cite: Hasaj, A.: Assessment of the Socio-Economic Impact of Flooding in Shkodra Region, Albania., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4091, https://doi.org/10.5194/egusphere-egu21-4091, 2021.

Despite improvements in the management of risk from climate related hazards and the introduction of new regulations, loss and damage caused by climate related hazards remains high. An important driver in many parts of the world is the continuation of new assets being built in hazard prone locations. Over the last decade over 120,000 new homes in England and Wales have been built in areas affected by different types of flooding. While the yearly rates of new homes in areas affected by river, coastal or surface water flooding have increased only moderately on the national level, significant differences between and within regions as well as between different flood types exist. Using property level data on new homes built over the last decade and information on the socio-economic development of neighbourhoods, we analyse spatial clusters of disproportional increase in exposure to single or multiple types of flooding from recently built homes and investigate how these patterns evolve under different future climate change scenarios. We find that a disproportionately higher number of homes built in struggling or declining neighbourhoods between 2008 and 2018 is expected to end up in areas at a high risk of flooding over their lifetime as a result of climate change. Based on these findings, we discuss issues regarding future spending on flood protection and affordability of flood insurance in the face of climate change as well as the transferability of the findings to other climate related hazards.

How to cite: Rözer, V. and Surminski, S.: New build homes, resilience and environmental justice – current and future trends in flood risk under climate change across England and Wales, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12494, https://doi.org/10.5194/egusphere-egu21-12494, 2021.

Abstract:

Climate change is one of the most pressing challenges of the 21st century and is likely to increase migration of the marginal communities from the coastal areas throughout the world. It is projected that 200 million people worldwide will be climate refugees by 2050. Owing to high exposure and poor adaptive capacity, low-lying coastal areas and islands in developing countries are the most vulnerable to the impacts of climate change. Therefore, it is imperative to understand how climate change is affecting the livelihoods, in turn, driving the migration in these regions.

The present study focuses on the Sundarbans region located along the coastal belt of West Bengal (India) as a part of Ganga-Brahmaputra mega delta. It is also a home of 4.7 million poor people, who earn below US$10 per month. The region is an exceedingly flat, low-lying, alluvial plain highly exposed to sea level rise, storm surge, tornedoes, cyclonic activity, riverbank erosion, salinization and subsequent mangrove depletion. Due to the climatic hazards, the basic livelihoods are at risk and their strategies towards livelihood collection remains largely unknown. Therefore, the present study provides insights into the nexus among climate stimuli, livelihood risks, and households’ strategies in the region, with special emphasize on climate change.

The study is based on field survey of 150 respondents representing migrant and non-migrant coastal communities from Gosaba, Basanti and Hingalganj block using structured questionnaires. More than 70% of respondents stated that livelihood risks mainly from climate change impacts as the major reason for inter-state migration, which is the main source of income supporting livelihood in the region. This environmental displacement in the Sundarbans region symbolizes the failure of adaptation to mitigate climate change induced sea level rise increasing the exposure to coastal flooding and storm surges, salinization, and erosion.  This study discusses potential mitigation strategies to combat the impacts of climate change on livelihoods of the coastal communities in the region.

How to cite: Pramanik, M., Szabo, S., Pal, I., and Udmale, P.: Climate Change – Livelihood – Migration Nexus: A Case Study from Sundarbans, India, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14256, https://doi.org/10.5194/egusphere-egu21-14256, 2021.

Social vulnerability is mostly described as specific social inequalities in the context of a disaster. Following this understanding, empirical research focuses on the unequal exposure of different groups to disasters and/or on the unequal capacities of groups to anticipate, cope and recover from the impact of a hazard. Although social vulnerability has recently gained attention in academia, Sweden lacks frameworks and indicators to assess it at a national level.

Following the large amount of publicly available data in Sweden, to address this gap, we present a method for quantifying social vulnerability to climate risks in Swedish municipalities. A large number of variables were collected and analyzed to create quantitative indicators that purport to measure a municipality’s vulnerability. Using Principal Component Analysis (PCA), the information in the variables was reduced to a smaller number of components and socioeconomic vulnerability scores for each Swedish municipality. The factor analysis resulted in five components explaining more than 75% of the total variance. The resulting components and the final index are mapped for each municipality.

The results show that socio-economic vulnerability is not evenly distributed across Sweden. Apart from those findings the fact that some municipal clusters are much more vulnerable than others, the developed method is a useful tool for comparing socio-economic conditions among municipalities and for identifying susceptible municipalities which are likely to face significant challenges in coping with future natural hazard events.

Preliminary results show similar trends of social vulnerability to natural hazards at a highly resolved spatial level of aggregation as comparted to municipal levels. As studies on social vulnerability are often data-driven and thus performed on larger administrative aggregations, the sub-set of socio-economic variables from Statistics Sweden used in this study was found useful in our approach. In order to explore social vulnerability in conjunction with coastal and fluvial flood scenarios, an interactive web map was created with ArcGIS Dashboards.

How to cite: Haas, J., Karagiorgos, K., Nyberg, L., and Pettersson, A.: A vulnerability index for climate related risks in Sweden, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5711, https://doi.org/10.5194/egusphere-egu21-5711, 2021.

Adaptation actions are imperative for building societal resilience to unforeseen and unavoidable impacts. With increasing extreme events and the need for sustainable development planning, a shift from reactive to more active anticipatory planning is essential to foster resilience within communities. Since assessments of vulnerability form the initial step to develop adaptation outcomes, we argue the need for differentiated vulnerability approaches for anticipatory adaptation planning for responding to the impacts of climate change-induced risks and social risks in the global south. The dominant conceptualization of adaptation within policy circles at regional and local levels remains overly simplistic with limited attention is to the ‘spatial’ and ‘social’ causes that differentiate vulnerability and adaptive capacity. The study proposes a differential vulnerability framework, based on our empirical findings in India, Ghana, and Ethiopia. We highlight the integration of differential vulnerability perspective, corresponding adaptation planning principles, and inclusive policy approach for overcoming the ‘adaptation deficit’ in the global south. Usage of differential vulnerability approach extends the anticipatory adaptation planning to not only incorporate the anticipation of multiple risks through future scenarios but also to identify locations that will be more acutely affected as a result of existing structural vulnerabilities. We emphasize the need to explicitly address the proximate causes of vulnerability emanating from the broader social and political regimes and help in the transformation of a prevailing governance mechanism for being more equitable, effective, and anticipatory.

How to cite: Shukla, R., Murken, L., and Gornott, C.: Differentiated vulnerability proposition for multi-risk adaptation planning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14005, https://doi.org/10.5194/egusphere-egu21-14005, 2021.

On October 29^th 2018, the Vaia storm hit the mountainous areas in North-Eastern Italy with high wind speeds, heavy gusts and extreme rainfall, leading to major socio-economic damages (two casualties, entire communities isolated for weeks, damages to buildings and infrastructures, etc.). It caused major damages to forests, losses to ecosystem services, and severe short as well as long term socio-economic consequences. As such, this event provides a concrete example of the scale of the hazard to communities and ecosystems and of the involved risks and impacts, including those on the economy, institutions and local communities.

Given this background, our study aims at understanding how individuals affected by the storm: i) detected the potentially dangerous circumstances, ii) reacted to the storm, iii) adapted their routine to cope with the consequence of the event, iv) changed their risk awareness and perception after the event.

To achieve these objectives, we developed a web-based survey addressing 1,500 ca. inhabitants of the Veneto and Trentino Alto Adige regions, two areas that suffered major consequences from the event. The survey quantitatively documented behavioural responses associated with the Vaia event and included questions related to: i) whether respondents changed their normal routine during the storm and if so for what reason; ii) information received before and during the event and how respondents reacted to it; iii) damage suffered during the event; iv) risk awareness and how it changed after the event; v) personal protection measures adopted before and after the event; vi) respondents' attitudinal and psychological traits, with specific reference to Protection Motivation Theory (Rogers, 1975, 1997; McMath and Prentice-Dunn,2005), a well-established theory on risk behaviour.

Data analysis is expected to reveal what are the key characteristics (maybe better factors?) affecting individual behaviours in a dangerous situation, with particular attention to the reasons that drive citizens to change their activities and daily routines during catastrophic events. Specifically, data will be used at first to develop a multivariate statistical analysis to define the determinants of adaptive behaviours and risk awareness. Secondly, they will be used to estimate probabilistic models (Latent Class models) that allow to segregate respondents (and hence the population of reference) in different groups sharing a similar profile in terms of behaviour and attitudes towards the catastrophic event under study. Probability to belong to different behavioural groups will be explained by individuals’ characteristics, such as socio-demographics and psychological traits related to the Protection Motivation Theory. The results will help to better understand societal responses to natural hazards and to explain why certain groups within broader communities are more risk aware and prepared than others. In turns, this will allow to design effective risk management strategies and inform policies and communication strategies aimed at increasing the citizen adaptive capacity.  

How to cite: Thiene, M., Franceschinis, C., Borga, M., and Scolobig, A.: Adaptive behaviours and risk awareness during catastrophic events: the case of the Vaia storm in North-Eastern Italy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6221, https://doi.org/10.5194/egusphere-egu21-6221, 2021.

Individual precautionary behaviour towards and in response to flooding has received much attention in current research, as precautionary behaviour can reduce flood impacts considerably. Therefore, private precautionary measures are increasingly considered in integrated flood risk management plans. Integrated flood risk management requires that all stakeholders threatened by flooding undertake action to limit adverse impacts. However, our current understanding of private precautionary measure employment has mostly been drawn from cross-sectional studies, i.e. data from one-time snapshots. While cross-sectional data has its uses in understanding individual behaviour and its drivers, other questions require the use of panel data, i.e. repeated surveys of the same individuals in order to correctly identify and understand temporal behavioural dynamics which cross-sectional data is unable to capture.
Here we use panel data to identify different types of dynamic adaptive behaviour. We applied and compared two classification methods to panel data from 227 individual households who were repeatedly interviewed across Germany about their implementation of precautionary measures after the widespread flood of June 2013: Latent Class Growth Analysis (LCGA) and Cluster Analysis based on k-means for longitudinal data. Results indicate three different groups of adaptive behavior over the survey period that are identified by both classification methods: (I) a group that maintains a “high standard” of protection, (II) a “performer” group that implements a fare share of precautionary measures after the flood and during the survey period and (III) a “non adaptive” group that shows little or no implementation of precautionary measures. As a considerable share of flood-prone residents did almost not adapt, results indicate that specific risk communications and funding schemes are needed in order to trigger adaptation of this group.

How to cite: Berghäuser, L., Bubeck, P., Hudson, P., and Thieken, A. H.: Identifying different types of individual flood precautionary behaviour from panel data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13600, https://doi.org/10.5194/egusphere-egu21-13600, 2021.