NH9.1

India is one of the world's most flood-prone countries, with 113 million people exposed to floods. Large-scale hydrological models integrated with complicated Navier–Stokes based hydraulic, and inundation models traditionally address flood preparedness, control, and mitigation. In addition to being highly data-intensive at the fine spatial and temporal resolution, this approach has a considerable computational cost that limits real-time applications. We employ the parameter-free Dynamic Budyko (DB) hydrological model to map observed precipitation with gridded runoff to overcome data scarcity. We propose a time-efficient Slope-corrected, Calibration-free, Iterative Flood Routing and Inundation Model (SCI-FRIM) framework that can be used with any hydrological model to generate a probability map of inundation. To model the catastrophic flood extents that the state of Kerala in India experienced during August 2018, we use gridded 0.25 deg × 0.25 deg IMD precipitation data. We use a parameter-free iterative approach to update flood velocity by assuming that river velocity does not fluctuate geographically across a particular river network at a given time instant. We pre-compute the iterative velocity and model the relationship between flood velocity-discharge and discharge-inundation height for each reach by combining the globally available SRTM/ASTER DEMs with empirically obtained river-reach geometry data (JPL). We compute the reach slope from the absolute vertical error-prone DEM by segmenting the river network into a series of independent channels and extracting the relationship between the channel pixel's elevation and the pixel's distance to the pour point. We use the Height Above Nearest Drainage (HAND) to map the probabilistic spatial extent corresponding to an ensemble of derived reach inundation heights. We then compare the proposed model with observed flood data points provided by the Kerala State Disaster Management Authority (KSDMA). The model captures up to 52% of 370,000 flood data points in a single run for the peak flood day within 15 minutes on a desktop computer. With reliable estimates of empirical bankfull discharge, the proposed model can achieve higher accuracy in lesser time.

How to cite: Unnithan, S. L. K., Biswal, B., Rüdiger, C., and Dubey, A. K.: Conceptual Flood Inundation Modelling: Computationally Efficient Methods for Large Data-scarce River Basins, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-450, https://doi.org/10.5194/egusphere-egu22-450, 2022.

In the context of climate change, probability of risk phenomena occurrence is more frequent and with greater intensity. This is especially valid for floods which cause significantly more damage and casualties, as flood-inducing conditions are met more often. The risk is emphasized by the fact that countless human settlements are located on the floodplain of river courses of different sizes and flow rates. The current study aims to detail an automatic GIS model that can easily compare drainage sub-basins of similar order, according to Horton-Strahler hierarchical classification, at large scale, for a given basin, based on morphometric parameters. This implies the use of a digital elevation model (DEM) as the only input layer, and setting a few parameters, in order to extract several quantifiable hydrological indicators, relevant to flood analysis. Some of the most relevant ones from the list are the elongation ratio, circularity ratio, relief ratio, roughness number, drainage density etc. All the functions have been integrated into a GIS tool, that would automatically aid in the fast creation of a final vector layer, that discerns between drainage basins with higher and lower degrees of relative vulnerability. This layer contains an attribute table with all the relevant parameters, as well as the result of the formula that assigns flood vulnerability values to each drainage basin, making possible the quantitative comparison between all the drainage sub-basins. The resulting table analysis is conducted in the background, based on the calculation of normalized values for each parameter, which are encompassed into a final vulnerability score. The model is easily applicable to most types of raster elevation layers, as long as they are in a projected coordinate system, regardless of pixel size. Furthermore, several functions were added to the model to mitigate potential errors that can occur in isolated cases, where the topography is particularly difficult to interpret by some native GIS tools. Therefore, this model is an easy to apply tool, that automatically identifies more vulnerable sub-basins, from a large drainage basin, over extended areas, with limited user-input, facilitating decision making in flood management, while providing quantifiable flood vulnerability results, in a very short period of time, without requiring extensive knowledge from the user.

How to cite: Enea, A., Iosub, M., and Stoleriu, C. C.: GIS automation of large-scale flood vulnerability analysis for drainage basins, based on a single Digital Elevation Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2895, https://doi.org/10.5194/egusphere-egu22-2895, 2022.

Floods are extreme natural hazards often with disastrous impacts on the economy and society. Flood risk assessments are required to better manage risk associated with floods. Nowadays, numerous flood risk models are available at various scales, from catchment to regional or even global scale. They involve a complex modelling chain that estimates risk as the product of probability of occurrence of an event (hazard) with its footprint (exposure) and the consequences over society and economy (vulnerability). Each component of this chain contains uncertainties, that propagate and contribute to the uncertainty in the model outputs. Much effort has been made to quantify such output uncertainty and attribute it to the various uncertainty sources in the modelling chain. However, the key drivers of uncertainty in flood risk estimates are still unclear because previous studies have reached conflicting conclusions.  Two things could possibly explain these ambiguous outcomes. First, these studies were implemented with different models and with different data, as well as different assumptions for the uncertainty and sensitivity analysis. Second, the studies were conducted at catchment and/or city scale with limited variability of physical and socio-economic characteristics within a study region, but with potentially large differences across study regions. In this project, we study the question of uncertainty quantification and attribution at much larger scale, namely the heterogeneous region of the Rhine River basin. In this way, we can identify spatial patterns of dominant input uncertainties and link them to characteristics, e.g. physical, socio-economic, in the different sub-basins. To this end, we use an industry flood risk model (catastrophe model) provided by JBA Risk Management which is capable of simulating flood risk across such a large region. Our ultimate goal is to provide evidence of how the importance of uncertainties varies across places with different climatic, hydrologic and socio-economic characteristics.

How to cite: Sarailidis, G., Pianosi, F., Wagener, T., Lamb, R., Styles, K., and Hutchings, S.: Linking the relative importance of input uncertainties of a flood risk model with basin characteristics., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3122, https://doi.org/10.5194/egusphere-egu22-3122, 2022.

Global flood models have undergone rapid development over the past decade. However, with each new generation of model it is essential to systematically evaluate simulation performance for a range of tests and against multiple sources of data. It is also important to take stock, document lessons learnt and contribute to the formation of better practice and modelling standards in the field. Here we illustrate some of the progress being made in global flood modelling by evaluating the latest 30 m resolution implementation of the LISFLOOD-FP/Fathom global flood model over the Central Highlands of Vietnam, and benchmark it against several previous incarnations of the model.

Two independent data sources are used to evaluate the model. The first of these maps recent flood extents using remotely sensed data from the Sentinal-1 missions and compares them to global flood model outputs of commensurate return periods. The second data set identifies land parcels (properties and agricultural fields) that flooded during the same events from a household survey, where uniquely all household land parcels in four villages were sampled. The independence of the date sets also allowed for cross-validation of the observations.

Substantial simulation enhancements are associated with the transition from SRTM and MERIT DEM’s at 90 m resolution to FABDEM, a version of Copernicus DEM at 30 m with forests and buildings removed. In addition to improvements derived from the DEM, more accurate river location, river width and discharge estimates combined with the inversion of river bathymetry via gradually varied rather than uniform flow theory also have an impact on performance.

How to cite: Neal, J., Hawker, L., Savage, J., Kirkpatrick, T., Zylberberg, Y., and Nam, P. K.: Evaluating the next generation of global flood models in the Central Highlands of Vietnam, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11682, https://doi.org/10.5194/egusphere-egu22-11682, 2022.

Reducing flood risk through improved disaster planning and risk management requires accurate and reliable estimates of flood damages.  Damage models commonly provide such information through calculating the impacts or costs of flooding to exposed assets, such as buildings within a community. At large scales, computational constraints or data coarseness leads to the common practice of aggregating asset data using a single statistic (e.g., the mean) prior to applying non-linear damage models. While this simplification has been shown to bias model results in other fields, like ecology, the influence of object aggregation on flood damage models has so far not been investigated. This study quantifies such errors in 12 published damage function sets and three levels of aggregation using simulated water depths. Preliminary findings show bias as high as 20% (of the damage estimate), with most damage functions having a positive bias for shallower depths (< 1 m) and a negative bias for larger depths (> 1 m). In other words, compared to an analogous model with object-specific asset data, aggregated models overestimate damages at shallow depths and underestimate damages at large depths. These findings identify a potentially significant source of error in large-scale flood damage assessments introduced, not by data quality or model transfer, but by modelling approach. With this information, risk modellers can make more informed decisions about when, where, and to what extent aggregation is appropriate. 

How to cite: Bryant, S., Kreibich, H., and Merz, B.: Flood damage model bias caused by aggregation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5679, https://doi.org/10.5194/egusphere-egu22-5679, 2022.

Economic inequality is today increasing in many contexts. Its consequences are multifaceted and relate to questions of justice, welfare, human well-being and health. Economic inequality also affects (directly or indirectly) society’s vulnerability to flood disasters. Research has previously shown that the ex-ante economic distribution within a country may affect the disaster outcomes. For instance, unequal societies also tend to exhibit spatial marginalization. If these marginalized areas are burdened with neglected infrastructure they also have a lower ability to divert flood water.

Our work highlights the role that economic inequality plays in explaining human flood losses, worldwide. We perform a statistical analysis using data for over a hundred countries and illustrate the importance of considering income distribution when building flood resilient societies. We also show how our results vary between different levels of economic development and discuss implications of our results on disaster research and risk reduction. 

How to cite: Lindersson, S., Raffetti, E., Brandimarte, L., Mård, J., Rusca, M., and Di Baldassarre, G.: A global analysis of economic inequality and flood losses, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7130, https://doi.org/10.5194/egusphere-egu22-7130, 2022.

Floods and tropical cyclones displaced more than 275 million people between 2008 and 2020, with the two hazards together being responsible for 86% of all displacements. It is important to understand the socio-economic drivers of displacement vulnerability to quantify future changes in risk, for instance, due to climate change, economic development, or social inequities. Here, we investigate globally and event-by-event the displacement vulnerability due to flooding and tropical cyclones (TCs), using remote sensing-derived hazard data. We create a database of displacement events associated with spatially explicit flood or TC hazard, by matching displacement data from the Internal Displacement Monitoring Center (IDMC) spatially and temporally with satellite imagery from the recently published Global Flood Database and a collection of tropical cyclone data. The resulting hazard footprints are overlaid with gridded population data to derive the number of affected people for each event, which is compared with estimated displacement to determine the event-specific vulnerability. Between and within continental regions, displacement vulnerability varies by several orders of magnitude. We generally find a negative trend between displacement vulnerability and increasing (socio-)economic prosperity indicators, such as GDP per capita or the Human Development Index (HDI). Indicator binning reveals further insights, for instance, a higher proportion of urbanization or female population tends to indicate a lower susceptibility towards TC impacts. We analyze the uncertainty associated with different population datasets and methods to compute the number of affected people. Our analysis provides new insights into patterns and potential drivers of displacement vulnerability across space and between socio-economic groups. To our knowledge, the usage of the extensive set of observational satellite imagery is an unprecedented approach for global flood vulnerability analysis, posing remote sensing as a suitable alternative for global models for future studies. 

How to cite: Mester, B., Frieler, K., and Schewe, J.: A global-scale vulnerability assessment of human displacement for floods and tropical cyclones, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5608, https://doi.org/10.5194/egusphere-egu22-5608, 2022.

Flood models typically produce flood maps with constant return periods in space, without considering the spatial structure of flood events. At a large scale, this can lead to a misestimation of flood risk and losses caused by extreme events. A stochastic approach to global flood modelling allows the simulation of sets of flood events with realistic spatial structure that can overcome this problem, but until recently this has been limited by the availability of gauge data. Previous research shows that simulated discharge data from global hydrological models can be used to develop a stochastic flood model of the United States (Wing et al., 2020) and suggests that the same approach can potentially be used to build large scale stochastic flood models elsewhere but this has not so far been tested.   

This research therefore focuses on using discharge hindcasts from global hydrological models to drive stochastic flood models in different areas of the world. By comparing the outputs of these simulations to a gauge-based approach, we analyse how a model-based approach can simulate spatial dependency in large scale flood modelling outside of well-gauged territories such as the US. Based on data availability we selected different areas in Australia, Southern Africa, Southeast Asia, South America and Europe for the analysis.

The results of this research show that the performance of a model-based approach in the different continents is promising and in most areas the errors are comparable to the results obtained in the United States by Wing et al. (2020). In the United States, with this magnitude of errors, the loss distribution obtained using the model-based approach is near identical to the one produced by the gauge-based method. This suggests that this method could be used in other regions to characterize losses. Using a network of synthetic gauges with data from global hydrological models would allow the development of a stochastic flood model with detailed spatial dependency, generating realistic event sets in data-scarce regions and loss exceedance curves where exposure and vulnerability data are available.

References

Wing, O. E. J., Quinn, N., Bates, P. D., Neal, J. C., Smith, A. M., Sampson, C. C., Coxon, G., Yamazaki, D., Sutanudjaja, E. H., & Alfieri, L. (2020). Toward Global Stochastic River Flood Modeling. Water Resources Research, 56(8). https://doi.org/10.1029/2020wr027692

How to cite: Olcese, G., Bates, P., Neal, J., Sampson, C., Wing, O., and Quinn, N.: Can hydrological models be used to characterize spatial dependency in global stochastic flood modelling?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9310, https://doi.org/10.5194/egusphere-egu22-9310, 2022.

Coastal flooding is driven by strong winds and low pressures in tropical and extratropical cyclones that generate a storm surge, and high tides. The combination of storm surge and the astronomical tide is defined as the storm tide. Currently over 600 million people live in coastal areas below 10 m elevation worldwide which is projected to increase to more than 1 billion people by 2050 under all Shared Economic Pathways. Towards the end of the 21^st century these growing coastal populations will be increasingly at risk of flooding due to SLR. To gain understanding into the threat imposed by coastal flooding and identify areas that are especially at risk, now and in the future, it is crucial to accurately model coastal inundation and assess the coastal flood hazard.

There are three main types of inundation models with complexity levels ranging from simple, to semi-advanced to advanced. Models capable of simulating inundation at the global scale follow a simple static approach. These models, often referred to as bathtub models, delineate the inundation zone by raising maximum water levels, that correspond to a return period, on a coastal DEM and select all areas that are below the specified water level height. The main limitations of this type of model is that they implicitly assume an infinite flood duration and do not capture relevant physical processes. Regional comparisons have shown that dynamic inundation models are much more accurate than static models in terms of flood extent and depth, and they can provide information on the flood duration.

In this study we develop a global dataset of storm tide hydrographs. These hydrographs represent the typical shape of an extreme sea level event at a certain location along the global coastline and can be used as boundary conditions for dynamic inundation models. This way we can move away from static to more advanced dynamic inundation models. To assess how different assumptions used for generating hydrographs influence the inundation extent and depth we perform a sensitivity analysis for several coastal regions.

How to cite: Dullaart, J., Muis, S., de Moel, H., Eilander, D., Ward, P., and Aerts, J.: Enabling dynamic modelling of global coastal flooding by defining storm tide hydrographs, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-179, https://doi.org/10.5194/egusphere-egu22-179, 2022.

Small Island Developing States are some of the most at risk places to flooding caused by tropical cyclone rainfall. However, there is a mismatch between existing flood risk assessment in small islands, and the increasing severity of projected tropical cyclone rainfall under current and future climate change. This research aims to address this gap by presenting the first application of an event-based rainfall-driven hydrodynamic model in a small island, for the Caribbean island of Puerto Rico. Applying an event set of 59,000 synthetic hurricane rainfall events, we represent hurricane rainfall spatially (~10km) and temporally (2-hourly), estimating flood hazard and population exposure at the island scale (9,100km²) at 20m model resolution using hydrodynamic model LISFLOOD-FP. Using this event-based approach, we aim to understand: i) what are the current estimates of population exposure to flooding from hurricane rainfall in Puerto Rico; and ii) how do these risk estimates change under 1.5°C and 2°C climate scenarios. We find that current population exposure to flooding from hurricane rainfall in Puerto Rico is high (8-9.80% of the population every 5 years), with an increase in population exposure of 1.60-15.20% and 0.70-22.30% under 1.5°C and 2°C climate change. This has critical implications for adaptation to more extreme flood risk in Puerto Rico, as well as underlining the important implications of the 1.5°C Paris Agreement target for small islands – a finding that is likely to be applicable to other small islands affected by tropical cyclones.

How to cite: Archer, L., Neal, J., Bates, P., Vosper, E., Sosa, J., and Mitchell, D.: Current and Future Flood Risk from Tropical Cyclones in Puerto Rico Under 1.5°C and 2°C Climate Change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2871, https://doi.org/10.5194/egusphere-egu22-2871, 2022.

Estimating site amplification of earthquake ground shaking at new sites and sites without any direct geotechnical measurements of site parameters remains a large challenge in seismic hazard assessment. Currently, the standard procedure is to use site proxies inferred from topographic slope from digital elevation models (DEMs). In this study, we test a geomorphological model for inferred regolith, soil and sediment depth by Pelletier et al. (2016). This model was originally developed as input for hydrology and ecosystem models and is based on several global values in addition to the topographic slope, including geological maps and water table data.

To test the suitability of the geomorphological model for ground-shaking prediction we derive the empirical site amplification for sites in Japan, Italy and California using different regional and global seismological datasets. We use the observed shaking amplification to test the correlation between the observed ground-shaking site amplification and the inferred site proxies and test the performance of site amplification models based on geomorphological proxies. We find that the geomorphological model works equally well or slightly better than the traditional inferred proxies. We therefore argue that this model is a promising alternative proxy that can be used for predicting site amplification on new sites and regions for which no geotechnical information exists (i.e. on a global level). This result has important implications for the development of the new generation of ground-shaking models used for shake maps and seismic hazard models.

How to cite: Loviknes, K. and Cotton, F.: Testing global geomorphological model as site proxy to predict ground-shaking amplification, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9586, https://doi.org/10.5194/egusphere-egu22-9586, 2022.

This research examines the responsibilities of and the interactions between the various research institutes, national agencies, regional groups, and local councils involved in monitoring, disseminating, and responding to official tsunami warnings in New Zealand. Specifically, the underlying issues within the separated structure of tsunami early warning and response in New Zealand is examined as to whether this enhances or restricts risk assessment.

In many countries, the same agency is responsible for both monitoring tsunami hazards and issuing tsunami warnings. However, in New Zealand, this process is split. GNS Science is the research institute responsible for monitoring tsunami hazards in New Zealand, if tsunami generation is confirmed GNS Science provides risk information to the nation’s official tsunami warning agency. The National Emergency Management Agency (NEMA) is the national agency responsible for issuing tsunami warnings in New Zealand. NEMA communicates national tsunami warnings to regional response groups as well as the public and media. The Civil Defence Emergency Management (CDEM) Groups are then responsible for coordinating regional tsunami evacuations, with New Zealand being split into 16 regional CDEM Groups. Within these regional groups, district and city councils can also tailor the evacuation information to communities at a local level.

Online social research methods were used to explore tsunami risk assessments in New Zealand. 106 documents and archives were collected and 57 semi-structured interviews conducted with tsunami researchers, warning specialists, and emergency managers. The majority of the interviewees were from New Zealand, with some participants also being recruited from Australia, the Pacific Islands, the UK, and the USA. This allowed for national, regional, and local responses in New Zealand to be compared to those in different countries to explore how warning systems operate in practice.

Key findings indicate that New Zealand having separate monitoring and warning agencies leads to the potential for error when passing information between organisations and delays can also be caused in disseminating official warnings. The warnings are communicated on a national scale, whilst the responses carried out vary between regions, having separate warning and evacuation agencies means there is a need for consistent messages and coordinated responses. GNS Science is capable of operating 24 hours per day, whereas NEMA and the CDEM Groups do not currently have this capacity. Again, this can cause delays in issuing and responding to official warnings. Variations in funding on a regional level also effect the number of staff and amount of resources in particular CDEM Groups.

These issues are underpinned by the ways in which knowledge is exchanged within the warning system and the lack of integration between national, regional, and local agencies. Tsunami researchers and warning specialists on a national level, and emergency managers on regional and local levels, must work together to effectively disseminate and respond to official tsunami warnings. This research concludes that the separated structure of tsunami early warning and response in New Zealand involves underlying issues which must be addressed in order to improve risk assessment.

How to cite: Fearnley, C., Hunt, R., Day, S., and Maslin, M.: The Responsibilities of and Interactions between Tsunami Early Warning and Response Agencies in New Zealand, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7321, https://doi.org/10.5194/egusphere-egu22-7321, 2022.

Large-magnitude volcanic eruptions have long been considered to pose a threat to the continued flourishing of humanity. The dominant narrative focuses on the nuclear-winter climatic scenarios that may develop as a result of a large-magnitude eruption (magnitudes 7+ on the Volcanic Explosivity Index (VEI)) propelling large quantities of ash and gas into our upper atmosphere and devastating global crop production. However, the probability of such an event remains rare, and this narrative fails to fully consider the vulnerability component of the risk equation. We propose that volcanic eruptions of even moderate magnitudes (VEI 3-6) could constitute a global catastrophic risk (events that might inflict damage to human welfare on a global scale) where the impacts of the eruption are amplified through cascading critical system failures.

Increased globalisation in our modern world has resulted in our overreliance on global critical system – networks and supply chains vital to the support and continued development of our societies (e.g. submarine cables, global shipping routes, transport and trade networks). We observe that many of these critical infrastructures and networks converge in regions where they could be exposed to moderate-scale volcanic eruptions (VEI 3-6). These regions of intersection, or pinch points, present localities where we have prioritised efficiency over resilience, and manufactured a new GCR landscape, presenting a scenario for global risk propagation. We present seven global pinch points, including the Strait of Malacca and the Mediterranean, which represent localities where disruption to any of these systems can result in a cascade of global disruptions. This is exemplified by the 2010 Eyjafjallajökull VEI 4 eruption which resulted in the closure of European airspace and cascaded to cause global disruption to just-in-time supply chains and transportation networks.

We suggest that volcanic risk assessments should incorporate interdisciplinary systems thinking in order to increase our resilience to volcanic GCRs.

How to cite: Mani, L., Tzachor, A., and Cole, P.: Lower magnitude volcanic eruptions as Global Catastrophic Risks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2338, https://doi.org/10.5194/egusphere-egu22-2338, 2022.

Costal reclaimed farmlands are commonly threatened by saltwater intrusion and peat-driven salinity, resulting in low and unstable agricultural productions. Climatic variables have a great effect on soil moisture and salinity influencing crop production during the various growing seasons. For this reason, monitoring soil water and salinity dynamics in the root zone during the crop growing season is fundamental to conceive mitigation strategies (e.g., precision irrigation techniques). To this end, a monitoring network was installed in an agricultural field located at the southern margin of the Venice Lagoon. Three soil-stations were placed along the main sandy paleochannel crossing the farmland southwest to northeast (stations S1, S2, and S3), while stations S4 and S5 were placed in two silty-loamy areas with high peat content. Each station was equipped with three T4e tensiometers (UMS GmbH, Munchen, Germany) at 0.3, 0.5, and 0.7 m, four Teros 12 sensors (METER Group, Inc., Pullman, WA, USA) measuring volumetric water content, temperature, and electrical conductivity (EC_b) at 0.1, 0.3, 0.5, and 0.7 m. In addition, a 2 m deep piezometer was installed to monitor groundwater electrical conductivity (EC_w) and depth to the water table. Soil samples were collected on each monitoring location and analyzed for texture, bulk density (BD), soil organic carbon (SOC), electrical conductivity (EC 1:5), pH, and cation exchange capacity (CEC). Moreover, a weather station was installed in the experimental field to accurately monitor the local meteorological conditions during the 2019 and 2020 growing seasons. The soil monitoring dataset shows that EC_b increases with depth at all locations. Moreover, rainfall events higher than 10 mm/day caused an increase in the EC_b at all layers and stations. The monitoring stations inside the paleochannel showed lower EC_b if compared to station S4 and S5, probably due to the highest hydraulic conductivity and, consequently, the highest leaching capacity. S5 was characterized by the highest peat content and showed the highest salinity in both soil and groundwater. In general, soil EC_b and groundwater EC_w showed similar behavior in 2019 and 2020, except for S4 and S5 that were saltier in 2019. These preliminary analyses demonstrated a strong influence of rainfall events on salinity behavior and highlights how climatic variables, soil heterogeneity, and saltwater intrusion at depth play an important role in the complex salinity dynamics within the root zone.

How to cite: Teatini, P., Ester, Z., and Francesco, M.: Assessing the effects of climatic variables on soil and groundwater salinity in a low-lying agricultural field near the Venice Lagoon, Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7756, https://doi.org/10.5194/egusphere-egu22-7756, 2022.

How to cite: Varis, O., Kummu, M., and Taka, M.: Geography of World’s Water Risks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7874, https://doi.org/10.5194/egusphere-egu22-7874, 2022.

Accurately estimating wildfire risk is essential for many use cases, such as prioritizing adaptation resources or offering insurance coverage for these devastating events. In collaboration with the Zurich-based InsurTech company CelsiusPro we present a globally consistent, open-source wildfire hazard, based on state-of-the-art fire models and providing high-resolution, probabilistic fire seasons suitable for risk analysis and insurance coverage pricing.

For the probabilistic part, we build upon the existing wildfire hazard model available on the open-source climate risk modelling platform CLIMADA (CLIMate ADAptation). This model creates stochastic wildfire events at 1 km resolution using a random walk generator that assigns a grid-point specific fire ignition and propagation probability based on Fire Information for Resource Management System (FIRMS) satellite data and physical constraints such as population density and land cover. However, this model does not account for key physical drivers, such as wind.

On the other hand, data from state-of-the-art fire models are available through the Fire Model Intercomparison Project (FireMIP), which coordinates the evaluation and comparison of these models. While most available models account for the complexity of fire ignition and propagation including relevant physical drivers, their resolution (ranging from 0.5° to 2.8°) is too coarse for the assessment of economic impacts as needed for insurance coverage pricing. In addition, most models are not fully probabilistic, but provide their outputs for present and future climate conditions.

In this work, we combine the annual fraction of burnt area provided as FireMIP output with CLIMADA’s stochastic model, resulting in a probabilistic, high-resolution wildfire hazard model that is based on state-of-the-art fire modelling. This allows us to compute a globally consistent economic risk of wildfires to physical assets by combining the newly developed hazard with an exposure and vulnerability.

How to cite: Steinmann, C. B., Lüthi, S., Gübeli, S., Guillod, B. P., and Bresch, D. N.: Downscaling global wildfire model output to a relevant scale for probabilistic wildfire risk assessment of economic impacts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8854, https://doi.org/10.5194/egusphere-egu22-8854, 2022.

Catastrophe models are very important tool to provide proper assessment and financial management of earthquake-related emergencies, which still create the largest protection gap across all other perils. Earthquake catastrophe models contain three main components: earthquake hazard, vulnerability and exposure. Simulating spatially-distributed ground-motion fields within either deterministic or probabilistic seismic hazard assessments poses a major challenge when site-related financial protection products are required. Several authors have demonstrated that the spatial correlation of earthquake ground-motion is period-, regionally- and scenario-dependent, so that the implementation of a unique correlation model may represent an oversimplification.

In this framework, we have established a joint research project between the University of Strathclyde and Impact Forecasting, Aon’s catastrophe model development centre of excellence, in order to advance the understanding of spatial correlations within the catastrophe modelling process. We developed correlation models for northern, central and southern Italian regions using both ad hoc and existing ground-motion models calibrated on different databases. Thereafter, we performed both deterministic scenario and event-based probabilistic hazard and risk assessments for Italy using the 2020 European Seismic Hazard and Risk Models. We employed the OpenQuake-engine for our calculations, which is an open-source tool suitable for accounting for the spatial correlation of earthquake ground-motion residuals. The results demonstrate the importance of considering not only the ground-motion spatial correlation, but also its associated uncertainty in risk analyses. Our findings have implications for (re)insurance companies evaluating the risk to high-value civil engineering infrastructures.

How to cite: Stripajova, S., Schiappapietra, E., Pazak, P., Douglas, J., and Trendafiloski, G.: Investigating the effect of spatial correlation on loss estimation in catastrophe models – a case study for Italy , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4950, https://doi.org/10.5194/egusphere-egu22-4950, 2022.

As the climate and the risks of extreme weather to society change, access to tools for researchers and decision makers to assess the possible evolution of impacts should be facilitated. The open-source modelling platform CLIMADA (CLIMate ADAptation) allows to investigate the present and future statistical risk of natural hazards to human and economic systems, from the local to the global scale. One of the latest additions to the platform is an Application Programming Interface (API) providing access to exposure and hazard data to perform risk assessments on a consistent 4km grid. Hazard sets for tropical cyclones, droughts, heat-waves, wildfires, river floods, and crop-yield are, or will imminently be available at a worldwide scale on the API. In addition, region-specific hazards such as European winter storms are available. As for the exposures at risk, both population count and assets can be considered based on the data produced trough the CLIMADA LitPop module.

Owing to the availability of globally consistent hazard and exposures datasets through the CLIMADA API, it is now possible to compute and combine the impacts from several hazards. In this first study making use of the API, we calculate global probabilistic economic impacts for tropical cyclones, river floods and reduced crop yields for historical data, as well as for future time steps based on the RCP2.6 and RCP8.5 climate scenarios. From these hazard sets, we compute probabilistic annual impact sets for each hazard. In the case that impacts are provided on an event-base and not on a yearly basis, the probabilistic annual impact sets are created by randomly sampling the number of events per year following a Poisson distribution. From the impact sets per hazard, we finally quantify the total combined cost in a same year and grid cell in order to investigate temporal and spatial correlations of the different hazards.

How to cite: Stalhandske, Z., Schmid, E., Steinmann, C. B., Kropf, C., and Bresch, D. N.: Many-hazard Risk Assessment with the CLIMADA Data API, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8673, https://doi.org/10.5194/egusphere-egu22-8673, 2022.

As part of the NARSIS (New Approach to Reactor Safety ImprovementS, www.narsis.eu) project, and the MYRIAD-EU (Multi-hazard and sYstemic framework for enhancing Risk-Informed mAnagement and Decision-making in the EU, www.myriadproject.eu) project, a compendium of existing open access software packages for risk modelling of natural hazards, as well as a review of multi-hazard projects has been undertaken with a clear focus on assessments in Europe.

There have been over 200 open access software packages produced for the evaluation of singular natural hazards, combinations of natural hazards and multi-hazard identified either propagating through to risk, or calculating extensive hazard metrics. By far, the most have been built for floods, and earthquakes, however a number have been designed for multi-hazard (RiskSCAPE, HAZUS and variants, CLIMADA, NARSIS-MHE, InaSAFE to name a few).

In around 120 of them, they have moved through to risk assessment, with the calculation of risk metrics. Many of these have been designed for scenario analysis, but there are also many which employ probabilistic methods or stochastic models to evaluate risk. In this work, the classification of the open access software packages follows that of previous studies (Daniell et al., 2014), but with a focus on the use for multi-hazard assessment rather than singular hazards.

Moving through to multi-risk, a number include different interconnected systems for assets (OOFIMS for instance from the EU SYNER-G project). Although there are very few that deal with consecutive or coinciding hazards, a number can be adapted to do this, and some even have the ability to be used for cascading hazard analysis.

By understanding the state-of-the-art in existing software packages as of 2022, a multi-hazard framework can be produced for various economic sectors such as ecosystems and forestry, energy, finance, food and agriculture, infrastructure and transport, as well as tourism, to solve some of the missing links when looking at the impacts of consecutive, coinciding or cascading hazards. In addition, relevant software packages have been found to conduct assessments on the European scale, but also on the local scale for more detailed analyses.

How to cite: Daniell, J., Schaefer, A., de Ruiter, M., Foerster, E., Ward, P., Brand, J., Khazai, B., Girard, T., and Wenzel, F.: Multi-hazard open access software package review with the potential for conducting sectoral risk assessments on a European or local scale, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12982, https://doi.org/10.5194/egusphere-egu22-12982, 2022.

Population projections for alternative socio-economic scenarios are crucial to understand climate change impacts. Current global gridded population projections are only available at coarse resolutions (~1km) that are inconsistent with the latest hazard models. Thus, climate change impact studies often utilise sub-optimum datasets by using coarse resolution gridded population predictions or present day population, and therefore may not adequately represent future population. To fill this gap, we use the latest datasets that align with the policy relevant Shared Socioeconomic Pathway (SSP) Scenarios and CMIP6 projections to create the first gridded population at ~90m resolution globally. We call this new dataset FuturePop. Projections are made at decadal intervals and extend to 2100 for each of the 5 SSP scenarios. Our method uses country level population and % urban projections from the SSP Database, redistributing population based on delineation of rural and urban areas. We add sophistication to our method by considering associated information such as travel time, and also include predictions of urban expansion. Comparison to existing global and regional datasets show FuturePop has considerable skill in predicting plausible population changes and redistribution. Lastly, we demonstrate the importance of using FuturePop for future flood risk compared to existing gridded population projections. Hazard footprints typically have horizontal length scales of tens to thousands of meters, thus it is crucial to depict populations at these scales to accurately estimate future flood exposure.

How to cite: Hawker, L., Bates, P., and Neal, J.: FuturePop - Global Gridded Population Projections at 90m resolution, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9606, https://doi.org/10.5194/egusphere-egu22-9606, 2022.