Abstract: The incidences of earthquakes in the north Indian state of Uttarkhand are broadly associated with the presence of active fault viz. Main Central Thrust and Alaknanda Fault in the north, Moradabad Fault and Himalayan Frontal Thrust in the southern margin, Martoli Thrust and Indus Suture in the eastern, Mahendragarh Dehrdun Fault in the west. Uttarakhand falls under Seismic Zone IV and V and has been struck by several devastating earthquakes viz. 1905 Kangra earthquake of M_W 7.8, 1991 Uttarkashi earthquake of M_W 6.8 and 1999 Chamoli earthquake of M_W 6.5 with maximum MM Intensity of IX observed in near-source region causing widespread damage and destruction in the study region. Uttarakhand region has undergone unprecedented development and population growth, emphasizing the importance of analysis of Seismic Hazard to ensure safe and secure progress in this seismically vulnerable region. Consideration of seismicity patterns, fault networks and similarity in the style of focal mechanisms yielded 10 areal seismogenic sources with additional active tectonic features in 0-25km, 25-70km, and 70-180km hypocentral depth ranges, along with 15 Ground Motion Prediction Equations for the tectonic provinces of Uttarakhand region yielding Probabilistic Peak Ground Acceleration (PGA) at engineering bedrock  seen to vary from 0.36g to 0.63g for 475years of return period which places the region in the moderate to high hazard zone necessitating a case study for site-specific seismic characterization of the region. Seismic site classification has been done based on an enriched geophysical, in-situ downhole, geotechnical database and surface geoscience attributes comprising of Geology, Geomorphology, Landform and Topographic Gradient derived shear wave velocity categorizes the region into Site Classes E, D4, D3, D2, D1, C4, C3, C2, C1, B and A. Using the input ground motion at bedrock level obtained from stochastic simulation for the near-source earthquakes, nonlinear site response analyses have been performed using PLAXIS-2D software package wherein site amplification has been mapped which is seen to vary in the range of 1.02 to 2.86. Surface-consistent probabilistic seismic hazard in terms of Peak Ground Acceleration (PGA) for a return period of 475 years has been assessed for the study region by convolving site amplification with bedrock hazard thus predicting a variation of PGA in the range of 0.51-1.61g. Additionally, assessment of liquefaction potential of the terrain and seismic hazard microzonation have been done for Dehradun city to identify areas with varying level of ground shaking and its associated liquefaction phenomenon during earthquakes, enabling the development of site-specific building codes and land-use regulations. The results of this investigation are expected to play vital roles in the earthquake–related disaster mitigation and management of the region.

How to cite: Bind, A. P. and Nath, S. K.: Site-specific Seismic Hazard Assessment of Uttarakhand, India with special emphasis on Liquefaction Potential  modelling of the terrain and Seismic Hazard Microzonation of Dehradun City, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14677, https://doi.org/10.5194/egusphere-egu24-14677, 2024.

Floods constantly occur in San Miguel de Ibarra's urban setting each year. Situated on the slopes of the Imbabura volcano, an integral component of the UNESCO Global Geopark Imbabura, this Ecuadorian city boasts an invaluable cultural and natural heritage. However, it has experienced multiple adverse impacts due to the overflow of rivers and streams. In 2022, an inventory of floods was compiled for the Geopark, revealing the persistent recurrence of this phenomenon within the city. Consequently, it became imperative to gather historical and contemporary data from diverse sources such as public institutions (GAD Ibarra 2023), digital newspapers, social networks, and aerial imagery (IGM 2014) to discern patterns and establish correlations related to these occurrences (SNGRE 2023).

In this way, the acquired information spanning the period from 1965 to the present, insights were gained into the distribution of flood-prone zones and their correlation with paleochannels. Additionally, discernment was achieved regarding alterations in land-use planning attributable to urban expansion in the city, which, in turn, contributes to the heightened susceptibility to floods. This meticulous analysis unveiled specific areas within the city consistently affected by such hazards, elucidating these events' characteristics and the ensuing damage to both public and private properties. The current publication presents preliminary findings utilized in the estimation of flood risk.

Keywords: Paleochannels, floods, Ibarra, Imbabura, Imbabura UNESCO Geopark

References:

GAD Ibarra (2023) Cartography of Ibarra canton at several scales

IGM (2014) Cartography of Ibarra canton 1:5.000

IGM (2023) Historical imagery of flights in Ecuador at several scales

SNGRE (2023) Data Base Events SNGRE. Period 2010 to 2023

How to cite: Torres-Ramírez, R.: Paleochannels and their correspondence with floods in the 21st century. Case study of Ibarra city, Imbabura, Ecuador., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14423, https://doi.org/10.5194/egusphere-egu24-14423, 2024.

Over 70% of flood events recorded in the past two decades in the Global Flood Database and WorldFloods dataset have occurred in locations where complex channel systems occur. Here we define complex channel systems as parts of the river network that diverge, such as bifurcations, multi-threaded channels, canals and deltas. Yet, large scale flood models have, until now, used only single-threaded networks due to the lack of a river network that reflects complex channel systems . Therefore, these large-scale models fundamentally misrepresent the physical processes in these often highly populated areas, leading to sub-optimal estimates of flood risk.

Using the new Global River Topology (GRIT) dataset, a global bifurcation and multi-directional river network (Wortmann et al. 2023), we extend the river channel bathymetry estimation routine of Neal et al. (2021) to model multi-channels with LISFLOOD-FP. We compare the multi-thread model results to observations and to previous versions of LISFLOOD-FP using a single-threaded river network in the Indus, Mekong and Niger rivers at 1 arc second (~30m). By using GRIT, we find marked improvements in model results, observing better connectivity to areas of the floodplain that are far from the main channel and more channel floodplain interactions in wetlands. This work paves the way to further our understanding of global flood risk and to finally consider the diverse, evolving nature of geomorphologically active river networks. As this work progresses, we will continue to model a typology of bifurcations and multi-directional rivers to help further our understanding of the significance of complex river systems.

Neal, J., Hawker, L., Savage, J., Durand, M., Bates, P., & Sampson, C. (2021). Estimating river channel bathymetry in large scale flood inundation models. Water Resources Research, 57(5), e2020WR028301.

Wortmann, M., Slater, L., Hawker, L., Liu, Y., & Neal, J. (2023). Global River Topology (GRIT) (0.4) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7629908

How to cite: Hawker, L., Neal, J., Wortmann, M., Slater, L., Liu, Y., Gebrechorkos, S. H., Leyland, J., Ashworth, P. J., Vahidi, E., Nicholas, A., Bennett, G., Boothroyd, R., Cloke, H., Griffith, H., Delorme, P., McLelland, S., Tatem, A. J., Parsons, D., and Darby, S. E.: When one becomes many: Including complex channel systems in large scale flood models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18718, https://doi.org/10.5194/egusphere-egu24-18718, 2024.

Understanding of hydrological and meteorological extremes is essential for flood risk management and flood protection. A primary focus in these professions is adequate estimation of extreme events that correspond to large return periods. Hydrological and meteorological observations only go back several decades, complicating frequency analysis of these large extremes. Capturing the tail behaviour of extremes is particularly challenging with such short records, resulting in high uncertainty of large precipitation and discharge extreme estimates.

This study proposes an alternative strategy for hydrological and meteorological frequency analysis. Long timeseries obtained from regional climate models are used to replace short observational datasets, leading to a substantial reduction of the statistical uncertainty of meteorological and hydrological extreme estimates. The approach was tested in the Meuse basin as part of the EMFloodresilience project, evaluating meteorological extremes from 16 synthetic ensembles of 65 years from the RACMO regional climate model (forced by the EC-EARTH global climate model). Hydrological extremes are analysed in a subsequent study from Rijkswaterstaat and Deltares, by forcing the wflow discharge model with the RACMO climate model dataset.

The study results reveal that bias-corrected model data is climatologically comparable to observational averages and extremes, exhibiting similar GEV location and scale parameters. Revealing a previously unexamined range of extremes, the model data offers a more plausible method to estimate the tails of annual extremes and likely provides a better estimate of the corresponding GEV shape parameter. Spatially, the model-derived parameter shows greater consistency across different sub-catchments of the Meuse basin compared to observations, suggesting a more robust insight in the tail behaviour of extremes. Additionally, a distinct separation between GEV distributions of summer and winter events is observed, indicating a transition in magnitude dominance from winter to summer maxima and possibly the presence of a double population. The existence of such a double population is difficult to obtain from observations, but can have an enormous impact on the return values of summer extremes. This emphasizes the need for further research on this area for adequate flood management.

How to cite: van Voorst, L., van den Brink, H., and Couasnon, A.: An evaluation of the use of regional climate model data applied to extreme precipitation in the Meuse basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17874, https://doi.org/10.5194/egusphere-egu24-17874, 2024.

This work outlines a methodology developed for considering aftershock-induced damage accumulation in seismic loss assessments. In particular, it adapts this methodology to the case of reinforced concrete (RC) frames in mainland France and incorporates it to an already-developed seismic loss assessment model.

The methodology consists in dividing the RC buildings into sub-categories of buildings, depending on parameters influencing the vulnerability of the structures. For each category, a set of discrete damage states is defined. For each state D_i, fragility functions are derived, enabling to compute the probability of transitioning to another damage state D_i+1, knowing the intensity of the ground motion. Therefore, this methodology allows to estimate the final damage state reached by a structure submitted to a series of ground motions.

In order to do so, the pool of French RC buildings is analysed so as to create realistic and general models of RC frames. Ground motions are selected from an open database, following some criteria. Fragility functions are then derived (for each type of building) by applying numerous ground motions to the models and assessing the probabilities of reaching each damage state. The methods for constructing those fragility functions are evaluated from the literature. The choice of relevant parameters measuring damage and measuring ground motion intensity is also scrutinized.

How to cite: Gouache, C. and Allemand, A.: Considering aftershock-induced damage accumulation in seismic loss assessments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2009, https://doi.org/10.5194/egusphere-egu24-2009, 2024.

Understanding the relationship between extreme temperature events and health outcomes necessitates integration of hazard and impact data. International databases of societal impacts from disasters serve as an important data source for empirical cross-country analyses. Yet, detailed and precise estimations of the hazard magnitude of these impact records are often lacking. Physical metrics play a pivotal role in, for instance, statistical analyses and exposure assessments.

In bridging this gap, our work leverages recent advancements in geocoding of disaster records alongside high-resolution meteorological datasets to construct an inventory of a diverse range of health-related climate metrics. Our global analysis spans over 200 records of extreme temperature disasters from the past fifty years. By doing so, we unveil insights into the properties of these disastrous heat- and cold-waves. We furthermore explore differences across space, time, metrics, and data sources. This work highlights the potential of utilizing this integrated approach to extract meaningful information from historical disaster records in global databases, aiding climate resilience and public health strategies.

How to cite: Lindersson, S. and Messori, G.: Quantifying health-related climate metrics of extreme temperature disasters: An international analysis over five decades, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9798, https://doi.org/10.5194/egusphere-egu24-9798, 2024.

The authors present a framework designed to model wildfire risk and the future projection of wildfire risk patterns, also in view of climate change scenarios. The adopted modeling framework is inherently multi scale, giving results at national scale, after a data gathering process developed at regional / supranational scale. The risk assessment comprises the computation of susceptibility, hazard, exposures, and damage layers. Machine learning techniques are used to assess the wildfire susceptibility and hazard at regional level, analogously to [1, 2]. To this end, a two-models approach has been adopted. The first model, based on the Random Forest Classifier, is trained at pan-European level to capture the climate variability of the European continent and related fire regimes. Building upon the outcome of this model, a wildfire susceptibility map representative for the historical
conditions at pan-European level is produced and used in input of a second machine learning model, to provide results at national level. The strength of this model lies in using high-resolution downscaled climate data and annual temporal resolution, with the objective of computing a high resolution annual susceptibility map for the specific region. This approach facilitates the generation of annual outcomes for both historical and future conditions, using the climate projections available in the ISIMIP framework. The result of five different climate models and three climate change scenarios have been used to estimate the average annual losses due to wildfires. The wildfire hazard has been evaluated through empirical approaches, building a wildfire hazard classes map combining fuel type/severity maps with wildfire susceptibility. Then, a burning probability is estimated for each hazard class: a statistical analysis on historical wildfires at pan-European level has been performed in order to retrieve the annual relative burned area per hazard class. The method allows to estimate the average annual probability to be affected by a fire given a wildfire event. Several exposed elements were used to estimate the losses ranging from infrastructure to forest and roads: Global Earthquake Model [3] provides a dataset featuring economic values under both present and future conditions across five categories of infrastructures at European level. JRC, OpenStreeMap, and Copernicus provide information on the presence of roads and forests. Empirical vulnerability functions establish a link between severity maps, the presence of exposed elements, and their economic value, leading to the estimation of potential damage maps. The assessment of average annual losses involves coupling spatial information on average annual probability with potential damage maps. This approach allows for the evaluation of average values across various future timeframes associating a variance accounting for both the year to year and climate models’ variability. Results have been produced at national level for several countries characterized by different wildfire regimes, land cover and climate, such as Croatia, Romania and Bulgaria.

How to cite: Ghasemiazma, F., Meschi, G., Trucchia, A., and Fiorucci, P.: Wildfire Risk Assessment under present and future climate at national scale: a pan european approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21315, https://doi.org/10.5194/egusphere-egu24-21315, 2024.

Assessing landslide risk is a fundamental step in planning prevention and mitigation actions in mountainous landscapes. To date, most landslide risk analyses address this topic at the scale of a slope or catchment. Whenever the scale involves regions, nations, or continents, the landslide risk analysis is hardly implemented. To test this theoretical framework, we present a practical case study, represented by the European landscape. In this contribution, we take the main Pan-European mountain ranges and provide an example of risk assessment at a continental scale. We consider challenges like cross-national variations landslide mapping and digital data storage. A two-stepped protocol is developed to identify areas more prone to failure. With this initial information, we then model the possible economic consequences, particularly in terms of human settlements and agricultural areas, as well as the exposed population. The analytical protocol firstly results in an unbiased landslide susceptibility map, which is combined with economic and population data. The landslide risk is presented in both the spatial distribution of possible economic losses and the identification of risk hotspots. The latters are defined through a bivariate classification scheme by combining the landslide susceptibility and exposure of human settlements. Ultimately, the exposed population is represented during the two sub-daily cycles across the study area.

How to cite: Caleca, F., Lombardo, L., Steger, S., Dahal, A., Tanyas, H., Raspini, F., and Tofani, V.: Assessing landslide risk on a Pan-European scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8660, https://doi.org/10.5194/egusphere-egu24-8660, 2024.

Climate-related disasters such as floods, droughts and storms often pose significant threats to human livelihoods, especially in developing countries. The extreme weather events often lead to destroying of shelter, harming of crops and livestock as well as fueling of conflicts, and the threat to human livelihoods are likely to increase due to climate change. While we know that climate change and conflict interact and reinforce each other, less is known in the context of natural disasters and disaster aid. In this paper we address this gap by studying how hazard severity, disaster exposure and drivers of vulnerability interact to produce humanitarian impacts, and if the delivery of emergency disaster aid alleviates these impacts. We do this by generating meteorological hazard severity measurements based on the reanalysis dataset ERA5, comparable across different climate-related disaster types, allowing us to study drivers of vulnerability to climate-related hazards. Secondarily, we study the role of aid allocation on limiting disaster mortality and displacement, with the results having broad implications for the understanding of disaster impacts and aid effectiveness.

How to cite: Berntell, E., von Uexkull, N., Rao, T., Bender, F., and Dellmuth, L.: Complex emergencies: drivers of the humanitarian impacts of climate-related disasters, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17738, https://doi.org/10.5194/egusphere-egu24-17738, 2024.

Drought is a slowly developing natural phenomenon that can occur in all climatic zones and propagates through the entire hydrological cycle with long-term socio-economic and environmental impacts. Intensified by anthropogenic climate change, drought has become one of the most significant natural hazards in Europe. Different definitions of drought exist, i.e. meteorological, hydrological, and agricultural droughts, which vary according to the time horizon and the variables considered. Just as there is no single definition of drought, there is no single index that accounts for all types of droughts. Consequently, capturing the evolution of drought dynamics and associated impacts across different temporal and spatial scales remains a critical challenge.

In this work, we first analyze different state-of-the-art standardized drought indexes in terms of their ability in detecting drought events at the pan-European scale, using hydro-meteorological variables from the E-HYPE hydrological model and forced with the HydroGFD v2.0 reanalysis dataset over the period 1993-2018. The findings suggest the need of adjusting the formulation of traditional drought indexes to better capture and represent drought-related impacts. Specifically, here we use the FRamework for Index-based Drought Analysis (FRIDA), a Machine Learning approach that allows the design of site-specific indexes to reproduce a surrogate of the drought impacts in the considered area, here represented by the Fraction of Absorbed Photosynthetically Active Radiation Anomaly (FAPAN). FRIDA builds a novel impact-based drought index combining all the relevant available information about the water circulating in the system identified by means of a feature extraction algorithm.

Our results reveal a general pattern among different indexes, that Southern England, Northern France, and Northern Italy are the regions with the highest number of drought events, whereas the areas experiencing longest drought durations are instead the Baltic Sea region and Normandy. Clustering the 35,408 European basins according to dominant hydrologic processes reveals that the variables mainly controlling the drought process vary across clusters. Similarly, we obtain diverse correlation between standardized drought indexes and the FAPAN in different clusters. Numerical results also show that, in one of the worst cases (cluster 10), the FRIDA index increases the correlation with FAPAN from 0.16 to 0.69. Lastly, the FRIDA indexes are computed for different climatic projections to investigate future trends in drought impacts.  Results show divergence with respect to the trends of the standardized drought indexes, with correlation values below 0.30. In conclusion, these findings can contribute in advancing drought-related climate services by enabling the analysis of projected drought impacts.

How to cite: Merlo, M., Giuliani, M., Du, Y., Pechlivanidis, I., and Castelletti, A.: Advancing drought detection and management using ML enhanced impact-based drought indexes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7875, https://doi.org/10.5194/egusphere-egu24-7875, 2024.

Modelling flood hazards at large scales – both uniform frequency hazard maps and event simulations whose frequency varies in space – is a relatively new scientific endeavour. Data and computation constraints have historically necessitated either a more local focus to modelling efforts, or the building of proof-of-concept global-scale models whose fidelity inhibits most practical applications.

Here, we present a global climate-conditioned flood catastrophe model; the culmination of decades of research into scaling inundation modelling, the incorporation of climate change, and synthetic event generation. 30 m resolution global maps representing fluvial, pluvial, and coastal flooding for given return periods were simulated using a hydrodynamic model with sub-grid channels whose inputs were defined using regional flood frequency analyses. Change factors from climate model cascades were flexibly used to perturb the local flood frequency a given flood map represents. Separately, a 10,000-year-long set of synthetic events were simulated using a conditional multivariate statistical model fitted to global fluvial-pluvial-coastal reanalysis data. The empirical return period of a given event is used to sample the corresponding flood map return period in order to build a long synthetic series of floods.

With a global exposure model built using a top-down approach – downscaling capital stock models to high-resolution satellite-derived land-use and building height data – and a global vulnerability model derived from an extensive review of modelling and engineering literature, we demonstrate the calibration and validation of the global risk model. We also show the software challenges overcome to run this model, as well as to enable end-users to flexibly calculate the flood risk of their own exposures in the Oasis Loss Modelling Framework.

How to cite: Wing, O., Quinn, N., Haylock, M., Lamb, C., Davies, R., Sampson, N., Probyn, I., Daniell, J., Elmer, F., Brand, J., and Bates, P.: A global stochastic flood risk model for any climate scenario, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9197, https://doi.org/10.5194/egusphere-egu24-9197, 2024.

Tropical cyclones are events responsible for the costliest meteorological catastrophes. On average per year over the last decade, they have affected 20 million people, with estimated economic losses US$51.5 billion (Krichene et al., 2023). These consequences reduce the economic growth of the affected countries (Berlemann & Wenzel, 2018). Take Jamaica, for instance, where annual damages caused by tropical cyclones are estimated at 0.5%, reaching up to 10% of the Gross Domestic Product (Adam & Bevan, 2020).

The climatology of tropical cyclone, defined as characteristics averaged over years, controls parameters like tracks, intensification, number of storms, all crucial for induced hazards (winds, precipitation, storm surge and waves). In recent years, anomalous tropical cyclones have impacted the coasts worldwide. In 2023, hurricane Otis, without precedent, rapidly intensified off the coast of the coast of Acapulco (Mexico), resulting in at least 52 deaths and estimated damage exceeding 10 billion USD. The track of tropical cyclone Kenneth struck areas of Mozambique where no previous tropical cyclone had impacted before, resulting in 45 casualties and $100 million in damage (Mawren et al., 2020). The future of tropical cyclones is impregnated with uncertainty and is a matter of concern, which have motivated the recent advance in this topic. Several authors asseverate an increase in intensity, reduce in frequency (Bloemendaal, et al., 2022; T. Knutson et al., 2020; T. R. Knutson et al., 2010), and their poleward displacement (Studholme et al., 2022). However, the global study of the displacement of tropical cyclones and their characteristics due to the migration of storms has not been integrated into large-scale adaptation planning.

This study identifies regions affected by the displacement of storms in the North Atlantic at the municipal administration level. Analysing characteristics under two climatology periods—a baseline climate (1980-2017) and a future high-emission climate scenario, Shared Socioeconomic Pathway SSP8.5 (2015-2050)—we used synthetic tracks (Bloemendaal, et al., 2022) generated with a model based on STORM  (Bloemendaal et al., 2020). Four Global Climate Models (CMCC, CNRM, EC-Earth, and HadGEM3) were examined to evaluate uncertainty, focusing on frequency, intensity, and critical parameters such as size, translation speed, track complexity, residence time in front of the coast, and relative direction to the shoreline.

This study identifies hotspots where tropical cyclone characteristics are spatially displaced, increasing the exposure to tropical cyclones in these regions. For example, the Canary Islands in Spain show that hurricanes of category 1, in present conditions, have a return period of 215 years, reducing to 62 years in the SSP8.5 scenario. This is in line with the recent records, the Hermine storm in 2022 almost impacted their coasts. The results raise questions about our public policies for future adaptation. In areas historically unaffected and unprepared for tropical cyclones, the corresponding government may lack and require prevention systems for tropical cyclones, such as warning alarms, reducing subsidies for coastal development or implementing disaster relief policies. 

How to cite: Odériz, I. and Losada, I.: Implications of the displacement of tropical cyclones for public policies in the North Atlantic, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17338, https://doi.org/10.5194/egusphere-egu24-17338, 2024.

A fundamental question in global hydrology is how global floods behaved in the past and are expected to behave in the future. Previous site-specific analyses might offer locally relevant insights, but little is known about how floods are connected in space and time as well as their synchronous responses to climate variability at the global scale. Here we carry out empirical analyses based on a comprehensive dataset of annual maximum flood peak series from 4407 stream gaging stations. We establish the link between any two stream gages if their annual maximum flood peak discharges are significantly correlated and the dates of their occurrences are sufficiently close (using event synchronization and complex network). Our results identify notable remote links of annual flood peak series over western Canada/US (e.g., upper Missouri River basin), northern Europe (e.g., Kemijoki River basin), southern China (e.g., middle Yangtze River basin), and northern South America (e.g., Amazon River basin). Annual flood peak series are linked to their local neighbors (within a distance of 4500 km) over eastern United States, central Europe, and eastern Australia. Remote links highlight the spatial dependence of riverine floods at the global scale. These links are dictated by the oscillation of dominant climate modes over the Pacific Ocean (e.g., El Niño Southern Oscillation, Pacific Decadal Oscillation) and their resultant anomalous atmospheric circulation patterns. Local flood clusters are more responsive to region-specific atmospheric forcings. The complex flood network plays an important role in regulating the dynamic behaviors of flood hazards. Our results offer new insights into global flood hydrology and their connections with large-scale climate forcings.

How to cite: Yang, Y., Yang, L., Wang, Q., and Villarini, G.: Connecting the dots: teleconnection of global floods and their association with climate variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13864, https://doi.org/10.5194/egusphere-egu24-13864, 2024.

The PROTECT project [1] includes a probabilistic integrated assessment of global population exposure to coastal flood hazard under climate-induced sea-level rise (SLR) over the next three centuries (to 2300). The assessment synthesises present-day datasets on population distribution [2], low lying coastal elevations [3] and extreme tides [4] with probabilistic projection datasets of population [5] and sea level [6] to 2300. For the scenarios considered (SSP1-2.6 & SSP2-4.5) and at a global scale, the median human exposure to coastal flood hazards grows substantially but then peaks in the early 2200s and subsequently slowly declines by 2300, despite continued rise in sea level.

Previous assessments have primarily focussed on shorter timeframes [2], typically to 2100, while it is widely acknowledged that even if temperatures are stabilised, sea levels are almost certain to continue to rise for many centuries [7][8][9]. Stakeholder workshops carried out with practitioners under the umbrella of PROTECT [10] and literature reviews [11][12] highlight the importance of extending sea-level rise information beyond 2100, to support strategic coastal adaptation and management, land-use planning, and critical infrastructure design.

Recent advancements in long term socio-economic modelling [13][5] now provide projections of global population and GDP at country level to 2300. These have already been applied to long-term risk assessments for other climate sectors [13][5][14].

For this assessment, the global coastline was split into ~29,000 segments, each assigned an extreme tide curve (from the COAST-RP dataset [4]) and a hypsometric curve, generated from a global terrain model [3] and present-day population distribution [2]. The hypsometric curves aggregate the total land-area and population at each elevation, including consideration of hydraulic connectivity to the coastline. This gives the land area and population that would be exposed at a given coastal flood level (up to 20mAMSL) for each coastal segment.

When sea-level scenarios [6] (SSP1-2.6 & SSP2-4.5) and socio-economic data [5] are combined, the human exposure and land area exposure to coastal flood hazard under a chosen extreme tide return period (or the annual average based on the event-exposure curve) is calculated.

This approach facilitates efficient computations, sampling across probabilistic data, and providing robust statistics at a high spatial resolution compared to traditional methods. The outputs at each coastal segment can be aggregated to sub-national, national, or the global scale.

In this analysis, it is found that the median exposure of people to coastal flood hazards increases fivefold to a peak in the early 2200s and subsequently slowly declines to 2300 in both SSPs, despite the continued rise in sea level. For the 80^th percentile population exposure grows even more (10- to 11-fold) but then stabilises rather than declines. These results reflect the interplay of sea level and demography with fall in global population in the latter half of the assessment period and are contrary to conventional wisdom. This analysis shows that in addition to sea-level rise, it is important to consider demographic trends when considering coastal futures.

Figure 1. Probabilistic annual average global population exposure to coastal flood hazard

References exceed the word limit so not included

How to cite: Heslop, J., Nicholls, R., Ballesteros Martinez, C., Linke, D., and Hinkel, J.: Global assessment of human exposure to sea-level rise to 2300, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20522, https://doi.org/10.5194/egusphere-egu24-20522, 2024.

Slow-moving landslides can cause damage to structures and infrastructure and result in thousands of casualties, if they fail catastrophically. Landslide motion may accelerate after prolonged rainfall, and with alterations to their surface hydrology caused by urbanization. As populations grow in mountainous regions, there will be more direct interactions between communities expanding onto landslides. Yet, the lack of systematic data has precluded a global overview of exposure. We address this by compiling a global database of 7,764 large landslides (>0.1 km² in area) reported to be slow-moving. Here, we assess the presence of human settlements in 2015 and estimate exposure across IPCC regions with projected landslide risk. We estimate that 9% of landslides in a given basin are occupied by human settlements. On 1195 km2 slow-moving landslides, settlement footprints total 55 km2 and cover an average of 12%, relative to the landslide area. We show regional influences of exposure to floods, average steepness, and urbanization on exposure across basins. Our estimates of exposure in East Asia (EAS) show the most credibility across regions facing growing landslide and flood risk by the IPCC. Apart from Central Asia, we find that urbanization in a basin increases the relative number of landslides inhabited. Furthermore, we find that regions with mountain risks projected to increase have highest uncertainty in our assessment.

How to cite: Ferrer, J. V. and Korup, O.: Slow-moving landslide exposure increases with population pressure, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20386, https://doi.org/10.5194/egusphere-egu24-20386, 2024.

Translation of geoscience research into tangible changes, such as modified decisions, processes or policy in the wider world is an important yet notably difficult process. Co-RISK is an accessible (i.e. open access, paper-based, zero cost) ‘toolkit’ for use by stakeholder groups within workshops, which is intended to aid this translation process. It is given a robust basis by incorporating paradox theory from organisation studies, which deals with navigating the genuine tensions between industry and research organizations that stem from their differing roles. Specifically designed to ameliorate the organizational paradox, a Co-RISK workshop draws up ‘Maps’ including key stakeholders (e.g. regulator, insurer, university) and their positionality (e.g. barriers, concerns, motivations), and identifies exactly the points where science might modify actions. Ultimately a Co-RISK workshop drafts simple and tailored project-specific frameworks that span from climate to hazard, to risk, to implications of that risk (e.g. solvency). The action research approach used to design Co-RISK (with Bank of England, Aon, Verisk), its implementation in a trial session for the insurance sector and its intellectual contribution are described and evaluated. The initial Co-RISK workshop was well received, so application is envisaged to other sectors (i.e. transport infrastructure, utilities, government).  Joint endeavours enabled by Co-RISK could fulfil the genuine need to quickly convert the latest insights from environmental research into real-world climate change adaptation strategies. 

https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1251/

How to cite: Hillier, J. K. and van Meeteren, M.: Co-RISK: A tool to co-create impactful university-industry projects for natural hazard risk mitigation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1684, https://doi.org/10.5194/egusphere-egu24-1684, 2024.