HS7.5

In a world of increasing global flood hazards, vulnerable populations (very young and elderly) are disproportionately affected by flooding due to their low self-reliance, weak political voice and insufficient inclusion into climate adaptation and emergency response plans. These individuals account for most flood casualties and often rely on emergency services due to flood induced injuries, exacerbated medical conditions, and requiring evacuative assistance. However, emergency service demand often exceeds the potential capacity whilst flooded roads and short emergency response timeframes decrease accessibility, service area, and population coverage; but how does this compare across the globe and what will the future hold?

To answer this question, a global analytical framework has been created to determine the spatial, temporal, and demographic variability of emergency service provision during floods. This is based on global fluvial and coastal flooding (at 10-year and 100-year return periods), and present and future flood conditions (present-day and 2050, under RCP 4.5 and RCP 8.5 climate scenarios). The framework includes a hotspot analysis to identify the extent and distribution of flood hazards and at-risk vulnerable populations, an accessibility analysis to identify emergency service accessibility to vulnerable populations based on restrictions of flood barriers and response-time frameworks, and a vulnerability analysis to compare the environmental injustice of emergency service provision between key demographic groups.

The highlighted geographical and temporal differences in emergency service provision globally and between regions, in addition to the framework itself, can be used by national and international organisations to inform strategic planning of emergency response operations and major investments of infrastructure, services, and facilities to maximise the benefit to the disproportionately affected vulnerable populations. This includes the production of more detailed flood hazard and evacuation maps that highlight vulnerability hotspots, the prioritisation of vulnerable population groups in emergency response plans to minimise geographic and population disparities of flood injuries and fatalities, and the allocation of emergency service hubs in regions of high vulnerability but low emergency response provision.

How to cite: Johnson, S., Wilby, R., Yu, D., and Matthews, T.: Global analysis of emergency service provision to vulnerable populations during floods of various magnitude under climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5214, https://doi.org/10.5194/egusphere-egu22-5214, 2022.

The Intensity and frequency of extreme storms have been increasing due to possible climate change, making it challenging to manage stormwaters in highly urbanized areas. Without an adequate and appropriate stormwater system, these storms may cause significant damage and losses to live and properties. Low Impact Development (LID) is a recent but widely accepted alternative for managing the increased stormwater. However, limited research is available to understand their effectiveness and optimize the mix of LIDs and conventional stormwater systems. This study evaluates the performance of several LIDs under current and future storm conditions, identify the best performing mixes of LIDs and convention stormwater system and provide a decision-making tool for urban stormwater management. The methodologies will be tested for Renton City, which is part of the Seattle Metropolitan Area.

In order to achieve our objective, first, a statistical rainfall-runoff model will be developed to assess the current stormwater system and estimate runoff for the current and future periods. The final results indicate a significant increase in runoff due to the increased rainfall in the future (2020-2040) compared to the past (1995-2014). The Stormwater Management Model (SWMM) will then be used to simulate the rainfall-runoff under conventional and LIDs (e.g., bio-retention, rain barrels, rain gardens, infiltration trenches, and permeable pavement) stormwater system. The final results show that the performance of LIDs in reducing total runoff volume varies with the types and combinations of LIDs. A 30% to 75% reduction in runoff was achieved for the past and future 50-year and 100-year storms. A Genetic Algorithm (GA) is used to optimize the LID and conventional stormwater system considering the reduction in runoff, installation and maintenance costs. The type, size, location, and number of different LIDs will be considered as decision variables for the GA. Finally, the study aims at developing a comprehensive framework to evaluate the performance of LIDs under present and future storms and identify cost and performance effective LIDs in a given urban area. The framework introduced in this study will help local authorities and practitioners to implement appropriate climate change adaptation strategies by maximizing the benefit from LIDs and ensure sustainable stormwater management for the current and future climates.

How to cite: Abduljaleel, Y. and Demissie, Y.: Evaluation and Optimization of Low Impact Development Designs for Sustainable Stormwater Management in a Changing Climate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13, https://doi.org/10.5194/egusphere-egu22-13, 2022.

Maps of flooding risk and exposure generally assume people and vehicles density constant over time, although this is not the case in the real world, as crowding is a highly dynamic process in urban areas. Monitoring and forecasting people mobility is a relevant aspect for metropolitan areas subjected to high risk of flooding. Information and communication technologies (ICT) along with big data are massively used, e.g., to support the optimization of traffic flows and the study of urban systems. In particular, mobile phone network data suits with the aim of producing dynamic information on people's movements that can be used to develop dynamic exposure to flood risk maps for areas with hydrogeological criticality, as done by Balistrocchi et al. (2020).

In this work we aim at proposing a time series modelling strategy to obtain “real time” traffic flows prediction. To do so we use mobile phone origin-destination signals on the flow of Telecom Italia Mobile (TIM) users among different census areas (ACE of ISTAT, the Italian National Statistical Institute), and for the MoSoRe Project 2020-2022 and recorded at hourly basis from September 2020 to August 2021.

An Harmonic Dynamic Regression (HDR) model (Hyndman, Athanasopoulos, 2021) as it follows:

Flow= α+Fourier.day (K_d )+Fourier.week (K_w )+ Month+ε_(ARIMA(p,d,q))                        (1)

is proposed, where multiple seasonal periods are modelled with a properly selected number of Fourier basis, month is a dummy variable to account for different levels of flows by months and the error component is structured as an ARIMA.

HDR model suits for our purposes due to the strong daily and weekly patterns in traffic flows, as also confirmed by preliminar results on the accuracy of prediction based on a cross-validation strategy.

In future developments, the model in equation 1 may be improved by adding proper features as explanatory variables to increase the prediction accuracy, such as, e.g., the presence of people in the census area of origin and in the census area of destination of the flow, or precipitation data.

People’s and vehicles’ exposure obtained from mobile phone data and processed with the above stochastic model are then combined to flooding hazard maps estimated for different storm return period in a urbanized area close to Brescia to estimate dynamic flood risk maps.      

References

Balistrocchi, M., Metulini, R, Carpita, M., Ranzi, R.: Dynamic maps of human exposure to floods based on mobile phone data. Natural Hazards and Earth System Sciences, 20: 3485{3500 (2020).

Hyndman, R. J., Athanasopoulos, G.: Forecasting: principles and practice. 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3 (2021)

How to cite: Razdar, B., Metulini, R., Carpita, M., and Ranzi, R.: Dynamic flood hazard maps based on traffic flow forecasts using mobile phone data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3200, https://doi.org/10.5194/egusphere-egu22-3200, 2022.

Flash floods are related to short duration and high intensity rainfalls, they are common phenomena in many parts of Europe as well as Italy. These events can result in debris flow, debris flood or water flood. The main differences are in the triggering, propagation, and depositional phases and more importantly in terms of velocity, impact forces and associated damage.
In Campania Region (Southern Italy) these phenomena historically involved the catchments several times, with an increase in frequency in the last decade. They are associated to small watershed – fan systems that fall in the southern Apennines characterized by intermittent flow. The alluvial fans in the outlet zones are highly urbanized, hence the population living in the deposition areas is exposed to high risk. 
In this study, the geomorphic response to flash floods is assessed through magnitude evaluation of some flash floods recently occurred in heterogeneous geological and geomorphological settings in both coastal and inland areas. Each scenario is reconstructed through the mapping of areal extent, water heights, particle sizes and estimate of volumes and built damage aiming at vulnerability definition, a relevant topic considering the global climate changes.
In this study, an approach aimed at developing vulnerability curves is proposed. It is based on a application of a typical method widely adopted in the earthquake engineering that in this case assume as intensity parameter the water height measured in post-event surveys. 
The results are expressed as vulnerability curves at different damage scenarios that can be valuable tools for local authorities, emergency, and disaster planners since they can assist decision making analysis of protection measures for future events.

How to cite: Forte, G., De Falco, M., Santangelo, N., and Santo, A.: Vulnerability scenarios for flash floods occurred in Campanian Apennines (South Italy), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4716, https://doi.org/10.5194/egusphere-egu22-4716, 2022.

Dams are useful for mitigation of floods, and at the same time there is a risk of dam breach or failure from floods, apart from seismic hazards and factors such as ageing of dam material. In recent decades, there is an alarming increase in dam breach events. This has drawn the attention of hydrologists to have a relook at methodologies being considered for dam risk analysis. Effective risk analysis requires accounting for both failure probability of dam and dam break consequences. There are numerous factors which effect the consequences, and there is considerable amount of uncertainty, vagueness and ambiguity among them due to lack of data and knowledge. To address this, we propose a new dam break risk assessment method in fuzzy framework. It considers fuzzy hierarchical model for risk assessment based on combination of static and variable fuzzy set theory. A hierarchical structure is devised for various factors influencing dam break consequences. Furthermore, weights are assigned to the factors using Fuzzy Analytical Hierarchy Process (FAHP). Thereafter, weighted information of different factors is comprehended to arrive at estimate of a risk index. The effectiveness of proposed method is demonstrated through case study on Hemavathi dam located in upper reaches of Cauvery River basin, India. It is a composite dam with masonary spillway and earthen flanges. The catchment area of river up to the dam site is 2904 sq. Km. Furthermore, height of dam above riverbed level is 44.5 m, and its gross storage capacity is 1047 Mm³. As per Bureau of Indian Standards (BIS) the dam is classified as large dam and therefore qualifies for Probable Maximum Flood (PMF) as design flood. Breach analysis of Hemavathi dam was performed using 1D-2D coupled HEC- RAS model to map the extent of flooding downstream of the dam using PMF (corresponding to 2-day PMP) as inflow and maintaining initial pool level in reservoir at maximum water level (MWL). For comprehensive risk assessment, life loss, economic loss, and social and environmental influence caused by dam break are considered in the model.

How to cite: Goel, A. and Srinivas, V. V.: New Dam Break Risk Assessment Method in Fuzzy Framework, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12786, https://doi.org/10.5194/egusphere-egu22-12786, 2022.

Recent evidences suggest that in Europe, flood frequency and precipitation frequencies are often not aligned. Beside other factors pre-storm conditions exert a significant impact on flood generation thus their knowledge is paramount for a proper flood forecasting. A number of predictors have been used in the past to understand how much precipitation is transformed into runoff (i.e., runoff coefficient, RC). Notable examples are the antecedent precipitation index (API), the prestorm river discharge and soil moisture. On top of these new products potentially available from satellite observations like surface soil moisture and total water storage anomalies (TWSA), root zone soil moisture from reanalysis and hydrological models can be used along with precipitation to predict in advance the severity of the storm runoff.Our goal here is to provide an objective description of the role played by different predictors for hydrologic forecasting in Europe. In particular, we aim at answering the following research questions:

• How variable is runoff coefficient across the European catchments?
• How much are surface and root zone soil moisture, river discharge, antecedent precipitation and total water storage anomalies able to explain the RC variability across European floods?
• Under which conditions (climate period, location and flood magnitude) are the different pre-storm indices able to predict this runoff coefficient variability?

We answered these questions using long term (1980-2016) precipitation and river discharge observations from more than 100 basins covering different European regions. Results demonstrated that root zone soil moisture and TWSA are the best predictors of prestorm conditions under a variety of climatic and geographic features and thus their correct representation in land surface and hydrological models is strategic for an effective flood forecasting.

How to cite: Massari, C., Marra, F., Tramblay, Y., Crow, W., Camici, S., Modanesi, S., Brocca, L., and Gruendemann, G.: Prestorm root zone soil moisture conditions critical for flood forecasting in Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1830, https://doi.org/10.5194/egusphere-egu22-1830, 2022.

Landslides represent a critical natural hazard in many mountain and hilly regions worldwide, provoking causalities and property damages. Landslide triggering thresholds are at the basis of early warning systems to protect livelihoods. Traditionally, landslide triggering thresholds are expressed in terms of not more than two or three precipitation variables, mostly rainfall event depth, and duration. Indeed, the availability of soil moisture information and its proxies (such as antecedent precipitation), can improve the performance of landslide triggering thresholds, thus calling for a multivariate approach.  

Given the above context, this study aims to develop regional landslide triggering thresholds by using multivariate statistical analysis to investigate the performance of multiple combinations of rainfall variables and event soil moisture data, in the identification of regional rainfall thresholds for landslide initiation. Lombardy region (northern Italy) was selected as a study area since it is one of the most susceptible Italian regions to landslide risk. The data on landslides were retrieved from the FraneIalia project that is a comprehensive spatio-temporal database of recent landslides affecting the Italian territory from 2010 onwards. For the Lombardy region, from 2010 to 2019, 592 landslides events triggered by rainfall were detected, all distributed within the mountain and hilly areas of the region.

Precipitation and soil moisture time series, instead, were derived from the ERA5-Land reanalysis dataset and the rainfall events were reconstructed using the CTRL-T code developed by IRPI-CNR, which characterizes each rainfall event by duration, mean intensity, total depth, and peak intensity. The most probable rainfall conditions associated with each landslide are, then, computed based on the distance between the rain gauge and the landslide location. Different combinations of precipitation and soil moisture variables are tested using dimensionality reduction multivariate statistical techniques. An optimization procedure is set up with the aim to maximize the True Skill Statistic (TSS) ROC index associated with parametric thresholds. Several multivariate combinations show better performances than the traditional depth-duration power-law thresholds.  

How to cite: Palazzolo, N., Peres, D. J., Creaco, E., and Cancelliere, A.: Identification of regional landslide triggering thresholds in the Lombardy region using multivariate statistical analysis , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2578, https://doi.org/10.5194/egusphere-egu22-2578, 2022.

Precipitation extremes affect the landscape differently and often drive numerous landslides widespread with disparate densities and features. Revealing the factors that govern this spatial variability is critical for understanding landslide susceptibility and developing prediction models. To this end, examining the peculiarities of the triggering rainfall event at spatial and temporal scales emerges as a promising method. Here, we relied on radar gauge-analyzed (R/A) rainfall estimates (period > 30 years, spatial resolution ≈ 5 km) and a landslide inventory for studying the spatial relationship between rainfall anomalies and triggered landslide density. The landslide inventory counts more than 7,600 shallow landslides distributed in about 550 km² and triggered by an extreme rainfall event that hit the Kyushu area in southern Japan in July 2017. A total of 23 R/A cells with different landslide densities were identified from the landslide inventory. A standard period of 72 h (P_std), where the cumulative rainfall during the triggering event is maximum, was used to evaluate the spatial rainfall peculiarities at short (1 – 24 h) and long (48 – 72) timescales. Subsequently, rainfall anomalies were discussed by plotting the mean intensities computed at multiple timescales within the P_std in the intensity duration frequency (IDF) curves developed for each R/A cell. The spatial density of triggered landslides was strongly influenced by the rainfall intensities that exceeded the 100-years return levels at disparate timescales and demonstrated anomalies. More than 65 % of the triggered landslides were located in only three R/A cells. In these cells, rainfall intensities of the triggering event exceeded the 100-years return level at the various timescales (from short to long) within the P_std, favoring numerous landslides of different geometric features. Rainfall intensities in cells with low landslide density reached the 100-years return levels at short timescales (3 – 24 h). However, this was not necessarily achieved in all low landslide density R/A cells. These preliminary results highlighted the spatial impacts of rainfall anomalies computed at multiple timescales on landslide densities and features and motivated further analysis.

How to cite: Mtibaa, S. and Tsunetaka, H.: Spatial relationship between extreme rainfall anomalies and density of the triggered landslides, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6884, https://doi.org/10.5194/egusphere-egu22-6884, 2022.

In this study, we investigate the relationship between land-falling atmospheric rivers (ARs) and landslides in western North America. ARs are channels of enhanced water vapor flux in the atmosphere and play an essential role in the water supply for precipitation in the midlatitudes. However, they can also trigger natural hazards such as floods and landslides. Our objective is to determine if the occurrence of landslides in western North America can be attributed to ARs hitting the western coastline and causing rainfall at the locations of the landslides and to characterize the strength and persistence of the ARs that lead to landslides. To that aim, we use landslide records with daily temporal resolution along with daily rainfall estimates from the ERA5 reanalysis, for the period between 1996 and 2018. We propose and run two attribution models to relate landslides to rainfall and rainfall to ARs and subsequently verify statistically if there is a unique and significant association between the landslides and the ARs. Our results show that the majority of the landslides reported along the western coast of North America are preceded by an AR. In the coastal regions, ARs and landslides are significantly correlated. Further inland, landslides are less likely, but those that do occur are significantly correlated with very intense ARs. Understanding and revealing the impacts of ARs on landslides in western North America will lead to better forecasts and risk assessments of these natural hazards.

How to cite: Vallejo-Bernal, S. M., Wolf, F., Luna, L., Boers, N., Marwan, N., and Kurths, J.: Relationship between atmospheric rivers and landslides in western North America, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8096, https://doi.org/10.5194/egusphere-egu22-8096, 2022.

 The probabilistic assessment of risk due to landslides for Disaster Risk Reduction (DRR) purposes in terms of absolute and quantitative metrics (e.g., number of expected fatalities, economic damage) is still quite challenging. If, on the one side, landslide susceptibility models based on the combined statistical analysis of observed events and geomorphological predisposing factors can be efficiently implemented, they must be integrated by further hypothesis and information to capture the complexity of landslides hazard and be efficiently used for the assessment of risk. For instance, most susceptibility models are static and do not formally account for main triggering conditions (e.g., rainfall or seismic activity). Furthermore, they do not include any probabilistic information on the frequency/magnitude relationships of the related events, hence conveying relative and partial information. In this contribution, a simplified framework for probabilistic landslides risk assessment is presented and its application for multi-hazard risk assessment in Burundi is discussed. The proposed approach is based on the integration of multi-temporal susceptibility models accounting for monthly average precipitation patterns into a heterogeneous Poisson point process model. The occurrence process model is used to generate a large portfolio of events, each associated with a feature representing its magnitude whose distribution is modelled by a simple power law. These events can be combined with exposure and fragility/vulnerability information to obtain a probabilistic assessment of risk of different adverse consequences on people, assets and infrastructure.

The proposed approach has been exemplified in the context of a multi-hazard risk assessment at national scale for Burundi and has proved successful in providing spatialised absolute and relative risk estimates that could be compared and combined with risk assessments related to other hazards (e.g., earthquakes and floods) with different characteristics and return periods.

 The practical implementation was based on the available data for the targeted region, which is limited, and relies on several assumptions and hypothesis that are accompanied by a significant level of uncertainty. The results have been preliminarily assessed using the data provided by the IOM Emergency Tracking Tool (ETT) from the period 2018-2021. The results indicate that the framework is flexible and can be used to obtain actionable information on risk due to landslides at different temporal and spatial scales. Our findings further highlight the importance of addressing landslide risk from a larger, interdisciplinary perspective, fostering the systematic collection of risk-oriented data (e.g., event inventories including information on damage and loss) and the synergies among different actors involved in DRR and Climate Change Adaptation. The potential and limitations of the proposed approach for regional landslide risk and for multi-hazard risk assessment will be discussed. The described research activities have been carried out within the framework of an international project funded by the European Union, implemented by the International Organization of Migration (IOM) and coordinated by IDOM (Spain).

How to cite: Pittore, M., Steger, S., Moreno, M., Campalani, P., Renner, K., Villacis, C., Piñón, J., Pérez, E., Rincón de la Rosa, L., Achour, I., and Noel, E.: Towards a quantitative spatiotemporal assessment of probabilistic landslide risk for large-area applications: challenges and perspectives.   , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11082, https://doi.org/10.5194/egusphere-egu22-11082, 2022.

In this work a multi-relaxation time (MRT) Lattice Boltzmann model based on the use of non-conventional collision operator is used to simulate the flood event in Venice Lagoon.

Numerical methods (finite difference, finite volume and finite element methods) that solve the macroscopic equations of fluid mechanics (Navier Stokes equations), are usually employed for these aims. Most of these methods put in evidence that the application of bed slope and friction forces can lead to inaccurate solutions due to numerical errors.

In addition, the extension of these schemes to complex geometries is not straightforward and some of these approaches are very computational expensive if applied to real flows. Since the problems are posed at a large scale, it should be the aim to develop a simple and accurate representation of the source term to simulate realistic shallow water flows.

The LBM approach is a versatile method and it has been extensively applied in different fields.

Non-conventional Lattice Boltzmann models based on central moments and cumulants collision operators allows to simulate large-scale hydraulic problems such as flooding events and the use of a GIS environment allows to set the information related to topography, initial conditions (water depth and velocity values distribution), boundary conditions (position and type of solid and inlet/outlet boundaries), external force (value and distribution of roughness coefficients, obstacles position) and to make this data available for the execution of the numerical model.

In order to validate the correctness of the proposed mathematical model for Venice Lagoon, the real flood event that took place on November 12, 2019 is simulated: several field data are available for this test case; the results, in terms of water level and velocity field are compared with recorded data, verifying the accordance. Moreover, technical solutions for hydraulic risk evaluation and mitigation, taking account of the expected sea level rise, due to climate change, are proposed.

How to cite: Padrone, J., Di Francesco, S., and Venturi, S.: Modelling flood events in Venice Lagoon with a cumulant CO lattice Boltzmann shallow water model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2474, https://doi.org/10.5194/egusphere-egu22-2474, 2022.

Future scenarios of climate change foresee an increase in frequency, duration, and severity of droughts, especially in arid and semiarid regions. This predictions require an intensive study of drought mechanics, starting with how past and present droughts behave, and continuing with the study of future droughts.
In this research, it has been studied how a precipitation decrease that causes a meteorological drought is related to hydrological drought, caused by a decrease in river streamflow. The area of study is located in the Guadalquivir River basin, south of the Iberian Peninsula, which serves as an example of semiarid region. Two different sources of streamflow data are used: observational data obtained from the Spanish Centre for Public Work Experimentation and Study (CEDEX), which takes into consideration regulation from reservoirs, and modelled data obtained with the Variable Infiltration Capacity (VIC) model. The use of two data sources allows for a comparison of results, serving as a validation for future projects that will rely on the use of modelled data to study the behaviour of droughts in the near future.
The numerical description and correlation of droughts is performed by means of drought indices, such as the Standardized Precipitation Evapotranspiration Index (SPEI) or the Standardized Streamflow Index (SSI), each describing one drought type, respectively meteorological and hydrological.

Keywords: Drought indices, Hydrological model, Observational data, Guadalquivir basin.

Acknowledgements
This work was funded by FEDER/Junta de Andalucía-Consejería de Economía y Conocimiento, project B-RNM-336-UGR18, by the Spanish Ministry of Economy and Competitiveness project CGL2017-89836-390 R with additional support from FEDER Funds, and by FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades (project P20_00035).

How to cite: Romero-Jiménez, E., García-Valdecasas Ojeda, M., Yeste, P., Rosa-Cánovas, J. J., Esteban-Parra, M. J., Castro-Díez, Y., and Gámiz-Fortis, S. R.: Correlation of Meteorological and Hydrological Droughts using Observational and Modelled Data in the Guadalquivir River Basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10067, https://doi.org/10.5194/egusphere-egu22-10067, 2022.

The entire strait is the base of the port aquatorium and a vital shipping artery from the Baltic Sea to the Curonian Lagoon as well as a complex water system connecting two water basins of different sizes and depths and nature: differing considerably in salinity and density. Although the quays are well protected from the waves of the open sea, dangerous water level fluctuations still occur in the port area, the origin of which is not yet well understood. This study aims to identify the occurrence and main characteristics of the long waves, with the period from minutes to several hours, to identify their origin and impact.

Analysis of the spectral composition of these oscillations is based on continuous pressure recordings at a frequency of 4 Hz in Klaipėda harbour during the stormy season 2016-2017 and repeated during calm and stormy seasons in 2021. Most of the oscillation energy is concentrated in two frequency bands. Significant water level changes occurred due to infragravity motions with periods of 30 s (0.03 Hz) and disturbances with the typical periods of wind waves on the Lithuanian coast with periods of 3-10 s (0.1-0.3 Hz). The highest peak in the wind wave frequency band corresponds to typical storm conditions in the Baltic Sea with periods of 5-9 s. While the typical amplitudes of the oscillations in this range were modest, hazardous changes in water level occurred at lower frequencies with amplitudes of 0.5 m. The record shows the presence of harbour oscillations with periods of 30-200 s (0.005-0.03 Hz) and seiches of the Curonian Lagoon with periods of 1200 s (0.0008 Hz).

The largest oscillations are created by a combination of wind waves and infragravity waves with periods that roughly match the natural seiche periods of Klaipėda Strait and harbour oscillations and seiches can be observed not only during the stormy season.

How to cite: Nesteckytė, L., Klepšaitė-Rimkienė, L., and Myrberg, K. A.: Long waves in the Port of Klaipėda , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4011, https://doi.org/10.5194/egusphere-egu22-4011, 2022.

Rainfall extremes are of major and increasing importance in semi-arid countries and their variability has strong implications for water resource and climate impacts on the local societies and environment. Here, we examine extremes intraseasonal descriptors (ISDs) in austral summer rainfall (November–February) over South Africa (SA). Using daily observations from 225 rain gauges, ERA5 reanalysis and satellite estimates (TRMM-3B42), we propose a novel typology of wet extreme events based on their spatial fraction, thus differentiating large- and small-scale extremes. Long-term variability of both types of extreme rainfall events is then extensively discussed. The relationship between these two types of rainfall extremes and different modes of climate variability is further explored at multiple timescales. At low-frequency modes, rainfall extremes are assessed at interannual (IV: 2−8 years) and quasi-decadal (QDV: 8−13 years) timescales which are primarily associated with El Niño Southern Oscillation (ENSO) and Interdecadal Pacific Oscillation (IPO) respectively. At high-frequency modes, rainfall extremes are evaluated with synoptic-scale variability related to seven convective regimes of Tropical Temperate Troughs (TTTs: 3–7 days) and intraseasonal variability associated with eight phases of the Madden-Julien Oscillation (MJO: 30–60 days).

The results demonstrate that using 7% of spatial fraction simultaneously exceeding the local threshold of the 90th percentile produces remarkable results in characterizing rainfall extremes into large- and small-scale extremes. Austral summer total rainfall is found to be primarily shaped by large-scale extremes which constitute more than half of the rainfall amount under observation, and nearly half in ERA5. Observation (ERA5) shows an average of 8 ± 5 (20 ± 7) days per season associated with large-scale extremes, which are comprised in 5 ± 3 (10 ± 3) spells with an average persistence of at least 2 days. Overall, we find a strong dependence of total rainfall on the number of wet days and wet spells that are associated with large-scale extremes. We also find that large- and small-scale extremes are well-organized and spatially coherent yet extreme conditions during small-scale events are found sporadic over the region, contrasting with large-scale events for which extreme conditions are found over a larger and coherent region.

Teleconnections with global SSTs confirm that La Niña conditions favor overall wet conditions and wet extremes in SA. The frequency of large-scale extremes is consistently related to warmer SSTs in the North Atlantic while their link with warmer Indian and tropical South Atlantic Ocean found stronger without ENSO influence. At low-frequency timescale, risk ratio assessment shows that the frequency (total rainfall) of large-scale extremes is significantly modified by IV (QDV) timescale. We note strong variations in the frequency (total rainfall) of large-scale (small-scale) extremes when IV timescale lies in strong positive phase (i.e., +0.5 standard deviation). At high-frequency timescale, the synoptic-scale variability associated with TTT events, are mostly responsible for changes in large-scale extremes as nearly 75% of such events occur during early to mature TTT regimes (3−5) whereas small-scale extremes were found equiprobable during all synoptic regimes. A risk ratio assessment suggests that the probability of large-scale extremes in TTT regime 5 significantly enhance (suppress) during MJO phases 6−8 (1−2).

How to cite: Ullah, A., Pohl, B., Pergaud, J., Dieppois, B., and Rouault, M.: Extremes in South African Rainfall: Mean Characteristics and Seamless Variability Across Multiple Timescales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1985, https://doi.org/10.5194/egusphere-egu22-1985, 2022.

The subject of this presentation is the assessment of the occurrence, intensity, and impact severity of weather-related events with socio-economic implications during the period 2000-2020 in Greece. The aim is to draw critical conclusions through the distribution of events at the temporal and spatial level and in relation to their societal impact as measured by a qualitative impact-severity index. The data derived from the High Impact Weather Events (HIWE) database that has been developed by the METEO Unit at the National Observatory of Athens (NOA), is systematically updated and publicly available. The analysis includes events related to floods, lightning activity, hail, snow/frost, windstorms, and tornados having caused impacts on life (injury or death) and/or infrastructure. The presentation provides an overview of the data used and methodology applied for assessing weather-related hazards, and the results of their analysis that include the evolution of events, the most damaging phenomena, and the areas most exposed to each phenomenon. This work was conducted in the frame of CLIMPACT – National Νetwork on Climate Change and its Impacts, a flagship initiative on climate change to coordinate a Pan-Hellenic network of institutions.

How to cite: Papagiannaki, K., Kotroni, V., Lagouvardos, K., and Bezes, A.: High-impact weather events in Greece: Analysis of the period 2000-2020, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4523, https://doi.org/10.5194/egusphere-egu22-4523, 2022.

The northeastern area of Italy, and specifically of Friuli Venezia Giulia region (FVG), is characterized by the heaviest precipitation annual totals in the country. Effects of both prolonged and extreme precipitation can be particularly damaging in this area, causing debris flow, flash floods, avalanches. Due to the very short times of concentration and hydrological response of the mountain watersheds of the analyzed area, extreme and short events are of particular interest. The region has a dense ground-station network which is managed by the regional Civil Protection Agency, constituted by 2 main rain-gauges networks, based on CAE and Micros-SIAP technology, respectively; this last is co-managed by the OSMER-ARPA (OSservatorio MEteorologico Regionale-Agenzia Regionale per la Protezione dell’Ambiente) FVG. The networks count a total of about 200 rain-gauges; for some stations, data at 5-minute resolution are available since the 1996 (CAE network), whereas Micros-SIAP works continuously and at high resolution since the early 2000s. Over the last two decades, the temporal resolution of stations has been progressively increased up to 1-minute step.

This work presents a comprehensive analysis of the available dataset at high temporal resolution (i.e. 30 min, 5 min and 1 min) to verify whether trends in very short rainfall duration are underway. The continuous time series of data recorded by a sample of rain-gauges by the two networks are first analyzed. A preliminary analysis aims at verifying the consistency of the dataset at the higher resolutions. Statistical trends are then assessed by comparing two methods, i.e., the classical Mann-Kendall and the quantile regression at different thresholds and durations. Differently than the traditional methods that require a subset of data (e.g., the rainfall annual maxima), the quantile regression method allows to detect changes in the tails of the rainfall distributions and to screen the whole rainfall time series.

How to cite: Arnone, E., Treppiedi, D., and Noto, L.: Preliminary analysis of high-resolution precipitation in Friuli Venezia Giulia region, Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6434, https://doi.org/10.5194/egusphere-egu22-6434, 2022.

The analysis of short-duration precipitation extremes is of foremost importance as heavy precipitation is directly related to many hazards, e.g. flash floods, landslides and crop damage. Here, we adopt an extreme value framework based on the concept of ordinary events, defined as independent realizations of the process of interest. In particular, we aim at investigating the link between the characteristics of ordinary storms (e.g. seasonality, average duration, autocorrelation) and the statistics of the emerging extremes at sub-daily durations (1-24 h). We used the Global Sub-Daily Rainfall (GSDR) dataset, which provides quality controlled hourly precipitation data from rain gauges over the Contiguous United States (CONUS). 

First, we tested the hypothesis that a Weibull distribution can describe the tail of ordinary events and independently reproduce the annual maxima. Then, we quantified the portion of ordinary events, termed tail hereinafter, which share the statistical properties with annual maxima. Analysis of the storm characteristics show shorter average duration storms (< 12h) in the central portion of CONUS, between latitudes 90ºW and 105ºW. Seasonality analysis showed predominance of summer events in all central and eastern areas, with exception to a region encompassing the northwestern areas of the southern US states, which are dominated by spring events. On the western coast, winter events dominate the tail of the distribution of the ordinary events. The majority of these events happened in the afternoon (12PM to 6PM) or night (6PM to 12AM). The parameters describing our extreme value distribution revealed insightful features. The scale parameter of the Weibull distribution describing the tail followed the local climatology, with higher values over the southeast of CONUS (region characterized for high intensity precipitation), and small values over the northwest. The shape parameter indicates heavier-tailed distributions on the north and central regions of the US, as opposed to the majority of stations CONUS-wide. On average, the number of events per year is larger in the east (50 to 100 events per year) when compared to the west (0 to 50 events per year) . 

Further analyses include investigating the influence of storm properties in the parameters of our extreme value distribution. This link, if proven significant, can be used to establish predictors for extreme precipitation statistics that stem from characteristics of ordinary storm events.

How to cite: Araujo, D., Marra, F., Ali, H., Fowler, H., and Nikolopoulos, E.: Storm characteristics and extreme sub-daily precipitation statistics over CONUS, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10722, https://doi.org/10.5194/egusphere-egu22-10722, 2022.

The compounding evidence on the aberrant behavior of extreme precipitation has drawn attention of hydrometeorologists towards re-evaluating the existing hydraulic design criteria for protection of large structures (e.g., spillways of dams, nuclear power plants) in changing climate. Traditionally, design flood estimates for those structures were based on Probable Maximum Precipitation (PMP) to minimize or avert the risk of failure and consequent catastrophic damage to mankind and the environment. PMP, as defined by the World Meteorological Organization (WMO), does not account for long-term climatic trends. However, in recent decades, there has been an increase in frequency and magnitude of extreme precipitation events in different parts of the globe. This necessitates devising potential strategies to arrive at effective PMP estimates to re-assess the existing design criteria.  Against this backdrop, researchers have been actively developing new methods or modifying the existing ones to adapt to changing climate. The majority of these methods are physics-based whose application demands voluminous data on various hydrometeorological variables and computationally intensive systems to run simulations on weather models. In comparison, statistical approaches are simple and not data intensive. Among available statistical approaches, Hershfield method is widely used due to its ease of application. There is a dearth of attempts to extend it for use in climate change scenarios.

In the present study, a new variant of Hershfield method is proposed which yields reliable PMP estimates by accounting for long-term trends in precipitation data for better estimation of at-site frequency factor in the climate change scenario. The applicability of the proposed method is illustrated over India considering 119 years (1901-2019) long 0.25-degree gridded precipitation records from IMD (India Meteorological Department). The country has more than 5000 dams, and currently PMP estimates are being considered for risk analyses of several ageing dams through the aid of the World Bank, under DRIP (Dam Rehabilitation and Improvement Project). The proposed methodology is applied to arrive at PMP estimates for sites/grids in homogeneous precipitation regions delineated in the country using cluster analysis. The overall impact of increasing/decreasing trend of precipitation on the regional estimate of frequency factor and one-day PMP estimates is clearly demonstrated using the proposed and conventional Hershfield methods.

How to cite: Bhatt, J. and Srinivas, V. V.: Accounting for long-term climatic trends in Probable Maximum Precipitation estimation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11993, https://doi.org/10.5194/egusphere-egu22-11993, 2022.

Between 13^th and 16^th July 2021, low-pressure system Bernd caused heavy flooding in parts of eastern Belgium, western Germany, and north-eastern France. In many of these areas, the 24 hours rainfall amounts exceeded the mean monthly precipitation (T. Junghänel et al. 2021). With at least 220 reported fatalities and insured loss estimates ranging between 10 and 13 EUR billion, it is one of the most devastating natural catastrophes in the central-European region of the last decades (GDV 2021).
Given the relevance of this event, a detailed reconstruction of the flood footprint would be of interest for both earth scientists and the insurance industry. For this purpose, a reconnaissance field trip was organised between 1^st and 3^rd November 2021 to affected municipalities in the German states of North Rhine-Westphalia, Rhineland-Palatinate, and the Belgian province of Liège. Remaining flood marks in buildings and other infrastructure were measured for over 200 locations, and water depths were inferred from them. In addition, information was collected on the degree of damage to buildings, as well as on the stage of reconstruction and clean-up. The focus was on areas that did not get much media attention back in July 2021, smaller ungauged streams, and, in general, any location where the flood depths and damages could not be easily inferred from other sources. The information collected during this field trip, combined with updated E-OBS precipitation data, river discharge gauge data, satellite imagery, as well as media and authorities’ reports was used to input, calibrate, and validate the different components of the RMS in-house flood model chain. In particular, the depth measurements from the reconnaissance trip were useful to calibrate the inundation model in municipalities affected by flash flooding from small to medium-sized ungauged streams, or by pluvial flooding. These point measurements allowed for a more detailed and comprehensive reconstruction of the flood depths over the entire affected area, beyond the better monitored larger river systems.

T. Junghänel, et al. (2021) Hydro-klimatologische Einordnung der Stark- und Dauerniederschläge in Teilen Deutschlands im Zusammenhang mit dem Tiefdruckgebiet „Bernd“ vom 12. bis 19. Juli 2021, DWD Geschäftsbereich Klima und Umwelt, https://www.dwd.de/DE/leistungen/besondereereignisse/niederschlag/20210721_bericht_starkniederschlaege_tief_bernd.pdf

GDV (2021) Hochwasserkatastrophe: Versicherer zahlen bereits über drei Milliarden Euro, https://www.gdv.de/de/medien/aktuell/hochwasserkatastrophe-versicherer-zahlen-bereits-ueber-drei-milliarden-euro--73798

How to cite: Salinas Illarena, J. L., Nicotina, L., Tillmanns, S., Bernet, D., Rentzos, P., Zanardo, S., Yang, Y., Li, S., and Hilberts, A.: Reconstruction of the July 2021 European floods footprint – from field measurements to hydraulic model calibration, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10468, https://doi.org/10.5194/egusphere-egu22-10468, 2022.

Within a few days of July 2021, extreme heavy rainfall associated with the low-pressure weather system “Bernd” caused severe flooding in Western Germany (North Rhine-Westphalia and Rhineland-Palatinate), as well as in Luxembourg, and parts of Belgium and the Netherlands. In Germany, this devastating event resulted in at least 184 fatalities.

In our presentation, we take a closer look at this event as classified in the Catalogue of Radar-based Heavy Rainfall Events (CatRaRE), derived from 21 years of climatological radar data (RADKLIM 1km,1h) for the area of Germany.

The CatRaRE Catalogue covers both the attributes of all classified heavy rainfall events as well as their spatial extent. The dataset is published annually by the German Meteorological Service and is freely available for all interested users at: dwd.de/catrare.

We present the extent and parameters of this extreme rainfall as an event classified in the CatRaRE together with a comprehensive analysis and comparison against all heavy precipitation events lasting between 1 to 72 hours which occurred in Germany in the period from 2001 to 2020. Apart from various extremity statistics such as return period, heavy precipitation index, and weather extremity indices, additional variables are examined as predictors for a potential impact: e.g. antecedent precipitation index, population density, land cover, imperviousness degree and topography indices.

How to cite: Walawender, E., Lengfeld, K., Winterrath, T., and Weigl, E.: Evaluation of the extreme rainfall event of July 2021 in Western Germany and its impact based on the Catalogue of Radar-based Heavy Rainfall Events (CatRaRE), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11559, https://doi.org/10.5194/egusphere-egu22-11559, 2022.

In July 2021, low-pressure system “Bernd” parked itself over central Europe in July 2021 and caused significant flooding in western Germany and neighbouring countries. The devastating flooding led to more than 180 causalities in Germany and caused catastrophic losses by disrupting infrastructure.

As the flood event unfolded, we at Verisk Extreme Event Solutions, re-modelled the event using state-of-the-art flood models by simulating river flows in our hydrological and flood inundation patterns in hydraulic model from observed precipitation fields derived from NASA’s Global Precipitation Measurement (GPM). Using the remodelled hazard and our Industry Exposure Database (IED), we provided a range of insured loss estimates for the insurance and reinsurance market. We will discuss the event with respect to hazard and uncertainties associated with risks, such as demand surge, cost inflation and infrastructure damage.

How to cite: Bhola, P., Doucet, M., Alarcon, S., and Reinhardt, B.: Comprehensive risk assessment of July 2021 European flooding including associated uncertainties, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12696, https://doi.org/10.5194/egusphere-egu22-12696, 2022.

The economic loss associated with natural hazards has drastically increased over the past decades, reaching over $210 billion dollars worldwide in 2020. The explication of regional-scale climate change effects with the tendency to exacerbate local climate criticalities has long jeopardized disaster resilience and the coping capacity of many communities. There is a lack of a robust operational linkage between the pre-disaster and post-disaster segments when a disaster occurs. This hampers an effective emergency response often leading to delayed humanitarian intervention and unplanned evacuations. Moreover, the great amount of openly available impact information on past events is commonly discarded and the forecast potential which the data yields has yet to be fully explored. In this context, the Impact-based Forecasting (IbF) approach aims to interconnect pre-emptive planning for early action with post-disaster impacts while taking advantage of historical data. The underlying principle of IbF is that the magnitude of an event is translated to site-specific impact information. Therefore, a paradigm shift from the conventional magnitude-likelihood relationship to impact-likelihood is proposed. This research develops a method to fully exploit the potential of IbF while overcoming the typical site-specificity of emergency response through remote sensing and automation. While the IbF framework allows for a multi-hazard approach, here we present a method targeting the ex-ante impact assessment of riverine floods. The analysis consists of two main components: i) the delineation of the flood extent from Sentinel-1 SAR imagery and ii) the definition of the event impact on the population, land and built environment. The IbF impact-likelihood relationship is ultimately derived by matching the two components for a historical event series. A fully automated Google Earth Engine algorithm for flood extent mapping with a 10 m spatial resolution has been developed to detect floodwater with a single-scene classification based on an automated thresholding method. The flood magnitude is then matched with open-access geodata such as human settlements, population density, land cover and infrastructure from the OpenStreetMap catalogue to generate the impact assessment. Once trained on several site or region specific past events, it can automatically forecast the impact associated with a given event magnitude. Here we apply the technique to three case studies including the flooding associated with the Tropical Cyclone Idai, which made landfall in Mozambique in March 2019 causing over 1200 fatalities and $2 billion worth of damage. The performance of the flood mapping algorithm has been evaluated as satisfactory for the impact application and further validation at two additional sites is ongoing. Therefore, local triggers can be set to ensure a valuable temporal window to promptly plan and estimate the cost of intervention on the field. This work is a first step to providing a consistent and regionally transferable disaster preparedness tool that allows for multi-hazard impact forecasts.

How to cite: Sarcinella, M., Pagán, B. R., Pal, J. S., Essenfelder, A. H., Landuyt, L., and Mysiak, J.: Impact-based Forecasting: Bridging the gap between forecast and post flood impact with remote sensing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12046, https://doi.org/10.5194/egusphere-egu22-12046, 2022.

In the context of disaster risk management and climate change adaptation, Nature-based Solutions (NBS) are being increasingly recognized and promoted as viable measures against hydro-meteorological hazards, while also being able to provide a range of environmental, social, and economic benefits. Yet, the employment of NBS to mitigate the impact of hydro-meteorological phenomena remains still sporadic and uncoordinated at the global and European level.

In order to assist competent authorities, practitioners and other stakeholders in developing successful NBS interventions for hydro-meteorological risk mitigation and climate change adaptation, while also raising general public awareness and community stewardship of NBS, the EU-H2020 project OPERANDUM has recently launched a multi-dimensional, open and user-friendly web-platform called GeoIKP (Geospatial Information Knowledge Platform).

GeoIKP follows a multi-stakeholder approach demonstrated through the integration of multiple modules related to science, policy and practice. This contribution offers an overview of GeoIKP and discusses in detail some of the innovative aspects and tools of the platform. It represents the first example of NBS web-platform with advanced interface customization. Functionalities and graphical interfaces are tailored to match specific user needs and interests for six different user profiles: 1) policy bodies (from international to local level), 2) knowledge-based organizations (research institutions, labs and data providers), 3) companies or private businesses, 4) associations, interest groups and grass-roots movements, 5) citizens and 6) other affected or interested parties (e.g. media outlets).

The platform combines the latest scientific and technological knowledge on the topic gathered within OPERANDUM with advanced webGIS functionalities, analytical algorithms, and a comprehensive repository for NBS data (and metadata) management and cataloging. The highly structured and comprehensive data model adopted here enables to query the database and/or filter the results based on a multitude of individual parameters which encompass all different dimensions of NBS (e.g. geophysical, societal, environmental, etc.). This not only allows for a straightforward and automatic association to one or more thematic aspects of NBS, but also enhances standardization, discoverability and interoperability of NBS data in the context of disaster risk management and climate change adaptation.

Among its functionalities, GeoIKP offers an interactive map which enables users to visualize and combine in real time geo-referenced datasets on a variety of thematic areas (hydro-meteorological hazards and associated socio-ecological risks, land cover/use characteristics, climate, Earth and ground observations, etc.), thus providing evidence-base support for the planning and management of NBS in a given geographic area. Through the map, the user can also access a geo-catalogue of existing NBS, and thus discover how NBS have been employed worldwide for hydro-meteorological risk reduction and climate change adaptation. At the same time, the platform serves as a hub for the growing NBS community to share information, tools, data, and experiences to reduce hydro-meteorological hazards. For example, scientists and practitioners can freely contribute to GeoIKP data repository as well as to the NBS catalogue, while the “Citizen Stories” functionality gives a voice to vulnerable, affected or concerned citizens to share personal experiences of how and why they started applying NBS to their areas, and to inspire others to take action.

How to cite: Leo, L. S., Kalas, M., Ommer, J., Vranić, S., Pavlova, I., Amirzada, Z., and Di Sabatino, S.: User-driven platform to facilitate community data access, collaboration, and knowledge sharing on Nature-Based Solutions as mitigation measures for hydro-meteorological hazards, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8229, https://doi.org/10.5194/egusphere-egu22-8229, 2022.

Local governments are faced with increasing levels of risk from extreme hydro-meteorological events such as (tropical) storms,  flooding, landslides, drought, heatwaves, wildfires, etc. The frequency and interaction of these events, also in combination with other events that do not have a direct climate driver, makes that it is likely that many areas are faced with higher impacts from compounding events. Global trends such as population growth, urbanization, increased dependency on technology also contributed to larger exposure and vulnerability. In order to plan for future developments, and for reducing the increasing levels of risk, local governments require to plan ahead and evaluate the options available for reducing the risk under future scenarios. For this task Spatial Decision Support Systems are required that allow local governments to make informed decisions, considering the current and future levels of risk. RiskChanges is a Spatial Decision Support System for the analysis of current and future multi-hazard risk at a local level, in order to analyze optimal risk reduction alternatives. The system is developed by the University of Twente in collaboration with the Asian Institute of Technology, GeoInformatics Centre. RiskChanges ( http://www.riskchanges.org/ ) is an Open-Source, web-based tool, based on a series of python scripts, which are integrated into a Graphical User Interface. The tool includes several major features: multi-hazard, multiple assets, a vulnerability curve database, multi-user approach, comparison of risk, and spatial analysis. Users can upload their own datasets (in the form of hazard maps, elements-at-risk maps, administrative unit maps, and vulnerability curves). The tool contains an open-source vulnerability curve database, allowing to sharing of physical vulnerability curves among users. Multiple users can collaborate on the same project, and provide different input data. The multi-hazard feature allows performing the risk assessment for multiple natural and manmade hazard interactions. Exposure and vulnerability are combined in a loss calculation for each combination of element-at-risk and hazard. Loss maps are integrated into a risk map, where the user indicates the interaction between the hazard types. The system allows to analyze the risk of multiple asset types with different spatial characteristics.  Users can compare the risk for the current situation and future scenarios and/or planning alternatives.  

How to cite: van Westen, C., Hazarika, M., Dahal, A., Kshetri, T., Shakya, A., and Nashrrullah, S.: The RiskChanges tool for multi-hazard risk-informed planning at local government level, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3026, https://doi.org/10.5194/egusphere-egu22-3026, 2022.

Tropical cyclones (TCs) displace millions of people every year. Displaced people are subject to heightened risks to their physical and mental well-being. We present the first results of a TC impact forecast system for population displacement, aiding the decision-making process for planning early prevention and mitigation actions. For example, planning precautionary evacuations and the allocation of humanitarian aid. We work closely with the Internal Displacement Monitoring Centre (IDMC) to develop a global TC impact forecast system that predicts the number of people potentially affected or displaced.

We build the impact forecast system using a python-based, open-source, globally consistent platform called CLIMADA (CLIMate ADAptation). The platform integrates probabilistic hazard, exposure, and vulnerability information to compute the potential impacts from TC events. The first prototype of the forecast system extracts information from ECMWF ensemble TC forecast tracks, a global population layer at ~1km resolution, and vulnerability functions that relate the exposed people to the intensity of TC wind speed. We show case studies of recent TC events to reveal the potential of the displacement forecast system, the uncertainties of the forecast results

The displacement forecast system will provide richer information for decision-makers and help improve warnings. The open-source data and codes of this implementation are also transferable to other users, hazards, and impact types. 

How to cite: Kam, P. M., Fairless, C., and Bresch, D. N.: Globally consistent tropical cyclones impact forecast system for population displacement, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1981, https://doi.org/10.5194/egusphere-egu22-1981, 2022.