Extreme hydro-meteorological events drive many hydrologic and geomorphic hazards, such as floods, landslides and debris flows, which pose a significant threat to modern societies on a global scale. The continuous increase of population and urban settlements in hazard-prone areas in combination with evidence of changes in extreme weather events lead to a continuous increase in the risk associated with weather-induced hazards. To improve resilience and to design more effective mitigation strategies, we need to better understand the aspects of vulnerability, risk, and triggers that are associated with these hazards.
This session aims at gathering contributions dealing with various hydro-meteorological hazards that address the aspects of vulnerability analysis, risk estimation, impact assessment, mitigation policies and communication strategies. Specifically, we aim to collect contributions from the academia, the industry (e.g. insurance) and government agencies (e.g. civil protection) that will help identify the latest developments and ways forward for increasing the resilience of communities at local, regional and national scales, and proposals for improving the interaction between different entities and sciences.
Contributions focusing on, but not limited to, novel developments and findings on the following topics are particularly encouraged:
- Physical and social vulnerability analysis and impact assessment of hydro-meteorological hazards
- Advances in the estimation of socioeconomic risk from hydro-meteorological hazards
- Characteristics of weather and precipitation patterns leading to high-impact events
- Relationship between weather and precipitation patterns and socio-economic impacts
- Hazard mitigation procedures
- Strategies for increasing public awareness, preparedness, and self-protective response
- Impact-based forecast and warning systems
- Insurance and reinsurance applications
vPICO presentations: Tue, 27 Apr
This presentation is going to address some of the main commonalities between hydrological research and hydrological practice, from the perspective of the Natural Catastrophe (Nat Cat) model developer. For example, hydrological research on the one hand, has a strong focus on the advancement of understanding hydrological processes. The hazard component of Nat Cat flood models, on the other hand, tends to be focused more on model suitability, accuracy and precision. However, it does rely heavily on a thorough understanding of the main hydro-meteorological drivers to describe catchment processes across the relevant spatial and temporal scales, and these are incorporated to achieve model realism and robustness, in particular when extrapolating outside the range of observed regimes. The latter is of importance when modelling extremes, which by definition are scarce.
The presentation will also go into detail on the feedbacks between hydrological research and hydrological practice. For example, how the latest generation of Natural Catastrophe models benefit from the advances in hydrological research, e.g. research on large scale hydroclimatic patterns like ENSO, or climate change research. Incorporating the latest research in hydrological hazard modeling into Catastrophe Models ultimately improves the risk assessment for a set of assets. Also, large-scale flood risk models using coupled model chains that are relatively new in the hydrological research literature, have been part of the standard methodology for the Nat Cat models for a couple of decades, and might be seen as an indicator for the societal demand to perform novel research in these fields.
How to cite: Salinas, J.: Intersections between hydrological research and hydrological practice – a Natural Catastrophe modeller perspective, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3235, https://doi.org/10.5194/egusphere-egu21-3235, 2021.
Detecting areas exposed to flood inundation in coastal zones is of paramount importance for reducing damages and preventing human and economic losses. In general, the Geomorphic Flood Index (GFI) method, based on a Digital Elevation Model (DEM) and mostly applied to riverine flood, provides a good representation of flood-prone areas with low requirements in terms of data, time and costs. However, the method does not account for inter-basin floodwater transfers and, therefore, performs poorly on coastal basins. The present work addresses this shortcoming by explicitly taking into account these potential inter-basin water transfers. We applied the GFI method with this new feature to a coastal basin located in southern Italy and the outcome was compared with a flood inundation map obtained by a two-dimensional hydraulic simulation for a return period of 300 years. Its transferability was tested in a second adjacent coastal basin using a threshold binary classification and the sensitivity of the methodology to the return period was investigated. Results show that coastal flood-prone areas are successfully delineated with performance metrics above 93%. This achievement represents a step further in the application of the GFI method, that can help stakeholders in flood risk management to rapidly and inexpensively characterize hazard-prone areas.
How to cite: Albertini, C., Miglino, D., Iacobellis, V., De Paola, F., and Manfreda, S.: Flood-prone areas delineation in coastal regions using the Geomorphic Flood Index, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3396, https://doi.org/10.5194/egusphere-egu21-3396, 2021.
Accumulating evidence on the increase of intensity and frequency of climate extremes such as droughts, necessitates the development of effective climate adaptation procedures. To inform adaptation and mitigation strategies we need to develop improved methodologies for assessing future drought risk. The outputs of such methodologies must be usable by various stakeholders (e.g. water, energy and biodiversity conservation managers) and must be scalable (from regional to global) and methodologically robust. Severity-duration-frequency (SDF) curves serve as a concise way to quantitatively and qualitatively represent anticipated changes in drought risk and thus offer an optimum way to convey information on future drought risk across scientific disciplines and stakeholders. In this work we are presenting a methodological framework for assessing future drought risk that integrates state-of-the-art high-resolution (~10km) climate data from ERA5-Land reanalysis and downscaled CMIP6 projections with novel statistical procedures for robust estimation of SDF curves. Results are presented for Australia and are based on meteorological drought identification based on the widely established indicators of Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Comparison between historic (1981-2019) and future (2020-2100) drought characteristics reveal that severity and duration tends to increase towards the end of the century. The spatial extent of severe and extreme droughts is also projected to increase, particularly in central and western Australia. The SDF analysis highlights a consistent increase in severity of extreme (i.e. 100yr) drought towards the end of century. While there is still significant uncertainty on the projected magnitude of increase, the multi-model analysis reveals that increasing trend of drought risk is consistent across models . The proposed framework can be applied at global scale and can be easily modified to incorporate additional drought indicators.
How to cite: Araujo, D., Marra, F., Merow, C., and Nikolopoulos, E.: A methodological framework for assessing changes in future drought risk: evaluation over Australia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12431, https://doi.org/10.5194/egusphere-egu21-12431, 2021.
In recent years, extreme events and their severe damage have become more common around the world. It is widely known that atmospheric greenhouse gases have contributed to global warming.
A set of appropriate indicators describing the extremes of climate change can be used to study the extent of climate change. This study reveals the trends of temperature extreme indices on the spatial scale in the western part of Northwest Himalayas. The study is conducted at 13 climate stations lies at a different altitude of the study area.The Daily maximum and minimum temperature data during 2000--2018 of stations obtained from the Pakistan Meteorological Department (PMD) and Water and Power Development Authority (WAPDA). The 12 extreme temperature indices (FD, SU, TXx , TXn., TNx, TNn, TN10p , TN90p, TX10p , TX90p, CSDI, WSDI) recommended by ETCCDI (Expert Team on Climate Change Detection and Indices) are used to study the variabilities in temperature extremes. These indices are characterized based on amplitude, persistence, and frequency. The analysis is performed by using R package of extremes “RClimDEX”. The analysis shows the frequency of summer days (Su) and warm spells (WSDI) have increasing trends in the Southwest region, whereas the frequency of cold spells and frost days have decreasing trends observed in the Northern region of the study areas. The maximum and minimum values of daily maximum temperature (TXX, TXN) increase in the foothill area of the region and decreasing trends in the high elevation region. The day and night get cool in the Northwest region, whereas the days and nights are showing warmer trends in low elevation regions of the study area. Overall, the study concludes that the Northwestern parts have cool trends while South West and South eastern parts have warm trends during the early 21st century.
Key words: Temperature Extremes, Northwest Himalayas, Trends, R-Climdex, Climate Change
How to cite: Aziz, F., Tariq, N., Rahim, A., and Mahmood, A.: Variability of Temperature Extremes in Northwest Himalayas during Early 21st Century., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-394, https://doi.org/10.5194/egusphere-egu21-394, 2021.
Geo-hydrological hazards like floods and landslides are common in mountain regions. During a disaster, evacuation shelters become a primary need of people. We develop a model to find suitable locations for emergency shelters in flood and landslide strikes in a rural mountain setting of the Western Ghat region, India. Firstly, susceptibility maps for flood and landslide hazards are prepared using a machine learning (Random forest) algorithm. Then location suitability modeling is done in GIS using the entropy method. The following entropy evolution factors are considered- flood susceptibility, landslide susceptibility, land use, distance from the road, distance from the hospital, distance from the market, distance from the fire station, distance from safe water sources, and the population of settlement cluster area. Model constraining factors like steep slope, high landslide, flood susceptible area, and protected area are accounted for using a cost matrix. The model is compared with community-based suitability mapping and evacuation centers during the past disaster of 2005. The study will contribute towards better disaster-resilient planning of rural mountainous settlements.
Keywords: Evacuation shelter, landslide, flood, random forest, entropy method, GIS
How to cite: Bera, S. and Gnyawali, K. R.: Evacuation shelter suitability modeling under combined geo-hydrological hazards in Western Ghat region, India, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7822, https://doi.org/10.5194/egusphere-egu21-7822, 2021.
Rainfall-induced landslides are widespread phenomena that cause casualties and economic losses every year. In Italy, intense or prolonged rainfall is the primary trigger of landslides. The identification of the rainfall conditions responsible for the initiation of landslides is a crucial issue and may contribute to reduce landslide risk at regional scale. In the literature, the most widely used criteria for the identification of rainfall conditions initiating slope failures are based on rainfall intensity-duration (I-D) or cumulative rainfall-duration (E-D) charts. In this study, a novel E-D procedure for the objective reconstruction of the rainfall conditions responsible for landslide occurrence is proposed. Rainfall measurements are derived from the satellite-based NASA Global Precipitation Measurement (GPM) database, which contains gridded precipitation estimates, with a half-hour temporal resolution and a 0.10-degree spatial resolution. Firstly, precipitation measurements are aggregated at hourly temporal resolution and the mean rainfall values over each territorial unit is calculated. Then, rainfall measurements are aggregated in order to obtain a sequence of rainfall events. Finally, for each rainfall event all the possible rainfall combinations are differentiated in two groups depending on whether they triggered or did not trigger landslides. The proposed procedure has been tested in a study area including six weather warning zones defined for hydrogeological risk management in Italy in the period between January 2010 and December 2018. Data on landslide occurrences are derived from the FraneItalia catalog (https://franeitalia.wordpress.com), a landslide inventory based on information retrieved from online Italian news. In the study area, the FraneItalia database reports 513 landslide events in the period 2010-2018. This procedure shall be a contribution toward objectively defining rainfall conditions responsible for landslides in different geographic areas, thus reducing the subjectivity inherent in the often-adopted heuristic treatment of rainfall and landslide data when defining rainfall thresholds for landslide occurrences.
How to cite: Calvello, M. and Pecoraro, G.: A procedure for identifying rainfall thresholds for the occurrence of landslides, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1792, https://doi.org/10.5194/egusphere-egu21-1792, 2021.
Stochastic modelling is an increasingly popular method to generate long rainfall time series as input for the subsequent hydrological applications, such as the design of urban drainage system. It aims to resemble the physical process of rainfall using parameters with physical meanings, instead of its statistical features. There are, however, two main challenges yet to be overcome in stochastic rainfall modelling. These are 1) reproduction of rainfall extremes at sub-hourly timescales, and 2) incorporation of the impact of climate change.
Some recent breakthroughs have been made to address the first challenge. Onof and Wang (2020) reformulated the equations of the randomised Bartlett-Lewis rectangular pulse (BLRP) models and showed that the improved models can well preserving rainfall extremes at sub-hourly (5- and 10-min) and hourly timescales.
The second challenge is however yet to be explored. Cross et al. (2020) recently presented a multivariate regression method that associates BLRP parameters to temperature estimates on a monthly basis, attempting to capture the dynamics of the underlying climate. However, the concept of ‘calendar month’ - an artificial period of time - was still employed to represent natural seasonality. This may fail capturing the natural shift and length difference of seasons between years. To address the above drawback, it is critical to ‘relax’ the concept of calendar month, so that the most similar climate conditions between different years can be better identified.
An innovative approach is proposed in this work to circumvent the above drawback, where two main improvements are implemented. First, instead of following calendar month, we slice the original rainfall time series using an overlapping moving window with 30-day window width and 10-day step size. This enables a stronger continuity in representing climate variations. Second, the dynamic time warping (DTW) algorithm is employed to quantify the similarity of climate conditions between different years. DTW is a widely-used algorithm in measuring the similarity between two time series, and is known to be less sensitive to the distortion in time axis as compared to the Euclidean distance metrics. Then, based upon DTW measures, we can identify the historical periods with the most similar climate conditions to the target ones. The statistical properties of the local gauge data for these specific periods are used to build the BLRP model in a dynamic fashion.
Selected atmospheric variables (including geopotential, temperature, U-component of wind, and V-component of wind ) from the ERA5 re-analysis datasets and five-minute rainfall data from 6 long recording rain gauges in Germany (one with 69 years of data; others with 49 years) are used to test the impact of the proposed approach. Preliminary results show that the statistical behaviours of newly identified periods of data are more analogous to the target period as compared to those identified from the traditional method relying on calendar month. This demonstrates the potential to use the proposed new approach to better incorporating the impact of climate change into stochastic rainfall time series modelling.
How to cite: Dai, T.-Y. and Wang, L.-P.: Modelling high-resolution rainfall extremes in a changing climate, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2436, https://doi.org/10.5194/egusphere-egu21-2436, 2021.
Global warming is expected to modify the regime of extreme precipitation. Physical laws translate increasing atmospheric heat into increasing atmospheric water content that, together with changes in the atmospheric dynamics, drive precipitation changes. The literature generally agrees that extreme precipitation is changing. However, the study of observed annual maximum time series suggests that trends are highly variable in space and uncertain, also as a result of the inherent large stochastic uncertainty of rainfall maxima. In the present work, we exploit the Meta-statistical Extreme Value (MEV) Distribution to investigate the statistical processes behind these trends and understand how they can be related to changing meteorological conditions. The MEV framework was recently proposed for the frequency analyses of extremes under pre-asymptotic conditions and was shown to significantly improve estimation uncertainty for extreme events by using ordinary events. The narrow confidence interval characterizing MEV is a clear advantage for trend analysis, and its ability to separate storm intensity and yearly occurrence permits to better understand the statistical processes underlying extremes. We gathered data from 33 stations in the Trentino region (Eastern Italian Alps) with at least 25 years of 5-minute resolution records (average density 1/190 km-2) and computed the parameters describing the yearly intensity distribution of events at multiple durations ranging from 15 minutes to 24 hours as well as their yearly number. The Regional Mann-Kendall test is used for evaluating the presence of trend in the distribution parameters, number of events per year, estimated quantiles and recorded annual maxima. Results confirm the presence of significant trends in the annual maxima. Trends in the 2-year quantiles estimated yearly using MEV are consistent with the observed trends in annual maxima, which are more marked for 15min to 1 hr duration and less marked for 3hr to 24 hr duration. Conversely, trends in rare quantiles (10-year, 100-year) are significant for durations up to 1 hour and become not significant for longer durations. Analysis of the parameters shows that these trends are likely due to a combination of (i) increasing number of storm events per year and increasing intensity of the storms, and (ii) changes in the tail properties of the storms.
How to cite: Dallan, E., Zaramella, M., Borga, M., and Marra, F.: Detecting and analyzing regional trends in sub-daily rainfall annual maxima by using the Meta-statistical Extreme Value Distribution, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13271, https://doi.org/10.5194/egusphere-egu21-13271, 2021.
Ghana is one of the countries most prone to floods in West Africa. Its annual occurrence often leads to disasters that are mostly felt by the urban poor. Despite the existence of salient activities conducted in order to reduce the flood risk in Ghana, there are still persisting challenges. Here, we evaluate these gaps and describe opportunities for further improving flood risk management (FRM) in Ghana. A mixed-method participatory approach comprising questionnaires, workshops, interviews with key stakeholders, and a systematic literature review were employed (Almoradie et al. 2020). Existing problems, discourses, FRM practices, and opportunities to enhance flood resilience were identified. Based on that, potential research directions on how to tackle these challenges were outlined. Results showed that the stakeholders interviewed construct the effectiveness of FRM differently and even in contradictory ways, embedded in diverse storylines. Furthermore, we found that Ghana’s FRM is still reactive rather than preventive and that research in the field of quantitative hazard and risk assessment is rather rudimentary. FRM policies and tools such as flood early warning systems (FEWS) are in place, but efforts should be directed towards their implementation and monitoring, investigation of socio-technical capacity aspects, and enhancement of institutions’ mandates, functions, and coordination. Based on these findings, we conceptualized a research and development project, which is based on participatory research, aiming to tackle some of the identified issues. To this end, we will implement a collaborative modelling approach and will develop a socio-technical tool, which comprises: (1) a tailored decision support system, (2) a citizen science-based data collection system, (3) a flood forecasting tool, and (4) an approach for modelling cascading risks.
Almoradie, A.*, de Brito, M.M.*Evers, M., Bossa, A., Lumor, M., Norman, C., Yacouba, Y., Hounkpe, J. (2020) Current flood risk management practices in Ghana: gaps and opportunities for improving resilience. International Journal of Flood Risk Management, doi:10.1111/jfr3.12664.
How to cite: Evers, M., Almoradie, A., de Brito, M. M., Höllermann, B., Ntajal, J., Lumor, M., Bossa, A., Norman, C., Yira Yacouba, Y. Y., and Jean Hounkpe, J. H.: Flood risk management in Ghana: gaps, opportunities, and socio-technical tools for improving resilience, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12683, https://doi.org/10.5194/egusphere-egu21-12683, 2021.
In the latest decades, the impact of floods has generated an increase of loss of human lives, as well as the interruption of economic activities in the affected areas. In this context, we present an implemented methodology for micro-scale flood risk evaluation that considers direct and tangible damages as a function of the hydrometric height and allows for quickly estimates of the damage level caused by alluvial events. The method has been applied and tested for economic and residential buildings in the town of Benevento (southern Italy), which was hit by destructive floods in the past. As the limitation of this original method is connected to the huge amounts of input data, we tried to overcome this limit by applying a simplified procedure in defining the physical data of buildings (e.g. type of buildings, n° of floors, presence of cellar). More specifically, during data collection on building features, two different criteria were used:1) data were acquired through a careful field survey, and 2) data were obtained through the topographical database of the Campania region and through the generalization of heights for each type of building. Data obtained using the first criterion result in a highly accurate risk assessment but, at the same time, the method is non-immediate and time-consuming. On the other hand, the second one is more expeditious. By comparison, the two criteria show very similar results and minimal differences, making the generalized data acquisition the most expeditious. In conclusion, the basic method allows estimating highly detailed potential losses for representative buildings categories in the urban context, but involves a higher degree of resolution; the generalised method, instead, thought the simplification of the data, responds to the need of reaching in a short time a damage value extremely similar to the real one.
How to cite: Festa, G. I., Revellino, P., Guadagno, F. M., Guerriero, L., Focareta, M., and Meoli, G.: A semplified method for flood risk evaluation at micro-scale level in Benevento city (Southern Italy), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15641, https://doi.org/10.5194/egusphere-egu21-15641, 2021.
Tropical Cyclones (TCs) are among the most dangerous natural hazards because they can cause severe economic losses and high mortality. Climate risk is defined as a metric that depends on social vulnerability and the occurrence of natural hazards. A social vulnerability index was constructed for this study using two metrics: the degree of local marginalization and the local social gap. The accumulated rainfall and duration of extreme precipitation associated with TC passages are examined as a natural hazard during the period 1981–2017. TC days are depicted as days when TC‐related rainfall exceeded the 95th percentile of daily precipitation from May to November, defined as summer precipitation. In this way, changes in climate risk under El Niño‐Southern Oscillation (ENSO) conditions are explored to determine regions where both social vulnerability and TC days are high. These changes are useful to find out when disasters have more chances to occur. In the present study, climate risk was found to increase more than 80% from average in southwestern Mexico during strong El Niño years. Under neutral conditions, climate risk values rise to more than 40% than average over northwestern Mexico. Under strong La Niña conditions, climate risk increases by more than 80% from average over the eastern coast of Mexico. Our approach is validated through a comparison between anomalies in climate risk and disaster costs (socioeconomic impacts). Both local vulnerability and ENSO conditions exacerbate socioeconomic impacts associated with TCs, and an analysis of linear trends in TC rainfall and TC days reveals that most of the coastal regions in Mexico have a significant rising trend in both variables. Thus, Mexico should be prepared to face more TC extreme rainfall events. Suggestions for how Mexico can meet the objectives of international risk agendas are discussed.
How to cite: Jaramillo, A. and Dominguez, C.: Are the socioeconomic impacts associated with tropical cyclones in Mexico exacerbated by local vulnerability and ENSO conditions?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-981, https://doi.org/10.5194/egusphere-egu21-981, 2021.
France experiences catastrophic floods on a yearly basis, with significant societal impacts. In this paper, we critically evaluate the French Flood Risk Governance (FRG) system with the aim of identifying any shortcoming and, thereby, to suggest improvements. To do so, we employ a historical assessment of catastrophic past flood events in the Provence-Alpes-Côte d'Azur (PACA) region and perform Strengths-Weaknesses-Opportunities-Threats (SWOT)-analysis. Our evaluation shows that despite persistent government efforts, the impacts of flood events in the region do not appear to have lessened over time. Identical losses in the same locations (e.g. Riou de l’Argentière watershed) can be observed after repetitive catastrophic events (e.g. 2015, 2019) triggering local inhabitant protests. We argue that the French FRG system can benefit from the following improvements: a) regular updates of the risk prevention plans and tools; b) the adoption of a Build Back Better logic instead of promoting the reconstruction of damaged elements in the same locations; c) taking into account undeclared damages into flood risk models (and not only those declared to flood insurance); d) increased communication between the actors of the different steps of each cycle (prepare, control, organise etc.); e) increased communication between three main elements of the cycle (risk prevention, emergency management and disaster recovery); f) an approach that extends the risk analysis outside the borders of the drainage basin (to be used in combination with the current basin risk models); and g) increased participation in FRG from local population. We also briefly discuss the use operational research methods for the optimisation of the French FRG.
How to cite: Kougkoulos, I., Merad, M., Cook, S., and Andredakis, I.: A critical analysis of French flood risk governance, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-349, https://doi.org/10.5194/egusphere-egu21-349, 2021.
Worldwide, floods have major impacts on people, economies, and the environment. In Myanmar, floods are the most frequently occurring hazard and have the highest contribution to average annual loss compared to all other hazards. Although the population has learned to adapt to yearly flooding, climate change exacerbates the frequency and magnitude of flood events to an extent where the population has little capacity to cope. Many factors such as poverty and dependency on agriculture make the Burmese people more vulnerable to major flood events. The need to better understand flood risk and its spatial patterns in Myanmar has become extremely important.
However, the state of the art on flood risk in Myanmar is not well developed. Analysis has mostly focused on flood loss, hazard, mitigation, and resilience, or future vulnerability to flooding. Here we present a comprehensive quantitative indicator-based risk assessment for a major flood event with a 100-year return period at the township level for Myanmar. This analysis will show the spatial distribution of major river flood risk based on the IPCC framing of risk while highlighting factors of vulnerability that contribute to risk. The analysis considered the present-day flood risk to people. Flood extent and population distribution were used to create a hazard/exposure indicator. Then, a systematic literature review was performed to identify relevant vulnerability indicators and drivers for Myanmar. Data for each vulnerability indicator was collected and compiled into one vulnerability index score. Then, we compared two different methods of aggregation of the elements into a risk index: multiplicative arithmetic aggregation and overlay of different quantiles of hazard/exposure and vulnerability. Post hoc analysis was conducted to test the relationship between elements for the multiplicative aggregation method.
The analysis showed that the highly exposed populations and townships are adjacent to rivers, with most flooding in the Ayeyarwady region. Major urban population centers such as Yangon and Mandalay cities have high exposure to flooding. Vulnerability to river flooding is primarily triggered by poverty, inadequate access to healthcare with a limited number of doctors and beds, poor road networks, and a small number of households with boats. Risk is highly concentrated in townships in the Ayeyarwady, Bago, and Rakhine regions in both aggregation methods.
Importantly, there are limitations in this study and future work could focus on addressing these gaps. For example, this assessment focused on a single hazard (flood) and a single exposed element (people) whereas Myanmar has a multi-hazard environment with complex social-ecological systems and high levels of resource dependency. Nevertheless, our study results remain essential for local and national authorities and related organizations in the field of disaster risk reduction as it has a strong conceptual foundation of risk with a clear focus on entry points for vulnerability and risk reduction.
How to cite: Wuit Yee Kyaw, H. and Dudley, A.: A risk assessment for major river flooding in Myanmar incorporating hazard, exposure, and vulnerability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14648, https://doi.org/10.5194/egusphere-egu21-14648, 2021.
An assessment of the compound hazard – extreme wind and extreme precipitation, of tropical cyclones (TCs) is of importance due to the enormous potential impact of TCs to the economic development and societal welfare of coastal regions. Recently, a new method to construct a large physically consistent TC event set (roughly 10,000 years of events) based on numerical weather prediction models has been developed (Ng & Leckebusch, 2021). However, a systematic method for the detailed analysis of the compound nature of the TC hazard with respect to damage relevant impacts is not yet available. In this presentation, we propose a new metric, TC compound meteorological hazard risk index (CMRI) to assess TC compound meteorological hazard risk in terms of potential economic loss for mainland China. CMRI considers TC-related extreme wind and extreme precipitation which are identified based on an impact-oriented tracking algorithm. CMRI is closely linked to the normalised economic loss in China between 1979-2014. We also present preliminary results of the application of CMRI in estimating the return period of the TC-related potential economic loss in mainland China.
How to cite: Leckebusch, G. and Ng, K.: An analysis of the meteorological compound hazard of typhoons, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5416, https://doi.org/10.5194/egusphere-egu21-5416, 2021.
The resilience of communities depends on how their citizens react during emergencies and how authorities implement systems to support appropriate self-protection responses from the public. Past events such as the storm Gloria in Spain demonstrate that one of the main challenges in risk communication remains citizens' inappropriate understanding of the upcoming risk and its potential impact in their daily lives. The current official warnings to the population in Spain continue to be based on the exceedances of the event's physical parameters, such as rainfall intensities and accumulations, that can be difficult for citizens to understand, personalize and translate into the expected local risks. The above can create a communication gap between what authorities provide to citizens in terms of risk information and what they actually need from a flood warning to make better decisions and react appropriately during an emergency.
Society is now demanding localized, people-centred risk communication for better social comprehension and acceptation, which provides understandable information about the expected local impacts and clear guidelines for ensuring citizens' safety during emergencies.
Thus, to support citizens' understanding and decision-making process at risk, we present a people-centred approach to design and implement new site-specific warnings (SSWs), i.e. warnings at problematic points based on local vulnerability and exposure information. The proposed methodology places people and communities at the core of the early warning system process. It blends meteorological information coming from radar-based rainfall nowcasting, numerical modelling and historical flood data to translate forecasts into relevant local impacts that the citizens may experience due to the coming weather-induced events and appropriate self-protection actions to help secure their lives. In this context, an active collaboration process with civil protection authorities, stakeholders and citizens is established from the start to incorporate their detailed local knowledge to the system and target their communication needs during emergencies. New technologies, such as smartphone applications, are used to disseminate the SSWs within the area of risk.
A first pilot based on the SSWs methodology is currently at the operational stage in Terrassa, Spain, for selected vulnerable points. Besides contributing to address the current gaps in risk communication, the implemented methodology in this study can help create a proactive, dynamic society by empowering its citizens to respond appropriately during the first instances of an emergency.
How to cite: Meléndez-Landaverde, E., Sempere-Torres, D., and Berenguer, M.: A people-centred approach for emergency communication: The case of site-specific warnings in Terrassa, Spain, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12638, https://doi.org/10.5194/egusphere-egu21-12638, 2021.
Understanding the different dimensions of vulnerability to floods is instrumental to gaining knowledge on flood impacts, to guide the development of appropriate risk analysis methods and to make critical decisions in risk management. Vulnerability assessment of complex systems, such as transportation infrastructure, demands an integrated framework to include various analytical methods to investigate the problem from the different characteristic perspectives related to their topological, functional, logic and dynamic properties. One approach to understand the impacts of transportation infrastructure disruptions on people is the accessibility-based vulnerability approach. Accessibility-based vulnerability analysis examines changes of access levels across a traffic network disrupted by floods, thereby providing insight on the impacts to a broader range of socio-economic aspects and to the society as a whole.
The presented study evaluates two different approaches. The first approach computes direct impacts and investigates different measures for extreme flood impacts to the road network. The second approach computes indirect impacts and
i) incorporates detailed information about the local road network in the accessibility-based vulnerability analysis by modifying the approach of calculating travel time between zones,
ii) includes additional contributing factors to the accessibility-based vulnerability analysis by considering residents and socio-economic opportunities in flood-affected areas,
iii) effectively identifies the most vulnerable traffic zones with respect to selected extreme flood scenarios, and
iv) investigates the influence of different spatial patterns of floods on accessibility-based vulnerability assessment.
We used three measures to assess direct flood impacts on the road network towards selecting the flood scenarios, which are representative for different flood patterns. Namely, Loss Index (LI), the total value of normalized edge betweenness centrality (Total-EBC), and the average normalized edge betweenness centrality (Mean-EBC). The Hansen integral accessibility approach was modified for two vulnerability indices considering traffic zones along with average shortest travel time as cost and applied for selected flood scenarios. The resulted vulnerability indices were additionally analyzed to identify the most vulnerable traffic zones for each approach and the spatial influence of the flood and network pattern as well as the distribution of population and opportunities. Finally, effects of the contributing factors to the vulnerability were investigated using correlation and comparison between the flood scenarios.
The results of the direct impact assessment show that different flood scenario and varying spatial extent are selected as extreme events based on Total-EBC and Mean-EBC. The comparisons of these different measures in assessing direct impact of extreme floods to road network allows to plan different services on disaster mitigation to place mitigation policies to be efficient. Most of the highly vulnerable traffic zones are related to the flood extent in these zones and affected population and opportunities in the traffic zones. However, the most remote traffic zones were also highly vulnerable in flood scenario, if some parts of the important connecting roads for these remote traffic zones were disturbed by a flood in traffic zones faraway. The overall results implicate those different types of flood scenarios could be classified into several groups according to their patterns of vulnerability.
How to cite: Papilloud, T., Zischg, A., and Keiler, M.: Road network vulnerability to extreme floods: accessibility-based analysis and patterns of vulnerability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1342, https://doi.org/10.5194/egusphere-egu21-1342, 2021.
Linear infrastructure systems such as Water Supply System (WSS), electricity and transportation are considered Critical Infrastructures (CIs) because their failure would jeopardize public health and economic security, with repercussions on the whole society (Fekete, 2019). CIs are exposed to natural hazards, such as flooding, which is the most frequent and damaging natural threat worldwide; in particular, ~7.5% of road and rail infrastructures are exposed to a 1/100-year flood event worldwide.
Flooding can damage CIs directly (when impacts are due to the physical contact with floodwaters, i.e. direct impacts) and indirectly (when impacts are not due to the physical contact, and/or occur outside the inundated area in space or time, i.e. indirect or cascade impacts). Whereas the assessment of direct impacts is well-advanced, the evaluation of indirect impacts is less frequently achieved (Arrighi et al. 2019).
This work presents the risk analysis of two linear infrastructure systems, i.e. the water distribution system (WSS) and the road network system. The evaluation of indirect flood impacts on the two networks is carried out for four flooding scenarios, obtained by a coupled 1D-quasi 2D hydraulic model. Two methods are used for assessing the impacts on the water distribution system and on the road network, a Pressure-Driven Demand network model 15 and a transport network disruption model respectively. The analysis is focused on the identification of: (i) common impact metrics; (ii) vulnerable elements exposed to the flood; (iii) similarities and differences of the methodological aspects for the
two networks; (iv) risks due to systemic interdependency. The study presents an application to the metropolitan area of Florence (Italy). When interdependencies are accounted for, results showed that the risk to the WSS in terms of Population Equivalent (PE/year) can be reduced by 71.5% and 41.8%, if timely repairs to the WSS stations are accomplished by 60 and 120 minutes respectively; the risk to WSS in terms of pipes length (km/year) reduces by 53.1% and 15.6% (Arrighi et al. 2020).
This study represents one of the first attempts to model flooding impact to CIs for real-world networks, considering mutual interconnections, and it is expected to be relevant to researchers, as well as practitioners. The study highlights that resilience is enhanced by system risk-informed planning, which ensures timely interventions on critical infrastructures; however, temporal and spatial scales are difficult to define for indirect impacts and cascade effects. Perspective research could further improve this work by applying a system-risk analysis to multiple urban infrastructures.
A Fekete (2019). Critical infrastructure and flood resilience: cascading effects beyond water. Water, 6, e1370. https://doi.org/10.1002/wat2.1370
C Arrighi, M Pregnolato, RJ Dawson, F Castelli (2019). Preparedness against mobility disruption by floods. Science of the Total Environment 654, 1010-1022. https://doi.org/10.1016/j.scitotenv.2018.11.191
C Arrighi, M Pregnolato, F Castelli (2020). Indirect flood impacts and cascade risk across interdependent linear infrastructures. Natural Hazards and Earth System Sciences Discussions, 1-18. https://doi.org/10.5194/nhess-2020-371
How to cite: Pregnolato, M. and Arrighi, C.: Modelling cascading impacts and risks across linear infrastructure systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2834, https://doi.org/10.5194/egusphere-egu21-2834, 2021.
The European Committee for Standardization provides coarse rules for the estimation of snow load maps for structural design. European countries can apply their own methodologies, resulting in inconsistencies for the 50-year return level of snow load at national borders. Commonly used approaches base on more or less sophisticated interpolation of snow depths with a subsequent assignment of snow density, or spatial extreme value interpolation of snow load measurements.
We propose a novel methodology for Austria, where snow load observations are not available. It is based on (1) modeling yearly snow load maxima with the specially developed ∆SNOW model, and (2) a generalized additive model, where explaining covariates and their combinations are represented by penalized regression splines, fitted to such derived snow load series. Results show an RMSE of 0.7 kN/m2, and a BIAS of -0.2 kN/m2 over all altitudes, thereby outperforming a smooth spatial extreme value model and the actual Austrian standard, when compared to locally estimated, “quasi-observed “ 50-year snow load maxima at 870 stations in and tightly around Austria.
The new approach requires no zoning and provides a reproducible and transparent approach. Due to the relatively ease of use and snow depth measurements as single prerequisite, the method is applicable in other countries as well. Negative BIASes, that significantly underestimate 50-year snow loads at a small number of stations, are the only objective problem that has to be solved before the new map can be proposed as a successor of the actual Austrian snow load map.
How to cite: Schellander, H., Winkler, M., and Hell, T.: Towards a reproducible snow load map – an example for Austria, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8859, https://doi.org/10.5194/egusphere-egu21-8859, 2021.
How to cite: Tuel, A. and Romppainen-Martius, O.: A global perspective on the sub-seasonal clustering of precipitation extremes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2050, https://doi.org/10.5194/egusphere-egu21-2050, 2021.
Safe and economical design of dams, highways, bridges, and other infrastructures require accurate estimates of the magnitude and frequency of peak floods obtained by flood frequency analysis (FFA). The Generalized Extreme Value (GEV) distribution is the traditional preference for FFA along with other distributions having location, scale, and shape parameters. In this poster, two alternative power-type distributions comprising one location and two shape parameters are explored, these are Burr type III (BrIII) and Burr type XII (BrXII) distributions. The performances of BrIII and BrXII are compared against that of GEV in describing annual maximum streamflow records at 1088 sites across Canada. A generic L-moment algorithm is developed to fit these distributions regardless of the unavailability of some of their analytical L-moment expressions. This algorithm is devised in the R package “LMoFit” on CRAN. The latter comparison shows that: (1) the three distributions perform equally-well in describing the observed peaks; (2) the BrIII and the BrXII distributions predict larger streamflow peaks increasing the heaviness of their right tails compared to that of the GEV distribution; (3) the predictions of the GEV distribution reach the upper limits of the distribution in 39% of the sites, while the corresponding predictions of BrIII and BrXII are not limited and exceed the reached limits of GEV; (4) the GEV distribution might be underestimating the risk of extreme events, especially for large return periods. Accordingly, there are potential limitations in using the GEV distribution for FFA and the findings suggest BrIII and BrXII distributions as consistent alternatives for future FFA practices. The “LMoFit” R package is devised to facilitate the future application of the suggested distributions.
How to cite: Zaghloul, M. A., Papalexiou, S. M., and Elshorbagy, A.: Paradigm Shift in distribution preferences for Flood Frequency Analysis and the ‘LMoFit’ R-Package, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3428, https://doi.org/10.5194/egusphere-egu21-3428, 2021.
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