HS7.5

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.

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.

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.

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.

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.

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.

Reference

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.

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.