Pub Date : 2023-06-23DOI: 10.5194/nhess-23-2289-2023
S. Massaro, Manuel Stocchi, Beatriz Martínez Montesinos, L. Sandri, J. Selva, R. Sulpizio, B. Giaccio, M. Moscatelli, E. Peronace, M. Nocentini, R. Isaia, Manuel Titos Luzón, P. Dellino, G. Naso, Antonio Costa
Abstract. Nowadays, modeling of tephra fallout hazard is coupled with probabilistic analysis that takes into account the natural variability of the volcanic phenomena in terms of eruption probability, eruption sizes, vent position, and meteorological conditions. In this framework, we present a prototypal methodology to carry out the long-term tephra fallout hazard assessment in southern Italy from the active Neapolitan volcanoes: Somma–Vesuvius, Campi Flegrei, and Ischia. The FALL3D model (v.8.0) has been used to run thousands of numerical simulations (1500 per eruption size class), considering the ECMWF ERA5 meteorological dataset over the last 30 years. The output in terms of tephra ground load has been processed within a new workflow for large-scale, high-resolution volcanic hazard assessment, relying on a Bayesian procedure, in order to provide the mean annual frequency with which the tephra load at the ground exceeds given critical thresholds at a target site within a 50-year exposure time. Our results are expressed in terms of absolute mean hazard maps considering different levels of aggregation, from the impact of each volcanic source and eruption size class to the quantification of the total hazard. This work provides, for the first time, a multi-volcano probabilistic hazard assessment posed by tephra fallout, comparable with those used for seismic phenomena and other natural disasters. This methodology can be applied to any other volcanic areas or over different exposure times, allowing researchers to account for the eruptive history of the target volcanoes that, when available, could include the occurrence of less frequent large eruptions, representing critical elements for risk evaluations.
{"title":"Assessing long-term tephra fallout hazard in southern Italy from Neapolitan volcanoes","authors":"S. Massaro, Manuel Stocchi, Beatriz Martínez Montesinos, L. Sandri, J. Selva, R. Sulpizio, B. Giaccio, M. Moscatelli, E. Peronace, M. Nocentini, R. Isaia, Manuel Titos Luzón, P. Dellino, G. Naso, Antonio Costa","doi":"10.5194/nhess-23-2289-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-2289-2023","url":null,"abstract":"Abstract. Nowadays, modeling of tephra fallout hazard is coupled with probabilistic analysis that takes into account the natural variability of the volcanic phenomena in terms of eruption probability, eruption sizes, vent position, and meteorological conditions. In this framework, we present a prototypal methodology to carry out the long-term tephra fallout hazard assessment in southern Italy from the active Neapolitan volcanoes: Somma–Vesuvius, Campi Flegrei, and Ischia. The FALL3D model (v.8.0) has been used to run thousands of numerical simulations (1500 per eruption size class), considering the ECMWF ERA5 meteorological dataset over the last 30 years. The output in terms of tephra ground load has been processed within a new workflow for large-scale, high-resolution volcanic hazard assessment, relying on a Bayesian procedure, in order to provide the mean annual frequency with which the tephra load at the ground exceeds given critical thresholds at a target site within a 50-year exposure time. Our results are expressed in terms of absolute mean hazard maps considering different levels of aggregation, from the impact of each volcanic source and eruption size class to the quantification of the total hazard. This work provides, for the first time, a multi-volcano probabilistic hazard assessment posed by tephra fallout, comparable with those used for seismic phenomena and other natural disasters. This methodology can be applied to any other volcanic areas or over different exposure times, allowing researchers to account for the eruptive history of the target volcanoes that, when available, could include the occurrence of less frequent large eruptions, representing critical elements for risk evaluations.\u0000","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47258602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-22DOI: 10.5194/nhess-23-2273-2023
C. Ferrarin, Florian Pantillon, S. Davolio, M. Bajo, M. Miglietta, E. Avolio, D. Carrió, I. Pytharoulis, C. Sánchez, Platon Patlakas, J. J. González-Alemán, E. Flaounas
Abstract. On 18 September 2020, Medicane Ianos hit the western coast of Greece, resulting in flooding and severe damage at several coastal locations. In this work, we aim at evaluating its impact on sea conditions and the associated uncertainty through the use of an ensemble of numerical simulations. We applied a coupled wave–current model to an unstructured mesh, representing the whole Mediterranean Sea, with a grid resolution increasing in the Ionian Sea along the cyclone path and the landfall area. To investigate the uncertainty in modelling sea levels and waves for such an intense event, we performed an ensemble of ocean simulations using several coarse (10 km) and high-resolution (2 km) meteorological forcings from different mesoscale models. The performance of the ocean and wave models was evaluated against observations retrieved from fixed monitoring stations and satellites. All model runs emphasized the occurrence of severe sea conditions along the cyclone path and at the coast. Due to the rugged and complex coastline, extreme sea levels are localized at specific coastal sites. However, numerical results show a large spread of the simulated sea conditions for both the sea level and waves, highlighting the large uncertainty in simulating this kind of extreme event. The multi-model and multi-physics approach allows us to assess how the uncertainty propagates from meteorological to ocean variables and the subsequent coastal impact. The ensemble mean and standard deviation were combined to prove the hazard scenarios of the potential impact of such an extreme event to be used in a flood risk management plan.
{"title":"Assessing the coastal hazard of Medicane Ianos through ensemble modelling","authors":"C. Ferrarin, Florian Pantillon, S. Davolio, M. Bajo, M. Miglietta, E. Avolio, D. Carrió, I. Pytharoulis, C. Sánchez, Platon Patlakas, J. J. González-Alemán, E. Flaounas","doi":"10.5194/nhess-23-2273-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-2273-2023","url":null,"abstract":"Abstract. On 18 September 2020, Medicane Ianos hit the western coast of Greece,\u0000resulting in flooding and severe damage at several coastal locations.\u0000In this work, we aim at evaluating its impact on sea conditions and the\u0000associated uncertainty through the use of an ensemble of numerical\u0000simulations. We applied a coupled wave–current model to an unstructured\u0000mesh, representing the whole Mediterranean Sea, with a grid resolution\u0000increasing in the Ionian Sea along the cyclone path and the landfall\u0000area. To investigate the uncertainty in modelling sea levels and waves\u0000for such an intense event, we performed an ensemble of ocean\u0000simulations using several coarse (10 km) and high-resolution (2 km)\u0000meteorological forcings from different mesoscale models. The performance of the ocean and wave models was evaluated against observations retrieved from fixed monitoring stations and satellites. All model runs emphasized the occurrence of severe sea conditions along the cyclone path and at the coast. Due to the rugged and complex coastline, extreme sea levels are localized at specific coastal sites. However, numerical results show a large spread of the simulated sea conditions for both the sea level and waves, highlighting the large uncertainty in simulating this kind of extreme event. The multi-model and multi-physics approach allows us to assess how the uncertainty propagates from meteorological to ocean variables and the subsequent coastal impact. The ensemble mean and standard deviation were combined to prove the hazard scenarios of the potential impact of such an\u0000extreme event to be used in a flood risk management plan.\u0000","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44286225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-21DOI: 10.5194/nhess-23-2251-2023
D. Eilander, A. Couasnon, F. Sperna Weiland, W. Ligtvoet, A. Bouwman, H. Winsemius, P. Ward
Abstract. In low-lying coastal areas floods occur from (combinations of) fluvial, pluvial, and coastal drivers. If these flood drivers are statistically dependent, their joint probability might be misrepresented if dependence is not accounted for. However, few studies have examined flood risk and risk reduction measures while accounting for so-called compound flooding. We present a globally applicable framework for compound flood risk assessments using combined hydrodynamic, impact, and statistical modeling and apply it to a case study in the Sofala province of Mozambique. The framework broadly consists of three steps. First, a large stochastic event set is derived from reanalysis data, taking into account co-occurrence of and dependence between all annual maximum flood drivers. Then, both flood hazard and impact are simulated for different combinations of drivers at non-flood and flood conditions. Finally, the impact of each stochastic event is interpolated from the simulated events to derive a complete flood risk profile. Our case study results show that from all drivers, coastal flooding causes the largest risk in the region despite a more widespread fluvial and pluvial flood hazard. Events with return periods longer than 25 years are more damaging when considering the observed statistical dependence compared to independence, e.g., 12 % for the 100-year return period. However, the total compound flood risk in terms of expected annual damage is only 0.55 % larger. This is explained by the fact that for frequent events, which contribute most to the risk, limited physical interaction between flood drivers is simulated. We also assess the effectiveness of three measures in terms of risk reduction. For our case, zoning based on the 2-year return period flood plain is as effective as levees with a 10-year return period protection level, while dry proofing up to 1 m does not reach the same effectiveness. As the framework is based on global datasets and is largely automated, it can easily be repeated for other regions for first-order assessments of compound flood risk. While the quality of the assessment will depend on the accuracy of the global models and data, it can readily include higher-quality (local) datasets where available to further improve the assessment.
{"title":"Modeling compound flood risk and risk reduction using a globally applicable framework: a pilot in the Sofala province of Mozambique","authors":"D. Eilander, A. Couasnon, F. Sperna Weiland, W. Ligtvoet, A. Bouwman, H. Winsemius, P. Ward","doi":"10.5194/nhess-23-2251-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-2251-2023","url":null,"abstract":"Abstract. In low-lying coastal areas floods occur from\u0000(combinations of) fluvial, pluvial, and coastal drivers. If these flood\u0000drivers are statistically dependent, their joint probability might be\u0000misrepresented if dependence is not accounted for. However, few studies have examined flood risk and risk reduction measures while accounting for\u0000so-called compound flooding. We present a globally applicable framework for\u0000compound flood risk assessments using combined hydrodynamic, impact, and\u0000statistical modeling and apply it to a case study in the Sofala province of\u0000Mozambique. The framework broadly consists of three steps. First, a large\u0000stochastic event set is derived from reanalysis data, taking into account\u0000co-occurrence of and dependence between all annual maximum flood drivers.\u0000Then, both flood hazard and impact are simulated for different combinations\u0000of drivers at non-flood and flood conditions. Finally, the impact of each\u0000stochastic event is interpolated from the simulated events to derive a\u0000complete flood risk profile. Our case study results show that from all\u0000drivers, coastal flooding causes the largest risk in the region despite a\u0000more widespread fluvial and pluvial flood hazard. Events with return periods longer than 25 years are more damaging when considering the observed\u0000statistical dependence compared to independence, e.g., 12 % for the\u0000100-year return period. However, the total compound flood risk in terms of\u0000expected annual damage is only 0.55 % larger. This is explained by the\u0000fact that for frequent events, which contribute most to the risk, limited\u0000physical interaction between flood drivers is simulated. We also assess the\u0000effectiveness of three measures in terms of risk reduction. For our case,\u0000zoning based on the 2-year return period flood plain is as effective as\u0000levees with a 10-year return period protection level, while dry proofing up\u0000to 1 m does not reach the same effectiveness. As the framework is based on\u0000global datasets and is largely automated, it can easily be repeated for\u0000other regions for first-order assessments of compound flood risk. While the\u0000quality of the assessment will depend on the accuracy of the global models\u0000and data, it can readily include higher-quality (local) datasets where\u0000available to further improve the assessment.\u0000","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44640044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-21DOI: 10.5194/nhess-23-2229-2023
A. Rosi, W. Frodella, Nicola Nocentini, Francesco Caleca, H. Havenith, A. Strom, Mirzo S. Saidov, Gany Amirgalievich Bimurzaev, V. Tofani
Abstract. Central Asia is an area characterized by complex tectonics and active deformation; the related seismic activity controls the earthquake hazard level that, due to the occurrence of secondary and tertiary effects, also has direct implications for the hazard related to mass movements such as landslides, which are responsible for an extensive number of casualties every year. Climatically, this region is characterized by strong rainfall gradient contrasts due to the diversity of climate and vegetation zones. The region is drained by large, partly snow- and glacier-fed rivers that cross or terminate in arid forelands; therefore, it is also affected by a significant river flood hazard, mainly in spring and summer seasons. The challenge posed by the combination of different hazards can only be tackled by considering a multi-hazard approach harmonized among the different countries, in agreement with the requirements of the Sendai Framework for Disaster Risk Reduction. This work was carried out within the framework of the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) project as part of a multi-hazard approach and is focused on the first landslide susceptibility analysis at a regional scale for Central Asia. To this aim the most detailed landslide inventories, covering both national and transboundary territories, were implemented in a random forest model, together with several independent variables. The proposed approach represents an innovation in terms of resolution (from 30 to 70 m) and extension of the analyzed area with respect to previous regional landslide susceptibility and hazard zonation models applied in Central Asia. The final aim was to provide a useful tool for land use planning and risk reduction strategies for landslide scientists, practitioners, and administrators.
{"title":"Comprehensive landslide susceptibility map of Central Asia","authors":"A. Rosi, W. Frodella, Nicola Nocentini, Francesco Caleca, H. Havenith, A. Strom, Mirzo S. Saidov, Gany Amirgalievich Bimurzaev, V. Tofani","doi":"10.5194/nhess-23-2229-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-2229-2023","url":null,"abstract":"Abstract. Central Asia is an area characterized by complex tectonics and active deformation; the related seismic activity controls the earthquake hazard level that, due to the occurrence of secondary and tertiary effects, also has direct implications for the hazard related to mass movements such as\u0000landslides, which are responsible for an extensive number of casualties\u0000every year. Climatically, this region is characterized by strong rainfall\u0000gradient contrasts due to the diversity of climate and vegetation zones.\u0000The region is drained by large, partly snow- and glacier-fed rivers that\u0000cross or terminate in arid forelands; therefore, it is also affected by a\u0000significant river flood hazard, mainly in spring and summer seasons. The\u0000challenge posed by the combination of different hazards can only be tackled by\u0000considering a multi-hazard approach harmonized among the different\u0000countries, in agreement with the requirements of the Sendai Framework for\u0000Disaster Risk Reduction. This work was carried out within the framework of\u0000the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) project as part of a multi-hazard approach and is\u0000focused on the first landslide susceptibility analysis at a regional scale\u0000for Central Asia. To this aim the most detailed landslide inventories,\u0000covering both national and transboundary territories, were implemented in a\u0000random forest model, together with several independent variables. The\u0000proposed approach represents an innovation in terms of resolution (from 30\u0000to 70 m) and extension of the analyzed area with respect to previous\u0000regional landslide susceptibility and hazard zonation models applied in\u0000Central Asia. The final aim was to provide a useful tool for land\u0000use planning and risk reduction strategies for landslide scientists,\u0000practitioners, and administrators.\u0000","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46861805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-20DOI: 10.5194/nhess-23-2203-2023
Juan Camilo Gomez- Zapata, M. Pittore, Nils Brinckmann, J. Lizarazo-Marriaga, S. Medina, N. Tarque, F. Cotton
Abstract. Multi-hazard risk assessments for building portfolios exposed to earthquake shaking followed by a tsunami are usually based on empirical vulnerability models calibrated on post-event surveys of damaged buildings. The applicability of these models cannot easily be extrapolated to other regions of larger/smaller events. Moreover, the quantitative evaluation of the damages related to each of the hazard types (disaggregation) is impossible. To investigate cumulative damage on extended building portfolios, this study proposes an alternative and modular method to probabilistically integrate sets of single-hazard vulnerability models that are constantly being developed and calibrated by experts from various research fields to be used within a multi-risk context. This method is based on the proposal of state-dependent fragility functions for the triggered hazard to account for the pre-existing damage and the harmonisation of building classes and damage states through their taxonomic characterisation, which is transversal to any hazard-dependent vulnerability. This modular assemblage also allows us to separate the economic losses expected for each scenario on building portfolios subjected to cascading hazards. We demonstrate its application by assessing the economic losses expected for the residential building stock of Lima, Peru, a megacity commonly exposed to consecutive earthquake and tsunami scenarios. We show the importance of accounting for damage accumulation on extended building portfolios while observing a dependency between the earthquake magnitude and the direct economic losses derived for each hazard scenario. For the commonly exposed residential building stock of Lima exposed to both perils, we find that classical tsunami empirical fragility functions lead to underestimations of predicted losses for lower magnitudes (Mw) and large overestimations for larger Mw events in comparison to our state-dependent models and cumulative-damage method.
{"title":"Scenario-based multi-risk assessment from existing single-hazard vulnerability models. An application to consecutive earthquakes and tsunamis in Lima, Peru","authors":"Juan Camilo Gomez- Zapata, M. Pittore, Nils Brinckmann, J. Lizarazo-Marriaga, S. Medina, N. Tarque, F. Cotton","doi":"10.5194/nhess-23-2203-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-2203-2023","url":null,"abstract":"Abstract. Multi-hazard risk assessments for building portfolios\u0000exposed to earthquake shaking followed by a tsunami are usually based on\u0000empirical vulnerability models calibrated on post-event surveys of damaged\u0000buildings. The applicability of these models cannot easily be extrapolated\u0000to other regions of larger/smaller events. Moreover, the quantitative\u0000evaluation of the damages related to each of the hazard types\u0000(disaggregation) is impossible. To investigate cumulative damage on extended building portfolios, this study proposes an alternative and modular method to probabilistically integrate sets of single-hazard vulnerability models\u0000that are constantly being developed and calibrated by experts from various\u0000research fields to be used within a multi-risk context. This method is based\u0000on the proposal of state-dependent fragility functions for the triggered\u0000hazard to account for the pre-existing damage and the harmonisation of\u0000building classes and damage states through their taxonomic characterisation, which is transversal to any hazard-dependent vulnerability. This modular assemblage also allows us to separate the economic losses expected for each scenario on building portfolios subjected to cascading hazards. We\u0000demonstrate its application by assessing the economic losses expected for\u0000the residential building stock of Lima, Peru, a megacity commonly exposed to\u0000consecutive earthquake and tsunami scenarios. We show the importance of\u0000accounting for damage accumulation on extended building portfolios while\u0000observing a dependency between the earthquake magnitude and the direct\u0000economic losses derived for each hazard scenario. For the commonly exposed\u0000residential building stock of Lima exposed to both perils, we find that\u0000classical tsunami empirical fragility functions lead to underestimations of predicted losses for lower magnitudes (Mw) and large overestimations for larger Mw events in comparison to our state-dependent models and cumulative-damage method.\u0000","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44484071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-16DOI: 10.5194/nhess-23-2157-2023
Charline Dalinghaus, G. Coco, P. Higuera
Abstract. We applied machine learning to improve the accuracy of present predictors of wave setup. Namely, we used an evolutionary-based genetic programming model and a previously published dataset, which includes various beach and wave conditions. Here, we present two new wave setup predictors: a simple predictor, which is a function of wave height, wavelength, and foreshore beach slope, and a fitter, but more complex predictor, which is also a function of sediment diameter. The results show that the new predictors outperform existing formulas. We conclude that machine learning models are capable of improving predictive capability (when compared to existing predictors) and also of providing a physically sound description of wave setup.
{"title":"A predictive equation for wave setup using genetic programming","authors":"Charline Dalinghaus, G. Coco, P. Higuera","doi":"10.5194/nhess-23-2157-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-2157-2023","url":null,"abstract":"Abstract. We applied machine learning to improve the accuracy of present predictors of wave setup. Namely, we used an evolutionary-based genetic programming model and a previously published dataset, which includes various beach and wave conditions. Here, we present two new wave setup predictors: a simple predictor, which is a function of wave height, wavelength, and foreshore beach slope, and a fitter, but more complex predictor, which is also a function of sediment diameter. The results show that the new predictors outperform existing formulas. We conclude that machine learning models are capable of improving predictive capability (when compared to existing predictors) and also of providing a physically sound description of wave setup.\u0000","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48492664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-16DOI: 10.5194/nhess-23-2171-2023
D. Gliksman, Paul Averbeck, N. Becker, B. Gardiner, V. Goldberg, J. Grieger, D. Handorf, K. Haustein, Alexia Karwat, F. Knutzen, H. Lentink, Rike Lorenz, Deborah Niermann, J. Pinto, Ronald Queck, A. Ziemann, C. Franzke
Abstract. Wind and windstorms cause severe damage to natural and human-made environments. Thus, wind-related risk assessment is vital for the preparation and mitigation of calamities. However, the cascade of events leading to damage depends on many factors that are environment-specific and the available methods to address wind-related damage often require sophisticated analysis and specialization. Fortunately, simple indices and thresholds are as effective as complex mechanistic models for many applications. Nonetheless, the multitude of indices and thresholds available requires a careful selection process according to the target sector. Here, we first provide a basic background on wind and storm formation and characteristics, followed by a comprehensive collection of both indices and thresholds that can be used to predict the occurrence and magnitude of wind and storm damage. We focused on five key sectors: forests, urban areas, transport, agriculture and wind-based energy production. For each sector we described indices and thresholds relating to physical properties such as topography and land cover but also to economic aspects (e.g. disruptions in transportation or energy production). In the face of increased climatic variability, the promotion of more effective analysis of wind and storm damage could reduce the impact on society and the environment.
{"title":"Review article: A European perspective on wind and storm damage – from the meteorological background to index-based approaches to assess impacts","authors":"D. Gliksman, Paul Averbeck, N. Becker, B. Gardiner, V. Goldberg, J. Grieger, D. Handorf, K. Haustein, Alexia Karwat, F. Knutzen, H. Lentink, Rike Lorenz, Deborah Niermann, J. Pinto, Ronald Queck, A. Ziemann, C. Franzke","doi":"10.5194/nhess-23-2171-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-2171-2023","url":null,"abstract":"Abstract. Wind and windstorms cause severe damage to natural and\u0000human-made environments. Thus, wind-related risk assessment is vital for the preparation and mitigation of calamities. However, the cascade of events leading to damage depends on many factors that are environment-specific and the available methods to address wind-related damage often require sophisticated analysis and specialization. Fortunately, simple indices and thresholds are as effective as complex mechanistic models for many applications. Nonetheless, the multitude of indices and thresholds available requires a careful selection process according to the target sector. Here, we first provide a basic background on wind and storm formation and characteristics, followed by a comprehensive collection of both indices and thresholds that can be used to predict the occurrence and magnitude of wind and storm damage. We focused on five key sectors: forests, urban areas, transport, agriculture and wind-based energy production. For each sector we described indices and thresholds relating to physical properties such as topography and land cover but also to economic aspects (e.g. disruptions in transportation or energy production). In the face of increased climatic variability, the promotion of more effective analysis of wind and storm damage could reduce the impact on society and the environment.\u0000","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49254295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-15DOI: 10.5194/nhess-23-2133-2023
Oya Kalaycıoğlu, Serhat Emre Akhanli, E. Menteşe, M. Kalaycıoğlu, S. Kalaycioglu
Abstract. To what extent an individual or group will be affected by the damage of a hazard depends not just on their exposure to the event but on their social vulnerability – that is, how well they are able to anticipate, cope with, resist, and recover from the impact of a hazard. Therefore, for mitigating disaster risk effectively and building a disaster-resilient society to natural hazards, it is essential that policy makers develop an understanding of social vulnerability. This study aims to propose an optimal predictive model that allows decision makers to identify households with high social vulnerability by using a number of easily accessible household variables. In order to develop such a model, we rely on a large dataset comprising a household survey (n = 41 093) that was conducted to generate a social vulnerability index (SoVI) in Istanbul, Türkiye. In this study, we assessed the predictive ability of socio-economic, socio-demographic, and housing conditions on the household-level social vulnerability through machine learning models. We used classification and regression tree (CART), random forest (RF), support vector machine (SVM), naïve Bayes (NB), artificial neural network (ANN), k-nearest neighbours (KNNs), and logistic regression to classify households with respect to their social vulnerability level, which was used as the outcome of these models. Due to the disparity of class size outcome variables, subsampling strategies were applied for dealing with imbalanced data. Among these models, ANN was found to have the optimal predictive performance for discriminating households with low and high social vulnerability when random-majority under sampling was applied (area under the curve (AUC): 0.813). The results from the ANN method indicated that lack of social security, living in a squatter house, and job insecurity were among the most important predictors of social vulnerability to hazards. Additionally, the level of education, the ratio of elderly persons in the household, owning a property, household size, ratio of income earners, and savings of the household were found to be associated with social vulnerability. An open-access R Shiny web application was developed to visually display the performance of machine learning (ML) methods, important variables for the classification of households with high and low social vulnerability, and the spatial distribution of the variables across Istanbul neighbourhoods. The machine learning methodology and the findings that we present in this paper can guide decision makers in identifying social vulnerability effectively and hence let them prioritise actions towards vulnerable groups in terms of needs prior to an event of a hazard.
{"title":"Using machine learning algorithms to identify predictors of social vulnerability in the event of a hazard: Istanbul case study","authors":"Oya Kalaycıoğlu, Serhat Emre Akhanli, E. Menteşe, M. Kalaycıoğlu, S. Kalaycioglu","doi":"10.5194/nhess-23-2133-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-2133-2023","url":null,"abstract":"Abstract. To what extent an individual or group will be affected by the damage of a hazard depends not just on their exposure to the event but on their social vulnerability – that is, how well they are able to anticipate, cope with, resist, and recover from the impact of a hazard. Therefore, for mitigating disaster risk effectively and building a disaster-resilient society to natural hazards, it is essential that policy makers develop an understanding of social vulnerability. This study aims to propose an optimal predictive model that allows decision makers to identify households with high social vulnerability by using a number of easily accessible household variables. In order to develop such a model, we rely on a large dataset comprising a household survey (n = 41 093) that was conducted to generate a social vulnerability index (SoVI) in Istanbul, Türkiye. In this study, we assessed the predictive ability of socio-economic, socio-demographic, and housing conditions on the household-level social vulnerability through machine learning models. We used classification and regression tree (CART), random forest (RF), support vector machine (SVM), naïve Bayes (NB), artificial neural network (ANN), k-nearest neighbours (KNNs), and logistic regression to classify households with respect to their social vulnerability level, which was used as the outcome of these models. Due to the disparity of class size outcome variables, subsampling strategies were applied for dealing with imbalanced data. Among these models, ANN was found to have the optimal predictive performance for discriminating households with low and high social vulnerability when random-majority under sampling was applied (area under the curve (AUC): 0.813). The results from the ANN method indicated that lack of social security, living in a squatter house, and job insecurity were among the most important predictors of social vulnerability to hazards. Additionally, the level of education, the ratio of elderly persons in the household, owning a property, household size, ratio of income earners, and savings of the household were found to be associated with social vulnerability. An open-access R Shiny web application was developed to visually display the performance of machine learning (ML) methods, important variables for the classification of households with high and low social vulnerability, and the spatial distribution of the variables across Istanbul neighbourhoods. The machine learning methodology and the findings that we present in this paper can guide decision makers in identifying social vulnerability effectively and hence let them prioritise actions towards vulnerable groups in terms of needs prior to an event of a hazard.\u0000","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45369857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-15DOI: 10.5194/nhess-23-2111-2023
Claudia Herbert, P. Döll
Abstract. Streamflow drought hazard indicators (SDHIs) are mostly lacking in large-scale drought early warning systems (DEWSs). This paper presents a new systematic approach for selecting and computing SDHIs for monitoring drought for human water supply from surface water and for river ecosystems. We recommend considering the habituation of the system at risk (e.g., a drinking water supplier or small-scale farmers in a specific region) to the streamflow regime when selecting indicators; i.e., users of the DEWSs should determine which type of deviation from normal (e.g., a certain interannual variability or a certain relative reduction of streamflow) the risk system of interest has become used to and adapted to. Distinguishing four indicator types, we classify indicators of drought magnitude (water anomaly during a predefined period) and severity (cumulated magnitude since the onset of the drought event) and specify the many relevant decisions that need to be made when computing SDHIs. Using the global hydrological model WaterGAP 2.2d, we quantify eight existing and three new SDHIs globally. For large-scale DEWSs based on the output of hydrological models, we recommend specific SDHIs that are suitable for assessing the drought hazard for (1) river ecosystems, (2) water users without access to large reservoirs, and (3) water users with access to large reservoirs, as well as being suitable for informing reservoir managers. These SDHIs include both drought magnitude and severity indicators that differ by the temporal averaging period and the habituation of the risk system to reduced water availability. Depending on the habituation of the risk system, drought magnitude is best quantified either by the relative deviation from the mean or by the return period of the streamflow value that is based on the frequency of non-exceedance. To compute the return period, we favor empirical percentiles over the standardized streamflow indicator as the former do not entail uncertainties due to the fitting of a probability distribution and can be computed for all streamflow time series. Drought severity should be assessed with indicators that imply habituation to a certain degree of interannual variability, to a certain reduction from mean streamflow, and to the ability to fulfill human water demand and environmental flows. Reservoir managers are best informed by the SDHIs of the grid cell that represents inflow into the reservoir. The DEWSs must provide comprehensive and clear explanations about the suitability of the provided indicators for specific risk systems.
{"title":"Analyzing the informative value of alternative hazard indicators for monitoring drought hazard for human water supply and river ecosystems at the global scale","authors":"Claudia Herbert, P. Döll","doi":"10.5194/nhess-23-2111-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-2111-2023","url":null,"abstract":"Abstract. Streamflow drought hazard indicators (SDHIs) are mostly lacking in\u0000large-scale drought early warning systems (DEWSs). This paper presents a new\u0000systematic approach for selecting and computing SDHIs for monitoring drought\u0000for human water supply from surface water and for river ecosystems. We\u0000recommend considering the habituation of the system at risk (e.g., a\u0000drinking water supplier or small-scale farmers in a specific region) to the\u0000streamflow regime when selecting indicators; i.e., users of the DEWSs should\u0000determine which type of deviation from normal (e.g., a certain\u0000interannual variability or a certain relative reduction of streamflow) the\u0000risk system of interest has become used to and adapted to. Distinguishing four\u0000indicator types, we classify indicators of drought magnitude (water anomaly\u0000during a predefined period) and severity (cumulated magnitude since the\u0000onset of the drought event) and specify the many relevant decisions that\u0000need to be made when computing SDHIs. Using the global hydrological model\u0000WaterGAP 2.2d, we quantify eight existing and three new SDHIs globally. For\u0000large-scale DEWSs based on the output of hydrological models, we recommend\u0000specific SDHIs that are suitable for assessing the drought hazard for (1) river ecosystems, (2) water users without access to large reservoirs, and (3) water users with access to large reservoirs, as well as being suitable for\u0000informing reservoir managers. These SDHIs include both drought magnitude and\u0000severity indicators that differ by the temporal averaging period and the\u0000habituation of the risk system to reduced water availability. Depending on\u0000the habituation of the risk system, drought magnitude is best quantified\u0000either by the relative deviation from the mean or by the return period of\u0000the streamflow value that is based on the frequency of non-exceedance. To\u0000compute the return period, we favor empirical percentiles over the\u0000standardized streamflow indicator as the former do not entail uncertainties\u0000due to the fitting of a probability distribution and can be computed for all\u0000streamflow time series. Drought severity should be assessed with indicators\u0000that imply habituation to a certain degree of interannual variability, to a\u0000certain reduction from mean streamflow, and to the ability to fulfill human\u0000water demand and environmental flows. Reservoir managers are best informed\u0000by the SDHIs of the grid cell that represents inflow into the reservoir. The\u0000DEWSs must provide comprehensive and clear explanations about the suitability\u0000of the provided indicators for specific risk systems.\u0000","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48190928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-13DOI: 10.5194/nhess-23-2089-2023
G. Ortner, M. Bründl, Chahan M. Kropf, T. Röösli, Y. Bühler, D. Bresch
Abstract. Snow avalanches are recurring natural hazards that affect the population and infrastructure in mountainous regions, such as in the recent avalanche winters of 2018 and 2019, when considerable damage was caused by avalanches throughout the Alps. Hazard decision makers need detailed information on the spatial distribution of avalanche hazards and risks to prioritize and apply appropriate adaptation strategies and mitigation measures and thus minimize impacts. Here, we present a novel risk assessment approach for assessing the spatial distribution of avalanche risk by combining large-scale hazard mapping with a state-of-the-art risk assessment tool, where risk is understood as the product of hazard, exposure and vulnerability. Hazard disposition is modeled using the large-scale hazard indication mapping method RAMMS::LSHIM (Rapid Mass Movement Simulation::Large-Scale Hazard Indication Mapping), and risks are assessed using the probabilistic Python-based risk assessment platform CLIMADA, developed at ETH Zürich. Avalanche hazard mapping for scenarios with a 30-, 100- and 300-year return period is based on a high-resolution terrain model, 3 d snow depth increase, automatically determined potential release areas and protection forest data. Avalanche hazard for 40 000 individual snow avalanches is expressed as avalanche intensity, measured as pressure. Exposure is represented by a detailed building layer indicating the spatial distribution of monetary assets. The vulnerability of buildings is defined by damage functions based on the software EconoMe, which is in operational use in Switzerland. The outputs of the hazard, exposure and vulnerability analyses are combined to quantify the risk in spatially explicit risk maps. The risk considers the probability and intensity of snow avalanche occurrence, as well as the concentration of vulnerable, exposed buildings. Uncertainty and sensitivity analyses were performed to capture inherent variability in the input parameters. This new risk assessment approach allows us to quantify avalanche risk over large areas and results in maps displaying the spatial distribution of risk at specific locations. Large-scale risk maps can assist decision makers in identifying areas where avalanche hazard mitigation and/or adaption is needed.
{"title":"Large-scale risk assessment on snow avalanche hazard in alpine regions","authors":"G. Ortner, M. Bründl, Chahan M. Kropf, T. Röösli, Y. Bühler, D. Bresch","doi":"10.5194/nhess-23-2089-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-2089-2023","url":null,"abstract":"Abstract. Snow avalanches are recurring natural hazards that affect the population and infrastructure in mountainous regions, such as in the recent avalanche winters of 2018 and 2019, when considerable damage was caused by avalanches throughout the Alps. Hazard decision makers need detailed information on the spatial distribution of avalanche hazards and risks to prioritize and apply appropriate adaptation strategies and mitigation measures and thus minimize impacts. Here, we present a novel risk assessment approach for assessing the spatial distribution of avalanche risk by combining large-scale hazard mapping with a state-of-the-art risk assessment tool, where risk is understood as the product of hazard, exposure and vulnerability. Hazard disposition is modeled using the large-scale hazard indication mapping method RAMMS::LSHIM (Rapid Mass Movement Simulation::Large-Scale Hazard Indication Mapping), and risks are assessed using the probabilistic Python-based risk assessment platform CLIMADA, developed at ETH Zürich. Avalanche hazard mapping for scenarios with a 30-, 100- and 300-year return period is based on a high-resolution terrain model, 3 d snow depth increase, automatically determined potential release areas and protection forest data. Avalanche hazard for 40 000 individual snow avalanches is expressed as avalanche intensity, measured as pressure. Exposure is represented by a detailed building layer indicating the spatial distribution of monetary assets. The vulnerability of buildings is defined by damage functions based on the software EconoMe, which is in operational use in Switzerland. The outputs of the hazard, exposure and vulnerability analyses are combined to quantify the risk in spatially explicit risk maps. The risk considers the probability and intensity of snow avalanche occurrence, as well as the concentration of vulnerable, exposed buildings. Uncertainty and sensitivity analyses were performed to capture inherent variability in the input parameters. This new risk assessment approach allows us to quantify avalanche risk over large areas and results in maps displaying the spatial distribution of risk at specific locations. Large-scale risk maps can assist decision makers in identifying areas where avalanche hazard mitigation and/or adaption is needed.\u0000","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49102905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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