Pub Date : 2024-05-14DOI: 10.5194/nhess-24-1635-2024
A. Androsov, S. Harig, N. Zamora, K. Knauer, N. Rakowsky
Abstract. This investigation addresses the tsunami inundation in Lima and Callao caused by the massive 1746 earthquake (Mw 9.0) along the Peruvian coast. Numerical modeling of the tsunami inundation processes in the nearshore includes strong nonlinear numerical terms. In a comparative analysis of the calculation of the tsunami wave effect, two numerical codes are used, Tsunami-HySEA and TsunAWI, which both solve the shallow water (SW) equations but with different spatial approximations. The comparison primarily evaluates the flow velocity fields in inundated areas. The relative importance of the various parts of the SW equations is determined, focusing on the nonlinear terms. Particular attention is paid to the contribution of momentum advection, bottom friction, and volume conservation. The influence of the nonlinearity on the degree and volume of inundation, flow velocity, and small-scale fluctuations is determined. The sensitivity of the solution concerning the bottom friction parameter is also investigated, showing the effects of nonlinearity processes in the inundated areas, wave heights, current velocity, and the spatial structure variations shown in tsunami inundation maps.
{"title":"Nonlinear processes in tsunami simulations for the Peruvian coast with focus on Lima and Callao","authors":"A. Androsov, S. Harig, N. Zamora, K. Knauer, N. Rakowsky","doi":"10.5194/nhess-24-1635-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-1635-2024","url":null,"abstract":"Abstract. This investigation addresses the tsunami inundation in Lima and Callao caused by the massive 1746 earthquake (Mw 9.0) along the Peruvian coast. Numerical modeling of the tsunami inundation processes in the nearshore includes strong nonlinear numerical terms. In a comparative analysis of the calculation of the tsunami wave effect, two numerical codes are used, Tsunami-HySEA and TsunAWI, which both solve the shallow water (SW) equations but with different spatial approximations. The comparison primarily evaluates the flow velocity fields in inundated areas. The relative importance of the various parts of the SW equations is determined, focusing on the nonlinear terms. Particular attention is paid to the contribution of momentum advection, bottom friction, and volume conservation. The influence of the nonlinearity on the degree and volume of inundation, flow velocity, and small-scale fluctuations is determined. The sensitivity of the solution concerning the bottom friction parameter is also investigated, showing the effects of nonlinearity processes in the inundated areas, wave heights, current velocity, and the spatial structure variations shown in tsunami inundation maps.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"16 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140979418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. In the context of natural hazard risk quantification and modeling of hazard interactions, some literature separates “Level I” (or occurrence) interactions from “Level II” (or consequence) interactions. The Level I interactions occur inherently due to the nature of the hazards, independently of the presence of physical assets. In such cases, one hazard event triggers or modifies the occurrence of another (e.g., flooding due to heavy rain, liquefaction and landslides triggered by an earthquake), thus creating a dependency between the features characterizing such hazard events. They differ from Level II interactions, which instead occur through impacts/consequences on physical assets/components and systems (e.g., accumulation of physical damage or social impacts due to earthquake sequences, landslides due to the earthquake-induced collapse of a retaining structure). Multi-hazard life cycle consequence (LCCon) analysis aims to quantify the consequences (e.g., repair costs, downtime, casualty rates) throughout a system’s service life and should account for both Level I and II interactions. The available literature generally considers Level I interactions – the focus of this study – mainly defining relevant taxonomies, often qualitatively, without providing a computational framework to simulate a sequence of hazard events incorporating the identified interrelations among them. This paper addresses this gap, proposing modeling approaches associated with different types of Level I interactions. It describes a simulation-based method for generating multi-hazard event sets (i.e., a sequence of hazard events and associated features throughout the system’s life cycle) based on the theory of competing Poisson processes. The proposed approach incorporates the different types of interactions in a sequential Monte Carlo sampling method. The method outputs multi-hazard event sets that can be integrated into LCCon frameworks to quantify interacting hazard consequences. An application incorporating several hazard interactions is presented to illustrate the potential of the proposed method.
摘要在自然灾害风险量化和灾害相互作用建模方面，一些文献将 "一级"（或发生）相互作用与 "二级"（或后果）相互作用区分开来。I 级相互作用是由于灾害的性质而固有发生的，与有形资产的存在无关。在这种情况下，一种危害事件会触发或改变另一种危害事件的发生（如暴雨引发的洪水、地震引发的液化和山体滑坡），从而在这些危害事件的特征之间产生依赖关系。它们与二级互动不同，二级互动是通过对有形资产/部件和系统的影响/后果发生的（例如，地震序列导致的有形损害或社会影响的累积，地震引发的挡土结构坍塌导致的山体滑坡）。多重危害生命周期后果（LCCon）分析旨在量化系统整个使用寿命期间的后果（如维修成本、停机时间、伤亡率），并应考虑一级和二级相互作用。现有文献一般考虑 I 级交互作用（本研究的重点），主要是定义相关的分类标准，通常是定性定义，而没有提供一个计算框架来模拟一连串的危险事件，其中包含已确定的它们之间的相互关系。本文针对这一空白，提出了与不同类型 I 级相互作用相关的建模方法。它描述了一种基于竞争泊松过程理论的模拟方法，用于生成多重危害事件集（即整个系统生命周期内的危害事件序列和相关特征）。所提出的方法将不同类型的交互作用纳入了顺序蒙特卡罗抽样方法。该方法输出的多危害事件集可集成到 LCCon 框架中，以量化相互作用的危害后果。本文介绍了一个包含多种危害相互作用的应用，以说明所提方法的潜力。
{"title":"Simulating multi-hazard event sets for life cycle consequence analysis","authors":"Leandro Iannacone, Kenneth Otárola, Roberto Gentile, Carmine Galasso","doi":"10.5194/nhess-24-1721-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-1721-2024","url":null,"abstract":"Abstract. In the context of natural hazard risk quantification and modeling of hazard interactions, some literature separates “Level I” (or occurrence) interactions from “Level II” (or consequence) interactions. The Level I interactions occur inherently due to the nature of the hazards, independently of the presence of physical assets. In such cases, one hazard event triggers or modifies the occurrence of another (e.g., flooding due to heavy rain, liquefaction and landslides triggered by an earthquake), thus creating a dependency between the features characterizing such hazard events. They differ from Level II interactions, which instead occur through impacts/consequences on physical assets/components and systems (e.g., accumulation of physical damage or social impacts due to earthquake sequences, landslides due to the earthquake-induced collapse of a retaining structure). Multi-hazard life cycle consequence (LCCon) analysis aims to quantify the consequences (e.g., repair costs, downtime, casualty rates) throughout a system’s service life and should account for both Level I and II interactions. The available literature generally considers Level I interactions – the focus of this study – mainly defining relevant taxonomies, often qualitatively, without providing a computational framework to simulate a sequence of hazard events incorporating the identified interrelations among them. This paper addresses this gap, proposing modeling approaches associated with different types of Level I interactions. It describes a simulation-based method for generating multi-hazard event sets (i.e., a sequence of hazard events and associated features throughout the system’s life cycle) based on the theory of competing Poisson processes. The proposed approach incorporates the different types of interactions in a sequential Monte Carlo sampling method. The method outputs multi-hazard event sets that can be integrated into LCCon frameworks to quantify interacting hazard consequences. An application incorporating several hazard interactions is presented to illustrate the potential of the proposed method.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"32 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140980259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-14DOI: 10.5194/nhess-24-1657-2024
A. Xhelaj, Massimiliano Burlando
Abstract. Downbursts winds, characterized by strong, localized downdrafts and subsequent horizontal straight-line winds, present a significant risk to civil structures. The transient nature and limited spatial extent present measurement challenges, necessitating analytical models for an accurate understanding and predicting their action on structures. This study analyzes the Sânnicolau Mare downburst event in Romania, on 25 June 2021, using a bi-dimensional analytical model coupled with the teaching–learning-based optimization (TLBO) algorithm. The intent is to understand the distinct solutions generated by the optimization algorithm and assess their physical validity. Supporting this examination are a damage survey and wind speed data recorded during the downburst event. Employed techniques include agglomerative hierarchical K-means clustering (AHK-MC) and principal component analysis (PCA) to categorize and interpret the solutions. Three main clusters emerge, each displaying different storm characteristics. Comparing the simulated maximum velocity with hail damage trajectories indicates that the optimal solution offers the best overlap, affirming its effectiveness in reconstructing downburst wind fields. However, these findings are specific to the Sânnicolau Mare event, underlining the need for a similar examination of multiple downburst events for broader validity.
摘要下沉气流风的特点是强烈的局部下沉气流和随后的水平直线风，对民用建筑结构构成重大威胁。其瞬时性和有限的空间范围给测量带来了挑战，因此需要分析模型来准确理解和预测其对结构的影响。本研究分析了 2021 年 6 月 25 日在罗马尼亚发生的 Sânnicolau Mare 下沉飓风事件，使用了一个双维分析模型和基于教学的优化（TLBO）算法。目的是了解优化算法生成的不同解决方案，并评估其物理有效性。为支持这项研究，还进行了损害调查，并记录了飓风袭击期间的风速数据。采用的技术包括聚类分层 K 均值聚类（AHK-MC）和主成分分析（PCA），以对解决方案进行分类和解释。结果显示有三个主要聚类，每个聚类都显示出不同的风暴特征。模拟最大速度与冰雹破坏轨迹的比较表明，最优解具有最佳的重叠性，这肯定了它在重建下爆风场方面的有效性。不过，这些发现仅针对 Sânnicolau Mare 事件，因此有必要对多个骤降事件进行类似研究，以获得更广泛的有效性。
{"title":"Application of the teaching–learning-based optimization algorithm to an analytical model of thunderstorm outflows to analyze the variability of the downburst kinematic and geometric parameters","authors":"A. Xhelaj, Massimiliano Burlando","doi":"10.5194/nhess-24-1657-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-1657-2024","url":null,"abstract":"Abstract. Downbursts winds, characterized by strong, localized downdrafts and subsequent horizontal straight-line winds, present a significant risk to civil structures. The transient nature and limited spatial extent present measurement challenges, necessitating analytical models for an accurate understanding and predicting their action on structures. This study analyzes the Sânnicolau Mare downburst event in Romania, on 25 June 2021, using a bi-dimensional analytical model coupled with the teaching–learning-based optimization (TLBO) algorithm. The intent is to understand the distinct solutions generated by the optimization algorithm and assess their physical validity. Supporting this examination are a damage survey and wind speed data recorded during the downburst event. Employed techniques include agglomerative hierarchical K-means clustering (AHK-MC) and principal component analysis (PCA) to categorize and interpret the solutions. Three main clusters emerge, each displaying different storm characteristics. Comparing the simulated maximum velocity with hail damage trajectories indicates that the optimal solution offers the best overlap, affirming its effectiveness in reconstructing downburst wind fields. However, these findings are specific to the Sânnicolau Mare event, underlining the need for a similar examination of multiple downburst events for broader validity.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"17 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140980636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-14DOI: 10.5194/nhess-24-1681-2024
M. Di Bacco, Daniela Molinari, A. R. Scorzini
Abstract. Accurate flood damage modelling is essential to estimate the potential impact of floods and to develop effective mitigation strategies. However, flood damage models rely on diverse sources of hazard, exposure and vulnerability data, which are often incomplete, inconsistent or totally missing. These issues with data quality or availability introduce uncertainties into the modelling process and affect the final risk estimations. In this study, we present INSYDE 2.0, a flood damage modelling tool that integrates detailed survey and desk-based data for enhanced reliability and informativeness of flood damage predictions, including an explicit representation of the effect of uncertainties arising from incomplete knowledge of the variables characterising the system under investigation.
{"title":"The value of multi-source data for improved flood damage modelling with explicit input data uncertainty treatment: INSYDE 2.0","authors":"M. Di Bacco, Daniela Molinari, A. R. Scorzini","doi":"10.5194/nhess-24-1681-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-1681-2024","url":null,"abstract":"Abstract. Accurate flood damage modelling is essential to estimate the potential impact of floods and to develop effective mitigation strategies. However, flood damage models rely on diverse sources of hazard, exposure and vulnerability data, which are often incomplete, inconsistent or totally missing. These issues with data quality or availability introduce uncertainties into the modelling process and affect the final risk estimations. In this study, we present INSYDE 2.0, a flood damage modelling tool that integrates detailed survey and desk-based data for enhanced reliability and informativeness of flood damage predictions, including an explicit representation of the effect of uncertainties arising from incomplete knowledge of the variables characterising the system under investigation.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"111 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140977769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-14DOI: 10.5194/nhess-24-1697-2024
C. Tacconi Stefanelli, W. Frodella, Francesco Caleca, Zh. T. Raimbekova, R. Umaraliev, V. Tofani
Abstract. Central Asia regions are characterized by active tectonics, high mountain chains with extreme topography with glaciers, and strong seasonal rainfall events. These key predisposing factors make large landslides a serious natural threat in the area, causing several casualties every year. The mountain crests are divided by wide lenticular or narrow, linear intermountain tectonic depressions, which are incised by many of the most important Central Asia rivers and are also subject to major seasonal river flood hazard. This multi-hazard combination is a source of potential damming scenarios, which can bring cascading effects with devastating consequences for the surrounding settlements and population. Different hazards can only be managed with a multi-hazard approach coherent within the different countries, as suggested by 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 with the aim of providing a damming susceptibility analysis at a regional scale for Central Asia. To achieve this, a semi-automated GIS-based mapping method, centered on a bivariate correlation of morphometric parameters defined by a morphological index, originally designed to assess the damming susceptibility at basin/regional scale, was modified to be adopted nationwide and applied to spatially assess the obstruction of the river network in Central Asia for mapped and newly formed landslides. The proposed methodology represents an improvement to the previously designed methodology, requiring a smaller amount of data, bringing new preliminary information on damming hazard management and risk reduction, and identifying the most critical area within the Central Asia regions.
{"title":"Assessing landslide damming susceptibility in Central Asia","authors":"C. Tacconi Stefanelli, W. Frodella, Francesco Caleca, Zh. T. Raimbekova, R. Umaraliev, V. Tofani","doi":"10.5194/nhess-24-1697-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-1697-2024","url":null,"abstract":"Abstract. Central Asia regions are characterized by active tectonics, high mountain chains with extreme topography with glaciers, and strong seasonal rainfall events. These key predisposing factors make large landslides a serious natural threat in the area, causing several casualties every year. The mountain crests are divided by wide lenticular or narrow, linear intermountain tectonic depressions, which are incised by many of the most important Central Asia rivers and are also subject to major seasonal river flood hazard. This multi-hazard combination is a source of potential damming scenarios, which can bring cascading effects with devastating consequences for the surrounding settlements and population. Different hazards can only be managed with a multi-hazard approach coherent within the different countries, as suggested by 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 with the aim of providing a damming susceptibility analysis at a regional scale for Central Asia. To achieve this, a semi-automated GIS-based mapping method, centered on a bivariate correlation of morphometric parameters defined by a morphological index, originally designed to assess the damming susceptibility at basin/regional scale, was modified to be adopted nationwide and applied to spatially assess the obstruction of the river network in Central Asia for mapped and newly formed landslides. The proposed methodology represents an improvement to the previously designed methodology, requiring a smaller amount of data, bringing new preliminary information on damming hazard management and risk reduction, and identifying the most critical area within the Central Asia regions.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"29 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140980154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-08DOI: 10.5194/nhess-24-1607-2024
Théo St. Pierre Ostrander, Thomé Kraus, Bruno Mazzorana, Johannes Holzner, A. Andreoli, Francesco Comiti, B. Gems
Abstract. Confluences are dynamic morphological nodes that are found in all river networks. In mountain regions, they are influenced by hydraulic and sedimentary processes that occur in steep channels during extreme events in small watersheds. Sediment transport in the tributary channel and aggradation in the confluence can be massive, potentially causing overbank flooding and sedimentation into adjacent settlement areas. Previous works dealing with confluences have mainly focused on lowland regions, and those that have focused on mountain areas have used sediment concentrations and channel gradients that are largely under-representative of mountain river conditions. The presented work contributes to filling this research gap with 45 experiments that use a large-scale physical model. Geometric model parameters, the applied grain size distribution, and the considered discharges represent the conditions at 135 confluences in South Tyrol (Italy) and Tyrol (Austria). The experimental program allowed for a comprehensive analysis of the effects of (i) the confluence angle, (ii) the tributary gradient, (iii) the channel discharges, and (iv) the tributary sediment concentration. In contrast to most research dealing with confluences, results indicate that, in the presence of an intense tributary sediment supply and a small tributary-to-main-channel discharge ratio (0.1), the confluence angle does not have a decisive effect on confluence morphology. Adjustments to the tributary channel gradient yielded the same results. A reoccurring range of depositional geomorphic units was observed in which a deposition cone transitioned to a bank-attached bar. The confluence morphology and tributary channel gradient rapidly adjusted, tending towards an equilibrium state to accommodate both water discharges and the sediment load from the tributary. Statistical analyses demonstrated that the confluence morphology was controlled by the combined channel discharge and the depositional or erosional extent was controlled by the sediment concentration. Applying conclusions drawn from lowland confluence dynamics could misrepresent depositional and erosional patterns and the related flood hazard at mountain river confluences.
{"title":"Limited effect of the confluence angle and tributary gradient on Alpine confluence morphodynamics under intense sediment loads","authors":"Théo St. Pierre Ostrander, Thomé Kraus, Bruno Mazzorana, Johannes Holzner, A. Andreoli, Francesco Comiti, B. Gems","doi":"10.5194/nhess-24-1607-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-1607-2024","url":null,"abstract":"Abstract. Confluences are dynamic morphological nodes that are found in all river networks. In mountain regions, they are influenced by hydraulic and sedimentary processes that occur in steep channels during extreme events in small watersheds. Sediment transport in the tributary channel and aggradation in the confluence can be massive, potentially causing overbank flooding and sedimentation into adjacent settlement areas. Previous works dealing with confluences have mainly focused on lowland regions, and those that have focused on mountain areas have used sediment concentrations and channel gradients that are largely under-representative of mountain river conditions. The presented work contributes to filling this research gap with 45 experiments that use a large-scale physical model. Geometric model parameters, the applied grain size distribution, and the considered discharges represent the conditions at 135 confluences in South Tyrol (Italy) and Tyrol (Austria). The experimental program allowed for a comprehensive analysis of the effects of (i) the confluence angle, (ii) the tributary gradient, (iii) the channel discharges, and (iv) the tributary sediment concentration. In contrast to most research dealing with confluences, results indicate that, in the presence of an intense tributary sediment supply and a small tributary-to-main-channel discharge ratio (0.1), the confluence angle does not have a decisive effect on confluence morphology. Adjustments to the tributary channel gradient yielded the same results. A reoccurring range of depositional geomorphic units was observed in which a deposition cone transitioned to a bank-attached bar. The confluence morphology and tributary channel gradient rapidly adjusted, tending towards an equilibrium state to accommodate both water discharges and the sediment load from the tributary. Statistical analyses demonstrated that the confluence morphology was controlled by the combined channel discharge and the depositional or erosional extent was controlled by the sediment concentration. Applying conclusions drawn from lowland confluence dynamics could misrepresent depositional and erosional patterns and the related flood hazard at mountain river confluences.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140998071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-06DOI: 10.5194/nhess-24-1579-2024
R. Baum, D. Brien, Mark E. Reid, William H. Schulz, Matthew J. Tello
Abstract. Hurricane Maria induced about 70 000 landslides throughout Puerto Rico, USA, including thousands each in three municipalities situated in Puerto Rico's rugged Cordillera Central range. By combining a nonlinear soil-depth model, presumed wettest-case pore pressures, and quasi-three-dimensional (3D) slope-stability analysis, we developed a landslide susceptibility map that has very good performance and continuous susceptibility zones having smooth, buffered boundaries. Our landslide susceptibility map enables assessment of potential ground-failure locations and their use as landslide sources in a companion assessment of inundation and debris-flow runout. The quasi-3D factor of safety, F3, showed strong inverse correlation to landslide density (high density at low F3). Area under the curve (AUC) of true positive rate (TPR) versus false positive rate (FPR) indicated success of F3 in identifying head-scarp points (AUC = 0.84) and source-area polygons (0.85 ≤ AUC ≤ 0.88). The susceptibility zones enclose specific percentages of observed landslides. Thus, zone boundaries use successive F3 levels for increasing TPR of landslide head-scarp points, with zones bounded by F3 at TPR = 0.75, very high; F3 at TPR = 0.90, high; and the remainder moderate to low. The very high susceptibility zone, with 118 landslides km−2, covered 23 % of the three municipalities. The high zone (51 landslides km−2) covered another 10 %.
{"title":"Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico","authors":"R. Baum, D. Brien, Mark E. Reid, William H. Schulz, Matthew J. Tello","doi":"10.5194/nhess-24-1579-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-1579-2024","url":null,"abstract":"Abstract. Hurricane Maria induced about 70 000 landslides throughout Puerto Rico, USA, including thousands each in three municipalities situated in Puerto Rico's rugged Cordillera Central range. By combining a nonlinear soil-depth model, presumed wettest-case pore pressures, and quasi-three-dimensional (3D) slope-stability analysis, we developed a landslide susceptibility map that has very good performance and continuous susceptibility zones having smooth, buffered boundaries. Our landslide susceptibility map enables assessment of potential ground-failure locations and their use as landslide sources in a companion assessment of inundation and debris-flow runout. The quasi-3D factor of safety, F3, showed strong inverse correlation to landslide density (high density at low F3). Area under the curve (AUC) of true positive rate (TPR) versus false positive rate (FPR) indicated success of F3 in identifying head-scarp points (AUC = 0.84) and source-area polygons (0.85 ≤ AUC ≤ 0.88). The susceptibility zones enclose specific percentages of observed landslides. Thus, zone boundaries use successive F3 levels for increasing TPR of landslide head-scarp points, with zones bounded by F3 at TPR = 0.75, very high; F3 at TPR = 0.90, high; and the remainder moderate to low. The very high susceptibility zone, with 118 landslides km−2, covered 23 % of the three municipalities. The high zone (51 landslides km−2) covered another 10 %.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"2 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141011357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-03DOI: 10.5194/nhess-24-1555-2024
Luca G. Severino, Chahan M. Kropf, Hilla Afargan‐Gerstman, C. Fairless, Andrie De Vries, Daniella I. V. Domeisen, D. Bresch
Abstract. Winter windstorms are among the most significant natural hazards in Europe linked to fatalities and substantial damage. However, projections of windstorm impact in Europe under climate change are highly uncertain. This study combines climate projections from 30 general circulation models participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) with the climate risk assessment model CLIMADA to obtain projections of windstorm-induced damage over Europe in a changing climate. We conduct an uncertainty–sensitivity analysis and find large uncertainties in the projected changes in the damage, with climate model uncertainty being the dominant factor of uncertainty in the projections. We investigate the spatial patterns of the climate change-induced modifications in windstorm damage and find an increase in the damage in northwestern and northern central Europe and a decrease over the rest of Europe, in agreement with an eastward extension of the North Atlantic storm track into Europe. We combine all 30 available climate models in an ensemble-of-opportunity approach and find evidence for an intensification of future climate windstorm damage, in which damage with return periods of 100 years under current climate conditions becomes damage with return periods of 28 years under future SSP585 climate scenarios. Our findings demonstrate the importance of climate model uncertainty for the CMIP6 projections of windstorms in Europe and emphasize the increasing need for risk mitigation due to extreme weather in the future.
{"title":"Projections and uncertainties of winter windstorm damage in Europe in a changing climate","authors":"Luca G. Severino, Chahan M. Kropf, Hilla Afargan‐Gerstman, C. Fairless, Andrie De Vries, Daniella I. V. Domeisen, D. Bresch","doi":"10.5194/nhess-24-1555-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-1555-2024","url":null,"abstract":"Abstract. Winter windstorms are among the most significant natural hazards in Europe linked to fatalities and substantial damage. However, projections of windstorm impact in Europe under climate change are highly uncertain. This study combines climate projections from 30 general circulation models participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) with the climate risk assessment model CLIMADA to obtain projections of windstorm-induced damage over Europe in a changing climate. We conduct an uncertainty–sensitivity analysis and find large uncertainties in the projected changes in the damage, with climate model uncertainty being the dominant factor of uncertainty in the projections. We investigate the spatial patterns of the climate change-induced modifications in windstorm damage and find an increase in the damage in northwestern and northern central Europe and a decrease over the rest of Europe, in agreement with an eastward extension of the North Atlantic storm track into Europe. We combine all 30 available climate models in an ensemble-of-opportunity approach and find evidence for an intensification of future climate windstorm damage, in which damage with return periods of 100 years under current climate conditions becomes damage with return periods of 28 years under future SSP585 climate scenarios. Our findings demonstrate the importance of climate model uncertainty for the CMIP6 projections of windstorms in Europe and emphasize the increasing need for risk mitigation due to extreme weather in the future.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"19 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141016991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-02DOI: 10.5194/nhess-24-1539-2024
Daniel Krieger, S. Brune, J. Baehr, Ralf Weisse
Abstract. Extratropical storms are one of the major coastal hazards along the coastline of the German Bight, the southeastern part of the North Sea, and a major driver of coastal protection efforts. However, the predictability of these regional extreme events on a seasonal scale is still limited. We therefore improve the seasonal prediction skill of the Max Planck Institute Earth System Model (MPI-ESM) large-ensemble decadal hindcast system for German Bight storm activity (GBSA) in winter. We define GBSA as the 95th percentiles of three-hourly geostrophic wind speeds in winter, which we derive from mean sea-level pressure (MSLP) data. The hindcast system consists of an ensemble of 64 members, which are initialized annually in November and cover the winters of 1960/61–2017/18. We consider both deterministic and probabilistic predictions of GBSA, for both of which the full ensemble produces poor predictions in the first winter. To improve the skill, we observe the state of two physical predictors of GBSA, namely 70 hPa temperature anomalies in September, as well as 500 hPa geopotential height anomalies in November, in areas where these two predictors are correlated with winter GBSA. We translate the state of these predictors into a first guess of GBSA and remove ensemble members with a GBSA prediction too far away from this first guess. The resulting subselected ensemble exhibits a significantly improved skill in both deterministic and probabilistic predictions of winter GBSA. We also show how this skill increase is associated with better predictability of large-scale atmospheric patterns.
{"title":"Improving seasonal predictions of German Bight storm activity","authors":"Daniel Krieger, S. Brune, J. Baehr, Ralf Weisse","doi":"10.5194/nhess-24-1539-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-1539-2024","url":null,"abstract":"Abstract. Extratropical storms are one of the major coastal hazards along the coastline of the German Bight, the southeastern part of the North Sea, and a major driver of coastal protection efforts. However, the predictability of these regional extreme events on a seasonal scale is still limited. We therefore improve the seasonal prediction skill of the Max Planck Institute Earth System Model (MPI-ESM) large-ensemble decadal hindcast system for German Bight storm activity (GBSA) in winter. We define GBSA as the 95th percentiles of three-hourly geostrophic wind speeds in winter, which we derive from mean sea-level pressure (MSLP) data. The hindcast system consists of an ensemble of 64 members, which are initialized annually in November and cover the winters of 1960/61–2017/18. We consider both deterministic and probabilistic predictions of GBSA, for both of which the full ensemble produces poor predictions in the first winter. To improve the skill, we observe the state of two physical predictors of GBSA, namely 70 hPa temperature anomalies in September, as well as 500 hPa geopotential height anomalies in November, in areas where these two predictors are correlated with winter GBSA. We translate the state of these predictors into a first guess of GBSA and remove ensemble members with a GBSA prediction too far away from this first guess. The resulting subselected ensemble exhibits a significantly improved skill in both deterministic and probabilistic predictions of winter GBSA. We also show how this skill increase is associated with better predictability of large-scale atmospheric patterns.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"18 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141020019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-02DOI: 10.5194/nhess-24-1521-2024
Paula Aguirre, Jorge León, Constanza González-Mathiesen, Randy Román, Manuela Penas, Alonso Ogueda
Abstract. Wildland–urban interface (WUI) regions are particularly vulnerable to wildfires due to their proximity to both nature and urban developments, posing significant risks to lives and property. To enhance our understanding of the risk profiles in WUI areas, we analysed seven fire case studies in central Chile. We developed a mixed-method approach for conducting local-scale analyses, which involved field surveys, remote-sensing through satellite and drone imagery, and GIS-based analysis of the collected data. The methodology led to the generation of a georeferenced dataset of damaged and undamaged dwellings, including 16 variables representing their physical characteristics, spatial arrangement, and the availability of fire suppression resources. A binary classification model was then used to assess the relative importance of these attributes as indicators of vulnerability. The analysis revealed that spatial arrangement factors have a greater impact on damage prediction than the structural conditions and fire preparedness of individual units. Specifically, factors such as dwelling proximity to neighbours, distance to vegetation, proximity to the border of dwelling groups, and distance from the origin of the fire substantially contribute to the prediction of fire damage. Other structural attributes associated with less affluent homes may also increase the likelihood of damage, although further data are required for confirmation. This study provides insights for the design, planning, and governance of WUI areas in Chile, aiding the development of risk mitigation strategies for both built structures and the broader territorial area.
{"title":"Modelling the vulnerability of urban settings to wildland–urban interface fires in Chile","authors":"Paula Aguirre, Jorge León, Constanza González-Mathiesen, Randy Román, Manuela Penas, Alonso Ogueda","doi":"10.5194/nhess-24-1521-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-1521-2024","url":null,"abstract":"Abstract. Wildland–urban interface (WUI) regions are particularly vulnerable to wildfires due to their proximity to both nature and urban developments, posing significant risks to lives and property. To enhance our understanding of the risk profiles in WUI areas, we analysed seven fire case studies in central Chile. We developed a mixed-method approach for conducting local-scale analyses, which involved field surveys, remote-sensing through satellite and drone imagery, and GIS-based analysis of the collected data. The methodology led to the generation of a georeferenced dataset of damaged and undamaged dwellings, including 16 variables representing their physical characteristics, spatial arrangement, and the availability of fire suppression resources. A binary classification model was then used to assess the relative importance of these attributes as indicators of vulnerability. The analysis revealed that spatial arrangement factors have a greater impact on damage prediction than the structural conditions and fire preparedness of individual units. Specifically, factors such as dwelling proximity to neighbours, distance to vegetation, proximity to the border of dwelling groups, and distance from the origin of the fire substantially contribute to the prediction of fire damage. Other structural attributes associated with less affluent homes may also increase the likelihood of damage, although further data are required for confirmation. This study provides insights for the design, planning, and governance of WUI areas in Chile, aiding the development of risk mitigation strategies for both built structures and the broader territorial area.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"15 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141020561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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