Pub Date : 2024-01-17DOI: 10.5194/nhess-24-79-2024
Soheil Mohammadi, Silvia De Angeli, G. Boni, F. Pirlone, S. Cattari
Abstract. Post-disaster recovery has been addressed in the literature by different sectoral perspectives and scientific communities. Nevertheless, studies providing holistic approaches to recovery, integrating reconstruction procedures and socio-economic impacts, are still lacking. Additionally, there is a gap in disaster recovery research addressing the additional challenges posed by the effect of complex, multiple, and interacting risks on highly interconnected urban areas. Furthermore, recovery has only been marginally explored from a pre-disaster perspective in terms of planning and actions to increase urban resilience and recoverability. This paper provides a critical review of existing literature and guidelines on multi-risk disaster recovery with the twofold aim of identifying current gaps and providing the layout to address multi-risk recovery planning tools for decision-making. The literature on disaster recovery is investigated in the paper by focusing on the definition of the recovery phase and its separation or overlapping with other disaster risk management phases, the different destinations and goals that an urban system follows through recovery pathways, the requirements to implement a holistic resilience-based recovery roadmap, the challenges for shifting from single-risk to multi-risk recovery approaches, and the available tools for optimal decision-making in the recovery planning. Finally, the current challenges in multi-risk recovery planning are summarized and discussed. This review can be a ground basis for new research directions in the field of multi-risk recovery planning to help stakeholders in decision-making and optimize their pre-disaster investments to improve the urban system's recoverability.
{"title":"Review article: Current approaches and critical issues in multi-risk recovery planning of urban areas exposed to natural hazards","authors":"Soheil Mohammadi, Silvia De Angeli, G. Boni, F. Pirlone, S. Cattari","doi":"10.5194/nhess-24-79-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-79-2024","url":null,"abstract":"Abstract. Post-disaster recovery has been addressed in the literature by different sectoral perspectives and scientific communities. Nevertheless, studies providing holistic approaches to recovery, integrating reconstruction procedures and socio-economic impacts, are still lacking. Additionally, there is a gap in disaster recovery research addressing the additional challenges posed by the effect of complex, multiple, and interacting risks on highly interconnected urban areas. Furthermore, recovery has only been marginally explored from a pre-disaster perspective in terms of planning and actions to increase urban resilience and recoverability. This paper provides a critical review of existing literature and guidelines on multi-risk disaster recovery with the twofold aim of identifying current gaps and providing the layout to address multi-risk recovery planning tools for decision-making. The literature on disaster recovery is investigated in the paper by focusing on the definition of the recovery phase and its separation or overlapping with other disaster risk management phases, the different destinations and goals that an urban system follows through recovery pathways, the requirements to implement a holistic resilience-based recovery roadmap, the challenges for shifting from single-risk to multi-risk recovery approaches, and the available tools for optimal decision-making in the recovery planning. Finally, the current challenges in multi-risk recovery planning are summarized and discussed. This review can be a ground basis for new research directions in the field of multi-risk recovery planning to help stakeholders in decision-making and optimize their pre-disaster investments to improve the urban system's recoverability.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139616676","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 : 2024-01-17DOI: 10.5194/nhess-24-63-2024
Yuting Zhang, Kai Liu, Xiaoyong Ni, Ming Wang, Jianchun Zheng, Mengting Liu, Dapeng Yu
Abstract. The accessibility of emergency medical services (EMSs) is not only determined by the distribution of emergency medical facilities but is also influenced by weather conditions. Inclement weather could affect the efficiency of the city's traffic network and further affect the response time of EMSs, which could therefore be an essential impact factor on the safety of human lives. This study proposes an EMSs-accessibility quantification method based on selected indicators, explores the influence of inclement weather on EMSs accessibility, and identifies the hotspots that have difficulty accessing timely EMSs. A case study was implemented in Beijing, which is a typical megacity in China, based on the ground-truth traffic data of the whole city in 2019. The results show that inclement weather has a general negative impact on EMSs accessibility. Under an inclement weather scenario, the area in the city that could get EMSs within 15 min would decrease by 13 % compared with a normal scenario (the average state of weekdays without precipitation), while in some suburban townships, the population that could get 15 min EMSs would decrease by 40 %. We found that snowfall has a greater impact on the accessibility of EMSs than rainfall. Although on the whole, the urban area would have more traffic speed reduction, towns in suburban areas with lower baseline EMSs accessibility are more vulnerable to inclement weather. Under the worst scenario in 2019, 12.6 % of the population (about 3.5 million people) could not get EMSs within 15 min, compared with 7.5 % with normal weather conditions. This study could provide a scientific reference for city planning departments to optimize traffic under inclement weather and the site selection of emergency medical facilities.
{"title":"Spatial accessibility of emergency medical services under inclement weather: a case study in Beijing, China","authors":"Yuting Zhang, Kai Liu, Xiaoyong Ni, Ming Wang, Jianchun Zheng, Mengting Liu, Dapeng Yu","doi":"10.5194/nhess-24-63-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-63-2024","url":null,"abstract":"Abstract. The accessibility of emergency medical services (EMSs) is not only determined by the distribution of emergency medical facilities but is also influenced by weather conditions. Inclement weather could affect the efficiency of the city's traffic network and further affect the response time of EMSs, which could therefore be an essential impact factor on the safety of human lives. This study proposes an EMSs-accessibility quantification method based on selected indicators, explores the influence of inclement weather on EMSs accessibility, and identifies the hotspots that have difficulty accessing timely EMSs. A case study was implemented in Beijing, which is a typical megacity in China, based on the ground-truth traffic data of the whole city in 2019. The results show that inclement weather has a general negative impact on EMSs accessibility. Under an inclement weather scenario, the area in the city that could get EMSs within 15 min would decrease by 13 % compared with a normal scenario (the average state of weekdays without precipitation), while in some suburban townships, the population that could get 15 min EMSs would decrease by 40 %. We found that snowfall has a greater impact on the accessibility of EMSs than rainfall. Although on the whole, the urban area would have more traffic speed reduction, towns in suburban areas with lower baseline EMSs accessibility are more vulnerable to inclement weather. Under the worst scenario in 2019, 12.6 % of the population (about 3.5 million people) could not get EMSs within 15 min, compared with 7.5 % with normal weather conditions. This study could provide a scientific reference for city planning departments to optimize traffic under inclement weather and the site selection of emergency medical facilities.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":" 726","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139617624","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 : 2024-01-10DOI: 10.5194/nhess-24-29-2024
Henrique M. D. Goulart, Irene Benito Lazaro, Linda van Garderen, Karin van der Wiel, D. Le Bars, E. Koks, B. van den Hurk
Abstract. High impact events like Hurricane Sandy (2012) significantly affect society and decision-making around weather/climate adaptation. Our understanding of the potential effects of such events is limited to their rare historical occurrences. Climate change might alter these events to an extent that current adaptation responses become insufficient. Furthermore, internal climate variability in the current climate might also lead to slightly different events with possible larger societal impacts. Therefore, exploring high impact events under different conditions becomes important for (future) impact assessment. In this study, we create storylines of Sandy to assess compound coastal flooding on critical infrastructure in New York City under different scenarios, including climate change effects (on the storm and through sea level rise) and internal variability (variations in the storm's intensity and location). We find that 1 m of sea level rise increases average flood volumes by 4.2 times, while maximised precipitation scenarios (internal variability) lead to a 2.5-fold increase in flood volumes. The maximised precipitation scenarios impact inland critical infrastructure assets with low water levels, while sea level rise impacts fewer coastal assets though with high water levels. The diversity in hazards and impacts demonstrates the importance of building a set of relevant scenarios, including those representing the effects of climate change and internal variability. The integration of a modelling framework connecting meteorological conditions to local hazards and impacts provides relevant and accessible information that can directly be integrated into high impact event assessments.
{"title":"Compound flood impacts from Hurricane Sandy on New York City in climate-driven storylines","authors":"Henrique M. D. Goulart, Irene Benito Lazaro, Linda van Garderen, Karin van der Wiel, D. Le Bars, E. Koks, B. van den Hurk","doi":"10.5194/nhess-24-29-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-29-2024","url":null,"abstract":"Abstract. High impact events like Hurricane Sandy (2012) significantly affect society and decision-making around weather/climate adaptation. Our understanding of the potential effects of such events is limited to their rare historical occurrences. Climate change might alter these events to an extent that current adaptation responses become insufficient. Furthermore, internal climate variability in the current climate might also lead to slightly different events with possible larger societal impacts. Therefore, exploring high impact events under different conditions becomes important for (future) impact assessment. In this study, we create storylines of Sandy to assess compound coastal flooding on critical infrastructure in New York City under different scenarios, including climate change effects (on the storm and through sea level rise) and internal variability (variations in the storm's intensity and location). We find that 1 m of sea level rise increases average flood volumes by 4.2 times, while maximised precipitation scenarios (internal variability) lead to a 2.5-fold increase in flood volumes. The maximised precipitation scenarios impact inland critical infrastructure assets with low water levels, while sea level rise impacts fewer coastal assets though with high water levels. The diversity in hazards and impacts demonstrates the importance of building a set of relevant scenarios, including those representing the effects of climate change and internal variability. The integration of a modelling framework connecting meteorological conditions to local hazards and impacts provides relevant and accessible information that can directly be integrated into high impact event assessments.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139439189","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 : 2024-01-09DOI: 10.5194/nhess-24-13-2024
Francesco Caleca, C. Scaini, W. Frodella, V. Tofani
Abstract. Landslides are widespread phenomena that occur in any terrestrial area with slopes, causing massive property damage and, in the worst-case scenario, loss of human life. This propensity to suffer losses is particularly high for developing countries due to their urban development, population growth and drastic land use changes. Social and economic consequences of landslides can be reduced through detailed planning and management strategies, which can be aided by risk analysis. In this study, we performed a detailed quantitative risk analysis for landslides in the whole of Central Asia (4 000 000 km2). Landslide-induced risk was computed, using a 200 m spatial resolution, in terms of exposed population and expected economic losses to buildings and linear infrastructures (roads and railways). The purpose of our study is to produce the first regional-scale landslide risk assessment for Central Asia in order to inform regional-scale risk mitigation strategies, and it represents an advanced step in the landslide risk analysis for extremely broad areas.
{"title":"Regional-scale landslide risk assessment in Central Asia","authors":"Francesco Caleca, C. Scaini, W. Frodella, V. Tofani","doi":"10.5194/nhess-24-13-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-13-2024","url":null,"abstract":"Abstract. Landslides are widespread phenomena that occur in any terrestrial area with slopes, causing massive property damage and, in the worst-case scenario, loss of human life. This propensity to suffer losses is particularly high for developing countries due to their urban development, population growth and drastic land use changes. Social and economic consequences of landslides can be reduced through detailed planning and management strategies, which can be aided by risk analysis. In this study, we performed a detailed quantitative risk analysis for landslides in the whole of Central Asia (4 000 000 km2). Landslide-induced risk was computed, using a 200 m spatial resolution, in terms of exposed population and expected economic losses to buildings and linear infrastructures (roads and railways). The purpose of our study is to produce the first regional-scale landslide risk assessment for Central Asia in order to inform regional-scale risk mitigation strategies, and it represents an advanced step in the landslide risk analysis for extremely broad areas.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139444307","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 : 2023-12-18DOI: 10.5194/nhess-23-3845-2023
Matthew D. K. Priestley, David B. Stephenson, Adam A. Scaife, Daniel Bannister, Christopher J. T. Allen, David Wilkie
Abstract. Windstorms are the most damaging natural hazard across western Europe. Risk modellers are limited by the observational data record to only ∼ 60 years of comprehensive reanalysis data that are dominated by considerable inter-annual variability. This makes estimating return periods of rare events difficult and sensitive to the choice of the historical period used. This study proposes a novel statistical method for estimating wind gusts across Europe based on observed windstorm footprints. A good description of extreme wind speeds is obtained by assuming that gust speed peaks over threshold are distributed exponentially, i.e. a generalised Pareto distribution having a zero shape parameter. The threshold and tail scale parameter are estimated at each location and used to calculate estimates of the 10- and 200-year return levels. The North Atlantic Oscillation (NAO) is particularly important for modulating lower return levels and modulating the threshold, with a less detectable influence on rarer extremes and the tail scale parameter. The length of historical data required to have the lowest error in estimating return levels is quantified using both observed and simulated time series of the historical NAO. For reducing errors in estimating 200-year return levels of an independent 10-year period, a data catalogue of at least 20 years is required. For lower return levels the NAO has a stronger influence on estimated return levels, and so there is more variability in estimates. Using theoretical estimates of future NAO states, return levels are largely outside the historical uncertainty, indicating significant increases in risk potential from windstorms in the next 100 years. Our method presents a framework for assessing high-return-period events across a range of hazards without the additional complexities of a full catastrophe model.
{"title":"Return levels of extreme European windstorms, their dependency on the North Atlantic Oscillation, and potential future risks","authors":"Matthew D. K. Priestley, David B. Stephenson, Adam A. Scaife, Daniel Bannister, Christopher J. T. Allen, David Wilkie","doi":"10.5194/nhess-23-3845-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-3845-2023","url":null,"abstract":"Abstract. Windstorms are the most damaging natural hazard across western Europe. Risk modellers are limited by the observational data record to only ∼ 60 years of comprehensive reanalysis data that are dominated by considerable inter-annual variability. This makes estimating return periods of rare events difficult and sensitive to the choice of the historical period used. This study proposes a novel statistical method for estimating wind gusts across Europe based on observed windstorm footprints. A good description of extreme wind speeds is obtained by assuming that gust speed peaks over threshold are distributed exponentially, i.e. a generalised Pareto distribution having a zero shape parameter. The threshold and tail scale parameter are estimated at each location and used to calculate estimates of the 10- and 200-year return levels. The North Atlantic Oscillation (NAO) is particularly important for modulating lower return levels and modulating the threshold, with a less detectable influence on rarer extremes and the tail scale parameter. The length of historical data required to have the lowest error in estimating return levels is quantified using both observed and simulated time series of the historical NAO. For reducing errors in estimating 200-year return levels of an independent 10-year period, a data catalogue of at least 20 years is required. For lower return levels the NAO has a stronger influence on estimated return levels, and so there is more variability in estimates. Using theoretical estimates of future NAO states, return levels are largely outside the historical uncertainty, indicating significant increases in risk potential from windstorms in the next 100 years. Our method presents a framework for assessing high-return-period events across a range of hazards without the additional complexities of a full catastrophe model.","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139173018","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 : 2023-12-18DOI: 10.5194/nhess-23-3863-2023
Ana Paez-Trujilo, Jeffer Cañon, Beatriz Hernandez, G. Corzo, Dimitri Solomatine
Abstract. The typical drivers of drought events are lower than normal precipitation and/or higher than normal evaporation. The region's characteristics may enhance or alleviate the severity of these events. Evaluating the combined effect of the multiple factors influencing droughts requires innovative approaches. This study applies hydrological modelling and a machine learning tool to assess the relationship between hydroclimatic characteristics and the severity of agricultural and hydrological droughts. The Soil Water Assessment Tool (SWAT) is used for hydrological modelling. Model outputs, soil moisture and streamflow, are used to calculate two drought indices, namely the Soil Moisture Deficit Index and the Standardized Streamflow Index. Then, drought indices are utilised to identify the agricultural and hydrological drought events during the analysis period, and the index categories are employed to describe their severity. Finally, the multivariate regression tree technique is applied to assess the relationship between hydroclimatic characteristics and the severity of agricultural and hydrological droughts. Our research indicates that multiple parameters influence the severity of agricultural and hydrological droughts in the Cesar River basin. The upper part of the river valley is very susceptible to agricultural and hydrological drought. Precipitation shortfalls and high potential evapotranspiration drive severe agricultural drought, whereas limited precipitation influences severe hydrological drought. In the middle part of the river, inadequate rainfall partitioning and an unbalanced water cycle that favours water loss through evapotranspiration and limits percolation cause severe agricultural and hydrological drought conditions. Finally, droughts are moderate in the basin's southern part (Zapatosa marsh and the Serranía del Perijá foothills). Moderate sensitivity to agricultural and hydrological droughts is related to the capacity of the subbasins to retain water, which lowers evapotranspiration losses and promotes percolation. Results show that the presented methodology, combining hydrological modelling and a machine learning tool, provides valuable information about the interplay between the hydroclimatic factors that influence drought severity in the Cesar River basin.
{"title":"Multivariate regression trees as an “explainable machine learning” approach to explore relationships between hydroclimatic characteristics and agricultural and hydrological drought severity: case of study Cesar River basin","authors":"Ana Paez-Trujilo, Jeffer Cañon, Beatriz Hernandez, G. Corzo, Dimitri Solomatine","doi":"10.5194/nhess-23-3863-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-3863-2023","url":null,"abstract":"Abstract. The typical drivers of drought events are lower than normal precipitation and/or higher than normal evaporation. The region's characteristics may enhance or alleviate the severity of these events. Evaluating the combined effect of the multiple factors influencing droughts requires innovative approaches. This study applies hydrological modelling and a machine learning tool to assess the relationship between hydroclimatic characteristics and the severity of agricultural and hydrological droughts. The Soil Water Assessment Tool (SWAT) is used for hydrological modelling. Model outputs, soil moisture and streamflow, are used to calculate two drought indices, namely the Soil Moisture Deficit Index and the Standardized Streamflow Index. Then, drought indices are utilised to identify the agricultural and hydrological drought events during the analysis period, and the index categories are employed to describe their severity. Finally, the multivariate regression tree technique is applied to assess the relationship between hydroclimatic characteristics and the severity of agricultural and hydrological droughts. Our research indicates that multiple parameters influence the severity of agricultural and hydrological droughts in the Cesar River basin. The upper part of the river valley is very susceptible to agricultural and hydrological drought. Precipitation shortfalls and high potential evapotranspiration drive severe agricultural drought, whereas limited precipitation influences severe hydrological drought. In the middle part of the river, inadequate rainfall partitioning and an unbalanced water cycle that favours water loss through evapotranspiration and limits percolation cause severe agricultural and hydrological drought conditions. Finally, droughts are moderate in the basin's southern part (Zapatosa marsh and the Serranía del Perijá foothills). Moderate sensitivity to agricultural and hydrological droughts is related to the capacity of the subbasins to retain water, which lowers evapotranspiration losses and promotes percolation. Results show that the presented methodology, combining hydrological modelling and a machine learning tool, provides valuable information about the interplay between the hydroclimatic factors that influence drought severity in the Cesar River basin.","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"26 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139172809","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 : 2023-11-30DOI: 10.5194/nhess-23-3685-2023
L. MacPherson, A. Arns, S. Fischer, F. Méndez, J. Jensen
Abstract. Developed coastlines require considerable investments into coastal protection measures to mitigate the effects of flooding caused by extreme sea levels (ESLs). To maximize the effectiveness of these measures, accurate estimates of the underlying hazard are needed. These estimates are typically determined by performing extreme value analysis on a sample of events taken from tide-gauge observations. However, such records are often limited in duration, and the resulting estimates may be highly uncertain. Furthermore, short records make it difficult to assess whether exceptionally large events within the record are appropriate for analysis or should be disregarded as outliers. In this study, we explore how historical information can be used to address both of these issues for the case of the German Baltic coast. We apply a Bayesian Markov chain Monte Carlo approach to assess ESLs using both systematic tide-gauge observations and historical information at seven locations. Apart from the benefits provided by incorporating historical information in extreme value analysis, which include reduced estimate uncertainties and the reclassification of outliers into useful samples, we find that the current tide-gauge records in the region alone are insufficient for providing accurate estimates of ESLs for the planning of coastal protection. We find long-range dependence in the series of ESLs at the site of Travemünde, which suggests the presence of some long-term variability affecting events in the region. We show that ESL activity over the full period of systematic observation has been relatively low. Consequently, analyses which consider only these data are prone to underestimations.
{"title":"Bayesian extreme value analysis of extreme sea levels along the German Baltic coast using historical information","authors":"L. MacPherson, A. Arns, S. Fischer, F. Méndez, J. Jensen","doi":"10.5194/nhess-23-3685-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-3685-2023","url":null,"abstract":"Abstract. Developed coastlines require considerable investments into coastal protection measures to mitigate the effects of flooding caused by extreme sea levels (ESLs). To maximize the effectiveness of these measures, accurate estimates of the underlying hazard are needed. These estimates are typically determined by performing extreme value analysis on a sample of events taken from tide-gauge observations. However, such records are often limited in duration, and the resulting estimates may be highly uncertain. Furthermore, short records make it difficult to assess whether exceptionally large events within the record are appropriate for analysis or should be disregarded as outliers. In this study, we explore how historical information can be used to address both of these issues for the case of the German Baltic coast. We apply a Bayesian Markov chain Monte Carlo approach to assess ESLs using both systematic tide-gauge observations and historical information at seven locations. Apart from the benefits provided by incorporating historical information in extreme value analysis, which include reduced estimate uncertainties and the reclassification of outliers into useful samples, we find that the current tide-gauge records in the region alone are insufficient for providing accurate estimates of ESLs for the planning of coastal protection. We find long-range dependence in the series of ESLs at the site of Travemünde, which suggests the presence of some long-term variability affecting events in the region. We show that ESL activity over the full period of systematic observation has been relatively low. Consequently, analyses which consider only these data are prone to underestimations.","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"554 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139204645","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 : 2023-11-30DOI: 10.5194/nhess-23-3703-2023
George Pacey, S. Pfahl, L. Schielicke, Kathrin Wapler
Abstract. Cold fronts provide an environment particularly favourable for convective initiation in the mid-latitudes and can also be associated with convective hazards such as flooding, wind, hail and lightning. We build a climatology of cold-frontal convective cells between 2007–2016 for April–September in a cell-front distance framework by combining a radar-based cell detection and tracking dataset and automatic front detection methods applied to reanalysis data. We find that on average around twice as many cells develop on cold-frontal cell days compared to non-cold-frontal cell days. Using the 700 hPa level as a reference point, we show the maximum cell frequency is 350–400 km ahead of the 700 hPa front, which is marginally ahead of the typical surface front location. The 700 hPa front location marks the minimum cell frequency and a clear shift in regime between cells, with a weakened diurnal cycle on the warm side of the 700 hPa cold front and strongly diurnally driven cells on the cold side of the 700 hPa front. High cell frequency is found several hundreds of kilometres ahead of the surface front, and cells in this region are most likely to be associated with mesocyclones, intense convective cores and lightning. Namely, mesocyclones were detected in around 5.0 % of pre-surface-frontal cells compared to only 1.5 % of non-cold-frontal cells. The findings in this study are an important step towards a better understanding of cold-frontal convection climatology and links between cold fronts and convective hazards.
{"title":"The climatology and nature of warm-season convective cells in cold-frontal environments over Germany","authors":"George Pacey, S. Pfahl, L. Schielicke, Kathrin Wapler","doi":"10.5194/nhess-23-3703-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-3703-2023","url":null,"abstract":"Abstract. Cold fronts provide an environment particularly favourable for convective initiation in the mid-latitudes and can also be associated with convective hazards such as flooding, wind, hail and lightning. We build a climatology of cold-frontal convective cells between 2007–2016 for April–September in a cell-front distance framework by combining a radar-based cell detection and tracking dataset and automatic front detection methods applied to reanalysis data. We find that on average around twice as many cells develop on cold-frontal cell days compared to non-cold-frontal cell days. Using the 700 hPa level as a reference point, we show the maximum cell frequency is 350–400 km ahead of the 700 hPa front, which is marginally ahead of the typical surface front location. The 700 hPa front location marks the minimum cell frequency and a clear shift in regime between cells, with a weakened diurnal cycle on the warm side of the 700 hPa cold front and strongly diurnally driven cells on the cold side of the 700 hPa front. High cell frequency is found several hundreds of kilometres ahead of the surface front, and cells in this region are most likely to be associated with mesocyclones, intense convective cores and lightning. Namely, mesocyclones were detected in around 5.0 % of pre-surface-frontal cells compared to only 1.5 % of non-cold-frontal cells. The findings in this study are an important step towards a better understanding of cold-frontal convection climatology and links between cold fronts and convective hazards.","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139198987","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 : 2023-11-29DOI: 10.5194/nhess-23-3671-2023
C. Molina, Badr-Eddine Boudriki Semlali, Guillermo González-Casado, Hyuk Park, Adriano Camps
Abstract. Ionospheric disturbances induced by seismic activity have been studied in recent years by many authors, showing an impact both before and after the occurrence of earthquakes. In this study, the ionospheric scintillation produced by the 2021 La Palma volcano eruption is analyzed. The Cumbre Vieja volcano was active from 19 September to 13 December 2021, and many earthquakes of magnitude 3–4 were recorded, with some of them reaching magnitude 5. Three methods, GNSS reference monitoring, GNSS reflectometry (GNSS-R) from NASA CYGNSS, and GNSS radio occultation (GNSS-RO) from COSMIC and Spire constellations, are used to compare and evaluate their sensitivity as proxies of earthquakes associated with the volcanic eruption. To compare the seismic activity with ionospheric scintillation, seismic energy release, and 95th percentile of the intensity scintillation parameter (S4), measurements have been computed at 6 h intervals for the whole duration of the volcanic eruption. GNSS-RO has shown the best correlation between earthquake energy and S4, with values up to 0.09 when the perturbations occur around 18 h after the seismic activity. GNSS reference monitoring station data also show some correlation 18 h and 7–8 d after. As expected, GNSS-R is the one that shows the smallest correlation, as the ionospheric signatures get masked by the signature of the surface where the reflection is taking place. Additionally, the three methods show a smaller correlation during the week before earthquakes. Given the small magnitude of the seismic activity, the correlation is barely detectable in this situation, and thus would be difficult to use in any application to find earthquake proxies.
{"title":"The 2021 La Palma volcanic eruption and its impact on ionospheric scintillation as measured from GNSS reference stations, GNSS-R and GNSS-RO","authors":"C. Molina, Badr-Eddine Boudriki Semlali, Guillermo González-Casado, Hyuk Park, Adriano Camps","doi":"10.5194/nhess-23-3671-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-3671-2023","url":null,"abstract":"Abstract. Ionospheric disturbances induced by seismic activity have been studied in recent years by many authors, showing an impact both before and after the occurrence of earthquakes. In this study, the ionospheric scintillation produced by the 2021 La Palma volcano eruption is analyzed. The Cumbre Vieja volcano was active from 19 September to 13 December 2021, and many earthquakes of magnitude 3–4 were recorded, with some of them reaching magnitude 5. Three methods, GNSS reference monitoring, GNSS reflectometry (GNSS-R) from NASA CYGNSS, and GNSS radio occultation (GNSS-RO) from COSMIC and Spire constellations, are used to compare and evaluate their sensitivity as proxies of earthquakes associated with the volcanic eruption. To compare the seismic activity with ionospheric scintillation, seismic energy release, and 95th percentile of the intensity scintillation parameter (S4), measurements have been computed at 6 h intervals for the whole duration of the volcanic eruption. GNSS-RO has shown the best correlation between earthquake energy and S4, with values up to 0.09 when the perturbations occur around 18 h after the seismic activity. GNSS reference monitoring station data also show some correlation 18 h and 7–8 d after. As expected, GNSS-R is the one that shows the smallest correlation, as the ionospheric signatures get masked by the signature of the surface where the reflection is taking place. Additionally, the three methods show a smaller correlation during the week before earthquakes. Given the small magnitude of the seismic activity, the correlation is barely detectable in this situation, and thus would be difficult to use in any application to find earthquake proxies.","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139213903","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 : 2023-11-29DOI: 10.5194/nhess-23-3651-2023
Francesco Battaglioli, P. Groenemeijer, I. Tsonevsky, T. Púčik
Abstract. Additive logistic regression models for lightning (ARlig) and large hail (ARhail) were developed using convective parameters from the ERA5 reanalysis, hail reports from the European Severe Weather Database (ESWD), and lightning observations from the Met Office Arrival Time Difference network (ATDnet). The models yield the probability of lightning and large hail in a given timeframe over a particular grid point. To explore the value of this approach to medium-range forecasting, the models were applied to the European Centre for Medium Range Weather Forecasts (ECMWF) reforecasts to reconstruct probabilistic lightning and large hail forecasts for 11 ensemble members, from 2008 to 2019 and for lead times up to 228 h. The lightning and large hail models were based on different predictor parameters: most unstable convective available potential energy (CAPE), 925–500 hPa bulk shear, mixed layer mixing ratio, wet bulb zero height (for large hail), most unstable lifted index, mean relative humidity between 850 and 500 hPa, 1 hourly accumulated convective precipitation and specific humidity at 925 hPa (for lightning). First, we compared the lightning and hail ensemble forecasts for different lead times with observed lightning and hail focusing on a recent hail outbreak. Second, we evaluated the predictive skill of the model as a function of forecast lead time using the area under the ROC curve (AUC) as a validation score. This analysis showed that ARhail has a very high predictive skill (AUC > 0.95) for a lead time up to 60 h. ARhail retains a high predictive skill even for extended forecasts (AUC = 0.86 at 180 h lead time). Although ARlig exhibits a lower predictive skill than ARhail, lightning forecasts are also skilful both in the short term (AUC = 0.92 at 60 h) and in the medium range (AUC = 0.82 at 180 h). Finally, we compared the performance of the 4-dimensional hail model with that of composite parameters such as the significant hail parameter (SHP) or the product of CAPE and the 925–500 hPa bulk shear (CAPESHEAR). Results show that ARhail outperforms CAPESHEAR at all lead times and SHP at short-to-medium lead times. These findings suggests that the combination of additive logistic regression models and ECMWF ensemble forecasts can create highly skilful medium-range hail and lightning forecasts for Europe.
{"title":"Forecasting large hail and lightning using additive logistic regression models and the ECMWF reforecasts","authors":"Francesco Battaglioli, P. Groenemeijer, I. Tsonevsky, T. Púčik","doi":"10.5194/nhess-23-3651-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-3651-2023","url":null,"abstract":"Abstract. Additive logistic regression models for lightning (ARlig) and large hail (ARhail) were developed using convective parameters from the ERA5 reanalysis, hail reports from the European Severe Weather Database (ESWD), and lightning observations from the Met Office Arrival Time Difference network (ATDnet). The models yield the probability of lightning and large hail in a given timeframe over a particular grid point. To explore the value of this approach to medium-range forecasting, the models were applied to the European Centre for Medium Range Weather Forecasts (ECMWF) reforecasts to reconstruct probabilistic lightning and large hail forecasts for 11 ensemble members, from 2008 to 2019 and for lead times up to 228 h. The lightning and large hail models were based on different predictor parameters: most unstable convective available potential energy (CAPE), 925–500 hPa bulk shear, mixed layer mixing ratio, wet bulb zero height (for large hail), most unstable lifted index, mean relative humidity between 850 and 500 hPa, 1 hourly accumulated convective precipitation and specific humidity at 925 hPa (for lightning). First, we compared the lightning and hail ensemble forecasts for different lead times with observed lightning and hail focusing on a recent hail outbreak. Second, we evaluated the predictive skill of the model as a function of forecast lead time using the area under the ROC curve (AUC) as a validation score. This analysis showed that ARhail has a very high predictive skill (AUC > 0.95) for a lead time up to 60 h. ARhail retains a high predictive skill even for extended forecasts (AUC = 0.86 at 180 h lead time). Although ARlig exhibits a lower predictive skill than ARhail, lightning forecasts are also skilful both in the short term (AUC = 0.92 at 60 h) and in the medium range (AUC = 0.82 at 180 h). Finally, we compared the performance of the 4-dimensional hail model with that of composite parameters such as the significant hail parameter (SHP) or the product of CAPE and the 925–500 hPa bulk shear (CAPESHEAR). Results show that ARhail outperforms CAPESHEAR at all lead times and SHP at short-to-medium lead times. These findings suggests that the combination of additive logistic regression models and ECMWF ensemble forecasts can create highly skilful medium-range hail and lightning forecasts for Europe.","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139210991","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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