Pub Date : 2024-07-26DOI: 10.5194/nhess-24-2577-2024
Manuel Grenier, Mathieu Boudreault, D. Carozza, Jérémie Boudreault, Sébastien Raymond
Abstract. Large-scale socioeconomic studies of the impacts of floods are difficult and costly for countries such as Canada and the United States due to the large number of rivers and size of watersheds. Such studies are however very important for analyzing spatial patterns and temporal trends to inform large-scale flood risk management decisions and policies. In this paper, we present different flood occurrence and impact models based upon statistical and machine learning methods of over 31 000 watersheds spread across Canada and the US. The models can be quickly calibrated and thereby easily run predictions over thousands of scenarios in a matter of minutes. As applications of the models, we present the geographical distribution of the modelled average annual number of people displaced due to flooding in Canada and the US, as well as various scenario analyses. We find for example that an increase of 10 % in average precipitation yields an increase in the displaced population of 18 % in Canada and 14 % in the US. The model can therefore be used by a broad range of end users ranging from climate scientists to economists who seek to translate climate and socioeconomic scenarios into flood probabilities and impacts measured in terms of the displaced population.
{"title":"Flood occurrence and impact models for socioeconomic applications over Canada and the United States","authors":"Manuel Grenier, Mathieu Boudreault, D. Carozza, Jérémie Boudreault, Sébastien Raymond","doi":"10.5194/nhess-24-2577-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-2577-2024","url":null,"abstract":"Abstract. Large-scale socioeconomic studies of the impacts of floods are difficult and costly for countries such as Canada and the United States due to the large number of rivers and size of watersheds. Such studies are however very important for analyzing spatial patterns and temporal trends to inform large-scale flood risk management decisions and policies. In this paper, we present different flood occurrence and impact models based upon statistical and machine learning methods of over 31 000 watersheds spread across Canada and the US. The models can be quickly calibrated and thereby easily run predictions over thousands of scenarios in a matter of minutes. As applications of the models, we present the geographical distribution of the modelled average annual number of people displaced due to flooding in Canada and the US, as well as various scenario analyses. We find for example that an increase of 10 % in average precipitation yields an increase in the displaced population of 18 % in Canada and 14 % in the US. The model can therefore be used by a broad range of end users ranging from climate scientists to economists who seek to translate climate and socioeconomic scenarios into flood probabilities and impacts measured in terms of the displaced population.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"23 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799515","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 : 2024-07-26DOI: 10.5194/nhess-24-2597-2024
Valerio Poggi, S. Parolai, N. Silacheva, A. Ischuk, K. Abdrakhmatov, Zainalobudin V. Kobuliev, V. Ismailov, Roman Ibragimov, Japar Karaev, Paola Ceresa, P. Bazzurro
Abstract. Central Asian countries, which include Kazakhstan, the Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan, are known to be highly exposed to natural hazards, particularly earthquakes, floods, and landslides. With the aim of enhancing financial resilience and risk-based investment, planning to promote disaster and climate resilience in Central Asia, the European Union, in collaboration with the World Bank and the Global Facility for Disaster Reduction and Recovery (GFDRR), launched the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) regional programme. Within this framework, a consortium of national and international scientific institutions was established and tasked with developing a regionally consistent multi-hazard and multi-asset probabilistic risk assessment. The overall goal was to improve scientific understanding on local perils and to provide local stakeholders and governments with up-to-date tools to support risk management strategies. However, the development of a comprehensive risk model can only be done with the basis of an accurate hazard evaluation, the reliability of which depends significantly on the availability of local data and direct observations. This paper describes the preparation of the input datasets required for the implementation of a probabilistic earthquake model for the Central Asian countries. In particular, it discusses the preparation of a new regional earthquake catalogue harmonized between countries and homogenized in moment magnitude (Mw), as well as the preparation of a regional database of selected active faults with associated slip rate information to be used for the construction of the earthquake source model. The work was carried out in collaboration with experts from the local scientific community, whose contribution proved essential for the rational compilation of the two harmonized datasets.
{"title":"Harmonizing seismicity information in Central Asian countries: earthquake catalogue and active faults","authors":"Valerio Poggi, S. Parolai, N. Silacheva, A. Ischuk, K. Abdrakhmatov, Zainalobudin V. Kobuliev, V. Ismailov, Roman Ibragimov, Japar Karaev, Paola Ceresa, P. Bazzurro","doi":"10.5194/nhess-24-2597-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-2597-2024","url":null,"abstract":"Abstract. Central Asian countries, which include Kazakhstan, the Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan, are known to be highly exposed to natural hazards, particularly earthquakes, floods, and landslides. With the aim of enhancing financial resilience and risk-based investment, planning to promote disaster and climate resilience in Central Asia, the European Union, in collaboration with the World Bank and the Global Facility for Disaster Reduction and Recovery (GFDRR), launched the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) regional programme. Within this framework, a consortium of national and international scientific institutions was established and tasked with developing a regionally consistent multi-hazard and multi-asset probabilistic risk assessment. The overall goal was to improve scientific understanding on local perils and to provide local stakeholders and governments with up-to-date tools to support risk management strategies. However, the development of a comprehensive risk model can only be done with the basis of an accurate hazard evaluation, the reliability of which depends significantly on the availability of local data and direct observations. This paper describes the preparation of the input datasets required for the implementation of a probabilistic earthquake model for the Central Asian countries. In particular, it discusses the preparation of a new regional earthquake catalogue harmonized between countries and homogenized in moment magnitude (Mw), as well as the preparation of a regional database of selected active faults with associated slip rate information to be used for the construction of the earthquake source model. The work was carried out in collaboration with experts from the local scientific community, whose contribution proved essential for the rational compilation of the two harmonized datasets.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"39 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141800227","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 : 2024-07-25DOI: 10.5194/nhess-24-2541-2024
Raphael Portmann, Timo Schmid, Leonie Villiger, D. Bresch, Pierluigi Calanca
Abstract. Hail represents a major threat to agriculture in Switzerland, and assessments of current and future hail risk are of paramount importance for decision-making in the insurance industry and the agricultural sector. However, relating observational information on hail with crop-specific damage is challenging. Here, we build and systematically assess an open-source model to predict hail damage footprints for field crops (wheat, maize, barley, rapeseed) and grapevine from the operational radar product Maximum Expected Severe Hail Size (MESHS) at different spatial resolutions. To this end, we combine the radar information with detailed geospatial information on agricultural land use and geo-referenced damage data from a crop insurer for 12 recent hail events in Switzerland. We find that for field crops model skill gradually increases when the spatial resolution is reduced from 1 km down to 8 km. For even lower resolutions, the skill is diminished again. In contrast, for grapevine, decreasing model resolution below 1 km tends to reduce skill, which is attributed to the different spatial distribution of field crops and grapevine in the landscape. It is shown that identifying a suitable MESHS thresholds to model damage footprints always involves trade-offs. For the lowest possible MESHS threshold (20 mm) the model predicts damage about twice as often as observed (high frequency bias and false alarm ratio), but it also has a high probability of detection (80 %). The frequency bias decreases for larger thresholds and reaches an optimal value close to 1 for MESHS thresholds of 30–40 mm. However, this comes at the cost of a substantially lower probability of detection (around 50 %), while overall model skill, as measured by the Heidke skill score (HSS), remains largely unchanged (0.41–0.44). We argue that, ultimately, the best threshold therefore depends on the relative costs of a false alarm versus a missed event. Finally, the frequency of false alarms is substantially reduced and skill is improved (HSS = 0.54) when only areas with high cropland density are considered. Results from this simple, open-source model show that modelling of hail damage footprints to crops from single-polarization radar in Switzerland is skilful and is best done at 8 km resolution for field crops and 1 km for grapevine.
{"title":"Modelling crop hail damage footprints with single-polarization radar: the roles of spatial resolution, hail intensity, and cropland density","authors":"Raphael Portmann, Timo Schmid, Leonie Villiger, D. Bresch, Pierluigi Calanca","doi":"10.5194/nhess-24-2541-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-2541-2024","url":null,"abstract":"Abstract. Hail represents a major threat to agriculture in Switzerland, and assessments of current and future hail risk are of paramount importance for decision-making in the insurance industry and the agricultural sector. However, relating observational information on hail with crop-specific damage is challenging. Here, we build and systematically assess an open-source model to predict hail damage footprints for field crops (wheat, maize, barley, rapeseed) and grapevine from the operational radar product Maximum Expected Severe Hail Size (MESHS) at different spatial resolutions. To this end, we combine the radar information with detailed geospatial information on agricultural land use and geo-referenced damage data from a crop insurer for 12 recent hail events in Switzerland. We find that for field crops model skill gradually increases when the spatial resolution is reduced from 1 km down to 8 km. For even lower resolutions, the skill is diminished again. In contrast, for grapevine, decreasing model resolution below 1 km tends to reduce skill, which is attributed to the different spatial distribution of field crops and grapevine in the landscape. It is shown that identifying a suitable MESHS thresholds to model damage footprints always involves trade-offs. For the lowest possible MESHS threshold (20 mm) the model predicts damage about twice as often as observed (high frequency bias and false alarm ratio), but it also has a high probability of detection (80 %). The frequency bias decreases for larger thresholds and reaches an optimal value close to 1 for MESHS thresholds of 30–40 mm. However, this comes at the cost of a substantially lower probability of detection (around 50 %), while overall model skill, as measured by the Heidke skill score (HSS), remains largely unchanged (0.41–0.44). We argue that, ultimately, the best threshold therefore depends on the relative costs of a false alarm versus a missed event. Finally, the frequency of false alarms is substantially reduced and skill is improved (HSS = 0.54) when only areas with high cropland density are considered. Results from this simple, open-source model show that modelling of hail damage footprints to crops from single-polarization radar in Switzerland is skilful and is best done at 8 km resolution for field crops and 1 km for grapevine.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"31 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806281","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 : 2024-07-25DOI: 10.5194/nhess-24-2559-2024
Helge Bormann, Jenny Kebschull, Lidia Gaslikova, R. Weisse
Abstract. In addition to storm tides, inland flooding due to intense rainfall has become an increasing threat at coastal lowlands. In particular, the coincidence of both types of events poses great challenges to regional water boards since their technical drainage capacities are limited. In this study, we analysed historical data and scenario-based simulations for gauge Knock near Emden at the German North Sea coast. The evaluation of observed inland flood events shows that mainly moderate storm tide series in combination with large-scale, intense precipitation led to an overload of inland drainage systems, whereas the highest individual storm tides or precipitation events alone could be handled well. Proactive risk management requires climate projections for the future. Therefore, a hydrological and a hydrodynamic ocean model were set up and driven by the same climate simulations to estimate future drainage system overloads. The evaluation of the simulations for the control period of two climate models confirms that the models can reproduce the generation mechanism of the compound events. The coincidence of storm tides and precipitation leads to the highest drainage system overloads, while system overload is also caused by intense rainfall events alone rather than by storm tides without intense precipitation. Scenario projections based on two climate models and two emission scenarios suggest that the intensity of compound events of rainfall and storm tides will increase consistently against the background of mean sea level rise for all investigated climate projections, while simulated system overload is higher for the RCP8.5 scenario compared to the RCP2.6 scenario. Comparable to the past, future compound events will cause more potential damage compared to single extreme events. The model results indicate an increasing frequency and intensity of inland drainage system overloads along the North Sea coast if timely adaptation measures are not taken.
{"title":"Model-based assessment of climate change impact on inland flood risk at the German North Sea coast caused by compounding storm tide and precipitation events","authors":"Helge Bormann, Jenny Kebschull, Lidia Gaslikova, R. Weisse","doi":"10.5194/nhess-24-2559-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-2559-2024","url":null,"abstract":"Abstract. In addition to storm tides, inland flooding due to intense rainfall has become an increasing threat at coastal lowlands. In particular, the coincidence of both types of events poses great challenges to regional water boards since their technical drainage capacities are limited. In this study, we analysed historical data and scenario-based simulations for gauge Knock near Emden at the German North Sea coast. The evaluation of observed inland flood events shows that mainly moderate storm tide series in combination with large-scale, intense precipitation led to an overload of inland drainage systems, whereas the highest individual storm tides or precipitation events alone could be handled well. Proactive risk management requires climate projections for the future. Therefore, a hydrological and a hydrodynamic ocean model were set up and driven by the same climate simulations to estimate future drainage system overloads. The evaluation of the simulations for the control period of two climate models confirms that the models can reproduce the generation mechanism of the compound events. The coincidence of storm tides and precipitation leads to the highest drainage system overloads, while system overload is also caused by intense rainfall events alone rather than by storm tides without intense precipitation. Scenario projections based on two climate models and two emission scenarios suggest that the intensity of compound events of rainfall and storm tides will increase consistently against the background of mean sea level rise for all investigated climate projections, while simulated system overload is higher for the RCP8.5 scenario compared to the RCP2.6 scenario. Comparable to the past, future compound events will cause more potential damage compared to single extreme events. The model results indicate an increasing frequency and intensity of inland drainage system overloads along the North Sea coast if timely adaptation measures are not taken.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"49 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805198","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 : 2024-07-24DOI: 10.5194/nhess-24-2523-2024
Tandin Wangchuk, R. Tsubaki
Abstract. The melting of glaciers has led to an unprecedented increase in the number and size of glacial lakes, particularly in the Himalayan region. A glacial lake outburst flood (GLOF) is a natural hazard in which water from a glacial or glacier-fed lake is swiftly discharged. GLOFs can significantly harm life, infrastructure, and settlements located downstream and can have considerable ecological, economic, and social impacts. Based on a dam breach model, BREACH, and a hydrodynamic model, HEC-RAS (Hydrologic Engineering Centre's River Analysis System), we examined the potential consequences of a GLOF originating from the Thorthomi glacial lake, located within the Phochhu river basin, one of Bhutan's largest and rapidly expanding glacial lakes. Our analysis revealed that following a breach the Thorthomi glacial lake will likely discharge a peak flow of 16 360 m3 s−1 within 4 h. Such a discharge could potentially cause considerable damage, with an estimated 245 ha of agricultural land and over 1277 buildings at risk of inundation. To mitigate ecological, economic, and social impacts on downstream areas, our results emphasise an urgent need for understanding and preparing for the potential consequences of a GLOF from Thorthomi lake. Our findings provide valuable insights for policymakers and stakeholders involved in disaster management and preparedness.
{"title":"A glacial lake outburst flood risk assessment for the Phochhu river basin, Bhutan","authors":"Tandin Wangchuk, R. Tsubaki","doi":"10.5194/nhess-24-2523-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-2523-2024","url":null,"abstract":"Abstract. The melting of glaciers has led to an unprecedented increase in the number and size of glacial lakes, particularly in the Himalayan region. A glacial lake outburst flood (GLOF) is a natural hazard in which water from a glacial or glacier-fed lake is swiftly discharged. GLOFs can significantly harm life, infrastructure, and settlements located downstream and can have considerable ecological, economic, and social impacts. Based on a dam breach model, BREACH, and a hydrodynamic model, HEC-RAS (Hydrologic Engineering Centre's River Analysis System), we examined the potential consequences of a GLOF originating from the Thorthomi glacial lake, located within the Phochhu river basin, one of Bhutan's largest and rapidly expanding glacial lakes. Our analysis revealed that following a breach the Thorthomi glacial lake will likely discharge a peak flow of 16 360 m3 s−1 within 4 h. Such a discharge could potentially cause considerable damage, with an estimated 245 ha of agricultural land and over 1277 buildings at risk of inundation. To mitigate ecological, economic, and social impacts on downstream areas, our results emphasise an urgent need for understanding and preparing for the potential consequences of a GLOF from Thorthomi lake. Our findings provide valuable insights for policymakers and stakeholders involved in disaster management and preparedness.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"59 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809173","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 : 2024-07-23DOI: 10.5194/nhess-24-2511-2024
D. Poelman, Hannes Kohlmann, Wolfgang Schulz
Abstract. Evaluating the risk of lightning strikes to a particular structure typically involves adhering to the guidance outlined in IEC 62305-2. Among the multitude of factors influencing the overall risk, flash density emerges as a crucial parameter. According to its definition, each flash is assigned only one contact point to ground. Nevertheless, it is well known that, on average, flashes exhibit multiple ground termination points as shown by high-speed camera observations. In this research, lightning data collected by the European Cooperation for Lightning Detection (EUCLID) network are utilized in combination with a ground strike point (GSP) algorithm that aggregates individual strokes within a flash into ground strike points. This approach enables the examination of spatio-temporal patterns of GSPs across Europe throughout a decade, spanning from 2013 to 2022. Average GSP densities exhibit variations across the European continent, mirroring the observed patterns in flash densities. The highest densities are concentrated along the Adriatic Sea and the western Balkan region, reaching peak values of up to 8.5 GSPs km−2 yr−1. The spatial distribution of the mean number of ground strike points per flash reveals a noticeable increase in the Mediterranean, Adriatic, and Baltic Sea regions compared to inland areas. Moreover, it has been determined that the average number of GSPs per flash reaches its peak between September and November. Additionally, a daily pattern is discernible, with the lowest number of GSPs per flash occurring between 12:00 and 18:00 UTC (universal time coordinated). It is found that 95 % of the separation distances between distinct GSPs are less than 6.7 km. Lastly, it is worth noting that the presence of the Alps has an impact on GSP behaviour, resulting in lower GSP counts in comparison to the surrounding areas, along with the shortest average distances between different GSPs.
{"title":"Insights into ground strike point properties in Europe through the EUCLID lightning location system","authors":"D. Poelman, Hannes Kohlmann, Wolfgang Schulz","doi":"10.5194/nhess-24-2511-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-2511-2024","url":null,"abstract":"Abstract. Evaluating the risk of lightning strikes to a particular structure typically involves adhering to the guidance outlined in IEC 62305-2. Among the multitude of factors influencing the overall risk, flash density emerges as a crucial parameter. According to its definition, each flash is assigned only one contact point to ground. Nevertheless, it is well known that, on average, flashes exhibit multiple ground termination points as shown by high-speed camera observations. In this research, lightning data collected by the European Cooperation for Lightning Detection (EUCLID) network are utilized in combination with a ground strike point (GSP) algorithm that aggregates individual strokes within a flash into ground strike points. This approach enables the examination of spatio-temporal patterns of GSPs across Europe throughout a decade, spanning from 2013 to 2022. Average GSP densities exhibit variations across the European continent, mirroring the observed patterns in flash densities. The highest densities are concentrated along the Adriatic Sea and the western Balkan region, reaching peak values of up to 8.5 GSPs km−2 yr−1. The spatial distribution of the mean number of ground strike points per flash reveals a noticeable increase in the Mediterranean, Adriatic, and Baltic Sea regions compared to inland areas. Moreover, it has been determined that the average number of GSPs per flash reaches its peak between September and November. Additionally, a daily pattern is discernible, with the lowest number of GSPs per flash occurring between 12:00 and 18:00 UTC (universal time coordinated). It is found that 95 % of the separation distances between distinct GSPs are less than 6.7 km. Lastly, it is worth noting that the presence of the Alps has an impact on GSP behaviour, resulting in lower GSP counts in comparison to the surrounding areas, along with the shortest average distances between different GSPs.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"85 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141812632","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 : 2024-07-22DOI: 10.5194/nhess-24-2495-2024
Nicola Loglisci, Giorgio Boni, A. Cauteruccio, F. Faccini, Massimo Milelli, G. Paliaga, Antonio Parodi
Abstract. Climate change in the Mediterranean region is manifesting itself as an increase in average air temperature and a change in the rainfall regime: the value of cumulative annual rainfall generally appears to be constant, but the intensity of annual rainfall maxima, between 1 and 24 h, is increasing, especially in the period between late summer and early autumn. The associated ground effects in urban areas consist of flash floods and pluvial floods, often in very small areas, depending on the physical-geographical layout of the region. In the context of global warming, it is therefore important to have an adequate monitoring network for rain events that are highly concentrated in space and time. This research analyses the meteo-hydrological features of the 27 and 28 August 2023 event that occurred in the city of Genoa, Italy, just 4 d after the record maximum air temperature was recorded: between 19:00 and 02:00 UTC almost 400 mm of rainfall was recorded in the eastern sector of the historic centre of Genoa, with significant ground effects such as flooding episodes and the overflowing of pressurised culverts. Rainfall observations and estimates were made using both official or “authoritative” networks (rain gauges and meteorological radar) and rain gauge networks inspired by citizen science principles. The combined analysis of observations from authoritative and citizen science networks reveals, for the event analysed, a spatial variability of the precipitation field at an hourly and a sub-hourly timescale that cannot be captured by the current spatial density of the authoritative measurement stations (which have one of the highest densities in Italy). Monthly total rainfall and short-duration annual maximum time series recorded by the authoritative rain gauge network of the Genoa area are then analysed. The results show significant variation even at distances of less than 2 km in the average rainfall depth accumulated over sub-hourly duration. Extreme weather monitoring activity is confirmed as one of the most important aspects in terms of flood prevention and protection in urban areas. The integration between authoritative and citizen science networks can prove to be a valid contribution to the monitoring of extreme events.
{"title":"The role of citizen science in assessing the spatiotemporal pattern of rainfall events in urban areas: a case study in the city of Genoa, Italy","authors":"Nicola Loglisci, Giorgio Boni, A. Cauteruccio, F. Faccini, Massimo Milelli, G. Paliaga, Antonio Parodi","doi":"10.5194/nhess-24-2495-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-2495-2024","url":null,"abstract":"Abstract. Climate change in the Mediterranean region is manifesting itself as an increase in average air temperature and a change in the rainfall regime: the value of cumulative annual rainfall generally appears to be constant, but the intensity of annual rainfall maxima, between 1 and 24 h, is increasing, especially in the period between late summer and early autumn. The associated ground effects in urban areas consist of flash floods and pluvial floods, often in very small areas, depending on the physical-geographical layout of the region. In the context of global warming, it is therefore important to have an adequate monitoring network for rain events that are highly concentrated in space and time. This research analyses the meteo-hydrological features of the 27 and 28 August 2023 event that occurred in the city of Genoa, Italy, just 4 d after the record maximum air temperature was recorded: between 19:00 and 02:00 UTC almost 400 mm of rainfall was recorded in the eastern sector of the historic centre of Genoa, with significant ground effects such as flooding episodes and the overflowing of pressurised culverts. Rainfall observations and estimates were made using both official or “authoritative” networks (rain gauges and meteorological radar) and rain gauge networks inspired by citizen science principles. The combined analysis of observations from authoritative and citizen science networks reveals, for the event analysed, a spatial variability of the precipitation field at an hourly and a sub-hourly timescale that cannot be captured by the current spatial density of the authoritative measurement stations (which have one of the highest densities in Italy). Monthly total rainfall and short-duration annual maximum time series recorded by the authoritative rain gauge network of the Genoa area are then analysed. The results show significant variation even at distances of less than 2 km in the average rainfall depth accumulated over sub-hourly duration. Extreme weather monitoring activity is confirmed as one of the most important aspects in terms of flood prevention and protection in urban areas. The integration between authoritative and citizen science networks can prove to be a valid contribution to the monitoring of extreme events.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"19 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141815828","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 : 2024-07-19DOI: 10.5194/nhess-24-2461-2024
Mithun Deb, James J. Benedict, Ning Sun, Zhaoqing Yang, R. Hetland, D. Judi, Taiping Wang
Abstract. Based on the projected increase in hurricane landfall frequency on the middle to lower US east coast, we examined the crucial role of the estuarine wind field in exacerbating coastal flooding. A regionally refined atmospheric and two high-resolution hydrology and ocean models are integrated to provide plausible and physically consistent ensembles of hurricane events and the associated flooding inside the Delaware Bay and River, a US mid-Atlantic estuary. Model results show that the hurricane propagation direction, estuarine geometry, remote surge from the open ocean, and direct nearshore upwind stress could magnify the flood magnitude. More specifically, inland-bound tracks that make landfall before reaching the mid-Atlantic coast produce a more significant surge within Delaware Bay than the shore-parallel tracks, where the estuarine wind direction plays the primary role in surge amplification. Ultimately, this study emphasized the need for integrated models to capture the nonlinear dynamics and interactions in flood hazard modeling.
{"title":"Estuarine hurricane wind can intensify surge-dominated extreme water level in shallow and converging coastal systems","authors":"Mithun Deb, James J. Benedict, Ning Sun, Zhaoqing Yang, R. Hetland, D. Judi, Taiping Wang","doi":"10.5194/nhess-24-2461-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-2461-2024","url":null,"abstract":"Abstract. Based on the projected increase in hurricane landfall frequency on the middle to lower US east coast, we examined the crucial role of the estuarine wind field in exacerbating coastal flooding. A regionally refined atmospheric and two high-resolution hydrology and ocean models are integrated to provide plausible and physically consistent ensembles of hurricane events and the associated flooding inside the Delaware Bay and River, a US mid-Atlantic estuary. Model results show that the hurricane propagation direction, estuarine geometry, remote surge from the open ocean, and direct nearshore upwind stress could magnify the flood magnitude. More specifically, inland-bound tracks that make landfall before reaching the mid-Atlantic coast produce a more significant surge within Delaware Bay than the shore-parallel tracks, where the estuarine wind direction plays the primary role in surge amplification. Ultimately, this study emphasized the need for integrated models to capture the nonlinear dynamics and interactions in flood hazard modeling.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"113 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141822140","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 : 2024-07-19DOI: 10.5194/nhess-24-2481-2024
Ming‐Huei Chang, Yen-Chen Huang, Yu‐Hsin Cheng, C. Terng, Jinyi Chen, Jyh Cherng Jan
Abstract. Global warming has enduring consequences in the ocean, leading to increased sea surface temperatures (SSTs) and subsequent environmental impacts, including coral bleaching and intensified tropical storms. It is imperative to monitor these trends to enable informed decision-making and adaptation. In this study, we comprehensively examine the methods for extracting long-term temperature trends, including STL, seasonal-trend decomposition procedure based on LOESS (locally estimated scatterplot smoothing), and the linear regression family, which comprises the ordinary least-squares regression (OLSR), orthogonal regression (OR), and geometric-mean regression (GMR). The applicability and limitations of these methods are assessed based on experimental and simulated data. STL may stand out as the most accurate method for extracting long-term trends. However, it is associated with notably sizable computational time. In contrast, linear regression methods are far more efficient. Among these methods, GMR is not suitable due to its inherent assumption of a random temporal component. OLSR and OR are preferable for general tasks but require correction to accurately account for seasonal signal-induced bias resulting from the phase–distance imbalance. We observe that this bias can be effectively addressed by trimming the SST data to ensure that the time series becomes an even function before applying linear regression, which is named “evenization”. We compare our methods with two commonly used methods in the climate community. Our proposed method is unbiased and better than the conventional SST anomaly method. While our method may have a larger degree of uncertainty than combined linear and sinusoidal fitting, this uncertainty remains within an acceptable range. Furthermore, linear and sinusoidal fitting can be unstable when applied to natural data containing significant noise.
{"title":"Revisiting regression methods for estimating long-term trends in sea surface temperature","authors":"Ming‐Huei Chang, Yen-Chen Huang, Yu‐Hsin Cheng, C. Terng, Jinyi Chen, Jyh Cherng Jan","doi":"10.5194/nhess-24-2481-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-2481-2024","url":null,"abstract":"Abstract. Global warming has enduring consequences in the ocean, leading to increased sea surface temperatures (SSTs) and subsequent environmental impacts, including coral bleaching and intensified tropical storms. It is imperative to monitor these trends to enable informed decision-making and adaptation. In this study, we comprehensively examine the methods for extracting long-term temperature trends, including STL, seasonal-trend decomposition procedure based on LOESS (locally estimated scatterplot smoothing), and the linear regression family, which comprises the ordinary least-squares regression (OLSR), orthogonal regression (OR), and geometric-mean regression (GMR). The applicability and limitations of these methods are assessed based on experimental and simulated data. STL may stand out as the most accurate method for extracting long-term trends. However, it is associated with notably sizable computational time. In contrast, linear regression methods are far more efficient. Among these methods, GMR is not suitable due to its inherent assumption of a random temporal component. OLSR and OR are preferable for general tasks but require correction to accurately account for seasonal signal-induced bias resulting from the phase–distance imbalance. We observe that this bias can be effectively addressed by trimming the SST data to ensure that the time series becomes an even function before applying linear regression, which is named “evenization”. We compare our methods with two commonly used methods in the climate community. Our proposed method is unbiased and better than the conventional SST anomaly method. While our method may have a larger degree of uncertainty than combined linear and sinusoidal fitting, this uncertainty remains within an acceptable range. Furthermore, linear and sinusoidal fitting can be unstable when applied to natural data containing significant noise.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":" 769","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141823318","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 : 2024-07-18DOI: 10.5194/nhess-24-2441-2024
E. Agayar, F. Aemisegger, Moshe Armon, Alexander Scherrmann, H. Wernli
Abstract. Understanding extreme precipitation events (EPEs) and their underlying dynamical processes and moisture transport patterns is essential to mitigating EPE-related risks. In this study, we investigate the dynamics of 82 EPEs (≥100 mm d−1) over the territory of Ukraine in the recent decades (1979–2019), of which the majority occurred in summer. The EPEs are identified based on precipitation observations from 215 meteorological stations and posts in Ukraine. The atmospheric variables for the case study analysis of selected EPEs and for climatological composites and trajectory calculations were taken from ERA5 reanalyses. Moisture sources contributing to the EPEs in Ukraine are identified with kinematic backward trajectories and the subsequent application of a moisture source identification scheme based on the humidity mass budget along these trajectories. The large-scale atmospheric circulation associated with EPEs was studied for a selection of representative EPEs in all seasons and with the aid of composites of all events per season. Results show that EPEs in summer occur all across Ukraine, but in other seasons EPE hotspots are mainly in the Carpathians and along the Black Sea and Sea of Azov. All EPEs were associated with a surface cyclone, with most having an upper-level trough, except for the winter events that occurred in situations with very strong westerly jets. Isentropic potential vorticity anomalies associated with EPEs in Ukraine show clear dipole structures in all seasons, however, interestingly with a different orientation of these anomaly dipoles between seasons. The analysis of moisture sources revealed a very strong case-to-case variability and often a combination of local and remote sources. Oceanic sources dominate in winter, but land evapotranspiration accounts for 60 %–80 % of the moisture that rains out in EPEs in the other seasons. Taken together, these findings provide a novel insight into large-scale characteristics of EPEs in Ukraine, a region with a unique geographical setting and with moisture sources as diverse as Newfoundland, the Azores, the Caspian Sea, and the Arctic Ocean.
{"title":"Precipitation extremes in Ukraine from 1979 to 2019: climatology, large-scale flow conditions, and moisture sources","authors":"E. Agayar, F. Aemisegger, Moshe Armon, Alexander Scherrmann, H. Wernli","doi":"10.5194/nhess-24-2441-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-2441-2024","url":null,"abstract":"Abstract. Understanding extreme precipitation events (EPEs) and their underlying dynamical processes and moisture transport patterns is essential to mitigating EPE-related risks. In this study, we investigate the dynamics of 82 EPEs (≥100 mm d−1) over the territory of Ukraine in the recent decades (1979–2019), of which the majority occurred in summer. The EPEs are identified based on precipitation observations from 215 meteorological stations and posts in Ukraine. The atmospheric variables for the case study analysis of selected EPEs and for climatological composites and trajectory calculations were taken from ERA5 reanalyses. Moisture sources contributing to the EPEs in Ukraine are identified with kinematic backward trajectories and the subsequent application of a moisture source identification scheme based on the humidity mass budget along these trajectories. The large-scale atmospheric circulation associated with EPEs was studied for a selection of representative EPEs in all seasons and with the aid of composites of all events per season. Results show that EPEs in summer occur all across Ukraine, but in other seasons EPE hotspots are mainly in the Carpathians and along the Black Sea and Sea of Azov. All EPEs were associated with a surface cyclone, with most having an upper-level trough, except for the winter events that occurred in situations with very strong westerly jets. Isentropic potential vorticity anomalies associated with EPEs in Ukraine show clear dipole structures in all seasons, however, interestingly with a different orientation of these anomaly dipoles between seasons. The analysis of moisture sources revealed a very strong case-to-case variability and often a combination of local and remote sources. Oceanic sources dominate in winter, but land evapotranspiration accounts for 60 %–80 % of the moisture that rains out in EPEs in the other seasons. Taken together, these findings provide a novel insight into large-scale characteristics of EPEs in Ukraine, a region with a unique geographical setting and with moisture sources as diverse as Newfoundland, the Azores, the Caspian Sea, and the Arctic Ocean.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141826007","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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