Pub Date : 2024-07-01DOI: 10.5194/nhess-24-2215-2024
A. Amengual, Romualdo Romero, M. Llasat, A. Hermoso, M. Llasat-Botija
Abstract. On 22 October 2019, the Francolí River basin in Catalonia, north-eastern Spain, experienced a heavy precipitation event that resulted in a catastrophic flash flood, causing six fatalities. Few studies comprehensively address both the physical and human dimensions and their interrelations during extreme flash flooding. This research takes a step forward towards filling this gap in knowledge by examining the alignment among all these factors. The hydrometeorological factors are investigated using the new Triangle-based Regional Atmospheric Model, radar-derived precipitation estimates, post-flood field and gauge observations, and the Kinematic Local Excess Model. The social dimension is assessed by examining the relationship between catchment dynamics and warning response times and by quantifying human behaviour during the course of the flash flood through a post-event citizen science campaign. Results reveal that a persistent south-easterly airflow brought low-level moisture and established convective instability in the region, while local orography was instrumental to triggering deep moist convection. A convective train promoted intense, copious, and prolonged precipitation over the north-western catchment headwaters. Basin response was significantly modulated by the very dry initial soil moisture conditions. After the long-lasting rainfall, an acute burst of precipitation resulted in extreme flash flooding. Fast and abrupt increases in streamflow affect small spatial scales and leave limited time for the effective implementation of protective measures. The institutional organization–protection–prevention cycle unfolded at the spatial and temporal scales typically dominated by the meteorological rather than hydrological scales. Although the citizen science campaign reveals the effectiveness of the warnings in reaching the population living in the most affected areas, a significant proportion of the respondents expressed a lack of adequate information or were unfamiliar with the intended meaning. In addition, a majority of the interviewees did not perceive any significant threat to life or property. In view of these results, this study identifies potential areas for improving social preparedness for similar natural hazards in the future.
{"title":"Hydrometeorological controls of and social response to the 22 October 2019 catastrophic flash flood in Catalonia, north-eastern Spain","authors":"A. Amengual, Romualdo Romero, M. Llasat, A. Hermoso, M. Llasat-Botija","doi":"10.5194/nhess-24-2215-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-2215-2024","url":null,"abstract":"Abstract. On 22 October 2019, the Francolí River basin in Catalonia, north-eastern Spain, experienced a heavy precipitation event that resulted in a catastrophic flash flood, causing six fatalities. Few studies comprehensively address both the physical and human dimensions and their interrelations during extreme flash flooding. This research takes a step forward towards filling this gap in knowledge by examining the alignment among all these factors. The hydrometeorological factors are investigated using the new Triangle-based Regional Atmospheric Model, radar-derived precipitation estimates, post-flood field and gauge observations, and the Kinematic Local Excess Model. The social dimension is assessed by examining the relationship between catchment dynamics and warning response times and by quantifying human behaviour during the course of the flash flood through a post-event citizen science campaign. Results reveal that a persistent south-easterly airflow brought low-level moisture and established convective instability in the region, while local orography was instrumental to triggering deep moist convection. A convective train promoted intense, copious, and prolonged precipitation over the north-western catchment headwaters. Basin response was significantly modulated by the very dry initial soil moisture conditions. After the long-lasting rainfall, an acute burst of precipitation resulted in extreme flash flooding. Fast and abrupt increases in streamflow affect small spatial scales and leave limited time for the effective implementation of protective measures. The institutional organization–protection–prevention cycle unfolded at the spatial and temporal scales typically dominated by the meteorological rather than hydrological scales. Although the citizen science campaign reveals the effectiveness of the warnings in reaching the population living in the most affected areas, a significant proportion of the respondents expressed a lack of adequate information or were unfamiliar with the intended meaning. In addition, a majority of the interviewees did not perceive any significant threat to life or property. In view of these results, this study identifies potential areas for improving social preparedness for similar natural hazards in the future.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"61 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141710225","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 : 2024-07-01DOI: 10.5194/nhess-24-2175-2024
M. Ghanavati, Ian Young, Ebru Kirezci, Jin Liu
Abstract. Numerous studies have demonstrated that significant global changes in wave and storm surge conditions have occurred over recent decades and are expected to continue out to at least 2100. This raises the question of whether the observed and projected changes in waves and storm surges will impact coastlines in the future. Previous global-scale analyses of these issues have been inconclusive. This study investigates the south-east coast of Australia over a period of 26 years (1988–2013). Over this period, this area has experienced some of the largest changes in wave climate of any coastal region globally. The analysis uses high-resolution hindcast data of waves and storm surge together with satellite observations of shoreline change. All datasets have been previously extensively validated against in situ measurements. The data are analysed to determine trends in each of these quantities over this period. The coastline is partitioned into regions and spatial consistency between trends in each of the quantities investigated. The results show that beaches along this region appear to have responded to the increases in wave energy flux and changes in wave direction. This has enhanced non-equilibrium longshore drift. Long sections of the coastline show small but measurable recession before sediment transported along the coast is intercepted by prominent headlands. The recession is largest where there are strong trends in increasing wave energy flux and/or changes in wave direction, with recession rates of up to 1 m yr−1. Although this is a regional study, this finding has global implications for shoreline stability in a changing climate.
{"title":"The impact of long-term changes in ocean waves and storm surge on coastal shoreline change: a case study of Bass Strait and south-east Australia","authors":"M. Ghanavati, Ian Young, Ebru Kirezci, Jin Liu","doi":"10.5194/nhess-24-2175-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-2175-2024","url":null,"abstract":"Abstract. Numerous studies have demonstrated that significant global changes in wave and storm surge conditions have occurred over recent decades and are expected to continue out to at least 2100. This raises the question of whether the observed and projected changes in waves and storm surges will impact coastlines in the future. Previous global-scale analyses of these issues have been inconclusive. This study investigates the south-east coast of Australia over a period of 26 years (1988–2013). Over this period, this area has experienced some of the largest changes in wave climate of any coastal region globally. The analysis uses high-resolution hindcast data of waves and storm surge together with satellite observations of shoreline change. All datasets have been previously extensively validated against in situ measurements. The data are analysed to determine trends in each of these quantities over this period. The coastline is partitioned into regions and spatial consistency between trends in each of the quantities investigated. The results show that beaches along this region appear to have responded to the increases in wave energy flux and changes in wave direction. This has enhanced non-equilibrium longshore drift. Long sections of the coastline show small but measurable recession before sediment transported along the coast is intercepted by prominent headlands. The recession is largest where there are strong trends in increasing wave energy flux and/or changes in wave direction, with recession rates of up to 1 m yr−1. Although this is a regional study, this finding has global implications for shoreline stability in a changing climate.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"277 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141692186","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 : 2024-07-01DOI: 10.5194/nhess-24-2191-2024
K. Rahman, S. Shang, Khaled S. Balkhair, H. Gabriel, K. Jadoon, Kifayat Zaman
Abstract. The impact of drought on environmental flow (EF) in 27 catchments of the Indus Basin is studied from 1980–2018 using indicators of hydrologic alterations (IHAs). The standardized precipitation evapotranspiration index (SPEI) was systematically propagated from one catchment to another using principal component analysis (PCA). Threshold regression is used to determine the severity of drought (scenario 1, drought severity that causes low flows) and the month (scenario 2, months where drought has resulted in low flows) that trigger low flows in the Indus Basin. The impact of drought on low EFs is quantified using range of variability analysis (RVA), which is an integrated component of the IHA used to study the hydrological alterations in environmental flow components (EFCs) by comparing the pre- and post-impact periods of human and/or climate interventions in EFCs. The hydrological alteration factor (HAF) is calculated for each catchment in the Indus Basin. The results show that most of the catchments were vulnerable to drought during the periods of 1984 to 1986, 1991/1992, 1997 to 2003, 2007 to 2008, 2012 to 2013, and 2017 to 2018. On a longer timescale (SPEI-12), drought is more severe in the lower Indus Basin (LIB) than in the upper Indus Basin (UIB). The IHA pointed out that drought significantly impacts the distribution of EFCs, particularly extremely low flow (ELF) and low flow (LF). The magnitude and frequency of the ELF and LF events increase as drought severity increases. The threshold regression provided useful insights, indicating that moderate drought can trigger ELF and LF at shorter timescales (SPEI-1 and SPEI-6) in the UIB and middle Indus Basin (MIB). Conversely, severe and extreme droughts trigger ELF and LF at longer timescales (SPEI-12) in the LIB. The threshold regression also divided the entire study period (1980–2018) into different time periods (scenario 2), which is useful for quantifying the impact of drought on low EFs using the SPEI coefficient. Higher SPEI coefficients are observed in the LIB, indicating high alterations in EF due to drought. HAF showed high alterations in EF in most of the catchments throughout the year except in August and September. Overall, this study provided useful insights for analysing the effects of drought on EF, especially during low flows.
{"title":"Catchment-scale assessment of drought impact on environmental flow in the Indus Basin, Pakistan","authors":"K. Rahman, S. Shang, Khaled S. Balkhair, H. Gabriel, K. Jadoon, Kifayat Zaman","doi":"10.5194/nhess-24-2191-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-2191-2024","url":null,"abstract":"Abstract. The impact of drought on environmental flow (EF) in 27 catchments of the Indus Basin is studied from 1980–2018 using indicators of hydrologic alterations (IHAs). The standardized precipitation evapotranspiration index (SPEI) was systematically propagated from one catchment to another using principal component analysis (PCA). Threshold regression is used to determine the severity of drought (scenario 1, drought severity that causes low flows) and the month (scenario 2, months where drought has resulted in low flows) that trigger low flows in the Indus Basin. The impact of drought on low EFs is quantified using range of variability analysis (RVA), which is an integrated component of the IHA used to study the hydrological alterations in environmental flow components (EFCs) by comparing the pre- and post-impact periods of human and/or climate interventions in EFCs. The hydrological alteration factor (HAF) is calculated for each catchment in the Indus Basin. The results show that most of the catchments were vulnerable to drought during the periods of 1984 to 1986, 1991/1992, 1997 to 2003, 2007 to 2008, 2012 to 2013, and 2017 to 2018. On a longer timescale (SPEI-12), drought is more severe in the lower Indus Basin (LIB) than in the upper Indus Basin (UIB). The IHA pointed out that drought significantly impacts the distribution of EFCs, particularly extremely low flow (ELF) and low flow (LF). The magnitude and frequency of the ELF and LF events increase as drought severity increases. The threshold regression provided useful insights, indicating that moderate drought can trigger ELF and LF at shorter timescales (SPEI-1 and SPEI-6) in the UIB and middle Indus Basin (MIB). Conversely, severe and extreme droughts trigger ELF and LF at longer timescales (SPEI-12) in the LIB. The threshold regression also divided the entire study period (1980–2018) into different time periods (scenario 2), which is useful for quantifying the impact of drought on low EFs using the SPEI coefficient. Higher SPEI coefficients are observed in the LIB, indicating high alterations in EF due to drought. HAF showed high alterations in EF in most of the catchments throughout the year except in August and September. Overall, this study provided useful insights for analysing the effects of drought on EF, especially during low flows.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"119 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141712855","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 : 2024-06-14DOI: 10.5194/nhess-24-2003-2024
Lichen Yu, Hao Qin, Shining Huang, Wei Wei, Haoyu Jiang, Lin Mu
Abstract. Storm surges are a common natural hazard in China's southern coastal area which usually cause a great loss of human life and financial damages. With the economic development and population concentration of coastal cities, storm surges may result in more impacts and damage in the future. Therefore, it is of vital importance to conduct risk assessment to identify high-risk areas and evaluate economic losses. However, quantitative study of storm surge risk assessment in undeveloped areas of China is difficult, since there is a lack of building character and damage assessment data. Aiming at the problem of data missing in undeveloped areas of China, this paper proposes a methodology for conducting storm surge risk assessment quantitatively based on deep learning and geographic information system (GIS) techniques. Five defined storm surge inundation scenarios with different typhoon return periods are simulated by the coupled FVCOM–SWAN (Finite Volume Coastal Ocean Model–Simulating WAves Nearshore) model, the reliability of which is validated using official measurements. Building footprints of the study area are extracted through the TransUNet deep learning model and remote sensing images, while building heights are obtained through unoccupied aerial vehicle (UAV) measurements. Subsequently, economic losses are quantitatively calculated by combining the adjusted depth–damage functions and overlaying an analysis of the buildings exposed to storm surge inundation. Zoning maps of the study area are provided to illustrate the risk levels according to economic losses. The quantitative risk assessment and zoning maps can help the government to provide storm surge disaster prevention measures and to optimize land use planning and thus to reduce potential economic losses in the coastal area.
{"title":"Quantitative study of storm surge risk assessment in an undeveloped coastal area of China based on deep learning and geographic information system techniques: a case study of Double Moon Bay","authors":"Lichen Yu, Hao Qin, Shining Huang, Wei Wei, Haoyu Jiang, Lin Mu","doi":"10.5194/nhess-24-2003-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-2003-2024","url":null,"abstract":"Abstract. Storm surges are a common natural hazard in China's southern coastal area which usually cause a great loss of human life and financial damages. With the economic development and population concentration of coastal cities, storm surges may result in more impacts and damage in the future. Therefore, it is of vital importance to conduct risk assessment to identify high-risk areas and evaluate economic losses. However, quantitative study of storm surge risk assessment in undeveloped areas of China is difficult, since there is a lack of building character and damage assessment data. Aiming at the problem of data missing in undeveloped areas of China, this paper proposes a methodology for conducting storm surge risk assessment quantitatively based on deep learning and geographic information system (GIS) techniques. Five defined storm surge inundation scenarios with different typhoon return periods are simulated by the coupled FVCOM–SWAN (Finite Volume Coastal Ocean Model–Simulating WAves Nearshore) model, the reliability of which is validated using official measurements. Building footprints of the study area are extracted through the TransUNet deep learning model and remote sensing images, while building heights are obtained through unoccupied aerial vehicle (UAV) measurements. Subsequently, economic losses are quantitatively calculated by combining the adjusted depth–damage functions and overlaying an analysis of the buildings exposed to storm surge inundation. Zoning maps of the study area are provided to illustrate the risk levels according to economic losses. The quantitative risk assessment and zoning maps can help the government to provide storm surge disaster prevention measures and to optimize land use planning and thus to reduce potential economic losses in the coastal area.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"14 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141341821","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 : 2024-06-13DOI: 10.5194/nhess-24-1975-2024
Christoph Nathanael von Matt, Regula Muelchi, L. Gudmundsson, Olivia Martius
Abstract. The co-occurrence of meteorological, agricultural, and hydrological droughts (multivariate compound droughts) in Switzerland during growing season is problematic due to limitations in water abstractions from rivers during low-flow periods, while at the same time the need for irrigation is high. We analyse compound droughts for 52 catchments in Switzerland during the extended summer season (May–October) using the transient climate and hydrological scenarios for Switzerland (CH2018 and Hydro-CH2018) for both a scenario with mitigation (representative concentration pathway 2.6 (RCP2.6), 8 model chains) and a scenario without mitigation (RCP8.5, 20 model chains). In the RCP8.5 scenario the number of compound drought days is projected to significantly increase by mid-century across all greater regions of Switzerland. The increased frequency is mainly a result of more frequent events (significant) rather than longer event durations (non-significant). Models generally agree on the sign of change. By 2085, compound drought events are projected to occur in median once per catchment per extended summer season north of the Alps and every 1–2 years south of the Alps. Further, the increases in compound drought days mainly occur between May–October, leading to a shift in the main agricultural production season and a more pronounced seasonality with the highest occurrence probabilities between mid-July and the beginning of October. Coupled to the increase in days and events, significantly more catchments are projected to be affected by compound droughts at the same time. In the RCP2.6 (mitigation) scenario, the increase in the number of compound drought days and events is not significant by the end of the 21st century. In comparison with RCP8.5, the number of compound drought days is reduced by 50 %–55 % north of the Alps and by up to 75 % south of the Alps by the end of the century. This emphasizes the need for coordinated adaptation in combination with mitigation measures taken at an early stage.
{"title":"Compound droughts under climate change in Switzerland","authors":"Christoph Nathanael von Matt, Regula Muelchi, L. Gudmundsson, Olivia Martius","doi":"10.5194/nhess-24-1975-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-1975-2024","url":null,"abstract":"Abstract. The co-occurrence of meteorological, agricultural, and hydrological droughts (multivariate compound droughts) in Switzerland during growing season is problematic due to limitations in water abstractions from rivers during low-flow periods, while at the same time the need for irrigation is high. We analyse compound droughts for 52 catchments in Switzerland during the extended summer season (May–October) using the transient climate and hydrological scenarios for Switzerland (CH2018 and Hydro-CH2018) for both a scenario with mitigation (representative concentration pathway 2.6 (RCP2.6), 8 model chains) and a scenario without mitigation (RCP8.5, 20 model chains). In the RCP8.5 scenario the number of compound drought days is projected to significantly increase by mid-century across all greater regions of Switzerland. The increased frequency is mainly a result of more frequent events (significant) rather than longer event durations (non-significant). Models generally agree on the sign of change. By 2085, compound drought events are projected to occur in median once per catchment per extended summer season north of the Alps and every 1–2 years south of the Alps. Further, the increases in compound drought days mainly occur between May–October, leading to a shift in the main agricultural production season and a more pronounced seasonality with the highest occurrence probabilities between mid-July and the beginning of October. Coupled to the increase in days and events, significantly more catchments are projected to be affected by compound droughts at the same time. In the RCP2.6 (mitigation) scenario, the increase in the number of compound drought days and events is not significant by the end of the 21st century. In comparison with RCP8.5, the number of compound drought days is reduced by 50 %–55 % north of the Alps and by up to 75 % south of the Alps by the end of the century. This emphasizes the need for coordinated adaptation in combination with mitigation measures taken at an early stage.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"29 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141346096","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 : 2024-06-12DOI: 10.5194/nhess-24-1951-2024
M. Igigabel, Marissa Yates, M. Vousdoukas, Youssef Diab
Abstract. In the context of climate change, height and frequency variations in extreme sea levels (ESLs) are studied using deterministic and probabilistic approaches. However, this type of approach does not highlight the dynamic effects (waves, currents) generated by metocean events (storms, cyclones, long swells, and tsunamis) beyond their effects on sea levels. In particular, ESL estimates are calculated by considering the main determining physical factors but cannot include all the effects of these factors. Ultimately, this can lead to confusion between ESL and hazard. This article proposes a systemic assessment method to analyze coastal hazard changes at regional scales, integrating parameters influencing sea levels, as well as factors describing the geomorphological context (length and shape of the coast, width of the continental shelf), metocean events, and the marine environment (e.g., coral reef state and sea ice extent). French mainland and overseas territories were selected to apply the method. The present study highlights the need to consider not only the sea level variability, but also the current and future characteristics of metocean events. The long, concave coasts bordered by a wide continental shelf appear particularly sensitive to variations in the intensity or trajectory of metocean events. Coral reef degradation in the tropics and the decrease in seasonal sea ice extent in the polar regions can also significantly change the nearshore hydrodynamics and impacts on the shoreline. These results help us to predict the types of hazard (shoreline erosion, rapid submersion, and/or permanent flooding) that will increase the most in different coastal zones.
{"title":"A systemic and comprehensive assessment of coastal hazard changes: method and application to France and its overseas territories","authors":"M. Igigabel, Marissa Yates, M. Vousdoukas, Youssef Diab","doi":"10.5194/nhess-24-1951-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-1951-2024","url":null,"abstract":"Abstract. In the context of climate change, height and frequency variations in extreme sea levels (ESLs) are studied using deterministic and probabilistic approaches. However, this type of approach does not highlight the dynamic effects (waves, currents) generated by metocean events (storms, cyclones, long swells, and tsunamis) beyond their effects on sea levels. In particular, ESL estimates are calculated by considering the main determining physical factors but cannot include all the effects of these factors. Ultimately, this can lead to confusion between ESL and hazard. This article proposes a systemic assessment method to analyze coastal hazard changes at regional scales, integrating parameters influencing sea levels, as well as factors describing the geomorphological context (length and shape of the coast, width of the continental shelf), metocean events, and the marine environment (e.g., coral reef state and sea ice extent). French mainland and overseas territories were selected to apply the method. The present study highlights the need to consider not only the sea level variability, but also the current and future characteristics of metocean events. The long, concave coasts bordered by a wide continental shelf appear particularly sensitive to variations in the intensity or trajectory of metocean events. Coral reef degradation in the tropics and the decrease in seasonal sea ice extent in the polar regions can also significantly change the nearshore hydrodynamics and impacts on the shoreline. These results help us to predict the types of hazard (shoreline erosion, rapid submersion, and/or permanent flooding) that will increase the most in different coastal zones.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"1 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141353615","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 : 2024-06-11DOI: 10.5194/nhess-24-1937-2024
Ran Zhu, Lei Chen
Abstract. This study examines the climatic characteristics of 202 Jianghuai cyclones and their linkage with precipitation during the Meiyu period from 1961 to 2020. The results show that cyclones mainly originate from the eastern and western Hubei Province. Additionally, we explore the statistical characteristics of intensity, radius and their positive correlation. In studying the decadal variation of cyclones, we find a similar evolution between the cyclones and Meiyu precipitation. Therefore, we further investigate the correlation between the Jianghuai cyclones and the precipitation during the Meiyu period. There is a positive correlation coefficient of 0.77 between them. Notably, the percentage of precipitation affected by cyclone activities can reach up to 47 %. The anomalous increase in precipitation caused by cyclones north of 27° N can reach a maximum of 7 mm d−1. When a cyclone exists, a significant negative geopotential height anomaly at the 500 hPa level over Mongolia can be traced back to day −4. The abnormally enhanced WPSH (western Pacific subtropical high), southwesterly low-level jet and negative geopotential height are the dominant factors causing abnormal precipitation during Jianghuai cyclones. Before and after the cyclone develops, water vapor flux and divergence from low latitudes increase abnormally, providing sufficient water vapor conditions for the generation of cyclone precipitation.
{"title":"Climatic characteristics of the Jianghuai cyclone and its linkage with precipitation during the Meiyu period from 1961 to 2020","authors":"Ran Zhu, Lei Chen","doi":"10.5194/nhess-24-1937-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-1937-2024","url":null,"abstract":"Abstract. This study examines the climatic characteristics of 202 Jianghuai cyclones and their linkage with precipitation during the Meiyu period from 1961 to 2020. The results show that cyclones mainly originate from the eastern and western Hubei Province. Additionally, we explore the statistical characteristics of intensity, radius and their positive correlation. In studying the decadal variation of cyclones, we find a similar evolution between the cyclones and Meiyu precipitation. Therefore, we further investigate the correlation between the Jianghuai cyclones and the precipitation during the Meiyu period. There is a positive correlation coefficient of 0.77 between them. Notably, the percentage of precipitation affected by cyclone activities can reach up to 47 %. The anomalous increase in precipitation caused by cyclones north of 27° N can reach a maximum of 7 mm d−1. When a cyclone exists, a significant negative geopotential height anomaly at the 500 hPa level over Mongolia can be traced back to day −4. The abnormally enhanced WPSH (western Pacific subtropical high), southwesterly low-level jet and negative geopotential height are the dominant factors causing abnormal precipitation during Jianghuai cyclones. Before and after the cyclone develops, water vapor flux and divergence from low latitudes increase abnormally, providing sufficient water vapor conditions for the generation of cyclone precipitation.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"21 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141359053","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 : 2024-06-06DOI: 10.5194/nhess-24-1913-2024
Praveen Kumar, Priyanka Priyanka, K. V. Uday, Varun Dutt
Abstract. Landslides threaten human life and infrastructure, resulting in fatalities and economic losses. Monitoring stations provide valuable data for predicting soil movement, which is crucial in mitigating this threat. Accurately predicting soil movement from monitoring data is challenging due to its complexity and inherent class imbalance. This study proposes developing machine learning (ML) models with oversampling techniques to address the class imbalance issue and develop a robust soil movement prediction system. The dataset, comprising 2 years (2019–2021) of monitoring data from a landslide in Uttarakhand, has a 70:30 ratio of training and testing data. To tackle the class imbalance problem, various oversampling techniques, including the synthetic minority oversampling technique (SMOTE), K-means SMOTE, borderline-SMOTE, and adaptive SMOTE (ADASYN), were applied to the training dataset. Several ML models, namely random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), adaptive boosting (AdaBoost), category boosting (CatBoost), long short-term memory (LSTM), multilayer perceptron (MLP), and a dynamic ensemble, were trained and compared for soil movement prediction. A 5-fold cross-validation method was applied to optimize the ML models on the training data, and the models were tested on the testing set. Among these ML models, the dynamic ensemble model with K-means SMOTE performed the best in testing, with an accuracy, precision, and recall rate of 0.995, 0.995, and 0.995, respectively, and an F1 score of 0.995. Additionally, models without oversampling exhibited poor performance in training and testing, highlighting the importance of incorporating oversampling techniques to enhance predictive capabilities.
摘要山体滑坡威胁着人类生命和基础设施，造成人员伤亡和经济损失。监测站为预测土壤移动提供了宝贵的数据，这对减轻这一威胁至关重要。由于监测数据的复杂性和固有的类不平衡，从监测数据中准确预测土壤移动具有挑战性。本研究提出利用超采样技术开发机器学习（ML）模型，以解决类不平衡问题，并开发出一种稳健的土壤移动预测系统。数据集包括来自北阿坎德邦滑坡的两年（2019-2021 年）监测数据，其中训练数据和测试数据的比例为 70:30。为解决类不平衡问题，对训练数据集采用了多种超采样技术，包括合成少数超采样技术（SMOTE）、K-means SMOTE、borderline-SMOTE 和自适应 SMOTE（ADASYN）。对随机森林（RF）、极梯度提升（XGBoost）、轻梯度提升机（LightGBM）、自适应提升（AdaBoost）、类别提升（CatBoost）、长短期记忆（LSTM）、多层感知器（MLP）和动态集合等多个 ML 模型进行了训练，并对其进行了比较。采用 5 倍交叉验证法对训练数据上的 ML 模型进行了优化，并在测试集上对模型进行了测试。在这些 ML 模型中，采用 K-means SMOTE 的动态集合模型在测试中表现最佳，准确率、精确率和召回率分别为 0.995、0.995 和 0.995，F1 分数为 0.995。此外，没有超采样的模型在训练和测试中表现不佳，这突出了采用超采样技术提高预测能力的重要性。
{"title":"Addressing class imbalance in soil movement predictions","authors":"Praveen Kumar, Priyanka Priyanka, K. V. Uday, Varun Dutt","doi":"10.5194/nhess-24-1913-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-1913-2024","url":null,"abstract":"Abstract. Landslides threaten human life and infrastructure, resulting in fatalities and economic losses. Monitoring stations provide valuable data for predicting soil movement, which is crucial in mitigating this threat. Accurately predicting soil movement from monitoring data is challenging due to its complexity and inherent class imbalance. This study proposes developing machine learning (ML) models with oversampling techniques to address the class imbalance issue and develop a robust soil movement prediction system. The dataset, comprising 2 years (2019–2021) of monitoring data from a landslide in Uttarakhand, has a 70:30 ratio of training and testing data. To tackle the class imbalance problem, various oversampling techniques, including the synthetic minority oversampling technique (SMOTE), K-means SMOTE, borderline-SMOTE, and adaptive SMOTE (ADASYN), were applied to the training dataset. Several ML models, namely random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), adaptive boosting (AdaBoost), category boosting (CatBoost), long short-term memory (LSTM), multilayer perceptron (MLP), and a dynamic ensemble, were trained and compared for soil movement prediction. A 5-fold cross-validation method was applied to optimize the ML models on the training data, and the models were tested on the testing set. Among these ML models, the dynamic ensemble model with K-means SMOTE performed the best in testing, with an accuracy, precision, and recall rate of 0.995, 0.995, and 0.995, respectively, and an F1 score of 0.995. Additionally, models without oversampling exhibited poor performance in training and testing, highlighting the importance of incorporating oversampling techniques to enhance predictive capabilities.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"87 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-16DOI: 10.5194/nhess-24-1757-2024
Jan Sodoge, C. Kuhlicke, Miguel D. Mahecha, M. D. de Brito
Abstract. Droughts often lead to cross-sectoral and interconnected socio-economic impacts, affecting human well-being, ecosystems, and economic development. Extended drought periods, such as the 2018–2022 event in Germany, amplify these impacts due to temporal carry-over effects. Yet, our understanding of drought impact dynamics during increasingly frequent multi-year drought periods is still in its infancy. In this study, we analyse the socio-economic impacts of the 2018–2022 multi-year drought in Germany and compare them to previous single-year events. Leveraging text-mining tools, we derive a dataset covering impacts reported by 260 news outlets on agriculture, forestry, livestock, waterways, aquaculture, fire, and social impacts spanning 2000 to 2022. We introduce the concept of drought impact profiles (DIPs) to describe spatio-temporal patterns of the reported co-occurrences of impacts. We employ a clustering algorithm to detect these DIPs and then use sequence mining and statistical tests to analyse spatio-temporal trends. Our results reveal that the 2018–2022 multi-year drought event had distinct impact patterns compared to prior single-year droughts regarding their spatial extent, impact diversity, and prevalent impact types. For the multi-year drought period, we identify shifts in how impacts have been perceived regionally, especially focusing on legacy and cascading effects on forestry and social activities. Also, we show how regional differences in relevant impacts are controlled by different land-cover types. Our findings enhance the understanding of the dynamic nature of drought impacts, highlighting the potential of text-mining techniques to study drought impact dynamics. The insights gained underscore the need for different strategies in managing multi-year droughts compared to single-year events.
{"title":"Text mining uncovers the unique dynamics of socio-economic impacts of the 2018–2022 multi-year drought in Germany","authors":"Jan Sodoge, C. Kuhlicke, Miguel D. Mahecha, M. D. de Brito","doi":"10.5194/nhess-24-1757-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-1757-2024","url":null,"abstract":"Abstract. Droughts often lead to cross-sectoral and interconnected socio-economic impacts, affecting human well-being, ecosystems, and economic development. Extended drought periods, such as the 2018–2022 event in Germany, amplify these impacts due to temporal carry-over effects. Yet, our understanding of drought impact dynamics during increasingly frequent multi-year drought periods is still in its infancy. In this study, we analyse the socio-economic impacts of the 2018–2022 multi-year drought in Germany and compare them to previous single-year events. Leveraging text-mining tools, we derive a dataset covering impacts reported by 260 news outlets on agriculture, forestry, livestock, waterways, aquaculture, fire, and social impacts spanning 2000 to 2022. We introduce the concept of drought impact profiles (DIPs) to describe spatio-temporal patterns of the reported co-occurrences of impacts. We employ a clustering algorithm to detect these DIPs and then use sequence mining and statistical tests to analyse spatio-temporal trends. Our results reveal that the 2018–2022 multi-year drought event had distinct impact patterns compared to prior single-year droughts regarding their spatial extent, impact diversity, and prevalent impact types. For the multi-year drought period, we identify shifts in how impacts have been perceived regionally, especially focusing on legacy and cascading effects on forestry and social activities. Also, we show how regional differences in relevant impacts are controlled by different land-cover types. Our findings enhance the understanding of the dynamic nature of drought impacts, highlighting the potential of text-mining techniques to study drought impact dynamics. The insights gained underscore the need for different strategies in managing multi-year droughts compared to single-year events.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"28 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140971086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.5194/nhess-24-1741-2024
Jiao Wang, Zhangxing Wang, Guanhua Sun, Hongming Luo
Abstract. In the current context of global climate change, geohazards such as earthquakes and extreme rainfall pose a serious threat to regional stability. We investigate a three-dimensional (3D) slope dynamic model under earthquake action, derive the calculation of seepage force and the normal stress expression of slip surface under seepage and earthquake, and propose a rigorous overall analysis method to solve the safety factor of slopes subjected to combined with rainfall and earthquake. The accuracy and reliability of the method is verified by two classical examples. Finally, the effects of soil permeability coefficient, porosity, and saturation on slope stability under rainfall in a project located in the Three Gorges Reservoir area are analyzed. The safety evolution of the slope combined with both rainfall and earthquake is also studied. The results indicate that porosity has a greater impact on the safety factor under rainfall conditions, while the influence of permeability coefficient and saturation is relatively small. With the increase of horizontal seismic coefficient, the safety factor of the slope decreases significantly. The influence of earthquake on slope stability is significantly greater than that of rainfall. The corresponding safety factor when the vertical seismic action is vertically downward is smaller than that when it is vertically upward. When considering both horizontal and vertical seismic effects, slope stability is lower.
{"title":"Analysis of three-dimensional slope stability combined with rainfall and earthquake","authors":"Jiao Wang, Zhangxing Wang, Guanhua Sun, Hongming Luo","doi":"10.5194/nhess-24-1741-2024","DOIUrl":"https://doi.org/10.5194/nhess-24-1741-2024","url":null,"abstract":"Abstract. In the current context of global climate change, geohazards such as earthquakes and extreme rainfall pose a serious threat to regional stability. We investigate a three-dimensional (3D) slope dynamic model under earthquake action, derive the calculation of seepage force and the normal stress expression of slip surface under seepage and earthquake, and propose a rigorous overall analysis method to solve the safety factor of slopes subjected to combined with rainfall and earthquake. The accuracy and reliability of the method is verified by two classical examples. Finally, the effects of soil permeability coefficient, porosity, and saturation on slope stability under rainfall in a project located in the Three Gorges Reservoir area are analyzed. The safety evolution of the slope combined with both rainfall and earthquake is also studied. The results indicate that porosity has a greater impact on the safety factor under rainfall conditions, while the influence of permeability coefficient and saturation is relatively small. With the increase of horizontal seismic coefficient, the safety factor of the slope decreases significantly. The influence of earthquake on slope stability is significantly greater than that of rainfall. The corresponding safety factor when the vertical seismic action is vertically downward is smaller than that when it is vertically upward. When considering both horizontal and vertical seismic effects, slope stability is lower.\u0000","PeriodicalId":508073,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"56 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140972378","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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