Pub Date : 2025-12-01DOI: 10.1016/j.nhres.2025.05.003
Yifang Chen , Zhitao Li , Suntao Chen , Yixuan Li , Jihua Fu
The cost and performance of earthquake early warning stations are always a trade-off. The high cost of traditional early warning stations limits their large-scale application. The emergence of low-cost MEMS accelerometers makes it possible to build dense earthquake early warning networks. However, the high noise level of MEMS accelerometers cannot be ignored, which affects the performance of magnitude estimation. In this paper, we propose a magnitude estimation method based on relative power which can directly use acceleration records for calculation without the need for integration and filtering. The attenuation relationship of magnitude, relative power and hypocentral distance can be derived from the statistical analysis of some seismic events. Comparative experiments showed that the magnitude estimation performance of Power method is comparable to the method and better than the method. In noise analysis, the Power method demonstrates superior performance in low Signal-to-Noise Ratio data. After station correction, the Power method can achieve robust magnitude estimation results with only five stations triggered following an earthquake. We validated the generalizability of this method using seismic data from Mexico.
{"title":"Magnitude estimation based on relative power in MEMS-based earthquake early warning system","authors":"Yifang Chen , Zhitao Li , Suntao Chen , Yixuan Li , Jihua Fu","doi":"10.1016/j.nhres.2025.05.003","DOIUrl":"10.1016/j.nhres.2025.05.003","url":null,"abstract":"<div><div>The cost and performance of earthquake early warning stations are always a trade-off. The high cost of traditional early warning stations limits their large-scale application. The emergence of low-cost MEMS accelerometers makes it possible to build dense earthquake early warning networks. However, the high noise level of MEMS accelerometers cannot be ignored, which affects the performance of magnitude estimation. In this paper, we propose a magnitude estimation method based on relative power which can directly use acceleration records for calculation without the need for integration and filtering. The attenuation relationship of magnitude, relative power and hypocentral distance can be derived from the statistical analysis of some seismic events. Comparative experiments showed that the magnitude estimation performance of Power method is comparable to the <span><math><mrow><msub><mi>P</mi><mi>v</mi></msub></mrow></math></span> method and better than the <span><math><mrow><msub><mi>P</mi><mi>d</mi></msub></mrow></math></span> method. In noise analysis, the Power method demonstrates superior performance in low Signal-to-Noise Ratio data. After station correction, the Power method can achieve robust magnitude estimation results with only five stations triggered following an earthquake. We validated the generalizability of this method using seismic data from Mexico.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 4","pages":"Pages 947-959"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145698249","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 : 2025-12-01DOI: 10.1016/j.nhres.2025.06.002
Fekadie Bazie Enyew , Dejene Sahlu , Gashaw Bimrew Tarekegn , Yakob Umer , Belen Marti-Cardona , Bedassa R. Cheneka , Daniel Asfaw Bekele , Daniel Ayalew Mengistu , Sarkawt Hama , Zinaw D. Shenga , Sisay E. Debele
Changing climate is increasingly influencing the rainy seasons and posing significant challenges for agriculture and water resource management in vulnerable regions. The study examines the spatiotemporal variation of rainy seasons in the Upper Blue Nile Basin (UBNB) of Northwestern Ethiopia using projections from the CMIP6 climate models to assess potential impacts on agricultural and water planning. We analyzed observed and projected precipitation data across three Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5, spanning near-term (2015–2044), mid-term (2045–2074), and long-term (2075–2100) periods. To correct systematic biases in model outputs, a power transformation technique was applied to precipitation data from seven CMIP6 models. The finding of the study showed that the INM-CM5-0 model is the most accurate in simulating precipitation patterns, forming the basis for our detailed analysis. Our findings reveal significant shifts in the timing and duration of rainy seasons across all scenarios, with more pronounced changes under the highest emission pathways, SSP5-8.5. The SSP5-8.5 scenario indicates the most substantial extension in the length of rainy season, particularly in lowland areas, due to both earlier onset and delayed cessation. Conversely, highland and midland regions are projected to experience shorter rainy seasons, lasting between 82 and 130 days, driven by delayed onset and earlier cessation. These shifts could profoundly affect agricultural productivity, necessitating adjustments in planting and harvesting schedules by several months. Under high-emission scenarios, crop cycles may become misaligned with traditional planting windows, especially in highland areas where shorter rainy seasons may constrain crop viability. This study highlights the urgent need for adaptive strategies, such as the practice of early-maturing crop varieties, to enhance resilience to the change of projected seasonal rainfall patterns. Proactive measures, particularly in highland communities, are crucial for maintaining food security and effective water resource management to enhance adaptation options to climate change impacts in the area. These insights can guide targeted agricultural policies and resource planning, helping to mitigate the adverse impacts of climate change on food and water security.
{"title":"Future changes in rainy seasons in the Upper Blue Nile Basin: Impacts on agriculture and water resources","authors":"Fekadie Bazie Enyew , Dejene Sahlu , Gashaw Bimrew Tarekegn , Yakob Umer , Belen Marti-Cardona , Bedassa R. Cheneka , Daniel Asfaw Bekele , Daniel Ayalew Mengistu , Sarkawt Hama , Zinaw D. Shenga , Sisay E. Debele","doi":"10.1016/j.nhres.2025.06.002","DOIUrl":"10.1016/j.nhres.2025.06.002","url":null,"abstract":"<div><div>Changing climate is increasingly influencing the rainy seasons and posing significant challenges for agriculture and water resource management in vulnerable regions. The study examines the spatiotemporal variation of rainy seasons in the Upper Blue Nile Basin (UBNB) of Northwestern Ethiopia using projections from the CMIP6 climate models to assess potential impacts on agricultural and water planning. We analyzed observed and projected precipitation data across three Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5, spanning near-term (2015–2044), mid-term (2045–2074), and long-term (2075–2100) periods. To correct systematic biases in model outputs, a power transformation technique was applied to precipitation data from seven CMIP6 models. The finding of the study showed that the INM-CM5-0 model is the most accurate in simulating precipitation patterns, forming the basis for our detailed analysis. Our findings reveal significant shifts in the timing and duration of rainy seasons across all scenarios, with more pronounced changes under the highest emission pathways, SSP5-8.5. The SSP5-8.5 scenario indicates the most substantial extension in the length of rainy season, particularly in lowland areas, due to both earlier onset and delayed cessation. Conversely, highland and midland regions are projected to experience shorter rainy seasons, lasting between 82 and 130 days, driven by delayed onset and earlier cessation. These shifts could profoundly affect agricultural productivity, necessitating adjustments in planting and harvesting schedules by several months. Under high-emission scenarios, crop cycles may become misaligned with traditional planting windows, especially in highland areas where shorter rainy seasons may constrain crop viability. This study highlights the urgent need for adaptive strategies, such as the practice of early-maturing crop varieties, to enhance resilience to the change of projected seasonal rainfall patterns. Proactive measures, particularly in highland communities, are crucial for maintaining food security and effective water resource management to enhance adaptation options to climate change impacts in the area. These insights can guide targeted agricultural policies and resource planning, helping to mitigate the adverse impacts of climate change on food and water security.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 4","pages":"Pages 960-974"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145698251","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 : 2025-12-01DOI: 10.1016/j.nhres.2025.03.010
Aalok Sharma Kafle, Kushum K. C, E.A. Hernandez, V. Uddameri
Flood damages to hydraulic infrastructure leads to devastating economic losses in the Southeast Texas/Southwest Louisiana region. This area lies at the cusp of the humid southeastern US and the more arid southern high plains and exhibits extreme climate variability. Traditional design protocols recommend the use of Log Pearson-III (LP3) based flood frequency analysis (FFA) using annual maximum flows (AMF). The reasonableness of this approach is increasingly being questioned due to intensification of flooding in recent years with possibility of nonstationary climate shifts. Using long-term historical data and an ensemble of downscaled climate data, the study demonstrates that streamflows exhibit stationary behavior and any noted shifts in precipitation lie within the high variability noted in the region. While nonstationarity is not of concern, the study also demonstrates that hydraulic designs based on LP3-AMF techniques are not resilient (defined here based on withstanding capacity and reliable operations over a design life-time). Two new resiliency indicators – Flood Withstanding Capacity (FWC) – defined as the ratio of anticipated flood quantile to the design flood quantile for a given return period and Reliability Reduction Factor (RRF) – defined as the ratio of the anticipated reliability to the design reliability over the design lifetime of the structure are developed. The level of conservatism within the study area was noted to be AMF-GEV ∼ AMF-GEV (nonstationary) < LP3-AMF < GP-PDS < LP3-PDS. Data are readily available for implementation of partial duration series, the non-inclusion of this approach in Flood frequency analysis is neglecting valuable information that lead to better resilient designs. The framework and resiliency indicators developed here are generic and can be applied in any part of the world.
{"title":"Resiliency of hydraulic infrastructure designs in a climate hot-spot at the intersection of two climate zones","authors":"Aalok Sharma Kafle, Kushum K. C, E.A. Hernandez, V. Uddameri","doi":"10.1016/j.nhres.2025.03.010","DOIUrl":"10.1016/j.nhres.2025.03.010","url":null,"abstract":"<div><div>Flood damages to hydraulic infrastructure leads to devastating economic losses in the Southeast Texas/Southwest Louisiana region. This area lies at the cusp of the humid southeastern US and the more arid southern high plains and exhibits extreme climate variability. Traditional design protocols recommend the use of Log Pearson-III (LP3) based flood frequency analysis (FFA) using annual maximum flows (AMF). The reasonableness of this approach is increasingly being questioned due to intensification of flooding in recent years with possibility of nonstationary climate shifts. Using long-term historical data and an ensemble of downscaled climate data, the study demonstrates that streamflows exhibit stationary behavior and any noted shifts in precipitation lie within the high variability noted in the region. While nonstationarity is not of concern, the study also demonstrates that hydraulic designs based on LP3-AMF techniques are not resilient (defined here based on withstanding capacity and reliable operations over a design life-time). Two new resiliency indicators – Flood Withstanding Capacity (FWC) – defined as the ratio of anticipated flood quantile to the design flood quantile for a given return period and Reliability Reduction Factor (RRF) – defined as the ratio of the anticipated reliability to the design reliability over the design lifetime of the structure are developed. The level of conservatism within the study area was noted to be AMF-GEV ∼ AMF-GEV (nonstationary) < LP3-AMF < GP-PDS < LP3-PDS. Data are readily available for implementation of partial duration series, the non-inclusion of this approach in Flood frequency analysis is neglecting valuable information that lead to better resilient designs. The framework and resiliency indicators developed here are generic and can be applied in any part of the world.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 4","pages":"Pages 835-850"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145698214","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 : 2025-12-01DOI: 10.1016/j.nhres.2025.03.005
Samya Lamhadri , Nadia Senechal , Abdessalam Ouallali , Mohammed El Hafyani , Driss Chahid , Mohammed karim Benhachmi
Coastal areas are essential for maintaining diverse ecosystems and providing key resources for human populations. However, Climate Change (CC) is a major driver of Sea Level Rise (SLR), manifesting through various impacts such as coastal erosion, flooding, and saline intrusion. This study uses the InVEST model to assess SLR exposure along 25 km of Morocco's Atlantic coastline in the Sale region. The model relies on a qualitative index based on bio-geophysical variables. Four scenarios were analyzed to evaluate the role of natural habitats in coastal protection and how SLR rates influence coastal exposure. The results indicate that with habitat protection, coastal exposure remains between low and moderate (50 %), while without protection, 50 % of the coastline faces high risk. The impact of SLR further amplifies this risk, with habitat loss resulting in 43.8 % of the coastline being classified as "very high risk" and 39.3 % as high risk. The southern coastline (Sidi Moussa) is more exposed than the northern part (Nation Beach), due to geomorphology, altitude, distance from the continental shelf, and habitat distribution. The exposure index showed notable spatial autocorrelation (Moran index = 0.7), indicating clustering of areas with similar exposure. The Moran index remained consistent across all scenarios, highlighting stable spatial dependency patterns. These findings help identify high risk districts along the Sale coastline, providing valuable support for coastal protection and sustainable development.
{"title":"Assessing coastal exposure to Sea Level Rise: A coupled approach of qualitative modeling and spatial autocorrelation analysis","authors":"Samya Lamhadri , Nadia Senechal , Abdessalam Ouallali , Mohammed El Hafyani , Driss Chahid , Mohammed karim Benhachmi","doi":"10.1016/j.nhres.2025.03.005","DOIUrl":"10.1016/j.nhres.2025.03.005","url":null,"abstract":"<div><div>Coastal areas are essential for maintaining diverse ecosystems and providing key resources for human populations. However, Climate Change (CC) is a major driver of Sea Level Rise (SLR), manifesting through various impacts such as coastal erosion, flooding, and saline intrusion. This study uses the InVEST model to assess SLR exposure along 25 km of Morocco's Atlantic coastline in the Sale region. The model relies on a qualitative index based on bio-geophysical variables. Four scenarios were analyzed to evaluate the role of natural habitats in coastal protection and how SLR rates influence coastal exposure. The results indicate that with habitat protection, coastal exposure remains between low and moderate (50 %), while without protection, 50 % of the coastline faces high risk. The impact of SLR further amplifies this risk, with habitat loss resulting in 43.8 % of the coastline being classified as \"very high risk\" and 39.3 % as high risk. The southern coastline (Sidi Moussa) is more exposed than the northern part (Nation Beach), due to geomorphology, altitude, distance from the continental shelf, and habitat distribution. The exposure index showed notable spatial autocorrelation (Moran index = 0.7), indicating clustering of areas with similar exposure. The Moran index remained consistent across all scenarios, highlighting stable spatial dependency patterns. These findings help identify high risk districts along the Sale coastline, providing valuable support for coastal protection and sustainable development.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 4","pages":"Pages 764-777"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145698210","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 : 2025-12-01DOI: 10.1016/j.nhres.2025.03.006
Jeff Dacosta Osei , Quisha Reed-Jones , Yaw A. Twumasi , Zhu H. Ning
Flooding poses a significant threat to the Alsen St/Irma Lee Community Village in East Baton Rouge Parish, driven by its low-lying terrain, complex hydrological dynamics, and inadequate drainage systems. This study integrates Geographic Information Systems (GIS) with the Soil and Water Assessment Tool (SWAT) to delineate catchments, analyze flood risks, and incorporate Land Use/Land Cover (LULC) changes between 1990 and 2025. Unlike conventional GIS-based flood assessments that often rely on static floodplain mapping or hydraulic models, this study employs a dynamic hydrological approach to capture the interplay between LULC changes and flood dynamics. Urban expansion, reflected in a 16.30 % (1.23 km2) increase in built-up areas, and vegetation loss, marked by a 6.5 % (1.52 km2) decline in closed forest cover, have intensified runoff and reduced natural water retention, increasing flood vulnerability. The study identifies 22 sub-basins contributing significantly to flood risks, with a 1-m flood depth scenario impacting 63 % of the community, endangering 1163 residents, 101 km of road networks, and 39 km of railway infrastructure. Validation with historical 2023 flood data demonstrates the model's robustness, achieving an RMSE of 0.002 and an NSE of 0.9983. In comparison to traditional methodologies, such as static hydraulic modeling, this approach incorporates temporally dynamic LULC changes, providing a more comprehensive and predictive framework for flood risk assessment for areas with limited data. These findings emphasize the need for proactive flood mitigation measures, including improved drainage systems, flood barriers, and community preparedness programs. Integrating LULC dynamics with hydrological modeling, this study offers a replicable framework that enhances the understanding of urbanization-driven flood risks in data-scarce regions, advancing both theory and practice in flood risk management.
{"title":"A GIS-based flood hazard mapping in the Alsen St./Irma Lee Community Village in the Louisiana state of USA","authors":"Jeff Dacosta Osei , Quisha Reed-Jones , Yaw A. Twumasi , Zhu H. Ning","doi":"10.1016/j.nhres.2025.03.006","DOIUrl":"10.1016/j.nhres.2025.03.006","url":null,"abstract":"<div><div>Flooding poses a significant threat to the Alsen St/Irma Lee Community Village in East Baton Rouge Parish, driven by its low-lying terrain, complex hydrological dynamics, and inadequate drainage systems. This study integrates Geographic Information Systems (GIS) with the Soil and Water Assessment Tool (SWAT) to delineate catchments, analyze flood risks, and incorporate Land Use/Land Cover (LULC) changes between 1990 and 2025. Unlike conventional GIS-based flood assessments that often rely on static floodplain mapping or hydraulic models, this study employs a dynamic hydrological approach to capture the interplay between LULC changes and flood dynamics. Urban expansion, reflected in a 16.30 % (1.23 km<sup>2</sup>) increase in built-up areas, and vegetation loss, marked by a 6.5 % (1.52 km<sup>2</sup>) decline in closed forest cover, have intensified runoff and reduced natural water retention, increasing flood vulnerability. The study identifies 22 sub-basins contributing significantly to flood risks, with a 1-m flood depth scenario impacting 63 % of the community, endangering 1163 residents, 101 km of road networks, and 39 km of railway infrastructure. Validation with historical 2023 flood data demonstrates the model's robustness, achieving an RMSE of 0.002 and an NSE of 0.9983. In comparison to traditional methodologies, such as static hydraulic modeling, this approach incorporates temporally dynamic LULC changes, providing a more comprehensive and predictive framework for flood risk assessment for areas with limited data. These findings emphasize the need for proactive flood mitigation measures, including improved drainage systems, flood barriers, and community preparedness programs. Integrating LULC dynamics with hydrological modeling, this study offers a replicable framework that enhances the understanding of urbanization-driven flood risks in data-scarce regions, advancing both theory and practice in flood risk management.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 4","pages":"Pages 778-799"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145698211","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}
Understanding the underlying causes of flood disasters is essential not only for developing effective flood management strategies but also for evaluating past policies and mitigation efforts. This study investigates the multi-dimensional causes and impacts of the increasing flood disasters in the Kathmandu Valley and the surrounding Roshi catchment, with a specific focus on the unprecedented September 2024 floods. Using a diverse range of data sources-including field observations, open-ended interviews, published studies and reports, remote sensing, socio-economic and hydro-meteorological data, as well as institutional, legal, and policy frameworks-we identify key factors contributing to the rising flood risk in and around the valley. The causes of flooding were broadly categorized into four main areas: catchment characteristics, anthropogenic activities, hydro-meteorological factors, and policy and institutional frameworks. The extreme rainfall events of September 2024 and the resulting floods further exposed the Kathmandu Valley's vulnerability, causing over three dozen fatalities and millions in economic losses. Unlike previous years, the flood impacts were exacerbated by debris flows and landslides from surrounding hillslopes, along with sediment contributions from mining sites and encroached riverbanks, intensifying the severity of inundation. Despite early warnings of heavy rainfall from concerned agencies, inadequate preparedness and response significantly amplified the disaster's impact, revealing critical gaps in Nepal's disaster management framework. Instead of a one-size-fits-all approach, effective flood management in the Kathmandu Valley requires a collaborative, multi-dimensional strategy tailored to its unique challenges. The September 2024 floods underscore the urgent need for systemic reforms in urban planning, policy reforms and enforcement, inter-agency collaboration, strengthened local government, and disaster risk management. Our analysis provide critical insights for enhancing flood resilience and improving future flood risk management strategies in a holistic manner.
{"title":"Unraveling the causes and impacts of increasing flood disasters in the kathmandu valley: Lessons from the unprecedented September 2024 floods","authors":"Kabin Lamichhane , Saroj Karki , Keshab Sharma , Bharat Khadka , Biplav Acharya , Kamal Biswakarma , Sumit Adhikari , Rajan Kc , Anusha Danegulu , Samana Bhattarai , Abhilasha Regmi , Mandip Subedi , Pawan Kumar Bhattarai","doi":"10.1016/j.nhres.2025.04.001","DOIUrl":"10.1016/j.nhres.2025.04.001","url":null,"abstract":"<div><div>Understanding the underlying causes of flood disasters is essential not only for developing effective flood management strategies but also for evaluating past policies and mitigation efforts. This study investigates the multi-dimensional causes and impacts of the increasing flood disasters in the Kathmandu Valley and the surrounding Roshi catchment, with a specific focus on the unprecedented September 2024 floods. Using a diverse range of data sources-including field observations, open-ended interviews, published studies and reports, remote sensing, socio-economic and hydro-meteorological data, as well as institutional, legal, and policy frameworks-we identify key factors contributing to the rising flood risk in and around the valley. The causes of flooding were broadly categorized into four main areas: catchment characteristics, anthropogenic activities, hydro-meteorological factors, and policy and institutional frameworks. The extreme rainfall events of September 2024 and the resulting floods further exposed the Kathmandu Valley's vulnerability, causing over three dozen fatalities and millions in economic losses. Unlike previous years, the flood impacts were exacerbated by debris flows and landslides from surrounding hillslopes, along with sediment contributions from mining sites and encroached riverbanks, intensifying the severity of inundation. Despite early warnings of heavy rainfall from concerned agencies, inadequate preparedness and response significantly amplified the disaster's impact, revealing critical gaps in Nepal's disaster management framework. Instead of a one-size-fits-all approach, effective flood management in the Kathmandu Valley requires a collaborative, multi-dimensional strategy tailored to its unique challenges. The September 2024 floods underscore the urgent need for systemic reforms in urban planning, policy reforms and enforcement, inter-agency collaboration, strengthened local government, and disaster risk management. Our analysis provide critical insights for enhancing flood resilience and improving future flood risk management strategies in a holistic manner.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 4","pages":"Pages 875-897"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145698248","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}
Tropical Cyclones pose significant threats to coastal regions, necessitating precise prediction and monitoring for effective disaster management. This study represents a comparative analysis of simulated and observed data for Tropical Cyclone Sitrang, focusing on key meteorological features such as temperature, pressure, rainfall, wind speed, and wind direction. The analysis utilizes the Weather Research and Forecasting Advanced Research WRF (WRF-ARW) model alongside geostationary satellite imagery from INSAT-3D, MSG, and multi-mission satellites. The novelty of this research lies in its dual approach, which combines high-resolution numerical modeling for 48 h in a single domain with a horizontal resolution of 27 km and real-time satellite observations to increase the accuracy of cyclone tracking and intensity prediction. By integrating these methodologies, we aim to bridge the gap between simulated data and observed phenomena, providing a more comprehensive understanding of cyclone dynamics. The time series graph showed good resemblances between the simulated outputs and observed data, providing evidence of the accuracy of the simulation. Additionally, the results derived from both models and satellites demonstrate a satisfactory level of concurrence with the observations. The rapid movement of cyclones, rather than being well predicted makes it challenging for decision-makers to deploy effective mitigation measures promptly. This comparative study underscores the importance of leveraging advanced modeling techniques and satellite technology to mitigate the adverse effects of such natural disasters on society.
{"title":"Simulation and observation of tropical cyclone Sitrang: A comparative study using WRF-ARW model and geostationary satellite imagery","authors":"Hassan Md. Naveed Anzum , Himel Bosu , Shakhawat Hossain , Quazi Aseer Faisal , Raiyan Ahamed , Md. Mehedi Hasan","doi":"10.1016/j.nhres.2025.04.004","DOIUrl":"10.1016/j.nhres.2025.04.004","url":null,"abstract":"<div><div>Tropical Cyclones pose significant threats to coastal regions, necessitating precise prediction and monitoring for effective disaster management. This study represents a comparative analysis of simulated and observed data for Tropical Cyclone Sitrang, focusing on key meteorological features such as temperature, pressure, rainfall, wind speed, and wind direction. The analysis utilizes the Weather Research and Forecasting Advanced Research WRF (WRF-ARW) model alongside geostationary satellite imagery from INSAT-3D, MSG, and multi-mission satellites. The novelty of this research lies in its dual approach, which combines high-resolution numerical modeling for 48 h in a single domain with a horizontal resolution of 27 km and real-time satellite observations to increase the accuracy of cyclone tracking and intensity prediction. By integrating these methodologies, we aim to bridge the gap between simulated data and observed phenomena, providing a more comprehensive understanding of cyclone dynamics. The time series graph showed good resemblances between the simulated outputs and observed data, providing evidence of the accuracy of the simulation. Additionally, the results derived from both models and satellites demonstrate a satisfactory level of concurrence with the observations. The rapid movement of cyclones, rather than being well predicted makes it challenging for decision-makers to deploy effective mitigation measures promptly. This comparative study underscores the importance of leveraging advanced modeling techniques and satellite technology to mitigate the adverse effects of such natural disasters on society.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 4","pages":"Pages 907-918"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145698252","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}
Recent climate change has resulted in the shrinkage of glaciers and the expansion of the glacial lakes in the Himalayas, thereby increasing the risk of Glacial Lake Outburst Floods (GLOFs). GLOFs from moraine-dammed glacial lakes are often liable to many casualties and colossal devastation of the downstream settlements and infrastructure. Nevertheless, these hazards have been largely overlooked in the Manaslu Region of the Nepal Himalaya, which witnessed a small GLOF from Birendra glacial lake on April 21, 2024, due to a snow-ice avalanche. Here, we used an integrated approach to study the evolution of Birendra Lake and its parent glaciers. We then conducted its GLOF hazard assessment in multi-scenarios by employing remote sensing, geographic information system (GIS), and hydrodynamic model. The results show that the parent glacier shrunk from 25.842 ± 1.21 to 21.56 ± 1.26 km2 between 1988 and 2024, and subsequently, the glacial lake expanded from 0.09 ± 0.02 km2 to 0.22 ± 0.03 km2. Three anticipated multi-scenario GLOF simulations were run using a two-dimensional (2D) dam break model, and the resulting flow was routed approximately 45 km downstream from the dam site. The results showed that the peak dam break flow ranges between 909 and 3768 m3/s in 10, 15, and 20 m breach height scenarios, and approximately 110 buildings on the downstream side will be submerged in the worst scenario. This study provides insights into the possible consequences of GLOFs in the Himalayan headwaters and contributes to planning and formulating disaster risk reduction and mitigation programs, particularly in the Manaslu region.
{"title":"Multi-breach GLOF hazard and exposure analysis of Birendra Lake in the Manaslu Region of Nepal","authors":"Utsav Poudel , Manish Raj Gouli , Kaiheng Hu , Nitesh Khadka , Ram Krishna Regmi , Bhesh Raj Thapa","doi":"10.1016/j.nhres.2025.03.007","DOIUrl":"10.1016/j.nhres.2025.03.007","url":null,"abstract":"<div><div>Recent climate change has resulted in the shrinkage of glaciers and the expansion of the glacial lakes in the Himalayas, thereby increasing the risk of Glacial Lake Outburst Floods (GLOFs). GLOFs from moraine-dammed glacial lakes are often liable to many casualties and colossal devastation of the downstream settlements and infrastructure. Nevertheless, these hazards have been largely overlooked in the Manaslu Region of the Nepal Himalaya, which witnessed a small GLOF from Birendra glacial lake on April 21, 2024, due to a snow-ice avalanche. Here, we used an integrated approach to study the evolution of Birendra Lake and its parent glaciers. We then conducted its GLOF hazard assessment in multi-scenarios by employing remote sensing, geographic information system (GIS), and hydrodynamic model. The results show that the parent glacier shrunk from 25.842 ± 1.21 to 21.56 ± 1.26 km<sup>2</sup> between 1988 and 2024, and subsequently, the glacial lake expanded from 0.09 ± 0.02 km<sup>2</sup> to 0.22 ± 0.03 km<sup>2</sup>. Three anticipated multi-scenario GLOF simulations were run using a two-dimensional (2D) dam break model, and the resulting flow was routed approximately 45 km downstream from the dam site. The results showed that the peak dam break flow ranges between 909 and 3768 m<sup>3</sup>/s in 10, 15, and 20 m breach height scenarios, and approximately 110 buildings on the downstream side will be submerged in the worst scenario. This study provides insights into the possible consequences of GLOFs in the Himalayan headwaters and contributes to planning and formulating disaster risk reduction and mitigation programs, particularly in the Manaslu region.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 4","pages":"Pages 800-813"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145698212","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}
Flood is a recurrent destructive natural calamity in the Kosi River Basin (KRB) in north Bihar in India. Geospatial modelling of these recurrent floods becomes imperative for effective disaster management. The KRB is renowned for its high vulnerability to flooding due to its sudden bending and heavy rainfall in the upper catchment of the basin located in Nepal. This study presents a comprehensive assessment of flood risk over the KRB, by utilizing the Analytical Hierarchy Process approach and Synthetic Aperture Radar data. The risk map was generated by considering multivariate set of factors including physical (elevation, slope), geological and hydrological variables (flood frequency, rainfall intensity, drainage network). Flood inundation and rainfall intensity are calculated over six years (2015–2020) to understand the dynamic nature of floods. The results of this analysis provide detailed flood inundation and risk maps, highlighting areas at varying levels of vulnerability and risk. Higher flood inundation was seen in downstream areas, which accounted for 6526.3 km2 (33%) of geographical areas. Flood inundation was highest in 2020 and 2019 accounting for 27.93% and 20.72% of areas, respectively, whereas the lowest flood inundation was seen in 2015 (4.14%). Areas under higher flood risk were 1383.7 km2 (7%), whereas 3820.9 km2 (19.4%) were at lower flood risk. Extremely flat downstream areas near riverbanks were at higher risk (7% of KRB) that has correspondence with higher flood frequency. The spatially explicit flood risk zone information can be invaluable for disaster preparedness and policymakers. Furthermore, flood risk assessment can reinforce resilience to improve land use planning, insurance planning, flood-prone area management, and raising public awareness of potential flood risks.
{"title":"Flood risk assessment of the Kosi River Basin in North Bihar using Synthetic Aperture Radar (SAR) data and AHP approach","authors":"Sourav Kumar , Bikash Ranjan Parida , K.K. Basheer Ahammed","doi":"10.1016/j.nhres.2025.02.002","DOIUrl":"10.1016/j.nhres.2025.02.002","url":null,"abstract":"<div><div>Flood is a recurrent destructive natural calamity in the Kosi River Basin (KRB) in north Bihar in India. Geospatial modelling of these recurrent floods becomes imperative for effective disaster management. The KRB is renowned for its high vulnerability to flooding due to its sudden bending and heavy rainfall in the upper catchment of the basin located in Nepal. This study presents a comprehensive assessment of flood risk over the KRB, by utilizing the Analytical Hierarchy Process approach and Synthetic Aperture Radar data. The risk map was generated by considering multivariate set of factors including physical (elevation, slope), geological and hydrological variables (flood frequency, rainfall intensity, drainage network). Flood inundation and rainfall intensity are calculated over six years (2015–2020) to understand the dynamic nature of floods. The results of this analysis provide detailed flood inundation and risk maps, highlighting areas at varying levels of vulnerability and risk. Higher flood inundation was seen in downstream areas, which accounted for 6526.3 km<sup>2</sup> (33%) of geographical areas. Flood inundation was highest in 2020 and 2019 accounting for 27.93% and 20.72% of areas, respectively, whereas the lowest flood inundation was seen in 2015 (4.14%). Areas under higher flood risk were 1383.7 km<sup>2</sup> (7%), whereas 3820.9 km<sup>2</sup> (19.4%) were at lower flood risk. Extremely flat downstream areas near riverbanks were at higher risk (7% of KRB) that has correspondence with higher flood frequency. The spatially explicit flood risk zone information can be invaluable for disaster preparedness and policymakers. Furthermore, flood risk assessment can reinforce resilience to improve land use planning, insurance planning, flood-prone area management, and raising public awareness of potential flood risks.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 3","pages":"Pages 618-632"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128448","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 : 2025-09-01DOI: 10.1016/j.nhres.2025.02.003
Andrew S. Pyle , Hillary Smith , Ryan P. Fuller
Our study presents a thematic analysis of 1582 posts from five digital emergent Facebook groups during Hurricane Florence, affecting North and South Carolina in the United States in 2018. We pose four research questions related to the formation and functioning of digital emergent groups during a disaster. Specifically, we examined how groups contributed to response efforts; messages developed across stages of the disaster; grassroots groups' organized disaster response; and how proximity affected group functioning. Participants contributed to disaster response by sharing and seeking information and aid. Groups coordinated logistics; offered affirmation; and provided commentary and humor. Group members supported the formal response through information sharing. Moreover, group members' proximity to the disaster and group structures designed to moderate groups were influential in the groups’ goal attainment. We see an opportunity for emergency managers to coordinate with digital emergent groups through trusted sources such as nonprofits active in disaster response. Implications for theory and practice are discussed.
{"title":"Emergent groups and the informal digital emergent response to hurricane florence in the United States","authors":"Andrew S. Pyle , Hillary Smith , Ryan P. Fuller","doi":"10.1016/j.nhres.2025.02.003","DOIUrl":"10.1016/j.nhres.2025.02.003","url":null,"abstract":"<div><div>Our study presents a thematic analysis of 1582 posts from five digital emergent Facebook groups during Hurricane Florence, affecting North and South Carolina in the United States in 2018. We pose four research questions related to the formation and functioning of digital emergent groups during a disaster. Specifically, we examined how groups contributed to response efforts; messages developed across stages of the disaster; grassroots groups' organized disaster response; and how proximity affected group functioning. Participants contributed to disaster response by sharing and seeking information and aid. Groups coordinated logistics; offered affirmation; and provided commentary and humor. Group members supported the formal response through information sharing. Moreover, group members' proximity to the disaster and group structures designed to moderate groups were influential in the groups’ goal attainment. We see an opportunity for emergency managers to coordinate with digital emergent groups through trusted sources such as nonprofits active in disaster response. Implications for theory and practice are discussed.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 3","pages":"Pages 633-643"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128450","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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