Objective:The objective of this study was to construct an early warning system(EWS)to facilitate risk assessment,early identification,and appropriate treatment of enteral nutrition feeding intolerance(FI)in patients w...Objective:The objective of this study was to construct an early warning system(EWS)to facilitate risk assessment,early identification,and appropriate treatment of enteral nutrition feeding intolerance(FI)in patients with stroke,so as to provide a reference for risk classification standards and interventions toward a complete EWSs for nursing care of stroke.Materials and Methods:Based on evidence and clinical nursing practice,a structured expert consultation method was adopted on nine experts over two rounds of consultation.Statistical analysis was used to determine the early warning index for FI in patients with stroke.Results:The expert authority coefficient was 0.89;the coefficients of variation for the two rounds of consultation were 0.088-0.312 and 0.096-0.214,respectively.There were significant differences in the Kendall’s concordance coefficient(P<0.05).Finally,22 items in five dimensions of patient age,disease,treatment,biochemical,and enteral nutrition-related factors were identified.Conclusion:The early warning index for FI in patients with a history of stroke is valid and practical.It provides a reference for the early clinical identification of FI risk.展开更多
Bringing together global efforts to enhance the implementation of warnings in managing vulnerabilities,hazards,risks,and disasters is essential to saving lives and for long-term vulnerability reduction.Ten years into ...Bringing together global efforts to enhance the implementation of warnings in managing vulnerabilities,hazards,risks,and disasters is essential to saving lives and for long-term vulnerability reduction.Ten years into the implementation of the Sendai Framework for Disaster Risk Reduction 2015-2030(SFDRR),there has been a renewed focus on warnings following the 2022 announcement by the United Nations Secretary-General of the five-year goal of Early Warnings for All.Delivering on Target G of the SFDRR has subsequently generated significant outcomes,however substantial gaps remain with implementing effective early warning systems(EWS).This article charts the policy evolution of warnings within the UN context and outlines the progress and remaining gaps of EWS in the SFDRR to date.Three key gaps that hinder the effective delivery of SFDRR and beyond are identified:(1)the need for common understanding of warning processes and terminology,such as multi-hazard EWS,and further elucidation of indicators used to measure and chart progress;(2)the need to mobilize and strengthen existing EWS,many of which are not formally recognized yet do the work of warnings across actors and entities,especially in fragile or resource-poor contexts;and(3)the need to foster collaboration between the multitude of actors and approaches involved in all forms of warnings,including people-centered warnings to address diversity and inclusivity,and integrate top-down and bottom-up approaches across sectors.Significant barriers to working across the numerous silos(institutional,geographical,political,and scientific)must be overcome to generate effective people-centered multi-hazard EWS to support disaster risk reduction in the future.Recommendations on how to fill these gaps in future frameworks are provided,to support people-centered,integrated warnings for all.展开更多
The third UN World Congress on Disaster Risk Reduction, held in Sendai, Japan in March 2015, agreed on a new framework to guide disaster risk reduction policy and practice for the next 15 years. The Sendai Framework f...The third UN World Congress on Disaster Risk Reduction, held in Sendai, Japan in March 2015, agreed on a new framework to guide disaster risk reduction policy and practice for the next 15 years. The Sendai Framework for Disaster Risk Reduction 2015–2030(SFDRR) leaves important implementation issues unspecified and potentially creates both problems and opportunities for complex,multilevel governance systems in coping with hazards and disastrous events. Early warning systems(EWS), if built into the mainstream of planning for development and disaster relief and recovery, could present a significant opportunity to realize many SFDRR goals. We explore the complexities of using hydrometeorological EWS to prepare for drought and flood disasters in the densely populated communities of Pakistan’s Indus River Basin in contrast to the African Sahel’s less densely settled grasslands. Multilevel governance systems are often dominated by a topdown, technocentric, centralized management bias and have great difficulty responding to the needs of peripheral and vulnerable populations. People-centered, bottom-up approaches that incorporate disaggregated communities with local knowledge into a balanced, multilevel disaster risk management and governance structure have adramatically better chance of realizing the SFDRR goals for disaster risk reduction.展开更多
Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall ...Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall short in capturing complex,nonlinear,and real-time environmental dynamics.In recent years,machine learning(ML)and deep learning(DL)techniques have emerged as promising alternatives for enhancing the accuracy,speed,and scalability of EWS.This review critically evaluates the evolution of ML models—such as Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—in coastal flood prediction,highlighting their architectures,data requirements,performance metrics,and implementation challenges.A unique contribution of this work is the synthesis of real-time deployment challenges including latency,edge-cloud tradeoffs,and policy-level integration,areas often overlooked in prior literature.Furthermore,the review presents a comparative framework of model performance across different geographic and hydrologic settings,offering actionable insights for researchers and practitioners.Limitations of current AI-driven models,such as interpretability,data scarcity,and generalization across regions,are discussed in detail.Finally,the paper outlines future research directions including hybrid modelling,transfer learning,explainable AI,and policy-aware alert systems.By bridging technical performance and operational feasibility,this review aims to guide the development of next-generation intelligent EWS for resilient and adaptive coastal management.展开更多
Earthquake early warning （EEW） systems are a new and effective way to mitigate the damage associated with earthquakes. A prototype EEW system is currently being constructed in the Fujian Province, a region along the...Earthquake early warning （EEW） systems are a new and effective way to mitigate the damage associated with earthquakes. A prototype EEW system is currently being constructed in the Fujian Province, a region along the Southeast coast of China. It is anticipated that the system will be completed in time to be tested at the end of this year （2013）. In order to evaluate how much advanced warning the EEW system will be able to provide different cities in Fujian, we established an EEW information release scheme based on the seismic monitoring stations distributed in the region. Based on this scheme, we selected 71 historical earthquakes. We then obtained the delineation of the region＇s potential seismic source data in order to estimate the highest potential seismic intensities for each city as well as the EEW system warning times. For most of the Fujian Province, EEW alarms would sound several seconds prior to the arrival of the destructive wave. This window of time gives city inhabitants the opportunity to take protective measures before the full intensity of the earthquake strikes.展开更多
Given the contemporary increase in the frequency,intensity,and duration of extreme anomalous hydromet hazards(droughts,foods,tropical storms,heatwaves),heightened attention of governments,scientists,media,and humanita...Given the contemporary increase in the frequency,intensity,and duration of extreme anomalous hydromet hazards(droughts,foods,tropical storms,heatwaves),heightened attention of governments,scientists,media,and humanitarian organizations is being given to hydromet early warning systems.The focus of this article is multidisciplinary and multifaceted:it involves connecting an earliest warning indicator associated with the Oceanic Niño Index,one that complements the existing National Oceanic and Atmospheric Administration indicator,with early warning early action and anticipatory action approaches for disaster risk reduction(DRR).This new indicator in theory at least could increase the lead time between the release of an ofcial forecast of an El Niño and the frst appearance of its adverse impacts,thereby serving as the earliest warning of an event.As such,this DRR research links new usable earliest warning information,providing additional time to initiate tactical actions to cope with El Niño-spawned hydromet hazards.Integrating an earliest indicator of the likely onset of an El Niño into early action frameworks would hasten humanitarian assistance by providing at-risk communities and humanitarian organizations with more time to consider a range of options for responding to El Niño’s impacts.展开更多
Clarifying the mechanisms through which coal mining affects groundwater storage(GWS)variations is crucial for water resource conservation and sustainable development.The Ordos Mining Region in China,a key energy base ...Clarifying the mechanisms through which coal mining affects groundwater storage(GWS)variations is crucial for water resource conservation and sustainable development.The Ordos Mining Region in China,a key energy base in China with significant strategic importance,has undergone intensive coal mining activities that have substantially disrupted regional groundwater circulation.This study integrated data from the Gravity Recovery and Climate Experiment Satellite(GRACE)and Famine Early Warning Systems Network(FEWS NET)Land Data Assimilation System(FLDAS)models,combined with weighted downscaling methodology and water balance principles,to reconstruct high-resolution(0.01°)terrestrial water storage(TWS)and GWS changes in the Ordos Mining Region,China from April 2002 to December 2021.The accuracy of GWS variations were validated through pumping test measurements.Subsequently,Geodetector analysis was implemented to quantify the contributions of natural and anthropogenic factors to groundwater storage dynamics.Key findings include:1)TWS in the study area showed a fluctuating but overall decreasing trend,with a total reduction of 8901.11 mm during study period.The most significant annual decrease occurred in 2021,reaching 1696.77 mm.2)GWS exhibited an accelerated decline,with an average annual change rate of 44.35 mm/yr,totaling a decrease of 887.05 mm.The lowest annual groundwater storage level was recorded in 2020,reaching 185.69 mm.3)Precipitation(PRE)contributed the most to GWS variation(q=0.52),followed by coal mining water consumption(MWS)(q=0.41).The interaction between PRE and MWS exhibited a nonlinear enhancement effect on GWS changes(0.54).The synergistic effect of natural hydrological factors has a great influence on the change of GWS,but coal mining water consumption will continue to reduce GWS.These findings provide critical references for the management and regulation of groundwater resource in mining regions.展开更多
Landslides induced by prolonged rainfalls are frequent mass movements along the northeastern portion of the Sierra Madre Oriental in Mexico,causing significant damage to infrastructure.In this work,we have studied the...Landslides induced by prolonged rainfalls are frequent mass movements along the northeastern portion of the Sierra Madre Oriental in Mexico,causing significant damage to infrastructure.In this work,we have studied the connection between rainfall and landslides in the Santa Rosa Canyon,a catchment located in the northeastern Mexico.A landslide database triggered by major storms and hurricanes that have hit the region over the past 30 years was analyzed.A total of 92 rainfall events in the Santa Rosa Canyon were studied to determine the amount of precipitation needed to trigger shallow landslides.For each event the duration(D,in hours)and the cumulated rainfall event(E,in mm)were determined by using historical rainfall data from weather stations located near the study area.We have proposed an ED threshold for rainfall-induced landslides with durations 0.5<D<120 hours to address the conditions that trigger these events in the study area.On analyzing the obtained threshold,it has been established that almost 60 mm of a daily rainfall accumulation is required to trigger shallow landslides in the study area.This estimation is consistent with other calculations made for areas close to the Santa Rosa Canyon.Finally,we validated the predictive capability of the threshold with a different set of rainfall data that did not result in landslides performing a straightforward receiver operating characteristic analysis.A good approach was obtained,especially for rainfall events with daily measurements.Results could be used as input information in the design of a landslide early warning system for the northeastern Mexico,and replicated for other landslide prone areas in the region.展开更多
Simple GNSS navigation receivers, developed for the mass market, can be used for positioning with sub centimeter accuracy in a wireless sensor network if the read-out of the carrier phase data is possible and all data...Simple GNSS navigation receivers, developed for the mass market, can be used for positioning with sub centimeter accuracy in a wireless sensor network if the read-out of the carrier phase data is possible and all data is permanently broadcast to a central computer for near real time processing of the respective base lines. Experiences gained in two research projects related to landslide monitoring are depicted in terms of quality and reliability of the results by the developed approach. As far as possible a modular system set up with commercial off-the-shelf components, e.g., standard WLAN fur commtmication, solar batteries with solar panels for autarkic power supply and in cooperation of existing proofed program tools is chosen. The challenge of the still ongoing development is to have a flexible and robust GNSS based sensor network available - concerned not only for landslide monitoring in future.展开更多
Earthquakes pose significant risks globally,necessitating effective seismic risk mitigation strategies like earthquake early warning(EEW)systems.However,developing and optimizing such systems requires thoroughly under...Earthquakes pose significant risks globally,necessitating effective seismic risk mitigation strategies like earthquake early warning(EEW)systems.However,developing and optimizing such systems requires thoroughly understanding their internal procedures and coverage limitations.This study examines a deep-learning-based on-site EEW framework known as ROSERS(Real-time On-Site Estimation of Response Spectra)proposed by the authors,which constructs response spectra from early recorded ground motion waveforms at a target site.This study has three primary goals:(1)evaluating the effectiveness and applicability of ROSERS to subduction seismic sources;(2)providing a detailed interpretation of the trained deep neural network(DNN)and surrogate latent variables(LVs)implemented in ROSERS;and(3)analyzing the spatial efficacy of the framework to assess the coverage area of on-site EEW stations.ROSERS is retrained and tested on a dataset of around 11,000 unprocessed Japanese subduction ground motions.Goodness-of-fit testing shows that the ROSERS framework achieves good performance on this database,especially given the peculiarities of the subduction seismic environment.The trained DNN and LVs are then interpreted using game theory-based Shapley additive explanations to establish cause-effect relationships.Finally,the study explores the coverage area of ROSERS by training a novel spatial regression model that estimates the LVs using geographically weighted random forest and determining the radius of similarity.The results indicate that on-site predictions can be considered reliable within a 2–9 km radius,varying based on the magnitude and distance from the earthquake source.This information can assist end-users in strategically placing sensors,minimizing blind spots,and reducing errors from regional extrapolation.展开更多
Climate-related hazards like drought are associated with loss of life and lead to food insecurity in many parts of sub-Saharan Africa. Food insecurity, which affects more than 220 million sub-Saharan Africans, manifes...Climate-related hazards like drought are associated with loss of life and lead to food insecurity in many parts of sub-Saharan Africa. Food insecurity, which affects more than 220 million sub-Saharan Africans, manifests as starvation that leads to more than 50% of children under the age of 5-years presenting as underweight for age in many communities on the continent. This household survey reports the means by which rural fisher folk and farming communities in Uganda gained access to early warning meteorological information. The survey covered five districts across different climatic zones in Uganda and recruited a total of 405 respondents with an average age of 41 years (SD 16). Economic activity was used to categorize each of the five districts into farming (crops and livestock) and fishing areas. The results showed that most respondents were unaware of drought as one of the climate-related hazards. Compared to respondents from the fishing communities, the respondents from farming communities were more likely to be receiving weather-related information (<em>P</em>-value < 0.01). There were 204/405 (50.37%) female respondents who, compared to male respondents, were less likely to have access to weather information, less willing to pay for weather information, and less likely to have and/or own devices like a radio for receiving weather information. The survey demonstrated that: 1) there were gaps in the knowledge about climate-related hazards, 2) there is a need for additional interventions targeting fisher folk communities access timely weather information, and 3) introducing user paid access to weather information may increase climate-related gender-based disparities.展开更多
incidents of extreme hyperbole and fraud in celebrity advertisements have occurred repeatedly because advertising participants are driven by commercial interests and it is also relevant to the deep social and cultural...incidents of extreme hyperbole and fraud in celebrity advertisements have occurred repeatedly because advertising participants are driven by commercial interests and it is also relevant to the deep social and cultural background. Therefore, great and prolonged efforts should be made to govern celebrity advertising in a multi-pronged way： strengthening legal supervision on the basis of clearly defined false advertising; establishing early warning and punishing systems including pre-qualification system, filing system and banning system; promoting public interest litigation system; increasing consumers＇ media literacy.展开更多
On 14 March 2019,Zimbabwe was hit by Cyclone Idai,leaving immeasurable destruction of unprecedented magnitude in its wake.In Chimanimani District,many lives were lost,many people were reported missing,and others were ...On 14 March 2019,Zimbabwe was hit by Cyclone Idai,leaving immeasurable destruction of unprecedented magnitude in its wake.In Chimanimani District,many lives were lost,many people were reported missing,and others were displaced.The question that immediately comes to mind is:Was the country prepared to manage the Cyclone Idai disaster?Reflecting on the community experiences,the purpose of this research was to interrogate the strength of the disaster risk reduction legislation and institutions in Zimbabwe in the face of meteorological hazards.The research also evaluated the extent of the impact Cyclone Idai had on the Chimanimani communities and the factors that increased the vulnerability to the cyclone.A mixed method approach that involved 1180 participants was used.The study found that disaster risk management legislation and institutions in Zimbabwe are weak.Cyclone Idai resulted in the loss of many human lives,loss of livelihoods,and massive damage to infrastructure.The cyclone exposed capacity and policy gaps in Zimbabwe’s disaster risk management system.The study makes a number of recommendations,including strengthening disaster legislation and policy,and disaster risk governance.Given the communities’response to the disaster occurrence,the study also recommends strengthening social capital.展开更多
1.Introduction When natural hazards such as floods,droughts,and wildfires continue to escalate globally,timely acquisition of disaster information remains crucial for rapid on ground situation assessment.Despite advan...1.Introduction When natural hazards such as floods,droughts,and wildfires continue to escalate globally,timely acquisition of disaster information remains crucial for rapid on ground situation assessment.Despite advances in satellite-based emergency mapping,delays persist highlighting the need to enhance early warning systems and overcome limitations of conventional,observation-based workflows.The integration of Big Data and Artificial Intelligence(BD&AI)through the fusion of available vast Remote Sensing(RS)datasets with Machine Learning(ML)algorithms allows us to achieve near real-time monitoring of hazards,improve their prediction.展开更多
Power systems are critical to modern society,providing the backbone for all economic,industrial,and residential activities.However,these systems face significant threats from both natural and man-made disasters,such a...Power systems are critical to modern society,providing the backbone for all economic,industrial,and residential activities.However,these systems face significant threats from both natural and man-made disasters,such as earthquakes,floods,hurricanes,cyber-attacks,and equipment failures.The vulnerability of power systems to these disasters can result in prolonged outages,economic losses,safety risks,and social consequences.This paper explores various disaster prevention and mitigation technologies employed in power systems to enhance resilience and ensure reliable electricity supply during and after disasters.It discusses early warning systems for natural hazards,resilient infrastructure designs,and cybersecurity measures to defend against man-made threats.Additionally,it examines fault detection,power system restoration,and disaster-resilient control strategies that aid in quick recovery from disruptions.The integration of smart grids,renewable energy sources,and advanced control technologies is emphasized as crucial for improving disaster preparedness and minimizing recovery time.Through the application of these technologies,power systems can better withstand and recover from the growing risks posed by both natural and human-induced disasters.展开更多
A crucial part of proposed earthquake early warning systems is a rapid estimate for earthquake magnitude.Most of these methods are focused on the first part of the P-wave train,the earlier and less destructive part of...A crucial part of proposed earthquake early warning systems is a rapid estimate for earthquake magnitude.Most of these methods are focused on the first part of the P-wave train,the earlier and less destructive part of the ground motion that follows an earthquake.A method has been proposed by using the period of the P-wave to determine the magnitude of a large earthquake at local distance,and a specific relation for the Sichuan region was calibrated according to acceleration records of Wenchuan earthquake.The Mw 6.6 earthquake hit Lushan County,Sichuan,on April 20,2013 and the largest aftershocks provide a useful dataset to validate the proposed relation and discuss the risks connected to the extrapolation of magnitude relations with a poor dataset of large earthquake waveforms.A discrepancy between the local magnitude(ML)estimated by means ofτc evaluation and the standard ML(6.4 vs.7.0)suggests using caution when ML vs.τc calibrations do not include a relevant dataset of large earthquakes.Effects from large residuals could be mitigated or removed by introducing selection rules onτc function,by regionalizing the ML vs.τc function in the presence of significant tectonic or geological heterogeneity,and by using probabilistic and evolutionary methods.展开更多
基金supported by the Young Teacher Project of the Beijing University of Chinese Medicine(No.:2018-JYB-JS155).
文摘Objective:The objective of this study was to construct an early warning system(EWS)to facilitate risk assessment,early identification,and appropriate treatment of enteral nutrition feeding intolerance(FI)in patients with stroke,so as to provide a reference for risk classification standards and interventions toward a complete EWSs for nursing care of stroke.Materials and Methods:Based on evidence and clinical nursing practice,a structured expert consultation method was adopted on nine experts over two rounds of consultation.Statistical analysis was used to determine the early warning index for FI in patients with stroke.Results:The expert authority coefficient was 0.89;the coefficients of variation for the two rounds of consultation were 0.088-0.312 and 0.096-0.214,respectively.There were significant differences in the Kendall’s concordance coefficient(P<0.05).Finally,22 items in five dimensions of patient age,disease,treatment,biochemical,and enteral nutrition-related factors were identified.Conclusion:The early warning index for FI in patients with a history of stroke is valid and practical.It provides a reference for the early clinical identification of FI risk.
文摘Bringing together global efforts to enhance the implementation of warnings in managing vulnerabilities,hazards,risks,and disasters is essential to saving lives and for long-term vulnerability reduction.Ten years into the implementation of the Sendai Framework for Disaster Risk Reduction 2015-2030(SFDRR),there has been a renewed focus on warnings following the 2022 announcement by the United Nations Secretary-General of the five-year goal of Early Warnings for All.Delivering on Target G of the SFDRR has subsequently generated significant outcomes,however substantial gaps remain with implementing effective early warning systems(EWS).This article charts the policy evolution of warnings within the UN context and outlines the progress and remaining gaps of EWS in the SFDRR to date.Three key gaps that hinder the effective delivery of SFDRR and beyond are identified:(1)the need for common understanding of warning processes and terminology,such as multi-hazard EWS,and further elucidation of indicators used to measure and chart progress;(2)the need to mobilize and strengthen existing EWS,many of which are not formally recognized yet do the work of warnings across actors and entities,especially in fragile or resource-poor contexts;and(3)the need to foster collaboration between the multitude of actors and approaches involved in all forms of warnings,including people-centered warnings to address diversity and inclusivity,and integrate top-down and bottom-up approaches across sectors.Significant barriers to working across the numerous silos(institutional,geographical,political,and scientific)must be overcome to generate effective people-centered multi-hazard EWS to support disaster risk reduction in the future.Recommendations on how to fill these gaps in future frameworks are provided,to support people-centered,integrated warnings for all.
基金funding from the National Science Foundation for EPS-1101317 project on ‘‘Research on Adaptation to Climate Change’’NSF-SESYNC/NIMBIOS DBI-1052875 project on ‘‘Integrating Human Risk Perception of Global Climate Change into Dynamic Earth System Models’’
文摘The third UN World Congress on Disaster Risk Reduction, held in Sendai, Japan in March 2015, agreed on a new framework to guide disaster risk reduction policy and practice for the next 15 years. The Sendai Framework for Disaster Risk Reduction 2015–2030(SFDRR) leaves important implementation issues unspecified and potentially creates both problems and opportunities for complex,multilevel governance systems in coping with hazards and disastrous events. Early warning systems(EWS), if built into the mainstream of planning for development and disaster relief and recovery, could present a significant opportunity to realize many SFDRR goals. We explore the complexities of using hydrometeorological EWS to prepare for drought and flood disasters in the densely populated communities of Pakistan’s Indus River Basin in contrast to the African Sahel’s less densely settled grasslands. Multilevel governance systems are often dominated by a topdown, technocentric, centralized management bias and have great difficulty responding to the needs of peripheral and vulnerable populations. People-centered, bottom-up approaches that incorporate disaggregated communities with local knowledge into a balanced, multilevel disaster risk management and governance structure have adramatically better chance of realizing the SFDRR goals for disaster risk reduction.
文摘Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall short in capturing complex,nonlinear,and real-time environmental dynamics.In recent years,machine learning(ML)and deep learning(DL)techniques have emerged as promising alternatives for enhancing the accuracy,speed,and scalability of EWS.This review critically evaluates the evolution of ML models—such as Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—in coastal flood prediction,highlighting their architectures,data requirements,performance metrics,and implementation challenges.A unique contribution of this work is the synthesis of real-time deployment challenges including latency,edge-cloud tradeoffs,and policy-level integration,areas often overlooked in prior literature.Furthermore,the review presents a comparative framework of model performance across different geographic and hydrologic settings,offering actionable insights for researchers and practitioners.Limitations of current AI-driven models,such as interpretability,data scarcity,and generalization across regions,are discussed in detail.Finally,the paper outlines future research directions including hybrid modelling,transfer learning,explainable AI,and policy-aware alert systems.By bridging technical performance and operational feasibility,this review aims to guide the development of next-generation intelligent EWS for resilient and adaptive coastal management.
基金National Key Technology R&D Program (2009BAK55B03)
文摘Earthquake early warning （EEW） systems are a new and effective way to mitigate the damage associated with earthquakes. A prototype EEW system is currently being constructed in the Fujian Province, a region along the Southeast coast of China. It is anticipated that the system will be completed in time to be tested at the end of this year （2013）. In order to evaluate how much advanced warning the EEW system will be able to provide different cities in Fujian, we established an EEW information release scheme based on the seismic monitoring stations distributed in the region. Based on this scheme, we selected 71 historical earthquakes. We then obtained the delineation of the region＇s potential seismic source data in order to estimate the highest potential seismic intensities for each city as well as the EEW system warning times. For most of the Fujian Province, EEW alarms would sound several seconds prior to the arrival of the destructive wave. This window of time gives city inhabitants the opportunity to take protective measures before the full intensity of the earthquake strikes.
基金support provided by the Office of U.S.Foreign Disaster Assistance,Bureau for Democracy,Conflict and Humanitarian Assistancem U.S. Agency for International Development
文摘Given the contemporary increase in the frequency,intensity,and duration of extreme anomalous hydromet hazards(droughts,foods,tropical storms,heatwaves),heightened attention of governments,scientists,media,and humanitarian organizations is being given to hydromet early warning systems.The focus of this article is multidisciplinary and multifaceted:it involves connecting an earliest warning indicator associated with the Oceanic Niño Index,one that complements the existing National Oceanic and Atmospheric Administration indicator,with early warning early action and anticipatory action approaches for disaster risk reduction(DRR).This new indicator in theory at least could increase the lead time between the release of an ofcial forecast of an El Niño and the frst appearance of its adverse impacts,thereby serving as the earliest warning of an event.As such,this DRR research links new usable earliest warning information,providing additional time to initiate tactical actions to cope with El Niño-spawned hydromet hazards.Integrating an earliest indicator of the likely onset of an El Niño into early action frameworks would hasten humanitarian assistance by providing at-risk communities and humanitarian organizations with more time to consider a range of options for responding to El Niño’s impacts.
基金Under the National Key R&D Program Key Project(No.2021YFC3201201)National Natural Science Foundation of China(No.52360032)+2 种基金Basic Scientific Research Business Fee Project of Colleges And Universities Directly Under the Inner Mongolia Autonomous Region(No.JBYYWF2022001)Development Plan of Innovation Team of Colleges And Universities in Inner Mongolia Autonomous Region(No.NMGIRT2313)the Innovation Team of‘Grassland Talents’。
文摘Clarifying the mechanisms through which coal mining affects groundwater storage(GWS)variations is crucial for water resource conservation and sustainable development.The Ordos Mining Region in China,a key energy base in China with significant strategic importance,has undergone intensive coal mining activities that have substantially disrupted regional groundwater circulation.This study integrated data from the Gravity Recovery and Climate Experiment Satellite(GRACE)and Famine Early Warning Systems Network(FEWS NET)Land Data Assimilation System(FLDAS)models,combined with weighted downscaling methodology and water balance principles,to reconstruct high-resolution(0.01°)terrestrial water storage(TWS)and GWS changes in the Ordos Mining Region,China from April 2002 to December 2021.The accuracy of GWS variations were validated through pumping test measurements.Subsequently,Geodetector analysis was implemented to quantify the contributions of natural and anthropogenic factors to groundwater storage dynamics.Key findings include:1)TWS in the study area showed a fluctuating but overall decreasing trend,with a total reduction of 8901.11 mm during study period.The most significant annual decrease occurred in 2021,reaching 1696.77 mm.2)GWS exhibited an accelerated decline,with an average annual change rate of 44.35 mm/yr,totaling a decrease of 887.05 mm.The lowest annual groundwater storage level was recorded in 2020,reaching 185.69 mm.3)Precipitation(PRE)contributed the most to GWS variation(q=0.52),followed by coal mining water consumption(MWS)(q=0.41).The interaction between PRE and MWS exhibited a nonlinear enhancement effect on GWS changes(0.54).The synergistic effect of natural hydrological factors has a great influence on the change of GWS,but coal mining water consumption will continue to reduce GWS.These findings provide critical references for the management and regulation of groundwater resource in mining regions.
文摘Landslides induced by prolonged rainfalls are frequent mass movements along the northeastern portion of the Sierra Madre Oriental in Mexico,causing significant damage to infrastructure.In this work,we have studied the connection between rainfall and landslides in the Santa Rosa Canyon,a catchment located in the northeastern Mexico.A landslide database triggered by major storms and hurricanes that have hit the region over the past 30 years was analyzed.A total of 92 rainfall events in the Santa Rosa Canyon were studied to determine the amount of precipitation needed to trigger shallow landslides.For each event the duration(D,in hours)and the cumulated rainfall event(E,in mm)were determined by using historical rainfall data from weather stations located near the study area.We have proposed an ED threshold for rainfall-induced landslides with durations 0.5<D<120 hours to address the conditions that trigger these events in the study area.On analyzing the obtained threshold,it has been established that almost 60 mm of a daily rainfall accumulation is required to trigger shallow landslides in the study area.This estimation is consistent with other calculations made for areas close to the Santa Rosa Canyon.Finally,we validated the predictive capability of the threshold with a different set of rainfall data that did not result in landslides performing a straightforward receiver operating characteristic analysis.A good approach was obtained,especially for rainfall events with daily measurements.Results could be used as input information in the design of a landslide early warning system for the northeastern Mexico,and replicated for other landslide prone areas in the region.
文摘Simple GNSS navigation receivers, developed for the mass market, can be used for positioning with sub centimeter accuracy in a wireless sensor network if the read-out of the carrier phase data is possible and all data is permanently broadcast to a central computer for near real time processing of the respective base lines. Experiences gained in two research projects related to landslide monitoring are depicted in terms of quality and reliability of the results by the developed approach. As far as possible a modular system set up with commercial off-the-shelf components, e.g., standard WLAN fur commtmication, solar batteries with solar panels for autarkic power supply and in cooperation of existing proofed program tools is chosen. The challenge of the still ongoing development is to have a flexible and robust GNSS based sensor network available - concerned not only for landslide monitoring in future.
文摘Earthquakes pose significant risks globally,necessitating effective seismic risk mitigation strategies like earthquake early warning(EEW)systems.However,developing and optimizing such systems requires thoroughly understanding their internal procedures and coverage limitations.This study examines a deep-learning-based on-site EEW framework known as ROSERS(Real-time On-Site Estimation of Response Spectra)proposed by the authors,which constructs response spectra from early recorded ground motion waveforms at a target site.This study has three primary goals:(1)evaluating the effectiveness and applicability of ROSERS to subduction seismic sources;(2)providing a detailed interpretation of the trained deep neural network(DNN)and surrogate latent variables(LVs)implemented in ROSERS;and(3)analyzing the spatial efficacy of the framework to assess the coverage area of on-site EEW stations.ROSERS is retrained and tested on a dataset of around 11,000 unprocessed Japanese subduction ground motions.Goodness-of-fit testing shows that the ROSERS framework achieves good performance on this database,especially given the peculiarities of the subduction seismic environment.The trained DNN and LVs are then interpreted using game theory-based Shapley additive explanations to establish cause-effect relationships.Finally,the study explores the coverage area of ROSERS by training a novel spatial regression model that estimates the LVs using geographically weighted random forest and determining the radius of similarity.The results indicate that on-site predictions can be considered reliable within a 2–9 km radius,varying based on the magnitude and distance from the earthquake source.This information can assist end-users in strategically placing sensors,minimizing blind spots,and reducing errors from regional extrapolation.
文摘Climate-related hazards like drought are associated with loss of life and lead to food insecurity in many parts of sub-Saharan Africa. Food insecurity, which affects more than 220 million sub-Saharan Africans, manifests as starvation that leads to more than 50% of children under the age of 5-years presenting as underweight for age in many communities on the continent. This household survey reports the means by which rural fisher folk and farming communities in Uganda gained access to early warning meteorological information. The survey covered five districts across different climatic zones in Uganda and recruited a total of 405 respondents with an average age of 41 years (SD 16). Economic activity was used to categorize each of the five districts into farming (crops and livestock) and fishing areas. The results showed that most respondents were unaware of drought as one of the climate-related hazards. Compared to respondents from the fishing communities, the respondents from farming communities were more likely to be receiving weather-related information (<em>P</em>-value < 0.01). There were 204/405 (50.37%) female respondents who, compared to male respondents, were less likely to have access to weather information, less willing to pay for weather information, and less likely to have and/or own devices like a radio for receiving weather information. The survey demonstrated that: 1) there were gaps in the knowledge about climate-related hazards, 2) there is a need for additional interventions targeting fisher folk communities access timely weather information, and 3) introducing user paid access to weather information may increase climate-related gender-based disparities.
文摘incidents of extreme hyperbole and fraud in celebrity advertisements have occurred repeatedly because advertising participants are driven by commercial interests and it is also relevant to the deep social and cultural background. Therefore, great and prolonged efforts should be made to govern celebrity advertising in a multi-pronged way： strengthening legal supervision on the basis of clearly defined false advertising; establishing early warning and punishing systems including pre-qualification system, filing system and banning system; promoting public interest litigation system; increasing consumers＇ media literacy.
基金the Tsuro Trust for providing the funding for the research。
文摘On 14 March 2019,Zimbabwe was hit by Cyclone Idai,leaving immeasurable destruction of unprecedented magnitude in its wake.In Chimanimani District,many lives were lost,many people were reported missing,and others were displaced.The question that immediately comes to mind is:Was the country prepared to manage the Cyclone Idai disaster?Reflecting on the community experiences,the purpose of this research was to interrogate the strength of the disaster risk reduction legislation and institutions in Zimbabwe in the face of meteorological hazards.The research also evaluated the extent of the impact Cyclone Idai had on the Chimanimani communities and the factors that increased the vulnerability to the cyclone.A mixed method approach that involved 1180 participants was used.The study found that disaster risk management legislation and institutions in Zimbabwe are weak.Cyclone Idai resulted in the loss of many human lives,loss of livelihoods,and massive damage to infrastructure.The cyclone exposed capacity and policy gaps in Zimbabwe’s disaster risk management system.The study makes a number of recommendations,including strengthening disaster legislation and policy,and disaster risk governance.Given the communities’response to the disaster occurrence,the study also recommends strengthening social capital.
文摘1.Introduction When natural hazards such as floods,droughts,and wildfires continue to escalate globally,timely acquisition of disaster information remains crucial for rapid on ground situation assessment.Despite advances in satellite-based emergency mapping,delays persist highlighting the need to enhance early warning systems and overcome limitations of conventional,observation-based workflows.The integration of Big Data and Artificial Intelligence(BD&AI)through the fusion of available vast Remote Sensing(RS)datasets with Machine Learning(ML)algorithms allows us to achieve near real-time monitoring of hazards,improve their prediction.
文摘Power systems are critical to modern society,providing the backbone for all economic,industrial,and residential activities.However,these systems face significant threats from both natural and man-made disasters,such as earthquakes,floods,hurricanes,cyber-attacks,and equipment failures.The vulnerability of power systems to these disasters can result in prolonged outages,economic losses,safety risks,and social consequences.This paper explores various disaster prevention and mitigation technologies employed in power systems to enhance resilience and ensure reliable electricity supply during and after disasters.It discusses early warning systems for natural hazards,resilient infrastructure designs,and cybersecurity measures to defend against man-made threats.Additionally,it examines fault detection,power system restoration,and disaster-resilient control strategies that aid in quick recovery from disruptions.The integration of smart grids,renewable energy sources,and advanced control technologies is emphasized as crucial for improving disaster preparedness and minimizing recovery time.Through the application of these technologies,power systems can better withstand and recover from the growing risks posed by both natural and human-induced disasters.
基金supported by the National Natural Science Foundation of China(Grant No.41404048)the Seismological Research Project(Grant No.201108002)
文摘A crucial part of proposed earthquake early warning systems is a rapid estimate for earthquake magnitude.Most of these methods are focused on the first part of the P-wave train,the earlier and less destructive part of the ground motion that follows an earthquake.A method has been proposed by using the period of the P-wave to determine the magnitude of a large earthquake at local distance,and a specific relation for the Sichuan region was calibrated according to acceleration records of Wenchuan earthquake.The Mw 6.6 earthquake hit Lushan County,Sichuan,on April 20,2013 and the largest aftershocks provide a useful dataset to validate the proposed relation and discuss the risks connected to the extrapolation of magnitude relations with a poor dataset of large earthquake waveforms.A discrepancy between the local magnitude(ML)estimated by means ofτc evaluation and the standard ML(6.4 vs.7.0)suggests using caution when ML vs.τc calibrations do not include a relevant dataset of large earthquakes.Effects from large residuals could be mitigated or removed by introducing selection rules onτc function,by regionalizing the ML vs.τc function in the presence of significant tectonic or geological heterogeneity,and by using probabilistic and evolutionary methods.
