Storm surge events(SSEs)involve multiple hazard-causing factors,such as surges,extreme rainfall,strong winds,waves,and ocean currents,which have destructive impacts on coastal regions.For a quantitative multi-hazard a...Storm surge events(SSEs)involve multiple hazard-causing factors,such as surges,extreme rainfall,strong winds,waves,and ocean currents,which have destructive impacts on coastal regions.For a quantitative multi-hazard assessment of SSEs,this study introduced the concept of the storm surge event seawater-atmosphere system(SSE-SAS)and proposed the system energy equivalence(SEE)model from a systemic energy perspective.SEE was obtained by employing a parameterization approach,and the hazard index(HI)and the concept of most significant hazard(MSH)were adopted to evaluate the severity of SSE-SAS.SEE at five stations in the Shandong Peninsula was calculated from 2005 to 2019,and probability analysis and hazard assessment were further conducted.Results show that the SEE of SSE-SAS ranges from 0.029×10^(3) to 30.418×10^(3) J/m^(2),and it exhibits an insignificant decreasing trend from 2005 to 2019.The SEE of SSE-SAS in the west of the Shandong Peninsula is greater than that in the east.Moreover,storm waves,storm surges,and storm rainfall are the major contributors to SEE,which exhibit different spatial patterns and characters in different SSE-SAS types.The HI of SSE-SAS at five stations is no more than medium hazard level,with MSH at return periods of 2-to 4-year level.This study provides a new approach for quantifying multi-hazard SSEs,which offers scientific insights for regional multi-hazard risk reduction and mitigation efforts.展开更多
A glacier hazard chain can form a long-runout mass flow and generate a large flood,affecting downstream areas hundreds of kilometers away from the initiating hazard site.This study focuses on the Yarlung Zangbo Daxiag...A glacier hazard chain can form a long-runout mass flow and generate a large flood,affecting downstream areas hundreds of kilometers away from the initiating hazard site.This study focuses on the Yarlung Zangbo Daxiagu.The objective is to address two key unresolved issues:the evolution of detached glacier materials into debris flows or debris floods and the amplification of the impact range and threats.A comprehensive framework is developed that considers the impacts of near-field and far-field hazards.Numerical modeling,remote sensing,and field investigations were integrated to understand the interactions,transformations,and amplifications of hazards in the glacier hazard chain.The results indicate that extensive,nearly saturated sediments on the glacier valley floor,when entrained,amplify the magnitude of the mass flow.The topography plays a crucial role.When the valley outlet is perpendicular to the river course,topographic obstacles cause immediate halting,resulting in the formation of high barrier dams.Conversely,when the glacier valley aligns nearly parallel to the river course,the mass flow can travel a much longer distance upon entering the river,causing an enlarged affected area.The barrier dams can breach rapidly,causing breaching floods that amplify the downstream impact from several kilometers to hundreds of kilometers.Our analysis reveals that the overall impacts remain spatially limited.Specifically,downstream areas along the Yarlung Zangbo-Brahmaputra River are unlikely to face greater threats from the upstream floods than local monsoon floods.Our findings provide the foundation for the management of glacier hazard chains.展开更多
This study advances the DRASTIC groundwater vulnerability assessment framework by integrating a multi-hazard groundwater index(MHGI)to account for the dynamic impacts of diverse anthropogenic activities and natural fa...This study advances the DRASTIC groundwater vulnerability assessment framework by integrating a multi-hazard groundwater index(MHGI)to account for the dynamic impacts of diverse anthropogenic activities and natural factors on both groundwater quality and quantity.Incorporating factors such as population growth,agricultural practices,and groundwater extraction enhances the framework’s ability to capture multi-dimensional,spatiotemporal changes in groundwater vulnerability.Additional improvements include refined weighting and rating scales for thematic layers based on available observational data,and the inclusion of distributed recharge.We demonstrate the practical utility of this dynamic DRASTIC-based framework through its application to the agro-urban regions of the Irrigated Indus Basin,a major groundwater-dependent agricultural area in South Asia.Results indicate that between 2005 and 2020,54%of the study area became highly vulnerable to pollution.The MHGI revealed a 13%decline in potential groundwater storage and a 25%increase in groundwater-stressed zones,driven primarily by population growth and intensive agriculture.Groundwater vulnerability based on both groundwater quality and quantity dimensions showed a 19%decline in areas of low to very low vulnerability and a 6%reduction in medium vulnerability zones by 2020.Sensitivity analyses indicated that groundwater vulnerability in the region is most influenced by groundwater recharge(42%)and renewable groundwater stress(38%).Validation with in-situ data yielded area under the curve values of 0.71 for groundwater quality vulnerability and 0.63 for MHGI.The framework provides valuable insights to guide sustainable groundwater management,safeguarding both environmental integrity and human well-being.展开更多
Economic losses and catastrophic casualties may occur once super high-rise structures are struck by low-probability but high-consequence scenarios of concurrent earthquakes and winds. Therefore, accurately predicting ...Economic losses and catastrophic casualties may occur once super high-rise structures are struck by low-probability but high-consequence scenarios of concurrent earthquakes and winds. Therefore, accurately predicting multi-hazard dynamic responses to super high-rise structures has significant engineering and scientific value. This study performed a parametric global sensitivity analysis (GSA) for multi-hazard dynamic response prediction of super high-rise structures using the multiple-degree-of-freedom shear (MFS) model. Polynomial chaos Kriging (PCK) was introduced to build a surrogate model that allowed GSA to be combined with Sobol’ indices. Monte Carlo simulation (MCS) is also conducted for the comparison to verify the accuracy and efficiency of the PCK method. Parametric sensitivity analysis is performed for a wide range of aleatory uncertainty (intensities of coupled multi-hazard), epistemic uncertainty (bending stiffness, k_(m);shear stiffness, kq;density, ρ;and damping ratio, ξ), probability distribution types, and coefficients of variation. The results indicate that epistemic uncertainty parameters, k_(m), ρ, and ξ dramatically affect the multi-hazard dynamic responses of super high-rise structures;in addition, Sobol’ indices between the normal and lognormal distributions are insignificant, while the variation levels have remarkably influenced the sensitivity indices.展开更多
The increasing frequency and intensity of natural disasters necessitate advanced prediction techniques to mitigate potential damage.This study presents a comprehensive multi-hazard early warning framework by integrati...The increasing frequency and intensity of natural disasters necessitate advanced prediction techniques to mitigate potential damage.This study presents a comprehensive multi-hazard early warning framework by integrating the multi-source data fusion technique.A multi-source data extraction method was introduced by extracting pressure level and average precipitation data based on the hazard event from the Cooperative Open Online Landslide Repository(COOLR)dataset across multiple temporal intervals(12 h to 1 h prior to events).Feature engineering was performed using Choquet fuzzy integral-based importance scoring,which enables the model to account for interactions and uncertainty across multiple features.Three individual Long Short-Term Memory(LSTM)models were trained for hazard location,average precipitation,and hazard category(i.e.,to detect the potential of natural disasters).These models were trained on varying temporal scales from 12 to 1 h prior to the event.These individual models achieved the performance of Mean Absolute Error(MAE)2.2 and 3.2,respectively,for the hazard location and average precipitation models,and an F1-score of 0.825 for the hazard category model.The results also indicate that the LSTM model outperformed traditional Machine Learning(ML)models,and the use of the fuzzy integral enhanced the prediction capability by 8.12%,2.6%,and 6.37%,respectively,for all three individual models.Furthermore,a rule-based algorithm was developed to synthesize the outputs from the individual models into a 3×3 grid of multi-hazard warnings.These findings underscore the effectiveness of the proposed framework in advancing multi-hazard forecasting and situational awareness,offering valuable support for timely and data-driven emergency response planning.展开更多
Multi-hazard susceptibility prediction is an important component of disasters risk management plan.An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions...Multi-hazard susceptibility prediction is an important component of disasters risk management plan.An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions.However,with the rapid development of artificial intelligence technology,multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck.In order to effectively solve this problem,this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks(CNN).First,we use historical flash flood,debris flow and landslide locations based on Google Earth images,extensive field surveys,topography,hydrology,and environmental data sets to train and validate the proposed CNN method.Next,the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria,i.e.,coefficient of determination,overall accuracy,mean absolute error and the root mean square error.Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods,debris flows and landslides.Finally,the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map.It can be observed from the map that 62.43%of the study area are prone to hazards,while 37.57%of the study area are harmless.In hazard-prone areas,16.14%,4.94%and 30.66%of the study area are susceptible to flash floods,debris flows and landslides,respectively.In terms of concurrent hazards,0.28%,7.11%and 3.13%of the study area are susceptible to the joint occurrence of flash floods and debris flow,debris flow and landslides,and flash floods and landslides,respectively,whereas,0.18%of the study area is subject to all the three hazards.The results of this study can benefit engineers,disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.展开更多
Many urban areas are located in regions of moderate seismicity and are subjected to strong wind. Buildings in these regions are often designed without seismic provisions. As a result, in the event of an earthquake, th...Many urban areas are located in regions of moderate seismicity and are subjected to strong wind. Buildings in these regions are often designed without seismic provisions. As a result, in the event of an earthquake, the potential for damage and loss of lives may not be known. In this paper, the performance of a typical high-rise building with a thick transfer plate （TP）, which is one type of building structure commonly found in Hong Kong, is assessed against both earthquake and wind hazards. Seismic- and wind-resistant performance objectives are first reviewed based on relevant codes and design guidelines for high-rise buildings. After a brief introduction of wind-resistant design of the building, various methodologies, including equivalent static load analysis （ESLA）, response spectrum analysis （RSA）, pushover analysis （POA）, linear and nonlinear time-history analysis （LTHA and NTHA）, are employed to assess the seismic performance of the building when subjected to frequent earthquakes, design based earthquakes and maximum credible earthquakes. The effects of design wind and seismic action with a common 50-year return period are also compared. The results indicate that most performance objectives can be satisfied by the building, but there are some objectives, such as inter-story drift ratio, that cannot be achieved when subjected to the frequent earthquakes. It is concluded that in addition to wind, seismic action may need to be explicitly considered in the design of buildings in regions of moderate seismicity.展开更多
Natural hazards are often studied in isolation.However,there is a great need to examine hazards holistically to better manage the complex of threats found in any region.Many regions of the world have complex hazard la...Natural hazards are often studied in isolation.However,there is a great need to examine hazards holistically to better manage the complex of threats found in any region.Many regions of the world have complex hazard landscapes wherein risk from individual and/or multiple extreme events is omnipresent.Extensive parts of Iran experience a complex array of natural hazards-floods,earthquakes,landslides,forest fires,subsidence,and drought.The effectiveness of risk mitigation is in part a function of whether the complex of hazards can be collectively considered,visualized,and evaluated.This study develops and tests individual and collective multihazard risk maps for floods,landslides,and forest fires to visualize the spatial distribution of risk in Fars Province,southern Iran.To do this,two well-known machine-learning algorithms-SVM and MARS-are used to predict the distribution of these events.Past floods,landslides,and forest fires were surveyed and mapped.The locations of occurrence of these events(individually and collectively) were randomly separated into training(70%) and testing(30%) data sets.The conditioning factors(for floods,landslides,and forest fires) employed to model the risk distributions are aspect,elevation,drainage density,distance from faults,geology,LULC,profile curvature,annual mean rainfall,plan curvature,distance from man-made residential structures,distance from nearest river,distance from nearest road,slope gradient,soil types,mean annual temperature,and TWI.The outputs of the two models were assessed using receiver-operating-characteristic(ROC) curves,true-skill statistics(TSS),and the correlation and deviance values from each models for each hazard.The areas-under-the-curves(AUC) for the MARS model prediction were 76.0%,91.2%,and 90.1% for floods,landslides,and forest fires,respectively.Similarly,the AUCs for the SVM model were 75.5%,89.0%,and 91.5%.The TSS reveals that the MARS model was better able to predict landslide risk,but was less able to predict flood-risk patterns and forest-fire risk.Finally,the combination of flood,forest fire,and landslide risk maps yielded a multi-hazard susceptibility map for the province.The better predictive model indicated that 52.3% of the province was at-risk for at least one of these hazards.This multi-hazard map may yield valuable insight for land-use planning,sustainable development of infrastructure,and also integrated watershed management in Fars Province.展开更多
In the U.S., the current Load and Resistance Factor Design （LRFD） Specifications for highway bridges is a reliability-based formulation that considers failure probabilities of bridge components due to the actions of...In the U.S., the current Load and Resistance Factor Design （LRFD） Specifications for highway bridges is a reliability-based formulation that considers failure probabilities of bridge components due to the actions of typical dead load and frequent vehicular loads. Various extreme load effects, such as earthquake and vessel collision, are on the same reliability-based platform. Since these extreme loads are time variables, combining them with not considered frequent. non- extreme loads is a significant challenge. The number of design limit state equations based on these failure probabilities can be unrealistically large and unnecessary from the view point of practical applications. Based on the opinion of AASHTO State Bridge Engineers, many load combinations are insignificant in their states. This paper describes the formulation of a criterion to include only the necessary load combinations to establish the design limit states. This criterion is established by examining the total failure probabilities for all possible time-invariant and time varying load combinations and breaking them down into partial terms. Then, important load combinations can be readily determined quantitatively,展开更多
Underground infrastructure(UI)plays a great important role in the urbanization and modernization of megacities in the world.However,the intensive development of the UI during the past decades has posed great risks to ...Underground infrastructure(UI)plays a great important role in the urbanization and modernization of megacities in the world.However,the intensive development of the UI during the past decades has posed great risks to the safety of city infrastructures under the impact of multi-hazards,especially with the condition of global climate change.In this paper,a general conceptualized framework to assess the resilience of UI in cities under multihazards impact is proposed.The urban tunnel system,e.g.,metro tunnel,road tunnel etc.,is selected as the typical underground infrastructure discussed with the emphasis both on the structural level in terms of mechanical behaviors and system level in terms of network efficiency.The hazards discussed in this paper include the natural hazards and human-related ones,with emphasis on earthquake,flood,and aggressive disturbances caused by human activities.After the general framework proposed for resilience of the structural and network behavior of the UI,two application examples are illustrated.The structural resilience of the shield tunnel under earthquake impact is analyzed by using the proposed resilience model,and the network resilience of the road tunnel system under the flood impact due to climate change is analyzed,respectively.The resilience enhancement by using the adaptive design strategy of real-time observational method is mathematically presented in this case.Some other practical engineering recovery measures are briefly discussed at the end of this application example.The findings in the application examples should be helpful to enhance the resilience-based design of the structural and network of tunnels from the component to the system level.展开更多
Yogyakarta is one of the large cities in Central Java,located on Java Island,Indonesia.The city,and the Pleret sub-district,where the study has taken place,is prone to earthquake hazards,because it is close to several...Yogyakarta is one of the large cities in Central Java,located on Java Island,Indonesia.The city,and the Pleret sub-district,where the study has taken place,is prone to earthquake hazards,because it is close to several seismically active zones,such as the Sunda Megathrust and the active fault known as the Opak Fault.Since a devastating earthquake of 2006,the population of the Pleret sub-district has increased significantly.Thus,the housing demand has increased,and so is the pace of low-cost housing that does not meet earthquake-safety requirements,and furthermore are often located on unstable slopes.The local alluvial material covering a jigsaw of unstable blocks and complex slope is conditions that can amplify the negative impacts of earthquakes.Within this context,this study is aiming to assess the multi-hazards and risks of earthquakes and related secondary hazards such as ground liquefaction,and coseismic landslides.To achieve this,we used geographic information systems and remote sensing methods supplemented with outcrop study and existing seismic data to derive shear-strain parameters.The results have revealed the presence of numerous uncharted active faults with movements visible from imagery and outcrops.show that the middle part of the study area has a complex geological structure,indicated by many unchartered faults in the outcrops.Using this newly mapped blocks combined with shear strain data,we reassessed the collapse probability of buildings that reach level>0.75 near the Opak River,in central Pleret sub-district.Classifying the buildings and from population distribution,we could determine that the highest risk was during nighttime as the buildings susceptible to fall are predominantly housing buildings.The secondary hazards follow a slightly different distribution with a concentration of risks in the West.展开更多
Hazard maps are usually prepared for each disaster, including seismic hazard maps, flood hazard maps, and landslide hazard maps. However, when the general public attempts to check their own disaster risk, most are lik...Hazard maps are usually prepared for each disaster, including seismic hazard maps, flood hazard maps, and landslide hazard maps. However, when the general public attempts to check their own disaster risk, most are likely not aware of the specific types of disaster. So, first of all, we need to know what kind<span style="font-family:;" "="">s</span><span style="font-family:;" "=""> of hazards are important. However, the information that integrates multiple hazards is not well maintained, and there are few such studies. On the other hand, in Japan, a lot of hazard information is being released on the Internet. So, we summarized and assessed hazard data that can be accessed online regarding shelters (where evacuees live during disasters) and their catchments (areas assigned to each shelter) in Yokohama City, Kanagawa Prefecture. Based on the results, we investigated whether a grouping by cluster analysis would allow for multi-hazard assessment. We used four natural disasters (seismic, flood, tsunami, sediment disaster) and six parameters of other population and senior population. However, since the characteristics of the population and the senior population were almost the same, only population data was used in the final examination. From the cluster analysis, it was found that it is appropriate to group the designated evacuation centers in Yokohama City into six groups. In addition, each of the six groups was found <span>to have explainable characteristics, confirming the effectiveness of multi-hazard</span> creation using cluster analysis. For example, we divided, all hazards are low, both flood and Seismic hazards are high, sediment hazards are high, etc. In many Japanese cities, disaster prevention measures have been constructed in consideration of ground hazards, mainly for earthquake disasters. In this paper, we confirmed the consistency between the evaluation results of the multi-hazard evaluated here and the existing ground hazard map and examined the usefulness of the designated evacuation center. Finally, the validity was confirmed by comparing this result with the ground hazard based on the actual measurement by the past research. In places where the seismic hazard is large, the two are consistent with the fact that the easiness of shaking by actual measurement is also large.</span>展开更多
The details of a multi-hazard and probabilistic risk assessment, developed for urban planning and emergency response activities in Manizales, Colombia, are presented in this article. This risk assessment effort was de...The details of a multi-hazard and probabilistic risk assessment, developed for urban planning and emergency response activities in Manizales, Colombia, are presented in this article. This risk assessment effort was developed under the framework of an integral disaster risk management project whose goal was to connect risk reduction activities by using open access and state-of-theart risk models. A probabilistic approach was used for the analysis of seismic, landslide, and volcanic hazards to obtain stochastic event sets suitable for probabilistic loss estimation and to generate risk results in different metrics after aggregating in a rigorous way the losses associated to the different hazards. Detailed and high resolution exposure databases were used for the building stock and infrastructure of the city together with a set of vulnerability functions for each of the perils considered. The urban and territorial ordering plan of the city was updated for socioeconomic development and land use using the hazard and risk inputs and determinants, which cover not only the current urban area but also those adjacent areas where the expansion of Manizales is expected to occur. The emergency response capabilities of the city were improved by taking into account risk scenarios and after updating anautomatic and real-time post-earthquake damage assessment.展开更多
Due to the frequent occurrence of multi-hazard disasters worldwide in recent years,effective multi-hazard sce-nario analysis is imperative for disaster rescue and emergency management.The response procedure for differ...Due to the frequent occurrence of multi-hazard disasters worldwide in recent years,effective multi-hazard sce-nario analysis is imperative for disaster rescue and emergency management.The response procedure for different single hazards were investigated and formulated before.However,the investigations of disaster scenario rarely systematically address the entire development and response process of multi-hazards,including the coupling mechanisms,evolution dynamics,scenario assessment and emergency response.To this end,this paper presents our methodology of multi-hazard disaster scenario that integrates experiment-simulation-field data,focusing on three dimensions consisting of multi-hazard coupling,structures and systems,and emergency management.The newly proposed scenario method mainly comprises three aspects:experiments and simulations,multi-hazard field investigation,scenario analysis and response.Specifically,in order to study the large-scale,high-intensity and multi-hazard coupling effects,we carried out reduced-scale experiments and field measurement experiments to develop experimental similarity theory and prototype simulations of multi-hazards.In addition,a variety of field rescue and survey equipment,such as robots,Unmanned Aerial Vehicle(UAV),and Virtual Reality/Augmented Reality(VR/AR)technologies were utilized to acquire real-time data of multi-hazard field.Furthermore,we also examine the mechanism and framework of multi-hazard scenarios to formulate the detailed procedures of man-agement and response.They are incorporated with the experiments,simulations,field data and models to con-struct a new scenario platform.The proposed scenario method was applied in a case study of the coupled wind and snow multi-hazard to verify its effectiveness.The new method contributes to the disaster relief,decision-making and emergency management for multi-hazard disaster to improve the urban resilience.展开更多
Preparation of accurate and up-to-date susceptibility maps at the regional scale is mandatory for disaster mitigation,site selection,and planning in areas prone to multiple natural hazards.In this study,we proposed a ...Preparation of accurate and up-to-date susceptibility maps at the regional scale is mandatory for disaster mitigation,site selection,and planning in areas prone to multiple natural hazards.In this study,we proposed a novel multi-hazard susceptibility assessment approach that combines expert-based and supervised machine learning methods for landslide,flood,and earthquake hazard assessments for a basin in Elazig Province,Türkiye.To produce the landslide susceptibility map,an ensemble machine learning algorithm,random forest,was chosen because of its known performance in similar studies.The modified analytical hierarchical process method was used to produce the flood susceptibility map by using factor scores that were defined specifically for the area in the study.The seismic hazard was assessed using ground motion parameters based on Arias intensity values.The univariate maps were synthesized with a Mamdani fuzzy inference system using membership functions designated by expert.The results show that the random forest provided an overall accuracy of 92.3%for landslide susceptibility mapping.Of the study area,41.24%were found prone to multi-hazards(probability value>50%),but the southern parts of the study area are more susceptible.The proposed model is suitable for multi-hazard susceptibility assessment at a regional scale although expert intervention may be required for optimizing the algorithms.展开更多
Global climate change,climate extremes,and overuse of natural resources are all major contributors to the risk brought on by cyclones.In I West Bengal state of India,the Pathar Pratima Block frequently experiences a v...Global climate change,climate extremes,and overuse of natural resources are all major contributors to the risk brought on by cyclones.In I West Bengal state of India,the Pathar Pratima Block frequently experiences a variety of risks that result in significant loss of life and livelihood.In order to govern coastal society,it is crucial to measure and map the multi-hazards risk status.To depict the multi-hazards vulnerability and risk status,no cutting-edge models are currently being applied.Predicting distinct physical vulnerabilities is possible using a variety of cutting-edge machine learning techniques.This study set out to precisely describe multi-hazard risk using powerful machine learning methods.This study involved the use of Analytic Hierarchical Analysis and two cutting-edge machine-learning algorithms-Random Forest and Artificial Neural Network,which are yet underutilized in this area.The multi-hazards risk was determined by taking into account six criteria.The southern and eastern regions of the research area are clearly identified by the multi-hazards risk maps as having high to extremely high hazards risk levels.Cyclonic hazards and embankment breaching are the main dominant factors among the multi-hazards.The machine learning approach is the most accurate model for mapping the multi-hazards risk where the ROC result of Random forest and artificial neural network is more than the conventional method AHP.Here RF is the most validated model than the other two.The effectiveness,root mean square error,true skill statistics,Friedman and Wilcoxon rank test,and area under the curve of receiver operating characteristic tests were used to evaluate the prediction capacity of newly constructed models.The RMSE values of 0.24 and 0.26,TSS values of 0.82 and 0.73,and AUC values of 88.20%and 89.10%as produced by RF and ANN models,respectively,were all excellent.展开更多
The World Meteorological Organization(WMO) is planning to implement a Global Multi-hazard Alert System(GMAS) to aggregate official warning^1 information issued by authorities around the world and to serve as a one-sto...The World Meteorological Organization(WMO) is planning to implement a Global Multi-hazard Alert System(GMAS) to aggregate official warning^1 information issued by authorities around the world and to serve as a one-stop shop to support the humanitarian organizations of the United Nations(UN), National Meteorological and Hydrological Services(NMHSs) and other global users including the media. It aims to enhance the authority and visibility of NMHSs and other alerting authorities. To aid effective dissemination of warnings to GMAS, the Common Alerting Protocol(CAP) was considered as a standard and robust format to use. In respect of GMAS infrastructure, the World Weather Information(WWIS) and the Severe Weather Information Centre(SWIC) of WMO as well as the WMO Alert Hub now being implemented are identified as core components, among others. The SWIC is being upgraded with GIS capability for displaying authoritative warnings and tropical cyclone(TC) information, and for use as a display platform of GMAS. Apart from warnings from NMHSs, authoritative TC warnings and advisories issued by Regional Specialized Meteorological Centres(RSMCs) and Tropical Cyclone Warning Centres(TCWCs) are also indispensable information for GMAS. As the existing TC warnings and advisories, now more or less in free text format, are mainly targeted for human users and are not intended for automatic parsing by computer software, it is proposed to make available the TC advisories in a machine-readable format so that TC information can be effectively ingested into GMAS and made available to the UN humanitarian organizations, NMHSs and other global users. In this respect, some enhancement measures to TC advisories are proposed. This calls for active collaboration of Members of the Typhoon Committee in the GMAS project.展开更多
基金supported by the Key Laboratory of Coastal Science and Integrated Management,Ministry of Natural Resources(No.2022COSIMQ002)the Shandong Provincial Social Science Planning Research Project(No.22CXSXJ15)+1 种基金the Guangxi Key Laboratory of Marine Environmental Science,Guangxi Academy of Sciences(No.GXKLHY21-04)the Hainan Province Marxism Project General Program(No.2023HNMGC03).
文摘Storm surge events(SSEs)involve multiple hazard-causing factors,such as surges,extreme rainfall,strong winds,waves,and ocean currents,which have destructive impacts on coastal regions.For a quantitative multi-hazard assessment of SSEs,this study introduced the concept of the storm surge event seawater-atmosphere system(SSE-SAS)and proposed the system energy equivalence(SEE)model from a systemic energy perspective.SEE was obtained by employing a parameterization approach,and the hazard index(HI)and the concept of most significant hazard(MSH)were adopted to evaluate the severity of SSE-SAS.SEE at five stations in the Shandong Peninsula was calculated from 2005 to 2019,and probability analysis and hazard assessment were further conducted.Results show that the SEE of SSE-SAS ranges from 0.029×10^(3) to 30.418×10^(3) J/m^(2),and it exhibits an insignificant decreasing trend from 2005 to 2019.The SEE of SSE-SAS in the west of the Shandong Peninsula is greater than that in the east.Moreover,storm waves,storm surges,and storm rainfall are the major contributors to SEE,which exhibit different spatial patterns and characters in different SSE-SAS types.The HI of SSE-SAS at five stations is no more than medium hazard level,with MSH at return periods of 2-to 4-year level.This study provides a new approach for quantifying multi-hazard SSEs,which offers scientific insights for regional multi-hazard risk reduction and mitigation efforts.
基金support from the National Natural Science Foundation of China(U20A20112,42061160480,42377196,and 52479095)the NSFC/RGC Joint Research Scheme(42061160480 and N_HKUST620/20)+1 种基金the Research Grants Council of the Hong Kong SAR Government(16203720,T22-606/23-R,and JRFS25266S09)the Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone(HZQB-KCZYB-2020083)。
文摘A glacier hazard chain can form a long-runout mass flow and generate a large flood,affecting downstream areas hundreds of kilometers away from the initiating hazard site.This study focuses on the Yarlung Zangbo Daxiagu.The objective is to address two key unresolved issues:the evolution of detached glacier materials into debris flows or debris floods and the amplification of the impact range and threats.A comprehensive framework is developed that considers the impacts of near-field and far-field hazards.Numerical modeling,remote sensing,and field investigations were integrated to understand the interactions,transformations,and amplifications of hazards in the glacier hazard chain.The results indicate that extensive,nearly saturated sediments on the glacier valley floor,when entrained,amplify the magnitude of the mass flow.The topography plays a crucial role.When the valley outlet is perpendicular to the river course,topographic obstacles cause immediate halting,resulting in the formation of high barrier dams.Conversely,when the glacier valley aligns nearly parallel to the river course,the mass flow can travel a much longer distance upon entering the river,causing an enlarged affected area.The barrier dams can breach rapidly,causing breaching floods that amplify the downstream impact from several kilometers to hundreds of kilometers.Our analysis reveals that the overall impacts remain spatially limited.Specifically,downstream areas along the Yarlung Zangbo-Brahmaputra River are unlikely to face greater threats from the upstream floods than local monsoon floods.Our findings provide the foundation for the management of glacier hazard chains.
基金funding from the National Science Foundation(NSF Award 2114701)of the United States.
文摘This study advances the DRASTIC groundwater vulnerability assessment framework by integrating a multi-hazard groundwater index(MHGI)to account for the dynamic impacts of diverse anthropogenic activities and natural factors on both groundwater quality and quantity.Incorporating factors such as population growth,agricultural practices,and groundwater extraction enhances the framework’s ability to capture multi-dimensional,spatiotemporal changes in groundwater vulnerability.Additional improvements include refined weighting and rating scales for thematic layers based on available observational data,and the inclusion of distributed recharge.We demonstrate the practical utility of this dynamic DRASTIC-based framework through its application to the agro-urban regions of the Irrigated Indus Basin,a major groundwater-dependent agricultural area in South Asia.Results indicate that between 2005 and 2020,54%of the study area became highly vulnerable to pollution.The MHGI revealed a 13%decline in potential groundwater storage and a 25%increase in groundwater-stressed zones,driven primarily by population growth and intensive agriculture.Groundwater vulnerability based on both groundwater quality and quantity dimensions showed a 19%decline in areas of low to very low vulnerability and a 6%reduction in medium vulnerability zones by 2020.Sensitivity analyses indicated that groundwater vulnerability in the region is most influenced by groundwater recharge(42%)and renewable groundwater stress(38%).Validation with in-situ data yielded area under the curve values of 0.71 for groundwater quality vulnerability and 0.63 for MHGI.The framework provides valuable insights to guide sustainable groundwater management,safeguarding both environmental integrity and human well-being.
基金Dalian Municipal Natural Science Foundation under Grant No.2019RD01。
文摘Economic losses and catastrophic casualties may occur once super high-rise structures are struck by low-probability but high-consequence scenarios of concurrent earthquakes and winds. Therefore, accurately predicting multi-hazard dynamic responses to super high-rise structures has significant engineering and scientific value. This study performed a parametric global sensitivity analysis (GSA) for multi-hazard dynamic response prediction of super high-rise structures using the multiple-degree-of-freedom shear (MFS) model. Polynomial chaos Kriging (PCK) was introduced to build a surrogate model that allowed GSA to be combined with Sobol’ indices. Monte Carlo simulation (MCS) is also conducted for the comparison to verify the accuracy and efficiency of the PCK method. Parametric sensitivity analysis is performed for a wide range of aleatory uncertainty (intensities of coupled multi-hazard), epistemic uncertainty (bending stiffness, k_(m);shear stiffness, kq;density, ρ;and damping ratio, ξ), probability distribution types, and coefficients of variation. The results indicate that epistemic uncertainty parameters, k_(m), ρ, and ξ dramatically affect the multi-hazard dynamic responses of super high-rise structures;in addition, Sobol’ indices between the normal and lognormal distributions are insignificant, while the variation levels have remarkably influenced the sensitivity indices.
文摘The increasing frequency and intensity of natural disasters necessitate advanced prediction techniques to mitigate potential damage.This study presents a comprehensive multi-hazard early warning framework by integrating the multi-source data fusion technique.A multi-source data extraction method was introduced by extracting pressure level and average precipitation data based on the hazard event from the Cooperative Open Online Landslide Repository(COOLR)dataset across multiple temporal intervals(12 h to 1 h prior to events).Feature engineering was performed using Choquet fuzzy integral-based importance scoring,which enables the model to account for interactions and uncertainty across multiple features.Three individual Long Short-Term Memory(LSTM)models were trained for hazard location,average precipitation,and hazard category(i.e.,to detect the potential of natural disasters).These models were trained on varying temporal scales from 12 to 1 h prior to the event.These individual models achieved the performance of Mean Absolute Error(MAE)2.2 and 3.2,respectively,for the hazard location and average precipitation models,and an F1-score of 0.825 for the hazard category model.The results also indicate that the LSTM model outperformed traditional Machine Learning(ML)models,and the use of the fuzzy integral enhanced the prediction capability by 8.12%,2.6%,and 6.37%,respectively,for all three individual models.Furthermore,a rule-based algorithm was developed to synthesize the outputs from the individual models into a 3×3 grid of multi-hazard warnings.These findings underscore the effectiveness of the proposed framework in advancing multi-hazard forecasting and situational awareness,offering valuable support for timely and data-driven emergency response planning.
基金supported by the Joint Funds of the National Natural Science Foundation of China(U21A2013)the State Key Laboratory of Biogeology and Environmental Geology,China University of Geosciences(GBL12107)the National Natural Science Foundation of China(61271408)。
文摘Multi-hazard susceptibility prediction is an important component of disasters risk management plan.An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions.However,with the rapid development of artificial intelligence technology,multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck.In order to effectively solve this problem,this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks(CNN).First,we use historical flash flood,debris flow and landslide locations based on Google Earth images,extensive field surveys,topography,hydrology,and environmental data sets to train and validate the proposed CNN method.Next,the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria,i.e.,coefficient of determination,overall accuracy,mean absolute error and the root mean square error.Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods,debris flows and landslides.Finally,the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map.It can be observed from the map that 62.43%of the study area are prone to hazards,while 37.57%of the study area are harmless.In hazard-prone areas,16.14%,4.94%and 30.66%of the study area are susceptible to flash floods,debris flows and landslides,respectively.In terms of concurrent hazards,0.28%,7.11%and 3.13%of the study area are susceptible to the joint occurrence of flash floods and debris flow,debris flow and landslides,and flash floods and landslides,respectively,whereas,0.18%of the study area is subject to all the three hazards.The results of this study can benefit engineers,disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.
基金Financial Support from Hong Kong PolytechnicUniversity Under Grant No. G-YX76
文摘Many urban areas are located in regions of moderate seismicity and are subjected to strong wind. Buildings in these regions are often designed without seismic provisions. As a result, in the event of an earthquake, the potential for damage and loss of lives may not be known. In this paper, the performance of a typical high-rise building with a thick transfer plate （TP）, which is one type of building structure commonly found in Hong Kong, is assessed against both earthquake and wind hazards. Seismic- and wind-resistant performance objectives are first reviewed based on relevant codes and design guidelines for high-rise buildings. After a brief introduction of wind-resistant design of the building, various methodologies, including equivalent static load analysis （ESLA）, response spectrum analysis （RSA）, pushover analysis （POA）, linear and nonlinear time-history analysis （LTHA and NTHA）, are employed to assess the seismic performance of the building when subjected to frequent earthquakes, design based earthquakes and maximum credible earthquakes. The effects of design wind and seismic action with a common 50-year return period are also compared. The results indicate that most performance objectives can be satisfied by the building, but there are some objectives, such as inter-story drift ratio, that cannot be achieved when subjected to the frequent earthquakes. It is concluded that in addition to wind, seismic action may need to be explicitly considered in the design of buildings in regions of moderate seismicity.
基金The study was supported by College of Agriculture,Shiraz University(Grant No.96GRD1M271143).
文摘Natural hazards are often studied in isolation.However,there is a great need to examine hazards holistically to better manage the complex of threats found in any region.Many regions of the world have complex hazard landscapes wherein risk from individual and/or multiple extreme events is omnipresent.Extensive parts of Iran experience a complex array of natural hazards-floods,earthquakes,landslides,forest fires,subsidence,and drought.The effectiveness of risk mitigation is in part a function of whether the complex of hazards can be collectively considered,visualized,and evaluated.This study develops and tests individual and collective multihazard risk maps for floods,landslides,and forest fires to visualize the spatial distribution of risk in Fars Province,southern Iran.To do this,two well-known machine-learning algorithms-SVM and MARS-are used to predict the distribution of these events.Past floods,landslides,and forest fires were surveyed and mapped.The locations of occurrence of these events(individually and collectively) were randomly separated into training(70%) and testing(30%) data sets.The conditioning factors(for floods,landslides,and forest fires) employed to model the risk distributions are aspect,elevation,drainage density,distance from faults,geology,LULC,profile curvature,annual mean rainfall,plan curvature,distance from man-made residential structures,distance from nearest river,distance from nearest road,slope gradient,soil types,mean annual temperature,and TWI.The outputs of the two models were assessed using receiver-operating-characteristic(ROC) curves,true-skill statistics(TSS),and the correlation and deviance values from each models for each hazard.The areas-under-the-curves(AUC) for the MARS model prediction were 76.0%,91.2%,and 90.1% for floods,landslides,and forest fires,respectively.Similarly,the AUCs for the SVM model were 75.5%,89.0%,and 91.5%.The TSS reveals that the MARS model was better able to predict landslide risk,but was less able to predict flood-risk patterns and forest-fire risk.Finally,the combination of flood,forest fire,and landslide risk maps yielded a multi-hazard susceptibility map for the province.The better predictive model indicated that 52.3% of the province was at-risk for at least one of these hazards.This multi-hazard map may yield valuable insight for land-use planning,sustainable development of infrastructure,and also integrated watershed management in Fars Province.
基金Federal Highway Administration at the University at Buffalo under Contract No.DTFH61-08-C-00012
文摘In the U.S., the current Load and Resistance Factor Design （LRFD） Specifications for highway bridges is a reliability-based formulation that considers failure probabilities of bridge components due to the actions of typical dead load and frequent vehicular loads. Various extreme load effects, such as earthquake and vessel collision, are on the same reliability-based platform. Since these extreme loads are time variables, combining them with not considered frequent. non- extreme loads is a significant challenge. The number of design limit state equations based on these failure probabilities can be unrealistically large and unnecessary from the view point of practical applications. Based on the opinion of AASHTO State Bridge Engineers, many load combinations are insignificant in their states. This paper describes the formulation of a criterion to include only the necessary load combinations to establish the design limit states. This criterion is established by examining the total failure probabilities for all possible time-invariant and time varying load combinations and breaking them down into partial terms. Then, important load combinations can be readily determined quantitatively,
基金substantially supported by the National Natural Science Foundation of China(No.52130805,51978516,52022070,52108381)the National Key R&D Program(No.2021YFF0502200 and 2021YFB2600804)。
文摘Underground infrastructure(UI)plays a great important role in the urbanization and modernization of megacities in the world.However,the intensive development of the UI during the past decades has posed great risks to the safety of city infrastructures under the impact of multi-hazards,especially with the condition of global climate change.In this paper,a general conceptualized framework to assess the resilience of UI in cities under multihazards impact is proposed.The urban tunnel system,e.g.,metro tunnel,road tunnel etc.,is selected as the typical underground infrastructure discussed with the emphasis both on the structural level in terms of mechanical behaviors and system level in terms of network efficiency.The hazards discussed in this paper include the natural hazards and human-related ones,with emphasis on earthquake,flood,and aggressive disturbances caused by human activities.After the general framework proposed for resilience of the structural and network behavior of the UI,two application examples are illustrated.The structural resilience of the shield tunnel under earthquake impact is analyzed by using the proposed resilience model,and the network resilience of the road tunnel system under the flood impact due to climate change is analyzed,respectively.The resilience enhancement by using the adaptive design strategy of real-time observational method is mathematically presented in this case.Some other practical engineering recovery measures are briefly discussed at the end of this application example.The findings in the application examples should be helpful to enhance the resilience-based design of the structural and network of tunnels from the component to the system level.
文摘Yogyakarta is one of the large cities in Central Java,located on Java Island,Indonesia.The city,and the Pleret sub-district,where the study has taken place,is prone to earthquake hazards,because it is close to several seismically active zones,such as the Sunda Megathrust and the active fault known as the Opak Fault.Since a devastating earthquake of 2006,the population of the Pleret sub-district has increased significantly.Thus,the housing demand has increased,and so is the pace of low-cost housing that does not meet earthquake-safety requirements,and furthermore are often located on unstable slopes.The local alluvial material covering a jigsaw of unstable blocks and complex slope is conditions that can amplify the negative impacts of earthquakes.Within this context,this study is aiming to assess the multi-hazards and risks of earthquakes and related secondary hazards such as ground liquefaction,and coseismic landslides.To achieve this,we used geographic information systems and remote sensing methods supplemented with outcrop study and existing seismic data to derive shear-strain parameters.The results have revealed the presence of numerous uncharted active faults with movements visible from imagery and outcrops.show that the middle part of the study area has a complex geological structure,indicated by many unchartered faults in the outcrops.Using this newly mapped blocks combined with shear strain data,we reassessed the collapse probability of buildings that reach level>0.75 near the Opak River,in central Pleret sub-district.Classifying the buildings and from population distribution,we could determine that the highest risk was during nighttime as the buildings susceptible to fall are predominantly housing buildings.The secondary hazards follow a slightly different distribution with a concentration of risks in the West.
文摘Hazard maps are usually prepared for each disaster, including seismic hazard maps, flood hazard maps, and landslide hazard maps. However, when the general public attempts to check their own disaster risk, most are likely not aware of the specific types of disaster. So, first of all, we need to know what kind<span style="font-family:;" "="">s</span><span style="font-family:;" "=""> of hazards are important. However, the information that integrates multiple hazards is not well maintained, and there are few such studies. On the other hand, in Japan, a lot of hazard information is being released on the Internet. So, we summarized and assessed hazard data that can be accessed online regarding shelters (where evacuees live during disasters) and their catchments (areas assigned to each shelter) in Yokohama City, Kanagawa Prefecture. Based on the results, we investigated whether a grouping by cluster analysis would allow for multi-hazard assessment. We used four natural disasters (seismic, flood, tsunami, sediment disaster) and six parameters of other population and senior population. However, since the characteristics of the population and the senior population were almost the same, only population data was used in the final examination. From the cluster analysis, it was found that it is appropriate to group the designated evacuation centers in Yokohama City into six groups. In addition, each of the six groups was found <span>to have explainable characteristics, confirming the effectiveness of multi-hazard</span> creation using cluster analysis. For example, we divided, all hazards are low, both flood and Seismic hazards are high, sediment hazards are high, etc. In many Japanese cities, disaster prevention measures have been constructed in consideration of ground hazards, mainly for earthquake disasters. In this paper, we confirmed the consistency between the evaluation results of the multi-hazard evaluated here and the existing ground hazard map and examined the usefulness of the designated evacuation center. Finally, the validity was confirmed by comparing this result with the ground hazard based on the actual measurement by the past research. In places where the seismic hazard is large, the two are consistent with the fact that the easiness of shaking by actual measurement is also large.</span>
文摘The details of a multi-hazard and probabilistic risk assessment, developed for urban planning and emergency response activities in Manizales, Colombia, are presented in this article. This risk assessment effort was developed under the framework of an integral disaster risk management project whose goal was to connect risk reduction activities by using open access and state-of-theart risk models. A probabilistic approach was used for the analysis of seismic, landslide, and volcanic hazards to obtain stochastic event sets suitable for probabilistic loss estimation and to generate risk results in different metrics after aggregating in a rigorous way the losses associated to the different hazards. Detailed and high resolution exposure databases were used for the building stock and infrastructure of the city together with a set of vulnerability functions for each of the perils considered. The urban and territorial ordering plan of the city was updated for socioeconomic development and land use using the hazard and risk inputs and determinants, which cover not only the current urban area but also those adjacent areas where the expansion of Manizales is expected to occur. The emergency response capabilities of the city were improved by taking into account risk scenarios and after updating anautomatic and real-time post-earthquake damage assessment.
基金other researchers in the National Key R&D Program of China(No.2017YFC0803300)for their great contribu-tions to this work.
文摘Due to the frequent occurrence of multi-hazard disasters worldwide in recent years,effective multi-hazard sce-nario analysis is imperative for disaster rescue and emergency management.The response procedure for different single hazards were investigated and formulated before.However,the investigations of disaster scenario rarely systematically address the entire development and response process of multi-hazards,including the coupling mechanisms,evolution dynamics,scenario assessment and emergency response.To this end,this paper presents our methodology of multi-hazard disaster scenario that integrates experiment-simulation-field data,focusing on three dimensions consisting of multi-hazard coupling,structures and systems,and emergency management.The newly proposed scenario method mainly comprises three aspects:experiments and simulations,multi-hazard field investigation,scenario analysis and response.Specifically,in order to study the large-scale,high-intensity and multi-hazard coupling effects,we carried out reduced-scale experiments and field measurement experiments to develop experimental similarity theory and prototype simulations of multi-hazards.In addition,a variety of field rescue and survey equipment,such as robots,Unmanned Aerial Vehicle(UAV),and Virtual Reality/Augmented Reality(VR/AR)technologies were utilized to acquire real-time data of multi-hazard field.Furthermore,we also examine the mechanism and framework of multi-hazard scenarios to formulate the detailed procedures of man-agement and response.They are incorporated with the experiments,simulations,field data and models to con-struct a new scenario platform.The proposed scenario method was applied in a case study of the coupled wind and snow multi-hazard to verify its effectiveness.The new method contributes to the disaster relief,decision-making and emergency management for multi-hazard disaster to improve the urban resilience.
文摘Preparation of accurate and up-to-date susceptibility maps at the regional scale is mandatory for disaster mitigation,site selection,and planning in areas prone to multiple natural hazards.In this study,we proposed a novel multi-hazard susceptibility assessment approach that combines expert-based and supervised machine learning methods for landslide,flood,and earthquake hazard assessments for a basin in Elazig Province,Türkiye.To produce the landslide susceptibility map,an ensemble machine learning algorithm,random forest,was chosen because of its known performance in similar studies.The modified analytical hierarchical process method was used to produce the flood susceptibility map by using factor scores that were defined specifically for the area in the study.The seismic hazard was assessed using ground motion parameters based on Arias intensity values.The univariate maps were synthesized with a Mamdani fuzzy inference system using membership functions designated by expert.The results show that the random forest provided an overall accuracy of 92.3%for landslide susceptibility mapping.Of the study area,41.24%were found prone to multi-hazards(probability value>50%),but the southern parts of the study area are more susceptible.The proposed model is suitable for multi-hazard susceptibility assessment at a regional scale although expert intervention may be required for optimizing the algorithms.
文摘Global climate change,climate extremes,and overuse of natural resources are all major contributors to the risk brought on by cyclones.In I West Bengal state of India,the Pathar Pratima Block frequently experiences a variety of risks that result in significant loss of life and livelihood.In order to govern coastal society,it is crucial to measure and map the multi-hazards risk status.To depict the multi-hazards vulnerability and risk status,no cutting-edge models are currently being applied.Predicting distinct physical vulnerabilities is possible using a variety of cutting-edge machine learning techniques.This study set out to precisely describe multi-hazard risk using powerful machine learning methods.This study involved the use of Analytic Hierarchical Analysis and two cutting-edge machine-learning algorithms-Random Forest and Artificial Neural Network,which are yet underutilized in this area.The multi-hazards risk was determined by taking into account six criteria.The southern and eastern regions of the research area are clearly identified by the multi-hazards risk maps as having high to extremely high hazards risk levels.Cyclonic hazards and embankment breaching are the main dominant factors among the multi-hazards.The machine learning approach is the most accurate model for mapping the multi-hazards risk where the ROC result of Random forest and artificial neural network is more than the conventional method AHP.Here RF is the most validated model than the other two.The effectiveness,root mean square error,true skill statistics,Friedman and Wilcoxon rank test,and area under the curve of receiver operating characteristic tests were used to evaluate the prediction capacity of newly constructed models.The RMSE values of 0.24 and 0.26,TSS values of 0.82 and 0.73,and AUC values of 88.20%and 89.10%as produced by RF and ANN models,respectively,were all excellent.
文摘The World Meteorological Organization(WMO) is planning to implement a Global Multi-hazard Alert System(GMAS) to aggregate official warning^1 information issued by authorities around the world and to serve as a one-stop shop to support the humanitarian organizations of the United Nations(UN), National Meteorological and Hydrological Services(NMHSs) and other global users including the media. It aims to enhance the authority and visibility of NMHSs and other alerting authorities. To aid effective dissemination of warnings to GMAS, the Common Alerting Protocol(CAP) was considered as a standard and robust format to use. In respect of GMAS infrastructure, the World Weather Information(WWIS) and the Severe Weather Information Centre(SWIC) of WMO as well as the WMO Alert Hub now being implemented are identified as core components, among others. The SWIC is being upgraded with GIS capability for displaying authoritative warnings and tropical cyclone(TC) information, and for use as a display platform of GMAS. Apart from warnings from NMHSs, authoritative TC warnings and advisories issued by Regional Specialized Meteorological Centres(RSMCs) and Tropical Cyclone Warning Centres(TCWCs) are also indispensable information for GMAS. As the existing TC warnings and advisories, now more or less in free text format, are mainly targeted for human users and are not intended for automatic parsing by computer software, it is proposed to make available the TC advisories in a machine-readable format so that TC information can be effectively ingested into GMAS and made available to the UN humanitarian organizations, NMHSs and other global users. In this respect, some enhancement measures to TC advisories are proposed. This calls for active collaboration of Members of the Typhoon Committee in the GMAS project.
