A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in vari...A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.展开更多
In Brazil, the prominent climate-induced disasters are floods and mass movements, with the latter being the most lethal. The spate of major landslide events, especially those in 2011, catalyzed the creation of CEMADEN...In Brazil, the prominent climate-induced disasters are floods and mass movements, with the latter being the most lethal. The spate of major landslide events, especially those in 2011, catalyzed the creation of CEMADEN (National Center for Monitoring and Early Warning of Natural Disasters). This article introduces one of CEMADEN’s pivotal systems for early landslide warnings and traces its developmental timeline. The highlighted SNAKE System epitomizes advancements in digital monitoring, forecasting, and alert mechanisms. By leveraging precipitation data from pluviometers in observed municipalities, the system bolsters early warnings related to potential mass movements, like planar slides and debris flows. Its deployment in CEMADEN’s Situation Room attests to its suitability for overseeing high-risk municipalities, attributed primarily to its robustness and precision.展开更多
Landslides not only cause property losses,but also kill and injure large numbers of people every year in the mountainous areas. These losses and casualties may be avoided to some extent by early warning systems for la...Landslides not only cause property losses,but also kill and injure large numbers of people every year in the mountainous areas. These losses and casualties may be avoided to some extent by early warning systems for landslides. In this paper, a realtime monitoring network and a computer-aided automatic early warning system(EWS) are presented with details of their design and an example of application in the Longjingwan landslide, Kaiyang County, Guizhou Province. Then, according to principle simple method of landslide prediction, the setting of alarm levels and the design of appropriate counter-measures are presented. A four-level early warning system(Zero, Outlook, Attention and Warning) has been adopted, and the velocity threshold was selected as the main warning threshold for the landslide occurrence, but expert judgment is included in the EWS to avoid false alarms. A case study shows the applicability and reliability for landslide risk management, and recommendations are presented for other similar projects.展开更多
In the central Nepal Himalaya,landslides form the major natural hazards annually resulting in many casualties and damage.Structural as well as non-structural measures are in place to minimize the risk of landslide haz...In the central Nepal Himalaya,landslides form the major natural hazards annually resulting in many casualties and damage.Structural as well as non-structural measures are in place to minimize the risk of landslide hazard.To reduce the landslide risk,a Landslide Early Warning System(LEWS)as a nonstructural measure has been piloted at Sundrawati village(Kalinchowk rural municipality,Dolakha district)to identify its effectiveness.Intensive discussions with stakeholders,aided by landslide susceptibility map,resulted in a better understanding of surface dynamics and the relationship between rainfall and surface movement.This led to the development of a LEWS comprised of extensometers,soil moisture sensors,rain gauge stations,and solar panels as an energy source that blows siren receiving signals via a micro-controller and interfacing circuit.The data generated through the system is transmitted via a Global System for Mobile Communications(GSM)network to responsible organizations in realtime to circulate the warning to local residents.This LEWS is user-friendly and can be easily operated by a community.The successful pilot early warning system has saved 495 people from 117 households in August 2018.However,landslide monitoring and dissemination of warning information remains a complex process where technical and communications skill should work closely together.展开更多
Landslide in alpine regions often causes heavy losses of both human lives and properties, most of the landslides are induced by heavy rainfall. In this paper, we put forward an early warning system of rain-induced lan...Landslide in alpine regions often causes heavy losses of both human lives and properties, most of the landslides are induced by heavy rainfall. In this paper, we put forward an early warning system of rain-induced landslide. From 2002, we carried on the demonstrative work of landslide monitoring and early warning in Yaan, Sichuan Province, China, and constructed the first county-scale landslide monitoring and early warning region. Yucheng District of Yaan City is located in the west of the Sichuan Basin, right in the intersection of SichuanBasin and the Tibetan Plateau. The slopes are made of Mesozoic sedimentary rock, sandstone inter-bedded with mudstone. Yucheng District has the title “sky funnel” because of the high precipitation, the annual precipitation is about 1750 mm. We carried out detailed landslide survey, and obtained the location, scale, characteristics, influence and triggering factors of the landslides. Then we assessed the regional landslide susceptibility. Based on the evolution law of the landslides, we selected ten factors to study the relationship between the factors and landslide. Using the bi-variate statistics method, we calculated the contribution to landslide from each factor, classified the susceptibility into four categories. We set up the regional rainfall monitoring network with 13 automatic CAWS600R rain gauges. Using the landslide survey data, we studied the rainfall influencing of the regional landslides. The one-day and three-day rainfall controls the occurrence of regional landslide. We also classified the triggering effect of rainfall into four categories. We presented a method to calculate the landslide danger degree using the susceptibility and triggering category. Utilizing the predicted rainfall data and real-time monitored rainfall data, together with the landslide susceptibility map, we developed a WebGIS-based landslide warning system, which greatly strengthened the capability for geohazard control.展开更多
Oil and gas pipelines are of great importance in China,and pipeline security problems pose a serious threat to society and the environment.Pipeline safety has therefore become an integral part of the entire national e...Oil and gas pipelines are of great importance in China,and pipeline security problems pose a serious threat to society and the environment.Pipeline safety has therefore become an integral part of the entire national economy.Landslides are the most harmful type of pipeline accident,and have directed increasing public attention to safety issues.Although some useful results have been obtained in the investigation and prevention of pipeline-landslide hazards,there remains a need for effective monitoring and early warning methods,especially when the complexity of pipeline-landslides is considered.Because oil and gas pipeline-landslides typically occur in the superficial soil layers,monitoring instruments must be easy to install and must cause minimal disturbance to the surrounding soil and pipeline.To address the particular characteristics of pipelinelandslides,we developed a multi-parameter integrated monitoring system called disaster reduction stick equipment.In this paper,we detail this monitoring and early warning system for pipeline-landslide hazards based on an on-site monitoring network and early warning algorithms.The functionality of our system was verified by its successful application to the Chongqing Loujiazhuang pipeline-landslide in China.The results presented here provide guidelines for the monitoring,early warning,and prevention of pipeline geological hazards.展开更多
Rainfall-induced landslides have occurred frequently in Southwestern China since the Wenchuan earthquake,resulting in massive loss of people’s life and property.Fortunately,landslide early-warning is one of the most ...Rainfall-induced landslides have occurred frequently in Southwestern China since the Wenchuan earthquake,resulting in massive loss of people’s life and property.Fortunately,landslide early-warning is one of the most important tools for landslide hazard prevention and mitigation.However,the accumulation of historical data of the landslides induced by rainfall is limited in many remote mountain areas and the stability of the slope is easily affected by human engineering activities and environmental changes,leading to difficulties to accurately realize early warning of landslide hazards by statistical methods.The proposed warning method is divided into rainfall warning component and deformation warning component because the deformation induced by rainfall has the characteristic of hysteretic nature.Rainfall,tilted angle and crack width are chosen as monitoring indexes.Rainfall grade level that contains rainfall intensity and duration information is graded according to the variation of the safety factor calculated by 3-D finite difference numerical simulation method,and then is applied using the strength reduction method and unascertained information theory to obtain the deformation grade level of several monitored points.Finally,based on the system reliability theory,we establish a comprehensive landslide warning level method that provides four early warning levels to reflect the safety factor reductions during and post rainfall events.The application of this method at a landslide site yield generally satisfactory results and provide a new method for performing multi-index and multi-level landslide early warnings.展开更多
In order to investigate the spatial distribution of early warning threshold for landslide induced by rainfall in China,the literatures about rainfall thresholds of landslides in China in recent 20 years are selected.S...In order to investigate the spatial distribution of early warning threshold for landslide induced by rainfall in China,the literatures about rainfall thresholds of landslides in China in recent 20 years are selected.Statistical analysis and visualization methods were employed to systematically analyze the research progress of rainfall early warning thresholds at various scales.Taking the typical rainfall intensity-duration(I-D)threshold model as the research object,combined with the geographical characteristics of China and the average annual rainfall of 20 years,the spatial distribution of early warning thresholds for rainfall-induced landslide in China is depicted.The results show that the inspired rain intensity coefficientαof the rainfall threshold(I-D curve)in China roughly increases gradually with the decrease of topography.Moreover,under consistent annual rainfall conditions,the scalar indexβexhibits regular changes corresponding to variations in terrain.Topography and rainfall are the two main factors strongly associated with the rainfall threshold.This research establishes a clear framework for studying the early warning thresholds for rainfall-induced landslides in China and holds significant scientific implications for developing more effective rainfall threshold models.展开更多
Rainfall-triggered landslides have posed significant threats to human lives and property each year in China. This paper proposed a meteorologicalgeotechnical early warning system GRAPES-LFM(GRAPES: Global and Regional...Rainfall-triggered landslides have posed significant threats to human lives and property each year in China. This paper proposed a meteorologicalgeotechnical early warning system GRAPES-LFM(GRAPES: Global and Regional Assimilation and Pr Ediction System; LFM: Landslide Forecast Model),basing on the GRAPES model and the landslide predicting model TRIGRS(Transient Rainfall Infiltration and Grid-based Regional Slope-Stability Model) for predicting rainfall-triggered landslides.This integrated system is evaluated in Dehua County,Fujian Province, where typhoon Bilis triggered widespread landslides in July 2006. The GRAPES model runs in 5 km×5 km horizontal resolution, and the initial fields and lateral boundaries are provided by NCEP(National Centers for Environmental Prediction) FNL(Final) Operational Global Analysis data. Quantitative precipitation forecasting products of the GRAPES model are downscaled to 25 m×25 m horizontal resolution by bilinear interpolation to drive the TRIGRS model. Results show that the observed areas locate in the high risk areas, and the GRAPES-LFM model could capture about 74% of the historical landslides with the rainfall intense 30mm/h. Meanwhile, this paper illustrates the relationship between the factor of safety(FS) and different rainfall patterns. GRAPES-LFM model enables us to further develop a regional, early warning dynamic prediction tool of rainfall-induced landslides.展开更多
文摘A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.
文摘In Brazil, the prominent climate-induced disasters are floods and mass movements, with the latter being the most lethal. The spate of major landslide events, especially those in 2011, catalyzed the creation of CEMADEN (National Center for Monitoring and Early Warning of Natural Disasters). This article introduces one of CEMADEN’s pivotal systems for early landslide warnings and traces its developmental timeline. The highlighted SNAKE System epitomizes advancements in digital monitoring, forecasting, and alert mechanisms. By leveraging precipitation data from pluviometers in observed municipalities, the system bolsters early warnings related to potential mass movements, like planar slides and debris flows. Its deployment in CEMADEN’s Situation Room attests to its suitability for overseeing high-risk municipalities, attributed primarily to its robustness and precision.
基金financially supported by the State Key Laboratory of Geo-hazard Prevention and Geo-environment Protection (Chengdu University of Technology) (Grant No. SKLGP2013Z007)the National Natural Science Foundation of China (Grant No. 41302242)
文摘Landslides not only cause property losses,but also kill and injure large numbers of people every year in the mountainous areas. These losses and casualties may be avoided to some extent by early warning systems for landslides. In this paper, a realtime monitoring network and a computer-aided automatic early warning system(EWS) are presented with details of their design and an example of application in the Longjingwan landslide, Kaiyang County, Guizhou Province. Then, according to principle simple method of landslide prediction, the setting of alarm levels and the design of appropriate counter-measures are presented. A four-level early warning system(Zero, Outlook, Attention and Warning) has been adopted, and the velocity threshold was selected as the main warning threshold for the landslide occurrence, but expert judgment is included in the EWS to avoid false alarms. A case study shows the applicability and reliability for landslide risk management, and recommendations are presented for other similar projects.
基金Government of NepalMinistry of Forests and Environment (MoFE)/DoFSCFood and Agricultural Organizations of the United Nations (FAO) for overall support to conduct this study
文摘In the central Nepal Himalaya,landslides form the major natural hazards annually resulting in many casualties and damage.Structural as well as non-structural measures are in place to minimize the risk of landslide hazard.To reduce the landslide risk,a Landslide Early Warning System(LEWS)as a nonstructural measure has been piloted at Sundrawati village(Kalinchowk rural municipality,Dolakha district)to identify its effectiveness.Intensive discussions with stakeholders,aided by landslide susceptibility map,resulted in a better understanding of surface dynamics and the relationship between rainfall and surface movement.This led to the development of a LEWS comprised of extensometers,soil moisture sensors,rain gauge stations,and solar panels as an energy source that blows siren receiving signals via a micro-controller and interfacing circuit.The data generated through the system is transmitted via a Global System for Mobile Communications(GSM)network to responsible organizations in realtime to circulate the warning to local residents.This LEWS is user-friendly and can be easily operated by a community.The successful pilot early warning system has saved 495 people from 117 households in August 2018.However,landslide monitoring and dissemination of warning information remains a complex process where technical and communications skill should work closely together.
文摘Landslide in alpine regions often causes heavy losses of both human lives and properties, most of the landslides are induced by heavy rainfall. In this paper, we put forward an early warning system of rain-induced landslide. From 2002, we carried on the demonstrative work of landslide monitoring and early warning in Yaan, Sichuan Province, China, and constructed the first county-scale landslide monitoring and early warning region. Yucheng District of Yaan City is located in the west of the Sichuan Basin, right in the intersection of SichuanBasin and the Tibetan Plateau. The slopes are made of Mesozoic sedimentary rock, sandstone inter-bedded with mudstone. Yucheng District has the title “sky funnel” because of the high precipitation, the annual precipitation is about 1750 mm. We carried out detailed landslide survey, and obtained the location, scale, characteristics, influence and triggering factors of the landslides. Then we assessed the regional landslide susceptibility. Based on the evolution law of the landslides, we selected ten factors to study the relationship between the factors and landslide. Using the bi-variate statistics method, we calculated the contribution to landslide from each factor, classified the susceptibility into four categories. We set up the regional rainfall monitoring network with 13 automatic CAWS600R rain gauges. Using the landslide survey data, we studied the rainfall influencing of the regional landslides. The one-day and three-day rainfall controls the occurrence of regional landslide. We also classified the triggering effect of rainfall into four categories. We presented a method to calculate the landslide danger degree using the susceptibility and triggering category. Utilizing the predicted rainfall data and real-time monitored rainfall data, together with the landslide susceptibility map, we developed a WebGIS-based landslide warning system, which greatly strengthened the capability for geohazard control.
基金financially supported by National Key R&D Program of China (No. 2018YFC1505201)National Natural Science Foundation of China (No. 41901008)+2 种基金Open Fund Project of Key Laboratory of Mountain Hazards and Surface Processes of the Chinese Academy of Sciencesthe Fundamental Research Funds for the Central Universities (Grant NO. 2682018CX05)financially supported by China Scholarship Council
文摘Oil and gas pipelines are of great importance in China,and pipeline security problems pose a serious threat to society and the environment.Pipeline safety has therefore become an integral part of the entire national economy.Landslides are the most harmful type of pipeline accident,and have directed increasing public attention to safety issues.Although some useful results have been obtained in the investigation and prevention of pipeline-landslide hazards,there remains a need for effective monitoring and early warning methods,especially when the complexity of pipeline-landslides is considered.Because oil and gas pipeline-landslides typically occur in the superficial soil layers,monitoring instruments must be easy to install and must cause minimal disturbance to the surrounding soil and pipeline.To address the particular characteristics of pipelinelandslides,we developed a multi-parameter integrated monitoring system called disaster reduction stick equipment.In this paper,we detail this monitoring and early warning system for pipeline-landslide hazards based on an on-site monitoring network and early warning algorithms.The functionality of our system was verified by its successful application to the Chongqing Loujiazhuang pipeline-landslide in China.The results presented here provide guidelines for the monitoring,early warning,and prevention of pipeline geological hazards.
基金sponsored by National KeyResearch and Development Program(2018YFC0809400)"Safety Guarantee Technology of Power Grid Facilities in Large Region under Extreme Conditions"and Scientific Program of State Grid Corporation of China(GCB17201800051)"Research for application of geological hazard analysis technology on strategic transmission channel of Sichuan-Tibet Plateau based on synthetic aperture radar"。
文摘Rainfall-induced landslides have occurred frequently in Southwestern China since the Wenchuan earthquake,resulting in massive loss of people’s life and property.Fortunately,landslide early-warning is one of the most important tools for landslide hazard prevention and mitigation.However,the accumulation of historical data of the landslides induced by rainfall is limited in many remote mountain areas and the stability of the slope is easily affected by human engineering activities and environmental changes,leading to difficulties to accurately realize early warning of landslide hazards by statistical methods.The proposed warning method is divided into rainfall warning component and deformation warning component because the deformation induced by rainfall has the characteristic of hysteretic nature.Rainfall,tilted angle and crack width are chosen as monitoring indexes.Rainfall grade level that contains rainfall intensity and duration information is graded according to the variation of the safety factor calculated by 3-D finite difference numerical simulation method,and then is applied using the strength reduction method and unascertained information theory to obtain the deformation grade level of several monitored points.Finally,based on the system reliability theory,we establish a comprehensive landslide warning level method that provides four early warning levels to reflect the safety factor reductions during and post rainfall events.The application of this method at a landslide site yield generally satisfactory results and provide a new method for performing multi-index and multi-level landslide early warnings.
基金support from the National Natural Science Foundation of China(U2005205)Science and Technology Innovation Platform Project of Fuzhou Science and Technology Bureau(No.2021-P-032)Opening Fund of Key Laboratory of Geohazard Prevention of Hilly Mountains,Ministry of Natural Resources(Fujian Key Laboratory of Geohazard Prevention)(FJKLGH2023K006).
文摘In order to investigate the spatial distribution of early warning threshold for landslide induced by rainfall in China,the literatures about rainfall thresholds of landslides in China in recent 20 years are selected.Statistical analysis and visualization methods were employed to systematically analyze the research progress of rainfall early warning thresholds at various scales.Taking the typical rainfall intensity-duration(I-D)threshold model as the research object,combined with the geographical characteristics of China and the average annual rainfall of 20 years,the spatial distribution of early warning thresholds for rainfall-induced landslide in China is depicted.The results show that the inspired rain intensity coefficientαof the rainfall threshold(I-D curve)in China roughly increases gradually with the decrease of topography.Moreover,under consistent annual rainfall conditions,the scalar indexβexhibits regular changes corresponding to variations in terrain.Topography and rainfall are the two main factors strongly associated with the rainfall threshold.This research establishes a clear framework for studying the early warning thresholds for rainfall-induced landslides in China and holds significant scientific implications for developing more effective rainfall threshold models.
基金supported by The National Basic Research Program of China(973)(Grant No.2013CB430106)National Natural Science Foundation of China(Grant No.41375108)Scientific Research&Innovation Projects for Academic Degree students of ordinary Universities of Jiangsu(Grant No.CXLX13_474)
文摘Rainfall-triggered landslides have posed significant threats to human lives and property each year in China. This paper proposed a meteorologicalgeotechnical early warning system GRAPES-LFM(GRAPES: Global and Regional Assimilation and Pr Ediction System; LFM: Landslide Forecast Model),basing on the GRAPES model and the landslide predicting model TRIGRS(Transient Rainfall Infiltration and Grid-based Regional Slope-Stability Model) for predicting rainfall-triggered landslides.This integrated system is evaluated in Dehua County,Fujian Province, where typhoon Bilis triggered widespread landslides in July 2006. The GRAPES model runs in 5 km×5 km horizontal resolution, and the initial fields and lateral boundaries are provided by NCEP(National Centers for Environmental Prediction) FNL(Final) Operational Global Analysis data. Quantitative precipitation forecasting products of the GRAPES model are downscaled to 25 m×25 m horizontal resolution by bilinear interpolation to drive the TRIGRS model. Results show that the observed areas locate in the high risk areas, and the GRAPES-LFM model could capture about 74% of the historical landslides with the rainfall intense 30mm/h. Meanwhile, this paper illustrates the relationship between the factor of safety(FS) and different rainfall patterns. GRAPES-LFM model enables us to further develop a regional, early warning dynamic prediction tool of rainfall-induced landslides.
