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
ZSM-5分子筛是合成三聚甲醛的有效催化剂。本工作通过XRF、XRD、SEM、NH3-TPD、Py-FTIR和27Al MAS NMR等手段对一系列不同SiO2/Al2O3物质的量比的ZSM-5分子筛催化剂进行了表征,研究了ZSM-5分子筛中BrΦnsted酸中心和Lewis酸中心对其甲...ZSM-5分子筛是合成三聚甲醛的有效催化剂。本工作通过XRF、XRD、SEM、NH3-TPD、Py-FTIR和27Al MAS NMR等手段对一系列不同SiO2/Al2O3物质的量比的ZSM-5分子筛催化剂进行了表征,研究了ZSM-5分子筛中BrΦnsted酸中心和Lewis酸中心对其甲醛合成三聚甲醛催化性能的影响。结果表明,SiO2/Al2O3物质的量比为250的ZSM-5分子筛具有合适的BrΦnsted酸中心用于催化甲醛缩聚为三聚甲醛的反应,同时其Lewis酸中心量极少,可有效抑制Cannizzaro或Tishchenko等副反应,提高三聚甲醛的选择性,因而具有最佳的合成三聚甲醛催化性能。寿命实验评价结果显示,SiO2/Al2O3物质的量比为250的ZSM-5分子筛具有良好的催化稳定性,单程寿命长达114 h,并且可通过550℃焙烧再生恢复其催化活性。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Supported by NNSF of China(10271048,10671073)Supported by Science and Technology Commission of Shanghai Municipality(07XD14011)Supported by Shanghai Leading Academic Discipline Project(B407)
文摘在这份报纸，我们在 orientable 为局部地 嵌入LEW 的 3-connected 图 G 显示出那表面，下列结果 hold:1 )每如此的 embeddings 是最小的类嵌入； 2 )面部周期正是暗示如此的 embeddings 的唯一的导致的 nonseparating 周期； 3 )每重叠图 O ( G ， C )是一张由两部组成的图， G 有一仅仅C桥 H 以便如果 C 是短的一个会缩的周期， C U H 比包含 C.This 的一个边的每个 noncontractible 周期延长的是
基金supported by the National Key R&D Program of China(2018YFB0604902).
文摘ZSM-5分子筛是合成三聚甲醛的有效催化剂。本工作通过XRF、XRD、SEM、NH3-TPD、Py-FTIR和27Al MAS NMR等手段对一系列不同SiO2/Al2O3物质的量比的ZSM-5分子筛催化剂进行了表征,研究了ZSM-5分子筛中BrΦnsted酸中心和Lewis酸中心对其甲醛合成三聚甲醛催化性能的影响。结果表明,SiO2/Al2O3物质的量比为250的ZSM-5分子筛具有合适的BrΦnsted酸中心用于催化甲醛缩聚为三聚甲醛的反应,同时其Lewis酸中心量极少,可有效抑制Cannizzaro或Tishchenko等副反应,提高三聚甲醛的选择性,因而具有最佳的合成三聚甲醛催化性能。寿命实验评价结果显示,SiO2/Al2O3物质的量比为250的ZSM-5分子筛具有良好的催化稳定性,单程寿命长达114 h,并且可通过550℃焙烧再生恢复其催化活性。
