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
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.
文摘目的 观察全杜仲胶囊对犬股骨头骨组织中的变化及对转录因子(RUNX2)、1型胶原蛋白(COL-1)蛋白表达的影响,探讨其修复股骨头坏死的机制。方法 选取健康成年比格犬(雌雄各6只,共12只,分笼饲养),随机数字表法分为正常组、模型组、全杜仲胶囊组、仙灵骨葆胶囊组,每组3只。除正常组外,其余3组均采用液氮冷冻法制备双侧股骨头坏死模型。术后第2天开始药物干预,正常组等量生理盐水灌胃,连续12周。观察犬的一般情况,苏木精-伊红染色法(HE)观察犬股骨头组织形态学改变;实时逆转录PCR(Real time RT-PCR)检测RUNX2、COL-1mRNA表达;免疫组化法检测RUNX2、COL-1蛋白表达。结果 与模型组相比较,HE病理染色方面,全杜仲组及仙灵骨葆胶囊组骨小梁面积、骨小梁体积有所恢复;免疫组化方面,与模型组相比,全杜仲胶囊组、仙灵骨葆胶囊组RUNX2蛋白和COL-1蛋白表达上升(P<0.05);Real time RT-PCR检测结果显示RUNX2、COL-1mRNA表达上升(P<0.05)。结论 全杜仲胶囊可以改善犬股骨头坏死组织的变化,这可能是通过调节COL-1及RUNX2蛋白及mRNA的表达调节骨代谢,进而达到防治股骨头坏死的目的。
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.
