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On 22 March 2014, a massive, catastrophic landslide occurred near Oso, Washington, USA, sweeping more than 1 km across the adjacent valley flats and killing 43 people. For the following 5 weeks, hundreds of workers engaged in an exhaustive search, rescue, and recovery effort directly in the landslide runout path. These workers could not avoid the risks posed by additional large-scale slope collapses. In an effort to ensure worker safety, multiple agencies cooperated to swiftly deploy a monitoring and alerting system consisting of sensors, automated data processing and web-based display, along with defined communication protocols and clear calls to action for emergency management and search personnel. Guided by the principle that an accelerating landslide poses a greater threat than a steadily moving or stationary mass, the system was designed to detect ground motion and vibration using complementary monitoring techniques. Near real-time information was provided by continuous GPS, seismometers/geophones, and extensometers. This information was augmented by repeat-assessment techniques such as terrestrial and aerial laser scanning and time-lapse photography. Fortunately, no major additional landsliding occurred. However, we did detect small headscarp failures as well as slow movement of the remaining landslide mass with the monitoring system. This was an exceptional response situation and the lessons learned are applicable to other landslide disaster crises. They underscore the need for cogent landslide expertise and ready-to-deploy monitoring equipment, the value of using redundant monitoring techniques with distinct goals, the benefit of clearly defined communication protocols, and the importance of continued research into forecasting landslide behavior to allow timely warning.
相似文献Within the Ararat Valley (Armenia), a continuously growing water demand (for irrigation and fish farming) and a simultaneous decline in groundwater recharge (due to climate change) result in increasing stress on the local groundwater resources. This detrimental development is reflected by groundwater-level drops and an associated reduction of the area with artesian conditions in the valley centre. This situation calls for increasing efforts aimed at more sustainable water resources management. The aim of this baseline study was the collection of data that allows for study on the origin and age distribution of the Ararat Valley groundwater based on environmental tracers, namely stable (δ2H, δ18O) and radioactive (35S, 3H) isotopes, as well as physical-chemical indicators. The results show that the Ararat Valley receives modern recharge, despite its (semi-)arid climate. While subannual groundwater residence times could be disproved (35S), the detected 3H pattern suggests groundwater ages of several decades, with the oldest waters being recharged around 60 years ago. The differing groundwater ages are reflected by varying scatter of stable isotope and hydrochemical signatures. The presence of young groundwater (i.e., younger that the 1970s), some containing nitrate, indicates groundwater vulnerability and underscores the importance of increased efforts to achieve sustainable management of this natural resource. Since stable isotope signatures indicate the recharge areas to be located in the mountains surrounding the valley, these efforts must not be limited to the central part of the valley where most of the abstraction wells are located.
相似文献Many geological phenomena are regularly measured over time to follow developments and changes. For many of these phenomena, the absolute values are not of interest, but rather the relative information, which means that the data are compositional time series. Thus, the serial nature and the compositional geometry should be considered when analyzing the data. Multivariate time series are already challenging, especially if they are higher dimensional, and latent variable models are a popular way to deal with this kind of data. Blind source separation techniques are well-established latent factor models for time series, with many variants covering quite different time series models. Here, several such methods and their assumptions are reviewed, and it is shown how they can be applied to high-dimensional compositional time series. Also, a novel blind source separation method is suggested which is quite flexible regarding the assumptions of the latent time series. The methodology is illustrated using simulations and in an application to light absorbance data from water samples taken from a small stream in Lower Austria.
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