We present elemental concentrations and magnetic susceptibility data from a new 270‐cm‐long sediment core collected from the western part of palaeolake Babicora and infer millennial‐scale hydrological variations over the last 27 cal. ka in the western Chihuahua Desert. Variations in the available water content at the sediment–air interface of the watershed, lake salinity and lake productivity are inferred from values of the chemical index of alteration (CIA), CaCO 3 and Corg, respectively. An abrupt increase in runoff at c. 24 cal. ka BP appears correlative with the Heinrich 2 (H2) event. Except for this event, diminished runoff between c. 27 and 19 cal. ka BP indicates lower annual precipitation (weak summer rainfall) during the Last Glacial Maximum. The deposition of chemically altered sediments between c. 25 and 22 cal. ka BP results from the higher sediment–water interaction in the watershed owing to lower evaporation, cooler conditions and higher precipitation during the H2 event. Since 19 cal. ka BP the runoff has been characterized by high‐amplitude fluctuations with intervals of reduced precipitation identified at c. 19, 18, 17.5, 13–14, 11.5, 10, 7.5 and 3 cal. ka BP. 相似文献
Coastal areas in East Africa are experiencing rapid economic, resource management, demographic and technological shifts. In response diverse Community-based Natural Resource Management (CBNRM) applications have been embraced to provide mutual conservation and use benefits. These initiatives have met with mixed success in practice. Reflecting on the limitations of past research using common pool resources theory theory to study CBNRM we use insights from actor oriented theory combined with satellite image analysis to describe and discuss the forces dynamically influencing institutional and mangrove forest cover change at Kisakasaka, Zanzibar focussing on the formal CBNRM project period between 1996 and 2001, but also considering the period before and after this. We examine the shifting social relations that affected the performance and viability of the formal CBNRM arrangements. An integrated approach was taken to the presentation and discussion of results where it was possible to enrich and expand explanations of socio-environmental change, which was driven by a lack of government support, the undermining effects of party political divisions, and the lack of institutional adaptive capacity. We conclude that this was a useful approach to explain CBNRM intervention events at Kisakasaka. 相似文献
Natural Hazards - We present a Risk Atlas of Mexico City based on a Geographical Information System (RA-GIS). We identified the prevalent social risk to the more relevant hazards in Mexico City... 相似文献
Sudden avulsions, unexpected channel migrations and backfilling phenomena are autogenic phenomena that can considerably change the propagation patterns of sediment-laden flows on alluvial fans. Once the initial and boundary conditions of the hazard scenario with a given return period are determined, the assessment of the associated exposed areas is based on one numerical, essentially deterministic, process simulation which may not adequately capture the underlying process variability. We generated sediment-laden flows on an experimental alluvial fan by following a “similarity-of-process concept”. Specifically, we considered a convexly shaped alluvial fan model layout featuring a curved guiding channel. As loading conditions, we defined a reference, an increased and a reduced level for the released water volume and the predisposed solid fraction, respectively. Further, we imposed two different stream power regimes and accomplished, for each factor combination, eight experimental runs. The associated exposure areas were recorded by video and mapped in a GIS. We then analysed exposure data and determined exposure probability maps superposing the footprints of the eight repetitions associated with each experimental loading condition. The patterns of exposure referred to the specific loading conditions showed a noticeable variability related to the main effects of the total event volume, the solid fraction, the interactions between them, and the imposed stream power in the feeding channel. Our research suggests that adopting a probabilistic notion of exposure in risk assessment and mitigation is advisable. Further, a major challenge consists in adapting numerical codes to better reflect the stochastics of process propagation for more reliable flood hazard assessments.
Geotechnical and Geological Engineering - An investigation performed on the interactions of silty soil treated with cement or lime demonstrates the strong relationship between microstructural... 相似文献
Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pre-trained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. We used these data to test the accuracy of the pre-trained SA classification model developed by MonkeyLearn to identify polarity in text data. This test explores the feasibility to automate the classification process to extract meaningful information from text data from SM in real-time in the future. We tested the no-code machine learning platform's performance using a confusion matrix. We obtained an overall accuracy (ACC) of 63% and a misclassification rate of 37%. We conclude that the ACC of the unsupervised classification is sufficient for a preliminary assessment, but further research is needed to determine if the accuracy is improved by customising the training model of the machine learning platform.
