Cyclogenesis and long-fetched winds along the southeastern coast of South America may lead to floods in populated areas, as the Buenos Aires Province, with important economic and social impacts. A numerical model (SMARA) has already been implemented in the region to forecast storm surges. The propagation time of the surge in such extensive and shallow area allows the detection of anomalies based on observations from several hours up to the order of a day prior to the event. Here, we investigate the impact and potential benefit of storm surge level data assimilation into the SMARA model, with the objective of improving the forecast. In the experiments, the surface wind stress from an ensemble prediction system drives a storm surge model ensemble, based on the operational 2-D depth-averaged SMARA model. A 4-D Local Ensemble Transform Kalman Filter (4D-LETKF) initializes the ensemble in a 6-h cycle, assimilating the very few tide gauge observations available along the northern coast and satellite altimeter data. The sparse coverage of the altimeters is a challenge to data assimilation; however, the 4D-LETKF evolving covariance of the ensemble perturbations provides realistic cross-track analysis increments. Improvements on the forecast ensemble mean show the potential of an effective use of the sparse satellite altimeter and tidal gauges observations in the data assimilation prototype. Furthermore, the effects of the localization scale and of the observational errors of coastal altimetry and tidal gauges in the data assimilation approach are assessed. 相似文献
The present study analysed the taphonomic characteristics of small mammal bone accumulations produced by small felids in an area from the central Monte Desert (Mendoza, Argentina). In order to provide criteria to identify the role that these predators had in the formation of zooarchaeological assemblages, the anatomical representation, bone breakage patterns and degrees of digestive corrosion were evaluated. The main taphonomic results are: low average values for the relative abundance of skeletal elements; greater representation of mandibles, maxillae, isolated incisors, humeri and femora than the remaining elements; preponderance of cranial elements with high proportion of isolated teeth; elevated frequencies of proximal limb bones compared with distal parts; high degree of breakage in all skeletal elements and digestive corrosion on almost all diagnostic bones (mainly moderate and heavy). The values of the studied taphonomic variables indicate that small felids in this area made severe alterations to the bones of their prey (mainly rodents), attributable to the category of extreme modifier, while preserving enough skeletal elements to allow their taphonomic characterization. Tooth marks or grooves on bone surfaces produced by scratching and chewing were not detected. The low relative abundance of skeletal elements, the high degree of breakage and the elevated frequency of elements with digestion traces represent general taphonomic patterns that fall within those reported for other South American small carnivores. 相似文献
The accuracies of three different evolutionary artificial neural network (ANN) approaches, ANN with genetic algorithm (ANN-GA), ANN with particle swarm optimization (ANN-PSO) and ANN with imperialist competitive algorithm (ANN-ICA), were compared in estimating groundwater levels (GWL) based on precipitation, evaporation and previous GWL data. The input combinations determined using auto-, partial auto- and cross-correlation analyses and tried for each model are: (i) GWLt?1 and GWLt?2; (ii) GWLt?1, GWLt?2 and Pt; (iii) GWLt?1, GWLt?2 and Et; (iv) GWLt?1, GWLt?2, Pt and Et; (v) GWLt?1, GWLt?2 and Pt?1 where GWLt, Pt and Et indicate the GWL, precipitation and evaporation at time t, individually. The optimal ANN-GA, ANN-PSO and ANN-ICA models were obtained by trying various control parameters. The best accuracies of the ANN-GA, ANN-PSO and ANN-ICA models were obtained from input combination (i). The mean square error accuracies of the ANN-GA and ANN-ICA models were increased by 165 and 124% using ANN-PSO model. The results indicated that the ANN-PSO model performed better than the other models in modeling monthly groundwater levels. 相似文献
Hurricane surge events have caused devastating damage in active-hurricane areas all over the world. The ability to predict surge elevations and to use this information for damage estimation is fundamental for saving lives and protecting property. In this study, we developed a framework for evaluating hurricane flood risk and identifying areas that are more prone to them. The approach is based on the joint probability method with optimal sampling (JPM-OS) using surge response functions (SRFs) (JPM-OS-SRF). Derived from a discrete set of high-fidelity storm surge simulations, SRFs are non-dimensional, physics-based empirical equations with an algebraic form, used to rapidly estimate surge as a function of hurricane parameters (i.e., central pressure, radius, forward speed, approach angle and landfall location). The advantage of an SRF-based approach is that a continuum of storm scenarios can be efficiently evaluated and used to estimate continuous probability density functions for surge extremes, producing more statistically stable surge hazard assessments without adding measurably to epistemic uncertainty. SRFs were developed along the coastline and then used to estimate maximum surge elevations with respect to a set of hurricane parameters. Integrating information such as ground elevation, property value and population with the JPM-OS-SRF allows quantification of storm surge-induced hazard impacts over the continuum of storm possibilities, yielding a framework to create the following risk-based products, which can be used to assist in hurricane hazard management and decision making: (1) expected annual loss maps; (2) flood damage versus return period relationships; and (3) affected business (e.g., number of business, number of employees) versus return period relationships. By employing several simplifying assumptions, the framework is demonstrated at three northern Gulf of Mexico study sites exhibiting similar surge hazard exposure. The framework results reveal Gulfport, MS, USA is at relatively more risk of economic loss than Corpus Christi, TX, USA, and Panama City, FL, USA. Note that economic processes are complex and very interrelated to most other human activities. Our intention here is to present a methodology to quantify the flood damage (i.e., infrastructure economic loss, number of businesses affected, number of employees in these affected businesses and sales volume in these affected businesses) but not to discuss the complex interactions of these damages with other economic activities and recovery plans.
This study demonstrated the usefulness of very long-range terrestrial laser scanning (TLS) for analysis of the spatial distribution of a snowpack, to distances up to 3000 m, one of the longest measurement range reported to date. Snow depth data were collected using a terrestrial laser scanner during 11 periods of snow accumulation and melting, over three snow seasons on a Pyrenean hillslope characterized by a large elevational gradient, steep slopes, and avalanche occurrence. The maximum and mean absolute snow depth error found was 0.5-0.6 and 0.2-0.3 m respectively, which may result problematic for areas with a shallow snowpack, but it is sufficiently accurate to determine snow distribution patterns in areas characterized by a thick snowpack. The results indicated that in most cases there was temporal consistency in the spatial distribution of the snowpack, even in different years. The spatial patterns were particularly similar amongst the surveys conducted during the period dominated by snow accumulation (generally until end of April), or amongst those conducted during the period dominated by melting processes (generally after mid of April or early May). Simple linear correlation analyses for the 11 survey dates, and the application of Random Forests analysis to two days representative of snow accumulation and melting periods indicated the importance of topography to the snow distribution. The results also highlight that elevation and the Topographic Position index (TPI) were the main variables explaining the snow distribution, especially during periods dominated by melting. The intra- and inter-annual spatial consistency of the snowpack distribution suggests that the geomorphological processes linked to presence/absence of snow cover act in a similar way in the long term, and that these spatial patterns can be easily identified through several years of adequate monitoring. 相似文献
A nearly continuous magnetostratigraphic polarity pattern was compiled from several ammonite-zoned carbonate successions of southern Poland and from a composite magnetostratigraphy from the Iberian Range of Spain. The array of sections spans the middle two-thirds of the Oxfordian within the Sub-Mediterranean Province (Cordatum through Bifurcatus ammonite zones). The average paleopole calculated from eight of these Polish sections is at 78.5°N, 184.9°E (δp = 2.6°, δm = 3.5°). The Sub-Mediterranean polarity pattern is consistent with an independent polarity pattern derived from the Boreal-realm sections of the British Isles, and improves the inter-correlation between these faunal realms. Cycle stratigraphy published for these ammonite subzones from southern France enabled temporal scaling of the polarity pattern, thereby facilitating correlation to marine magnetic anomalies M28 through M33 as modeled from deep-tow magnetometer surveys in the Western Pacific. The bases of the Middle and Upper Oxfordian substages as defined in the Sub-Mediterranean zonation in Poland correspond approximately to chrons M33 and M29 of that Pacific M-sequence model. 相似文献
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