A review of the plant-based traditions of the Cocoa Panyols of Trinidad

This paper reviews the plants used by Spanish speakers in Trinidad and Tobago as documented in a 1994 publication. The plant uses were reviewed to determine whether the plants and uses were different from other ethnic groups in Trinidad and the wider region and to draw conclusions from the review. The review covers 148 plants. With few exceptions, the plants were Native and so were the uses. Several plants have been little studied (Ambroisa cumanensis, Aristolochia rugosa, Bauhinia cumanensis, Bauhinia excisa, Begonia humilis, Croton conduplicatus, Croton flavens, Cyperus diffusus, Desmodium incanum, Fleurya aestuans, Heliconia bihai). The Cocoa Panyols retained the knowledge of at least two plants first described in the 1800s that were no longer mentioned to researchers in Trinidad and Tobago after 1981 by the rest of the population. The Cocoa Panyols preserved their cultural and linguistic heritage by concentrating on cocoa growing in rural areas and their movement from place to place to establish cocoa plantations may account for the uniformity of information on ethnomedicine in Trinidad, which additionally is similar to medicinal plant knowledge of the original populations of South America.     

Rapid visual screening of soft-story buildings from street view images using deep learning classification

Rapid and accurate identification of potential structural deficiencies is a crucial task in evaluating seismic vulnerability of large building inventories in a region. In the case of multi-story structures, abrupt vertical variations of story stiffness are known to significantly increase the likelihood of collapse during moderate or severe earthquakes. Identifying and retrofitting buildings with such irregularities—generally termed as soft-story buildings—is, therefore, vital in earthquake preparedness and loss mitigation efforts. Soft-story building identification through conventional means is a labor-intensive and time-consuming process. In this study, an automated procedure was devised based on deep learning techniques for identifying soft-story buildings from street-view images at a regional scale. A database containing a large number of building images and a semi-automated image labeling approach that effectively annotates new database entries was developed for developing the deep learning model. Extensive computational experiments were carried out to examine the effectiveness of the proposed procedure, and to gain insights into automated soft-story building identification.

     

The Upper Stratospheric Ozone Budget: An Update of Calculations Based on HALOE Data

On the basis of data obtained by the Halogen Occultation Experiment (HALOE) on the Upper Atmosphere Research Satellite (UARS) box model calculations are performed to investigate the ozone budget in the upper stratosphere. The HALOE data comprise measurements of major source gases and key chemical species involved in the ozone destruction cycles. In comparison to earlier calculations using version 17 of the HALOE data, the calculated ozone destruction rate increases when the updated data version 18 is used. However, as with the previous study using version 17 of the HALOE data, no evidence for a significant model ozone deficit is found.     

Young basalts of the central Washington Cascades, flux melting of the mantle, and trace element signatures of primary arc magmas

Basaltic lavas from the Three Sisters and Dalles Lakes were erupted from two isolated vents in the central Washington Cascades at 370–400 ka and 2.2 Ma, respectively, and have distinct trace element compositions that exemplify an important and poorly understood feature of arc basalts. The Three Sisters lavas are calc-alkaline basalts (CAB) with trace element compositions typical of most arc magmas: high ratios of large-ion-lithophile to high-field-strength elements (LILE/HFSE), and strong negative Nb and Ta anomalies. In contrast, the Dalles Lakes lavas have relatively low LILE/HFSE and no Nb or Ta anomalies, similar to ocean-island basalts (OIB). Nearly all Washington Cascade basalts with high to moderate incompatible element concentrations show this CAB or OIB-like compositional distinction, and there is pronounced divergence between the two magma types with a large compositional gap between them. We show that this trace element distinction can be easily explained by a simple model of flux-melting of the mantle wedge by a fluid-rich subduction component (SC), in which the degree of melting (F) of the peridotite source is correlated with the amount of SC added to it. Distinctive CAB and OIB-like trace element compositions are best explained by a flux-melting model in which dF/dSC decreases with increasing F, consistent with isenthalpic (heat-balanced) melting. In the context of this model, CAB trace element signatures simply reflect large degrees of melting of strongly SC-fluxed peridotite along relatively low dF/dSC melting trends, consistent with derivation from relatively cold mantle. Under other conditions (i.e., small degrees of melting or large degrees of melting of weakly SC-fluxed peridotite [high dF/dSC]), either OIB- or MORB (mid-ocean ridge basalt)-like compositions are produced. Trace element and isotopic compositions of Washington Cascade basalts are easily modeled by a correlation between SC and F across a range of mantle temperatures. This implies that the dominant cause of arc magmatism in this region is flux melting of the mantle wedge. Received: 2 March 1999 / Accepted: 18 August 1999     

Lagrangian coherent structures in the California Current System – sensitivities and limitations

We report on numerical experiments to test the sensitivity of Lagrangian coherent structures (LCSs), found by identifying ridges of the finite-time Lyapunov exponent (FTLE), to errors in two systems representing the California Current System (CCS). First, we consider a synthetic mesoscale eddy field generated from Fourier filtering satellite altimetry observations of the CCS. Second, we consider the full observational satellite altimetry field in the same region. LCS are found to be relatively insensitive to both sparse spatial and temporal resolution and to the velocity field interpolation method. Strongly attracting and repelling LCS are robust to perturbations of the velocity field of over 20% of the maximum regional velocity. Contours of the Okubo–Weiss (OW) parameter are found to be consistent with LCS in large mature eddies in the unperturbed systems. The OW parameter is unable to identify eddies at the uncertainty level expected for altimetry observations of the CCS. At this expected error level, the FTLE method is reliable for locating boundaries of large eddies and strong jets. Small LCS features such as lobes are not well resolved even at low error levels, suggesting that reliable determination of lobe dynamics from altimetry will be problematic.     

Dependence of Bayesian Model Selection Criteria and Fisher Information Matrix on Sample Size

Geostatistical analyses require an estimation of the covariance structure of a random field and its parameters jointly from noisy data. Whereas in some cases (as in that of a Matérn variogram) a range of structural models can be captured with one or a few parameters, in many other cases it is necessary to consider a discrete set of structural model alternatives, such as drifts and variograms. Ranking these alternatives and identifying the best among them has traditionally been done with the aid of information theoretic or Bayesian model selection criteria. There is an ongoing debate in the literature about the relative merits of these various criteria. We contribute to this discussion by using synthetic data to compare the abilities of two common Bayesian criteria, BIC and KIC, to discriminate between alternative models of drift as a function of sample size when drift and variogram parameters are unknown. Adopting the results of Markov Chain Monte Carlo simulations as reference we confirm that KIC reduces asymptotically to BIC and provides consistently more reliable indications of model quality than does BIC for samples of all sizes. Practical considerations often cause analysts to replace the observed Fisher information matrix entering into KIC with its expected value. Our results show that this causes the performance of KIC to deteriorate with diminishing sample size. These results are equally valid for one and multiple realizations of uncertain data entering into our analysis. Bayesian theory indicates that, in the case of statistically independent and identically distributed data, posterior model probabilities become asymptotically insensitive to prior probabilities as sample size increases. We do not find this to be the case when working with samples taken from an autocorrelated random field.     

Development of the Coastal Storm Modeling System (CoSMoS) for predicting the impact of storms on high-energy,active-margin coasts

The Coastal Storm Modeling System (CoSMoS) applies a predominantly deterministic framework to make detailed predictions (meter scale) of storm-induced coastal flooding, erosion, and cliff failures over large geographic scales (100s of kilometers). CoSMoS was developed for hindcast studies, operational applications (i.e., nowcasts and multiday forecasts), and future climate scenarios (i.e., sea-level rise + storms) to provide emergency responders and coastal planners with critical storm hazards information that may be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. The prototype system, developed for the California coast, uses the global WAVEWATCH III wave model, the TOPEX/Poseidon satellite altimetry-based global tide model, and atmospheric-forcing data from either the US National Weather Service (operational mode) or Global Climate Models (future climate mode), to determine regional wave and water-level boundary conditions. These physical processes are dynamically downscaled using a series of nested Delft3D-WAVE (SWAN) and Delft3D-FLOW (FLOW) models and linked at the coast to tightly spaced XBeach (eXtreme Beach) cross-shore profile models and a Bayesian probabilistic cliff failure model. Hindcast testing demonstrates that, despite uncertainties in preexisting beach morphology over the ~500 km alongshore extent of the pilot study area, CoSMoS effectively identifies discrete sections of the coast (100s of meters) that are vulnerable to coastal hazards under a range of current and future oceanographic forcing conditions, and is therefore an effective tool for operational and future climate scenario planning.     

Flood and coastal erosion risk management policy evolution in Northern Ireland: “Incremental or leapfrogging?”

Climate change poses a significant challenge for the future of Northern Ireland’s coast due to impacts that include, inter alia, mean sea level rise of between 13 cm and 74 cm by 2050. Whilst flooding is regarded as a major hazard in the United Kingdom (UK), to date Northern Ireland’s experiences of coastal flooding have been infrequent and less severe compared to those in England and Wales. Similarly, coastal erosion has historically been, and remains, only a minor concern in Northern Ireland. Partly as a result of this, Government administrative arrangements for Flood and Coastal Erosion Risk Management (FCERM) in Northern Ireland operate in the absence of any statutory provision for coastal erosion, as well as without formal or strategic shoreline management planning and any integrated flood and coastal erosion risk management policy. This paper provides a commentary on Northern Ireland’s approach to FCERM, comparing this with its UK counterparts, highlighting both congruence and divergence in policy evolution and development. It is noted that the recent EU Floods Directive has been a significant catalyst and that the current institutional landscape for FCERM is in flux.