(Appendix) Petrographic information, trace element and Sr isotope ratios of Pito Deep samples

Fluid flow through the axial hydrothermal system at fast spreading ridges is investigated using the Sr-isotopic composition of upper crustal samples recovered from a tectonic window at Pito Deep (NE Easter microplate). Samples from the sheeted dike complex collected away from macroscopic evidence of channelized fluid flow, such as faults and centimeter-scale hydrothermal veins, show a range of 87Sr/86Sr from 0.7025 to 0.7030 averaging 0.70276 relative to a protolith with 87Sr/86Sr of ~0.7024. There is no systematic variation in 87Sr/86Sr with depth in the sheeted dike complex. Comparison of these new data with the two other localities that similar data sets exist for (ODP Hole 504B and the Hess Deep tectonic window) reveals that the extent of Sr-isotope exchange is similar in all of these locations. Models that assume that fluid-rock reaction occurs during one-dimensional (recharge) flow lead to significant decreases in the predicted extent of isotopic modification of the rock with depth in the crust. These model results show systematic misfits when compared with the data that can only be avoided if the fluid flow is assumed to be focused in isolated channels with very slow fluid-rock exchange. In this scenario the fluid at the base of the crust is little modified in 87Sr/86Sr from seawater and thus unlike vent fluids. Additionally, this model predicts that some rocks should show no change from the fresh-rock 87Sr/86Sr, but this is not observed. Alternatively, models in which fluid-rock reaction occurs during upflow (discharge) as well as downflow, or in which fluids are recirculated within the hydrothermal system, can reproduce the observed lack of variation in 87Sr/86Sr with depth in the crust. Minimum time-integrated fluid fluxes, calculated from mass balance, are between 1.5 and 2.6 * 10**6 kg/m**2 for all areas studied to date. However, new evidence from both the rocks and a compilation of vent fluid compositions demonstrates that some Sr is leached from the crust. Because this leaching lowers the fluid 87Sr/86Sr without changing the rock 87Sr/86Sr, these mass balance models must underestimate the time-integrated fluid flux. Additionally, these values do not account for fluid flow that is channelized within the crust.

Gridded climate data from 5 Global Climate Models (GCM) of the Last Glacial Maximum (LGM) downscaled to 30 arc seconds for Europe, with links to NetCDF files

Studies on the impact of historical, current and future global change require very high-resolution climate data (less or equal 1km) as a basis for modelled responses, meaning that data from digital climate models generally require substantial rescaling. Another shortcoming of available datasets on past climate is that the effects of sea level rise and fall are not considered. Without such information, the study of glacial refugia or early Holocene plant and animal migration are incomplete if not impossible. Sea level at the last glacial maximum (LGM) was approximately 125m lower, creating substantial additional terrestrial area for which no current baseline data exist. Here, we introduce the development of a novel, gridded climate dataset for LGM that is both very high resolution (1km) and extends to the LGM sea and land mask. We developed two methods to extend current terrestrial precipitation and temperature data to areas between the current and LGM coastlines. The absolute interpolation error is less than 1°C and 0.5 °C for 98.9% and 87.8% of all pixels for the first two 1 arc degree distance zones. We use the change factor method with these newly assembled baseline data to downscale five global circulation models of LGM climate to a resolution of 1km for Europe. As additional variables we calculate 19 'bioclimatic' variables, which are often used in climate change impact studies on biological diversity. The new LGM climate maps are well suited for analysing refugia and migration during Holocene warming following the LGM.

Age models and summer sea surface temperature and winter sea ice concentration for the EPILOG-LGM time slice in the Pacific Southern Ocean

Sea surface temperatures and sea-ice extent are the most critical variables to evaluate the Southern Ocean paleoceanographic evolution in relation to the development of the global carbon cycle, atmospheric CO2 variability and ocean-atmosphere circulation. In contrast to the Atlantic and the Indian sectors, the Pacific sector of the Southern Ocean has been insufficiently investigated so far. To cover this gap of information we present diatom-based estimates of summer sea surface temperature (SSST) and winter sea-ice concentration (WSI) from 17 sites in the polar South Pacific to study the Last Glacial Maximum (LGM) at the EPILOG time slice (19,000-23,000 cal. years BP). Applied statistical methods are the Imbrie and Kipp Method (IKM) and the Modern Analog Technique (MAT) to estimate temperature and sea-ice concentration, respectively. Our data display a distinct LGM east-west differentiation in SSST and WSI with steeper latitudinal temperature gradients and a winter sea-ice edge located consistently north of the Pacific-Antarctic Ridge in the Ross sea sector. In the eastern sector of our study area, which is governed by the Amundsen Abyssal Plain, the estimates yield weaker latitudinal SSST gradients together with a variable extended winter sea-ice field. In this sector, sea-ice extent may have reached sporadically the area of the present Subantarctic Front at its maximum LGM expansion. This pattern points to topographic forcing as major controller of the frontal system location and sea-ice extent in the western Pacific sector whereas atmospheric conditions like the Southern Annular Mode and the ENSO affected the oceanographic conditions in the eastern Pacific sector. Although it is difficult to depict the location and the physical nature of frontal systems separating the glacial Southern Ocean water masses into different zones, we found a distinct temperature gradient in latitudes straddled by the modern Southern Subtropical Front. Considering that the glacial temperatures north of this zone are similar to the modern, we suggest that this represents the Glacial Southern Subtropical Front (GSSTF), which delimits the zone of strongest glacial SSST cooling (>4K) to its North. The southern boundary of the zone of maximum cooling is close to the glacial 4°C isotherm. This isotherm, which is in the range of SSST at the modern Antarctic Polar Front (APF), represents a circum-Antarctic feature and marks the northern edge of the glacial Antarctic Circumpolar Current (ACC). We also assume that a glacial front was established at the northern average winter sea ice edge, comparable with the modern Southern Antarctic Circumpolar Current Front (SACCF). During the glacial, this front would be located in the area of the modern APF. The northward deflection of colder than modern surface waters along the South American continent leads to a significant cooling of the glacial Humboldt Current surface waters (4-8K), which affects the temperature regimes as far north as into tropical latitudes. The glacial reduction of ACC temperatures may also result in the significant cooling in the Atlantic and Indian Southern Ocean, thus may enhance thermal differentiation of the Southern Ocean and Antarctic continental cooling. Comparison with temperature and sea ice simulations for the last glacial based on numerical simulations show that the majority of modern models overestimate summer and winter sea ice cover and that there exists few models that reproduce our temperature data rather well.

Parameterization of applied general equilibrium models with flexible trade specifications based on the Armington, Krugman, and Melitz models

This paper explains how the Armington-Krugman-Melitz supermodel developed by Dixon and Rimmer can be parameterized, and demonstrates that only two kinds of additional information are required in order to extend a standard trade model to include Melitz-type monopolistic competition and heterogeneous firms. Further, it is shown how specifying too much additional information leads to violations of the model constraints, necessitating adjustment and reconciliation of the data. Once a Melitz-type model is parameterized, a Krugman-type model can also be parameterized using the calibrated values in the Melitz-type model without any additional data. Sample code for the General Algebraic Modeling System (GAMS) has also been prepared to promote the innovative supermodel in the AGE community.

The location of manufacturing firms and imperfect information in transport market

It is well know that transport charges are not symmetric: fronthaul and backhaul costs on a route may differ, because they are affected by the distribution of economic acitivities. This paper develops a two-regional general equilibrium model in which transport costs are determined endogenously as a result of a search and matching process. It is shown that economies or diseconomies of transport density emerge, depending on the search costs of transport firms and the relative importance of the possibility of backhaul transportation. It is found that the symmetry of the distribution of economic activity may break owing to economies of transport density when the additional search costs are small enough.

Neutrality in the choice of number of firms or level of fixed costs in calibrating an Armington-Krugman-Melitz encompassing module for applied general equilibrium models

This paper shows how an Armington-Krugman-Melitz encompassing module based on Dixon and Rimmer (2012) can be calibrated, and clarifies the choice of initial levels for two kinds of number of firms, or parameter values for two kinds of fixed costs, that enter a Melitz-type specification can be set freely to any preferred value, just as the cases we derive quantities from given value data assuming some of the initial prices to be unity. In consequence, only one kind of additional information, which is on the shape parameter related to productivity, just is required in order to incorporate Melitz-type monopolistic competition and heterogeneous firms into a standard applied general equilibrium model. To be a Krugman-type, nothing is needed. This enables model builders in applied economics to fully enjoy the featured properties of the theoretical models invented by Krugman (1980) and Melitz (2003) in practical policy simulations at low cost.

How does firm heterogeneity information impact the estimation of embodied carbon emissions in Chinese exports?

Using an augmented Chinese input–output table in which information about firm ownership and type of traded goods are explicitly reported, we show that ignoring firm heterogeneity causes embodied CO2 emissions in Chinese exports to be overestimated by 20% at the national level, with huge differences at the sector level, for 2007. This is because different types of firm that are allocated to the same sector of the conventional Chinese input–output table vary greatly in terms of market share, production technology and carbon intensity. This overestimation of export-related carbon emissions would be even higher if it were not for the fact that 80% of CO2 emissions embodied in exports of foreign-owned firms are, in fact, emitted by Chinese-owned firms upstream of the supply chain. The main reason is that the largest CO2 emitter, the electricity sector located upstream in Chinese domestic supply chains, is strongly dominated by Chinese-owned firms with very high carbon intensity.

Using ecological niche models to support tree species selection for forest restoration planning in largely deforested regions

Species selection for forest restoration is often supported by expert knowledge on local distribution patterns of native tree species. This approach is not applicable to largely deforested regions unless enough data on pre-human tree species distribution is available. In such regions, ecological niche models may provide essential information to support species selection in the framework of forest restoration planning. In this study we used ecological niche models to predict habitat suitability for native tree species in "Tierra de Campos" region, an almost totally deforested area of the Duero Basin (Spain). Previously available models provide habitat suitability predictions for dominant native tree species, but including non-dominant tree species in the forest restoration planning may be desirable to promote biodiversity, specially in largely deforested areas were near seed sources are not expected. We used the Forest Map of Spain as species occurrence data source to maximize the number of modeled tree species. Penalized logistic regression was used to train models using climate and lithological predictors. Using model predictions a set of tools were developed to support species selection in forest restoration planning. Model predictions were used to build ordered lists of suitable species for each cell of the study area. The suitable species lists were summarized drawing maps that showed the two most suitable species for each cell. Additionally, potential distribution maps of the suitable species for the study area were drawn. For a scenario with two dominant species, the models predicted a mixed forest (Quercus ilex and a coniferous tree species) for almost one half of the study area. According to the models, 22 non-dominant native tree species are suitable for the study area, with up to six suitable species per cell. The model predictions pointed to Crataegus monogyna, Juniperus communis, J.oxycedrus and J.phoenicea as the most suitable non-dominant native tree species in the study area. Our results encourage further use of ecological niche models for forest restoration planning in largely deforested regions.

Improving runoff estimates from regional climate models: a performance analysis in Spain

An important step to assess water availability is to have monthly time series representative of the current situation. In this context, a simple methodology is presented for application in large-scale studies in regions where a properly calibrated hydrologic model is not available, using the output variables simulated by regional climate models (RCMs) of the European project PRUDENCE under current climate conditions (period 1961–1990). The methodology compares different interpolation methods and alternatives to generate annual times series that minimise the bias with respect to observed values. The objective is to identify the best alternative to obtain bias-corrected, monthly runoff time series from the output of RCM simulations. This study uses information from 338 basins in Spain that cover the entire mainland territory and whose observed values of natural runoff have been estimated by the distributed hydrological model SIMPA. Four interpolation methods for downscaling runoff to the basin scale from 10 RCMs are compared with emphasis on the ability of each method to reproduce the observed behaviour of this variable. The alternatives consider the use of the direct runoff of the RCMs and the mean annual runoff calculated using five functional forms of the aridity index, defined as the ratio between potential evapotranspiration and precipitation. In addition, the comparison with respect to the global runoff reference of the UNH/GRDC dataset is evaluated, as a contrast of the “best estimator” of current runoff on a large scale. Results show that the bias is minimised using the direct original interpolation method and the best alternative for bias correction of the monthly direct runoff time series of RCMs is the UNH/GRDC dataset, although the formula proposed by Schreiber (1904) also gives good results

Linked Data at the Spanish National Library and the application of IFLA RDFS models

The Spanish National Library (Biblioteca Nacional de España1. BNE) and the Ontology Engineering Group2 of Universidad Politécnica de Madrid are working on the joint project ?Preliminary Study of Linked Data?, whose aim is to enrich the Web of Data with the BNE authority and bibliographic records. To this end, they are transforming the BNE information to RDF following the Linked Data principles3 proposed by Tim Berners Lee.

Refining predictions of iberian plant distribution: lessons from p. nigra and p. sylvestris palaeoecological-based habitat suitability models

In recent years, challenged by the climate scenarios put forward by the IPCC and its potential impact on plant distribution, numerous predictive techniques -including the so called habitat suitability models (HSM)- have been developed. Yet, as the output of the different methods produces different distribution areas, developing validation tools are strong needs to reduce uncertainties. Focused in the Iberian Peninsula, we propose a palaeo-based method to increase the robustness of the HSM, by developing an ecological approach to understand the mismatches between the palaeoecological information and the projections of the HSMs. Here, we present the result of (1) investigating causal relationships between environmental variables and presence of Pinus sylvestris L. and P. nigra Arn. available from the 3rd Spanish Forest Inventory, (2) developing present and past presence-predictions through the MaxEnt model for 6 and 21 kyr BP, and (3) assessing these models through comparisons with biomized palaeoecological data available from the European Pollen Database for the Iberian Peninsula.

Factored Translation Models for improving a Speech into Sign Language Translation System

This paper proposes the use of Factored Translation Models (FTMs) for improving a Speech into Sign Language Translation System. These FTMs allow incorporating syntactic-semantic information during the translation process. This new information permits to reduce significantly the translation error rate. This paper also analyses different alternatives for dealing with the non-relevant words. The speech into sign language translation system has been developed and evaluated in a specific application domain: the renewal of Identity Documents and Driver’s License. The translation system uses a phrase-based translation system (Moses). The evaluation results reveal that the BLEU (BiLingual Evaluation Understudy) has improved from 69.1% to 73.9% and the mSER (multiple references Sign Error Rate) has been reduced from 30.6% to 24.8%.

Reduced order adaptive models for systems of PDEs using POD

In this work we propose a method to accelerate time dependent numerical solvers of systems of PDEs that require a high cost in computational time and memory. The method is based on the combined use of such numerical solver with a proper orthogonal decomposition, from which we identify modes, a Galerkin projection (that provides a reduced system of equations) and the integration of the reduced system, studying the evolution of the modal amplitudes. We integrate the reduced model until our a priori error estimator indicates that our approximation in not accurate. At this point we use again our original numerical code in a short time interval to adapt the POD manifold and continue then with the integration of the reduced model. Application will be made to two model problems: the Ginzburg-Landau equation in transient chaos conditions and the two-dimensional pulsating cavity problem, which describes the motion of liquid in a box whose upper wall is moving back and forth in a quasi-periodic fashion. Finally, we will discuss a way of improving the performance of the method using experimental data or information from numerical simulations

Level Set Segmentation with Shape and Appearance Models Using Affine Moment Descriptors

We propose a level set based variational approach that incorporates shape priors into edge-based and region-based models. The evolution of the active contour depends on local and global information. It has been implemented using an efficient narrow band technique. For each boundary pixel we calculate its dynamic according to its gray level, the neighborhood and geometric properties established by training shapes. We also propose a criterion for shape aligning based on affine transformation using an image normalization procedure. Finally, we illustrate the benefits of the our approach on the liver segmentation from CT images.

Hydrologic models for emergency decision support using Bayesian networks

In the presence of a river flood, operators in charge of control must take decisions based on imperfect and incomplete sources of information (e.g., data provided by a limited number sensors) and partial knowledge about the structure and behavior of the river basin. This is a case of reasoning about a complex dynamic system with uncertainty and real-time constraints where bayesian networks can be used to provide an effective support. In this paper we describe a solution with spatio-temporal bayesian networks to be used in a context of emergencies produced by river floods. In the paper we describe first a set of types of causal relations for hydrologic processes with spatial and temporal references to represent the dynamics of the river basin. Then we describe how this was included in a computer system called SAIDA to provide assistance to operators in charge of control in a river basin. Finally the paper shows experimental results about the performance of the model.