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Entropy plays a crucial role in designing new functional materials or enhancing material performance. The design of high entropy ceramics (HECs) introduces a larger composition space, thus the understanding of the correlation between the composition, entropy effect, and the performance of HECs is becoming increasingly important. In this work, to pursue superior properties, a series of CeNbO 4 -based solid solution ceramics with a maximum of 9 species cations in a single sublattice were synthesized by using high entropy configuration strategy. The interaction of composition, size disorder, and configurational entropy on the properties of HECs was emphasized. The potential conflict between high resistivity and low material constant of thermosensitive ceramics was overcome and thus achieving excellent thermal sensitivity properties having an extremely wide temperature range from room temperature to 1623 K and long-term high-temperature stability. This will bring innovative prospects for the design and application of high-temperature functional ceramics. [ABSTRACT FROM AUTHOR]

Background: Due to the high recurrence and low 5-year survival rates of esophageal squamous cell carcinoma (ESCC) after treatment, the discovery of novel drugs for recurrence chemoprevention is of particular importance. Methods: We screened the FDA-approved drug library and found that Nuplazid, an atypical antipsychotic that acts as an effective 5-HT 2 A receptor inverse agonist, could potentially exert anticancer effects in vitro and in vivo on ESCC. Results: Pull-down results indicated that Nuplazid binds with p21-activated kinase 4 (PAK4), and a kinase assay showed that Nuplazid strongly suppressed PAK4 kinase activity. Moreover, Nuplazid exhibited inhibitory effects on ESCC in vivo. Conclusions: Our findings indicate that Nuplazid can suppress ESCC progression through targeting PAK4. [ABSTRACT FROM AUTHOR]

Background: Due to the high recurrence and low 5-year survival rates of esophageal squamous cell carcinoma (ESCC) after treatment, the discovery of novel drugs for recurrence chemoprevention is of particular importance.Methods: We screened the FDA-approved drug library and found that Nuplazid, an atypical antipsychotic that acts as an effective 5-HT 2 A receptor inverse agonist, could potentially exert anticancer effects in vitro and in vivo on ESCC.Results: Pull-down results indicated that Nuplazid binds with p21-activated kinase 4 (PAK4), and a kinase assay showed that Nuplazid strongly suppressed PAK4 kinase activity. Moreover, Nuplazid exhibited inhibitory effects on ESCC in vivo.Conclusions: Our findings indicate that Nuplazid can suppress ESCC progression through targeting PAK4. [ABSTRACT FROM AUTHOR]

Nitrogen is an important element for plant growth, photosynthesis and productivity. It has been presented to have a close correlation with wetland purification and estuarine eutrophication. The quantification of nitrogen concentration in wetland plants from hyperspectral data can provide valuable information to help develop approaches to research wetland ecosystem functioning and biophysical processes. This paper developed a revised nitrogen index (revised NDNI) on the basis of NDNI and SAVI to estimate canopy nitrogen concentration of wetland reed from ground-measured and spaceborne hyperspectral data while reducing wetland background effects. The SAVI was revised through replacing the red band by the 1510 nm band and a soil self-adjustable factor to increase vegetation sensitivity and diminish background noise. The revised NDNI integrated advantages of NDNI that was sensitive to canopy nitrogen concentration and revised SAVI that was used as the denominator in revised NDNI to reduce contributions of wetland background reflectance. The results showed that the revised NDNI was more robust than NDNI for nitrogen predictions of reeds, with a relatively higher accuracy ( R 2 = 0.84, 0.88, 0.78). It was more insensitive to the variations of LAI than the compared indices, with a lower correlation coefficient ( R 2 = 0.27). These results demonstrated that the revised NDNI was both sensitive to canopy nitrogen status of reeds and resistant to the background effects. A validation was performed using Hyperion image over two experimental fields in the study area. The results of canopy nitrogen concentration estimations exhibited a good correlation with ground-based measurements ( R 2 = 0.79). The revised NDNI was proved to be more suitable for canopy nitrogen concentration prediction of aquatic vegetation (such as Phragmites australis ) in the context of wetlands. [ABSTRACT FROM AUTHOR]

Abstract: One of the earliest investigations of provenance was inspired by applications in GIS in the early 1990’s. Provenance records the processing history of a data product. It provides an information context to help users determine the reliability of data products. Conventional provenance applications in GIS focus on provenance capture, representation, and usage in a stand-alone environment such as a desktop-based GIS software system. They cannot support wide sharing and open access of provenance in a distributed environment. The growth of service-oriented sharing and processing of geospatial data brings some new challenges in provenance-aware applications. One is how to share geospatial provenance in an interoperable way. This paper describes the development of provenance service for geospatial data products using the ebXML Registry Information Model (ebRIM) of a geospatial catalog service, which follows the interface specifications of the OGC Catalogue Services for the Web (CSW). This approach fits well the current service stack of the GIS domain and facilitates the management of geospatial data provenance in an open and distributed environment. [Copyright &y& Elsevier]

Abstract: Nowadays, geographic data, such as NASA''s Earth Observation System (EOS) data, are playing an increasing role in many areas, including academic research, government decisions and even in people''s every lives. As the quantity of geographic data becomes increasingly large, a major problem is how to fully make use of such data in a distributed, heterogeneous network environment. In order for a user to effectively discover and retrieve the specific information that is useful, the geographic metadata should be described and managed properly. Fortunately, the emergence of XML and Web Services technologies greatly promotes information distribution across the Internet. The research effort discussed in this paper presents a method and its implementation for transforming Hierarchical Data Format (HDF)-EOS metadata from the NASA ECS model to ISO 19115-based XML, which will be managed by the Open Geospatial Consortium (OGC) Catalogue Services—Web Profile (CSW). Using XML and international standards rather than domain-specific models to describe the metadata of those HDF-EOS data, and further using CSW to manage the metadata, can allow metadata information to be searched and interchanged more widely and easily, thus promoting the sharing of HDF-EOS data. [Copyright &y& Elsevier]

The flower pollination algorithm (FPA) is a novel heuristic optimization algorithm inspired by the pollination behavior of flowers in nature. However, the global and local search processes of the FPA are sensitive to the search direction and parameters. To solve this issue, an improved flower pollination algorithm based on cosine cross-generation differential evolution (FPA-CCDE) is proposed. The algorithm uses cross-generation differential evolution to guide the local search process, so that the optimal solution is achieved and sets cosine inertia weights to increase the search convergence speed. At the same time, the external archiving mechanism and the adaptive adjustment of parameters realize the dynamic update of scaling factor and crossover probability to enhance the population richness as well as reduce the number of local solutions. Then, it combines the cross-generation roulette wheel selection mechanism to reduce the probability of falling into the local optimal solution. In comparing to the FPA-CCDE with five state-of-the-art optimization algorithms in benchmark functions, we can observe the superiority of the FPA-CCDE in terms of stability and optimization features. Additionally, we further apply the FPA-CCDE to solve the robot path planning issue. The simulation results demonstrate that the proposed algorithm has low cost, high efficiency, and attack resistance in path planning, and it can be applied to a variety of intelligent scenarios. [ABSTRACT FROM AUTHOR]

Abstract: Remote sensing imagery has been commonly used by intelligence analysts to discover geospatial features, including complex ones. The overwhelming volume of routine image acquisition requires automated methods or systems for feature discovery instead of manual image interpretation. The methods of extraction of elementary ground features such as buildings and roads from remote sensing imagery have been studied extensively. The discovery of complex geospatial features, however, is still rather understudied. A complex feature, such as a Weapon of Mass Destruction (WMD) proliferation facility, is spatially composed of elementary features (e.g., buildings for hosting fuel concentration machines, cooling towers, transportation roads, and fences). Such spatial semantics, together with thematic semantics of feature types, can be used to discover complex geospatial features. This paper proposes a workflow-based approach for discovery of complex geospatial features that uses geospatial semantics and services. The elementary features extracted from imagery are archived in distributed Web Feature Services (WFSs) and discoverable from a catalogue service. Using spatial semantics among elementary features and thematic semantics among feature types, workflow-based service chains can be constructed to locate semantically-related complex features in imagery. The workflows are reusable and can provide on-demand discovery of complex features in a distributed environment. [Copyright &y& Elsevier]

Earth science research and applications usually use Distributed Geospatial Information Processing (DGIP) services and powerful computing capabilities to extract information and knowledge from large volumes of distributed geospatial data. Conceptually, such processing can be abstracted into a logical model that utilizes geospatial domain knowledge to produce new geospatial products. Using this idea, the geo-tree concept and the proposed geospatial Abstract Information Model (AIM) have been used to develop a Grid workflow engine complying with geospatial standards and the Business Process Execution Language. Upon a user's request, the engine generates virtual geospatial data/information/knowledge products from existing DGIP data and services. This article details how to (1) define and describe the AIM in XML format, (2) describe the process logically with an AIM, including the geospatial semantic logic, (3) conceptually describe the process of producing a particular geospatial product step by step from raw geospatial data, (4) instantiate AIM as a concrete Grid-service workflow by selecting the optimal service instances and data sets, and (5) design a Grid workflow engine to execute the concrete workflows to produce geospatial products. To verify the advantages and applicability of this Grid-enabled virtual geospatial product system, its performance is evaluated, and a sample application is provided. [ABSTRACT FROM AUTHOR]

Geospatial service taxonomies represent the knowledge about the characteristics of geospatial services from the enterprise, computational, information, engineering, infrastructure, or technology viewpoints. This paper presents a lightweight taxonomy of geospatial services with the aim of promoting the global sharing of and interoperability among geospatial service instances. This taxonomy focuses on the knowledge connected with service interoperability. As a hierarchical taxonomy, it consists of six layers: service category, service type, version, profile, binding and uniform resource name (URN), from the root down to the leaves. Each layer is composed of classification nodes, with each node identifying one classification concept. Each concept, with a concrete semantic meaning, can be used to classify service instances. The application of this classification scheme to the Global Earth Observation System of Systems (GEOSS) Component and Service registry is also introduced. The results of this study may lead to the further development of service taxonomy to thoroughly capture the knowledge about geospatial services. The lessons learned may be useful to others representing and manipulating geoscientific knowledge. [Copyright &y& Elsevier]

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ‐GUESS, ORCHIDEE, SiB‐3, and SiB‐CASA. All models were wrapped in a software framework driven with common forcing data, spin‐up, and run protocols specified by the Multi‐scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901–2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open‐source, cloud‐based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges. Plain Language Summary: Comparing models is fundamental to our understanding of how the land surface responds to changes in climate. However, these comparisons are challenging to conduct, requiring the organization of multiple, decentralized teams throughout the world. We explored centralizing these models on a single supercomputing system. The models were all run the same way. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) the centralized system takes a lot of burden off individual teams; (b) running models the same way helps to identify differences in how the world is represented in the models; and (c) the system allows us to run many model experiments relatively quickly. The challenges are: (a) lots of models require lots of data storage and transfer needs; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. Another system, called PEcAn, which has a lot of tools that can help overcome these challenges, can potentially be used in future work. Key Points: We ran nine terrestrial biosphere models centralized on a common computing frameworkThe Farm allows multiple MIP experiments to be run relatively quickly and uniformlyChallenges included technological demand, model versioning, and interpretation of results [ABSTRACT FROM AUTHOR]

Terrestrial vegetation removes CO2 from the atmosphere; an important climate regulation service that slows global warming. This 119 Pg C per annum transfer of CO2 into plants—gross primary productivity (GPP)—is the largest land carbon flux globally. While understanding past and anticipated future GPP changes is necessary to support carbon management, the factors driving long-term changes in GPP are largely unknown. Here we show that 1901 to 2010 changes in GPP have been dominated by anthropogenic activity. Our dual constraint attribution approach provides three insights into the spatiotemporal patterns of GPP change. First, anthropogenic controls on GPP change have increased from 57% (1901 decade) to 94% (2001 decade) of the vegetated land surface. Second, CO2 fertilization and nitro gen deposition are the most important drivers of change, 19.8 and 11.1 Pg C per annum (2001 decade) respectively, especially in the tropics and industrialized areas since the 1970's. Third, changes in climate have functioned as fertilization to enhance GPP (1.4 Pg C per annum in the 2001 decade). These findings suggest that, from a land carbon balance perspective, the Anthropocene began over 100 years ago and that global change drivers have allowed GPP uptake to keep pace with anthropogenic emissions. [ABSTRACT FROM AUTHOR]

Climate change, rising CO2 concentration, and land use and land cover change (LULCC) are primary driving forces for terrestrial gross primary productivity (GPP), but their impacts on the temporal changes in GPP are uncertain. In this study, the effects of the three main factors on the interannual variation (IAV) and seasonal cycle amplitude (SCA) of GPP in China were investigated using 12 terrestrial biosphere models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project. The simulated ensemble mean value of China's GPP between 1981 and 2010, driven by common climate forcing, LULCC and CO2 data, was found to be 7.4±1.8 Pg C yr -1. In general, climate was the dominant control factor of the annual trends, IAV and seasonality of China's GPP. The overall rising CO2 led to enhanced plant photosynthesis, thus increasing annual mean and IAV of China's total GPP, especially in northeastern and southern China, where vegetation is dense. LULCC decreased the IAV of China's total GPP by ∼7 %, whereas rising CO2 induced an increase of 8 %. Compared to climate change and elevated CO2 , LULCC showed less contributions to GPP's temporal variation, and its impact acted locally, mainly in southwestern China. Furthermore, this study also examined subregional contributions to the temporal changes in China's total GPP. Southern and southeastern China showed higher contributions to China's annual GPP, whereas southwestern and central parts of China explained larger fractions of the IAV in China's GPP. [ABSTRACT FROM AUTHOR]

Generalized Equilibrium Feedback Assessment (GEFA) is a potentially valuable multivariate statistical tool for extracting vegetation feedbacks to the atmosphere in either observations or coupled models. GEFA's reliability at capturing the terrestrial impacts on regional climate is demonstrated using the Community Earth System Model (CESM). The feedback is assessed statistically by applying GEFA to output from a fully coupled control run. In order to reduce the sampling error caused by short data records, the traditional full GEFA is refined through stepwise GEFA by dropping unimportant forcings. Two ensembles of dynamical experiments, against which GEFA-based vegetation feedbacks are evaluated, are developed for the Sahel or West African monsoon region. In these experiments, regional leaf area index (LAI) is modified either alone or in conjunction with soil moisture. Stepwise GEFA boasts higher consistency between statistically- and dynamically-assessed atmospheric responses to land surface anomalies than full GEFA, especially with short data records. Both the statistical and dynamical assessments reveal a negative vegetation-rainfall feedback in the Sahel, associated with an atmospheric stability mechanism, versus a weaker, positive feedback in the West African monsoon region, associated with a moisture recycling mechanism, in CESM. [ABSTRACT FROM AUTHOR]

This study advances the practicality and stability of the traditional multivariate statistical method, generalized equilibrium feedback assessment (GEFA), for decomposing the key oceanic drivers of regional atmospheric variability, especially when available data records are short. An advanced stepwise GEFA methodology is introduced, in which unimportant forcings within the forcing matrix are eliminated through stepwise selection. Method validation of stepwise GEFA is performed using the CESM, with a focused application to northern and tropical Africa (NTA). First, a statistical assessment of the atmospheric response to each primary oceanic forcing is carried out by applying stepwise GEFA to a fully coupled control run. Then, a dynamical assessment of the atmospheric response to individual oceanic forcings is performed through ensemble experiments by imposing sea surface temperature anomalies over focal ocean basins. Finally, to quantify the reliability of stepwise GEFA, the statistical assessment is evaluated against the dynamical assessment in terms of four metrics: the percentage of grid cells with consistent response sign, the spatial correlation of atmospheric response patterns, the area-averaged seasonal cycle of response magnitude, and consistency in associated mechanisms between assessments. In CESM, tropical modes, namely El Niño-Southern Oscillation and the tropical Indian Ocean Basin, tropical Indian Ocean dipole, and tropical Atlantic Niño modes, are the dominant oceanic controls of NTA climate. In complementary studies, stepwise GEFA is validated in terms of isolating terrestrial forcings on the atmosphere, and observed oceanic and terrestrial drivers of NTA climate are extracted to establish an observational benchmark for subsequent coupled model evaluation and development of process-based weights for regional climate projections. [ABSTRACT FROM AUTHOR]

The seasonal-cycle amplitude (SCA) of the atmosphere-ecosystem carbon dioxide (CO2) exchange rate is a useful metric of the responsiveness of the terrestrial biosphere to environmental variations. It is unclear, however, what underlying mechanisms are responsible for the observed increasing trend of SCA in atmospheric CO2 concentration. Using output data from the Multi-scale Terrestrial Model Intercomparison Project (MsTMIP), we investigated how well the SCA of atmosphere-ecosystem CO2 exchange was simulated with 15 contemporary terrestrial ecosystem models during the period 1901-2010. Also, we made attempt to evaluate the contributions of potential mechanisms such as atmospheric CO2, climate, land-use, and nitrogen deposition, through factorial experiments using different combinations of forcing data. Under contemporary conditions, the simulated global-scale SCA of the cumulative net ecosystem carbon flux of most models was comparable in magnitude with the SCA of atmospheric CO2 concentrations. Results from factorial simulation experiments showed that elevated atmospheric CO2 exerted a strong influence on the seasonality amplification. When the model considered not only climate change but also land-use and atmospheric CO2 changes, the majority of the models showed amplification trends of the SCAs of photosynthesis, respiration, and net ecosystem production (+0.19 % to +0.50 % yr-1). In the case of land-use change, it was difficult to separate the contribution of agricultural management to SCA because of inadequacies in both the data and models. The simulated amplification of SCA was approximately consistent with the observational evidence of the SCA in atmospheric CO2 concentrations. Large inter-model differences remained, however, in the simulated global tendencies and spatial patterns of CO2 exchanges. Further studies are required to identify a consistent explanation for the simulated and observed amplification trends, including their underlying mechanisms. Nevertheless, this study implied that monitoring of ecosystem seasonality would provide useful insights concerning ecosystem dynamics. [ABSTRACT FROM AUTHOR]

Federal hydropower plants account for approximately half of installed US conventional hydropower capacity, and are an important part of the national renewable energy portfolio. Utilizing the strong linear relationship between the US Geological Survey WaterWatch runoff and annual hydropower generation, a runoff-based assessment approach is introduced in this study to project changes in annual and regional hydropower generation in multiple power marketing areas. Future climate scenarios are developed with a series of global and regional climate models, and the model output is bias-corrected to be consistent with observed data for the recent past. Using this approach, the median change in annual generation at federal projects is projected to be -2 TWh, with an estimated ensemble uncertainty of ±9 TWh. Although these estimates are similar to the recently observed variability in annual hydropower generation, and may therefore appear to be manageable, significantly seasonal runoff changes are projected and it may pose significant challenges in water systems with higher limits on reservoir storage and operational flexibility. Future assessments will be improved by incorporating next-generation climate models, by closer examination of extreme events and longer-term change, and by addressing the interactions among hydropower and other water uses. [ABSTRACT FROM AUTHOR]

Soil moisture plays an important role in the coupled water, energy, and carbon cycles. In addition to surface processes such as evapotranspiration, the boundary fluxes that influence soil moisture are closely related to surface or subsurface runoff. To elucidate how uncertainties in representing surface and subsurface hydrology may influence simulations of the carbon cycle, numerical experiments were performed using version 4 of the Community Land Model with two widely adopted runoff generation parameterizations from the TOPMODEL and Variable Infiltration Capacity (VIC) model under the same protocol. The results showed that differences in the runoff generation schemes caused a relative difference of 36% and 34% in global mean total runoff and soil moisture, respectively, with substantial differences in their spatial distribution and seasonal variability. Changes in the simulated gross primary production (GPP) were found to correlate well with changes in soil moisture through its effects on leaf photosynthesis (An) and leaf area index (LAI), which are the two dominant components determining GPP. Soil temperature, which is influenced by soil moisture, also affects LAI and GPP for the seasonal-deciduous and stress-deciduous plant functional types that dominate in cold regions. Consequently, the simulated global mean GPP differs by 20.4% as a result of differences in soil moisture and soil temperature simulated between the two models. Our study highlights the significant interactions among the water, energy, and carbon cycles and the need for reducing uncertainty in the hydrologic parameterization of land surface models to better constrain carbon cycle modeling. [ABSTRACT FROM AUTHOR]

Motivated by the question of whether and how a state-of-the-art regional chemical transport model (CTM) can facilitate characterization of CO2spatiotemporal variability and verify CO2fossil-fuel emissions, we for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate CO2. This paper presents methods, input data, and initial results for CO2simulation using CMAQ over the contiguous United States in October 2007. Modeling experiments have been performed to understand the roles of fossil-fuel emissions, biosphere–atmosphere exchange, and meteorology in regulating the spatial distribution of CO2near the surface over the contiguous United States. Three sets of net ecosystem exchange (NEE) fluxes were used as input to assess the impact of uncertainty of NEE on CO2concentrations simulated by CMAQ. Observational data from six tall tower sites across the country were used to evaluate model performance. In particular, at the Boulder Atmospheric Observatory (BAO), a tall tower site that receives urban emissions from Denver, CO, the CMAQ model using hourly varying, high-resolution CO2fossil-fuel emissions from the Vulcan inventory and CarbonTracker optimized NEE reproduced the observed diurnal profile of CO2reasonably well but with a low bias in the early morning. The spatial distribution of CO2was found to correlate with NOx, SO2, and CO, because of their similar fossil-fuel emission sources and common transport processes. These initial results from CMAQ demonstrate the potential of using a regional CTM to help interpret CO2observations and understand CO2variability in space and time. The ability to simulate a full suite of air pollutants in CMAQ will also facilitate investigations of their use as tracers for CO2source attribution. This work serves as a proof of concept and the foundation for more comprehensive examinations of CO2spatiotemporal variability and various uncertainties in the future.Implications: Atmospheric CO2 has long been modeled and studied on continental to global scales to understand the global carbon cycle. This work demonstrates the potential of modeling and studying CO2 variability at fine spatiotemporal scales with CMAQ, which has been applied extensively, to study traditionally regulated air pollutants. The abundant observational records of these air pollutants and successful experience in studying and reducing their emissions may be useful for verifying CO2 emissions. Although there remains much more to further investigate, this work opens up a discussion on whether and how to study CO2 as an air pollutant. [ABSTRACT FROM AUTHOR]

A global energy crop productivity model that provides geospatially explicit quantitative details on biomass potential and factors affecting sustainability would be useful, but does not exist now. This study describes a modeling platform capable of meeting many challenges associated with global-scale agro-ecosystem modeling. We designed an analytical framework for bioenergy crops consisting of six major components: (i) standardized natural resources datasets, (ii) global field-trial data and crop management practices, (iii) simulation units and management scenarios, (iv) model calibration and validation, (v) high-performance computing ( HPC) simulation, and (vi) simulation output processing and analysis. The HPC-Environmental Policy Integrated Climate ( HPC- EPIC) model simulated a perennial bioenergy crop, switchgrass ( Panicum virgatum L.), estimating feedstock production potentials and effects across the globe. This modeling platform can assess soil C sequestration, net greenhouse gas ( GHG) emissions, nonpoint source pollution (e.g., nutrient and pesticide loss), and energy exchange with the atmosphere. It can be expanded to include additional bioenergy crops (e.g., miscanthus, energy cane, and agave) and food crops under different management scenarios. The platform and switchgrass field-trial dataset are available to support global analysis of biomass feedstock production potential and corresponding metrics of sustainability. [ABSTRACT FROM AUTHOR]

We develop an approach for estimating net ecosystem exchange ( NEE) using inventory-based information over North America ( NA) for a recent 7-year period (ca. 2000-2006). The approach notably retains information on the spatial distribution of NEE, or the vertical exchange between land and atmosphere of all non-fossil fuel sources and sinks of CO2, while accounting for lateral transfers of forest and crop products as well as their eventual emissions. The total NEE estimate of a −327 ± 252 TgC yr−1 sink for NA was driven primarily by CO2 uptake in the Forest Lands sector (−248 TgC yr−1), largely in the Northwest and Southeast regions of the US, and in the Crop Lands sector (−297 TgC yr−1), predominantly in the Midwest US states. These sinks are counteracted by the carbon source estimated for the Other Lands sector (+218 TgC yr−1), where much of the forest and crop products are assumed to be returned to the atmosphere (through livestock and human consumption). The ecosystems of Mexico are estimated to be a small net source (+18 TgC yr−1) due to land use change between 1993 and 2002. We compare these inventory-based estimates with results from a suite of terrestrial biosphere and atmospheric inversion models, where the mean continental-scale NEE estimate for each ensemble is −511 TgC yr−1 and −931 TgC yr−1, respectively. In the modeling approaches, all sectors, including Other Lands, were generally estimated to be a carbon sink, driven in part by assumed CO2 fertilization and/or lack of consideration of carbon sources from disturbances and product emissions. Additional fluxes not measured by the inventories, although highly uncertain, could add an additional −239 TgC yr−1 to the inventory-based NA sink estimate, thus suggesting some convergence with the modeling approaches. [ABSTRACT FROM AUTHOR]

geospatial catalogue service provides a network-based meta-information repository and interface for advertising and discovering shared geospatial data and services. Descriptive information (i.e., metadata) for geospatial data and services is structured and organized in catalogue services. The approaches currently available for searching and using that information are often inadequate. Semantic Web technologies show promise for better discovery methods by exploiting the underlying semantics. Such development needs special attention from the Cyberinfrastructure perspective, so that the traditional focus on discovery of and access to geospatial data can be expanded to support the increased demand for processing of geospatial information and discovery of knowledge. Semantic descriptions for geospatial data, services, and geoprocessing service chains are structured, organized, and registered through extending elements in the ebXML Registry Information Model (ebRIM) of a geospatial catalogue service, which follows the interface specifications of the Open Geospatial Consortium (OGC) Catalogue Services for the Web (CSW). The process models for geoprocessing service chains, as a type of geospatial knowledge, are captured, registered, and discoverable. Semantics-enhanced discovery for geospatial data, services/service chains, and process models is described. Semantic search middleware that can support virtual data product materialization is developed for the geospatial catalogue service. The creation of such a semantics-enhanced geospatial catalogue service is important in meeting the demands for geospatial information discovery and analysis in Cyberinfrastructure. [ABSTRACT FROM AUTHOR]

Abstract: A catalogue service facilitates sharing, discovery, retrieval, management of, and access to large volumes of distributed geospatial resources, for example data, services, applications, and their replicas on the Internet. Grid computing provides an infrastructure for effective use of computing, storage, and other resources available online. The Open Geospatial Consortium has proposed a catalogue service specification and a series of profiles for promoting the interoperability of geospatial resources. By referring to the profile of the catalogue service for Web, an innovative information model of a catalogue service is proposed to offer Grid-enabled registry, management, retrieval of and access to geospatial resources and their replicas. This information model extends the e-business registry information model by adopting several geospatial data and service metadata standards—the International Organization for Standardization (ISO)’s 19115/19119 standards and the US Federal Geographic Data Committee (FGDC) and US National Aeronautics and Space Administration (NASA) metadata standards for describing and indexing geospatial resources. In order to select the optimal geospatial resources and their replicas managed by the Grid, the Grid data management service and information service from the Globus Toolkits are closely integrated with the extended catalogue information model. Based on this new model, a catalogue service is implemented first as a Web service. Then, the catalogue service is further developed as a Grid service conforming to Grid service specifications. The catalogue service can be deployed in both the Web and Grid environments and accessed by standard Web services or authorized Grid services, respectively. The catalogue service has been implemented at the George Mason University/Center for Spatial Information Science and Systems (GMU/CSISS), managing more than 17TB of geospatial data and geospatial Grid services. This service makes it easy to share and interoperate geospatial resources by using Grid technology and extends Grid technology into the geoscience communities. [Copyright &y& Elsevier]

Earth and space science research and applications typically involve collecting and analyzing large volumes of geospatial data much of which is derived from other existing data by applying a scientific workflow. Such a step-by-step process can be viewed as a process of geospatial knowledge transformation, which often involves hypotheses, inferences and integrations to derive user-specific data products from the knowledge of domain experts. Our research is focused on reducing the transformation effort by providing component inference and integration tools. The Semantic Web envisions a new standardized information infrastructure to enable interoperable machine-to-machine interactions and automatic or semi-automatic service chaining for deriving knowledge over networks. This paper describes a generic framework and implementation of how the Semantic Web proceeds through the life cycle of geospatial knowledge transformation, from geospatial modeling (knowledge formalization), through model instantiation (service chain) to model execution (data product). Our approach relies on semantic integrations. A number of ontologies used to capture domain knowledge are introduced in this paper as the basis of knowledge bases for describing and reasoning geospatial data and services. Also, a semantically enabled geospatial catalog service is described to enable more effective discovery, automation and integration of geospatial data and services. [Copyright &y& Elsevier]

Abstract: Recent advances in Sensor Web geospatial data capture, such as high-resolution in satellite imagery and Web-ready data processing and modeling technologies, have led to the generation of large numbers of datasets from real-time or near real-time observations and measurements. Finding which sensor or data complies with criteria such as specific times, locations, and scales has become a bottleneck for Sensor Web-based applications, especially remote-sensing observations. In this paper, an architecture for use of the integration Sensor Observation Service (SOS) with the Open Geospatial Consortium (OGC) Catalogue Service-Web profile (CSW) is put forward. The architecture consists of a distributed geospatial sensor observation service, a geospatial catalogue service based on the ebXML Registry Information Model (ebRIM), SOS search and registry middleware, and a geospatial sensor portal. The SOS search and registry middleware finds the potential SOS, generating data granule information and inserting the records into CSW. The contents and sequence of the services, the available observations, and the metadata of the observations registry are described. A prototype system is designed and implemented using the service middleware technology and a standard interface and protocol. The feasibility and the response time of registry and retrieval of observations are evaluated using a realistic Earth Observing-1 (EO-1) SOS scenario. Extracting information from SOS requires the same execution time as record generation for CSW. The average data retrieval response time in SOS+CSW mode is 17.6% of that of the SOS-alone mode. The proposed architecture has the more advantages of SOS search and observation data retrieval than the existing sensor Web enabled systems. [Copyright &y& Elsevier]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

The ability to accurately predict ecosystem drought response and recovery is necessary to produce reliable forecasts of land carbon uptake and future climate. Using a suite of models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we assessed modeled net primary productivity (NPP) response to, and recovery from, drought events against a benchmark derived from tree ring observations between 1948 and 2008 across forested regions of the US and Europe. We find short lag times (0-6 months) between climate anomalies and modeled NPP response. Although models accurately simulate the direction of drought legacy effects (i.e. NPP decreases), projected effects are approximately four times shorter and four times weaker than observations suggest. This discrepancy between observed and simulated vegetation recovery from drought reveals a potential critical model deficiency. Since productivity is a crucial component of the land carbon balance, models that underestimate drought recovery time could overestimate predictions of future land carbon sink strength and, consequently, underestimate forecasts of atmospheric CO2. [ABSTRACT FROM AUTHOR]