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The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the first 7-year segment of its latest atmospheric reanalysis: ERA-5 over the period 2010–2016. ERA-5 has important changes relative to the former ERA-Interim atmospheric reanalysis including higher spatial and temporal resolutions as well as a more recent model and data assimilation system. ERA-5 is foreseen to replace ERA-Interim reanalysis and one of the main goals of this study is to assess whether ERA-5 can enhance the simulation performances with respect to ERA-Interim when it is used to force a land surface model (LSM). To that end, both ERA-5 and ERA-Interim are used to force the ISBA (Interactions between Soil, Biosphere, and Atmosphere) LSM fully coupled with the Total Runoff Integrating Pathways (TRIP) scheme adapted for the CNRM (Centre National de Recherches Météorologiques) continental hydrological system within the SURFEX (SURFace Externalisée) modelling platform of Météo-France. Simulations cover the 2010–2016 period at half a degree spatial resolution. The ERA-5 impact on ISBA LSM relative to ERA-Interim is evaluated using remote sensing and in situ observations covering a substantial part of the land surface storage and fluxes over the continental US domain. The remote sensing observations include (i) satellite-driven model estimates of land evapotranspiration, (ii) upscaled ground-based observations of gross primary production, (iii) satellite-derived estimates of surface soil moisture and (iv) satellite-derived estimates of leaf area index (LAI). The in situ observations cover (i) soil moisture, (ii) turbulent heat fluxes, (iii) river discharges and (iv) snow depth. ERA-5 leads to a consistent improvement over ERA-Interim as verified by the use of these eight independent observations of different land status and of the model simulations forced by ERA-5 when compared with ERA-Interim. This is particularly evident for the land surface variables linked to the terrestrial hydrological cycle, while variables linked to vegetation are less impacted. Results also indicate that while precipitation provides, to a large extent, improvements in surface fields (e.g. large improvement in the representation of river discharge and snow depth), the other atmospheric variables play an important role, contributing to the overall improvements. These results highlight the importance of enhanced meteorological forcing quality provided by the new ERA-5 reanalysis, which will pave the way for a new generation of land-surface developments and applications.

Animal populations are often comprised of both foraging specialists and generalists. For instance, some individuals show higher foraging site fidelity (spatial specialization) than others. Such individual differences in degree of specialization can persist over time‐scales of months or even years in long‐lived animals, but the mechanisms leading to these different individual strategies are not fully understood. There is accumulating evidence that individual variation in foraging behaviour is shaped by animal personality traits, such as boldness. Despite this, the potential for boldness to drive differences in the degree of specialization is unknown. In this study, we used novel object tests to measure boldness in black‐legged kittiwakes (Rissa tridactyla) breeding at four colonies in Svalbard and deployed GPS loggers to examine their at‐sea foraging behaviour. We estimated the repeatability of foraging trips and used a hidden Markov model to identify locations of foraging sites in order to quantify individual foraging site fidelity. Across the breeding season, bolder birds were more repeatable than shy individuals in the distance and range of their foraging trips, and during the incubation period (but not chick rearing), bolder individuals were more site‐faithful. Birds exhibited these differences while showing high spatial similarity in foraging areas, indicating that site selection was not driven by personality‐dependent spatial partitioning. We instead suggest that a relationship between boldness and site fidelity may be driven by differences in behavioural flexibility between bold and shy individuals. Together, these results provide a potential mechanism by which widely reported individual differences in foraging specialization may emerge.

This paper presents a comprehensive analysis of the impact of adaptive cruise control on energy consumption in real-world driving conditions based on a natural experiment: a large-scale observational dataset of driving data from a diverse fleet of vehicles and drivers. The analysis is conducted at two different fidelity levels: (1) a macroscopic trip-level benefit estimate that compares trips with and without cruise control in a counterfactual way using statistical methods, and (2) a situation-based comparison achieved through the segmentation of trips into distinct driving situations such as acceleration, braking, cruising, and other maneuvers. The results of this research show that the effect of cruise control on energy consumption varies across different driving situations and levels of analysis. In a macroscopic trip-level analysis, cruise control engagement is associated with a slight increase in fuel consumption across the fleet. As revealed later by the situation-based analysis, this result can be attributed to the negative impact of cruise control on energy consumption in cruising mode, which is the most common driving situation. However, the situation-based comparison demonstrates that cruise control can provide fuel consumption benefits in situations involving acceleration and braking, particularly when a preceding vehicle is present. The study also emphasizes the importance of controlling for various factors that can influence both fuel consumption and the likelihood of cruise control engagement to properly evaluate its effects. Efficient automated driving technology to improve upon human driving behaviour offer a promising pathway to energy and fuel savings. Here, authors analyse real-world data to determine how adaptive cruise control affects fuel consumption and identify specific driving situations where it can be optimized.

PurposeDespite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis.Design/methodology/approachThe model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs).FindingsEvaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model’s utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations.Originality/valuePresents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning.

Conserved lands provide multiple ecosystem services, including opportunities for nature-based recreation. Managing this service requires understanding the landscape attributes underpinning its provision, and how changes in land management affect its contribution to human wellbeing over time. However, evidence from both spatially explicit and temporally dynamic analyses is scarce, often due to data limitations. In this study, we investigated nature-based recreation within conserved lands in Vermont, USA. We used geotagged photographs uploaded to the photo-sharing website Flickr to quantify visits by in-state and out-of-state visitors, and we multiplied visits by mean trip expenditures to show that conserved lands contributed US $1.8 billion (US $0.18-20.2 at 95% confidence) to Vermont's tourism industry between 2007 and 2014. We found eight landscape attributes explained the pattern of visits to conserved lands; visits were higher in larger conserved lands, with less forest cover, greater trail density and more opportunities for snow sports. Some of these attributes differed from those found in other locations, but all aligned with our understanding of recreation in Vermont. We also found that using temporally static models to inform conservation decisions may have perverse outcomes for nature-based recreation. For example, static models suggest conserved land with less forest cover receive more visits, but temporally dynamic models suggest clearing forests decreases, rather than increases, visits to these sites. Our results illustrate the importance of understanding both the spatial and temporal dynamics of ecosystem services for conservation decision-making.

Car sharing is a new transport mode which combines characteristics of private and collective traditional transport means. Understanding the relationship of this mode with existing ones is very important for policy makers to create an efficient transport system and to properly address public resources. This paper aims to analyze the interaction of car sharing with the existing offer of competing modes, using data from a specific travel survey administered in the city of Turin, where both free-floating and one-way station based car sharing services are offered. All transport modes operating in the study area were considered. Bivariate models were estimated to study the propensity to have a car sharing subscription and the substitution patterns between different travel means for a representative random sample of trips taken by the Turin population. Results show that the current car sharing system is perceived as efficient and useful; car sharing members are young males, living in high-income and low-size household with, in particular, a high number of workers and low number of available cars; moreover, the presence of private parking near home has a strong negative impact. There is evidence that car sharing can substitute car driving trips, while the evidence that the same can happen with biking and walking trips is not supported by models but only marginally seen from descriptive statistics. There is also some complementarity between car sharing and public transport and a strong complementarity between car sharing and bike sharing, so that policy makers should jointly promote those modes.

The artisanal fisheries of Ecuador operate within one of the most dynamic and productive marine ecosystems of the world. This study investigates the catch composition of the Ecuadorian artisanal fishery for large pelagic fishes, including aspects of its spatio-temporal dynamics. The analyses of this study are based on the most extensive dataset available to date for this fishery: a total of 106,963 trip-landing inspection records collected at its five principal ports during 2008 ‒ 2012. Ecuadorian artisanal fisheries remove a substantial amount of biomass from the upper trophic-level predatory fish community of the eastern tropical Pacific Ocean. It is estimated that at least 135 thousand metric tons (mt) (about 15.5 million fish) were landed in the five principal ports during the study period. The great novelty of Ecuadorian artisanal fisheries is the \"oceanic-artisanal\" fleet component, which consists of mother-ship (nodriza) boats with their towed fiber-glass skiffs (fibras) operating with pelagic longlines. This fleet has fully expanded into oceanic waters as far offshore as 100°W, west of the Galapagos Archipelago. It is estimated that nodriza operations produce as much as 80% of the total catches of the artisanal fishery. The remainder is produced by independent fibras operating in inshore waters with pelagic longlines and/or surface gillnets. A multivariate regression tree analysis was used to investigate spatio-environmental effects on the nodriza fleet (n = 6,821 trips). The catch species composition of the nodriza fleet is strongly influenced by the northwesterly circulation of the Humboldt Current along the coast of Peru and its associated cold waters masses. The target species and longline gear-type used by nodrizas change seasonally with the incursion of cool waters (< 25°C) from the south and offshore. During this season, dolphinfish (Coryphaena hippurus) dominates the catches. However, in warmer waters, the fishery changes to tuna-billfish-shark longline gear and the catch composition becomes much more diverse.

Over the past few years, the semantics community has developed several ontologies to describe concepts and relationships for internet of things (IoT) applications. A key problem is that most of the IoT-related semantic descriptions are not as widely adopted as expected. One of the main concerns of users and developers is that semantic techniques increase the complexity and processing time, and therefore, they are unsuitable for dynamic and responsive environments such as the IoT. To address this concern, we propose IoT-Lite, an instantiation of the semantic sensor network ontology to describe key IoT concepts allowing interoperability and discovery of sensory data in heterogeneous IoT platforms by a lightweight semantics. We propose 10 rules for good and scalable semantic model design and follow them to create IoT-Lite. We also demonstrate the scalability of IoT-Lite by providing some experimental analysis and assess IoT-Lite against another solution in terms of round trip time performance for query-response times. We have linked IoT-Lite with stream annotation ontology, to allow queries over stream data annotations, and we have also added dynamic semantics in the form of MathML annotations to IoT-Lite. Dynamic semantics allows the annotation of spatio-temporal values, reducing storage requirements and therefore the response time for queries. Dynamic semantics stores mathematical formulas to recover estimated values when actual values are missing.

Recreational fisheries can have a significant impact on fish populations and can suffer from the same symptoms of open access as commercial fisheries. However, recreational fisheries receive little attention compared with their commercial counterparts. Regulations designed to allocate scarce fish, such as seasonal closures and bag limits, can result in significant losses of value to anglers. We provide an estimate of these foregone benefits by estimating the potential gains to implementing management reforms of the headboat portion of the recreational red snapper fishery in the US Gulf of Mexico. This fishery has suffered from a regulatory spiral of shortened seasons and lowered bag limits in spite of rebuilding stocks. We gather primary survey data of headboat anglers that elicit trip behavior and their planned number and seasonal distribution of trips under status-quo and alternative management approaches. We use these data to estimate a model of anglers’ seasonal trip demand as a function of the ability to retain red snapper, bag limits, and fees. We find that a hypothetical rights-based policy, whereby vessels with secure rights to a portion of annual catch could offer their customers year-round fishing in exchange for lower per-angler retention and increased fees, could raise the average angler’s welfare by $139/y. When placed in the global context of recreational fishing, these estimates suggest that status-quo management may deprive anglers of billions of dollars of lost economic value per year.

Smart card data are widely used in generating the origin and destination (O–D) matrix for public transit, which contains important information for transportation planning and operation. However, the generation of the O–D matrix is limited by the smart card data information that includes the boarding (origin) information without the alighting (destination) information. To solve this problem, trip chain methods have been proposed, thereby greatly contributing in estimating the destination using the smart card data. Nevertheless, unlinked trips, that is, trips with unknown destinations, are a persisting issue. The purpose of this study is to develop a method for estimating the destination of unlinked trips, in which trip chain methods cannot be applied, using temporal travel patterns and historical boarding records of the passengers based on long-term smart card data. The passengers were clustered by k-means clustering, and the time-of-day travel patterns were estimated for each cluster using a Gaussian mixture model. The travel patterns were formulated to estimate the destination of the passengers from the smart card data. The proposed method was verified using the 2018 smart card data collected in Sejong City, South Korea. The existing trip chain method matched the destinations of 60.0% of the total trips, whereas the proposed method improved the matching to 74.9% by additionally matching the destinations of 37.2% of the unlinked trips.