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As water resources become further stressed due to increasing levels of societal demand, understanding the effect of climate and landuse change on various components of the water cycle is of strategic importance. In this study we used a previously developed hydrologic model of the Black Sea Catchment (BSC) to assess the impact of potential climate and landuse changes on the fresh water availability. The BSC model was built, calibrated, and validated against observed daily river discharge for the period of 1973-2006 using the Soil andWater Assessment Tool (SWAT) as the modeling tool. We employed the A2 and B2 scenarios of 2017-2050 generated by the Danish Regional Climate Model (HIRHAM), and four potential future landuse scenarios based on the Intergovernmental Panel of Climate Change (IPCC)'s special report on emissions scenarios (SRES) storylines, to analyze the impact of climate change and landuse change on the water resources of the BSC. The detailed modeling and the ensemble of the scenarios showed that a substantial part of the catchment will likely experience a decrease in freshwater resources by 30 to 50%. [ABSTRACT FROM AUTHOR]

Data discoverability, accessibility, and integration are frequent barriers for scientists and a major obstacle for favorable results on environmental research. To tackle this issue, the Group on Earth Observations (GEO) is leading the development of the Global Earth Observation System of Systems (GEOSS), a voluntary effort that connects Earth Observation resources world-wide, acting as a gateway between producers and users of environmental data. GEO recognizes the importance of capacity building and education to reach large adoption, acceptance and commitment on data sharing principles to increase the capacity to access and use Earth Observations data. This article presents 'Bringing GEOSS services into practice' (BGSIP), an integrated set of teaching material and software to facilitate the publication and use of environmental data through standardized discovery, view, download, and processing services, further facilitating the registration of data into GEOSS. So far, 520 participants in 10 countries have been trained using this material, leading to numerous Spatial Data Infrastructure implementations and 1,000 tutorial downloads. This workshop lowers the entry barriers for both data providers and users, facilitates the development of technical skills, and empowers people. [ABSTRACT FROM AUTHOR]

Summary Improved data sharing is needed for hydrological modeling and water management that require better integration of data, information and models. Technological advances in Earth observation and Web technologies have allowed the development of Spatial Data Infrastructures (SDIs) for improved data sharing at various scales. International initiatives catalyze data sharing by promoting interoperability standards to maximize the use of data and by supporting easy access to and utilization of geospatial data. A series of recent European projects are contributing to the promotion of innovative Earth observation solutions and the uptake of scientific outcomes in policy. Several success stories involving different hydrologists’ communities can be reported around the World. Gaps still exist in hydrological, agricultural, meteorological and climatological data access because of various issues. While many sources of data exists at all scales it remains difficult and time-consuming to assemble hydrological information for most projects. Furthermore, data and sharing formats remain very heterogeneous. Improvements require implementing/endorsing some commonly agreed standards and documenting data with adequate metadata. The brokering approach allows binding heterogeneous resources published by different data providers and adapting them to tools and interfaces commonly used by consumers of these resources. The challenge is to provide decision-makers with reliable information, based on integrated data and tools derived from both Earth observations and scientific models. Successful SDIs rely therefore on various aspects: a shared vision between all participants, necessity to solve a common problem, adequate data policies, incentives, and sufficient resources. New data streams from remote sensing or crowd sourcing are also producing valuable information to improve our understanding of the water cycle, while field sensors are developing rapidly and becoming less costly. More recent data standards are enhancing interoperability between hydrology and other scientific disciplines, while solutions exist to communicate uncertainty of data and models, which is an essential pre-requisite for decision-making. Distributed computing infrastructures can handle complex and large hydrological data and models, while Web Processing Services bring the flexibility to develop and execute simple to complex workflows over the Internet. The need for capacity building at human, infrastructure and institutional levels is also a major driver for reinforcing the commitment to SDI concepts. [ABSTRACT FROM AUTHOR]

Aim Climate warming and land use change represent a major challenge for both species and conservation managers. Temporally and spatially explicit projections of the future distribution of species have been extensively developed to support decision-making in conservation. The aim of this study was to move beyond the simple projections of likely impacts of global change to identify the most vulnerable species. We suggest an original vulnerability index that integrates estimations of projected range change and different proxies of species resilience in a quantitative way. The proposed index is generally applicable, completely quantitative, and it allows ranking species so as to prioritize conservation actions. Location We illustrate the applicability of the vulnerability index using breeding birds in Switzerland as an example of conservation target. Methods The vulnerability index relies on five indicators quantifying different aspects of the projected change in distributional area, the reservoirs available for the species and their recent population trend. Species distribution was modelled using three different techniques (GAM, MARS and BRT) and then projected for 2050 and 2100 according to two different IPCC scenarios of climate change coupled with two regional land use scenarios to represent different magnitudes of the stressors and the range of possible outcomes. Results According to the different contributions of the base indicators, different patterns of vulnerability can be distinguished. In Switzerland, breeding birds inhabiting coniferous woodlands, alpine habitats and wetlands have significantly higher vulnerability to climate and land use change than species in other habitats. Main conclusions The proposed vulnerability index represents an early warning system as it identifies species that are currently not threatened, but are very likely to become so. As such, it complements the assessment of risk of species' extinction based on the Red List and on their international importance. [ABSTRACT FROM AUTHOR]

Aim This study used data from temperate forest communities to assess: (1) five different stepwise selection methods with generalized additive models, (2) the effect of weighting absences to ensure a prevalence of 0.5, (3) the effect of limiting absences beyond the environmental envelope defined by presences, (4) four different methods for incorporating spatial autocorrelation, and (5) the effect of integrating an interaction factor defined by a regression tree on the residuals of an initial environmental model. Location State of Vaud, western Switzerland. Methods Generalized additive models (GAMs) were fitted using the grasp package (generalized regression analysis and spatial predictions, http://www.cscf.ch/grasp). Results Model selection based on cross-validation appeared to be the best compromise between model stability and performance (parsimony) among the five methods tested. Weighting absences returned models that perform better than models fitted with the original sample prevalence. This appeared to be mainly due to the impact of very low prevalence values on evaluation statistics. Removing zeroes beyond the range of presences on main environmental gradients changed the set of selected predictors, and potentially their response curve shape. Moreover, removing zeroes slightly improved model performance and stability when compared with the baseline model on the same data set. Incorporating a spatial trend predictor improved model performance and stability significantly. Even better models were obtained when including local spatial autocorrelation. A novel approach to include interactions proved to be an efficient way to account for interactions between all predictors at once. Main conclusions Models and spatial predictions of 18 forest communities were significantly improved by using either: (1) cross-validation as a model selection method, (2) weighted absences, (3) limited absences, (4) predictors accounting for spatial autocorrelation, or (5) a factor variable accounting for interactions between all predictors. The final choice of model strategy should depend on the nature of the available data and the specific study aims. Statistical evaluation is useful in searching for the best modelling practice. However, one should not neglect to consider the shapes and interpretability of response curves, as well as the resulting spatial predictions in the final assessment. [ABSTRACT FROM AUTHOR]

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Prediction of species’ distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species’ distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species’ occurrence data. Presence-only data were effective for modelling species’ distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editors discuss topics within the issue including addressing of new environmental and societal challenges at the Black Sea through environmental data sharing, shift of Alpine grasslands and morphological changes on coast of the Danube Delta in Romania.

• Boundary organizations straddle traditional institutional boundaries and silos. • They foster science-policy interactions and inter-/transdisciplinary collaboration. • They help overcome barriers to enable scientific evidence use in policy-making. Boundary organizations are non-traditional structures that can foster transdisciplinary relationships and help catalyze the exchange of ideas, trust, and ultimately the implementation of scientific evidence into policy. Here, we describe GE-21, a group composed of researchers and public servants interested in promoting biodiversity and ecosystem services in Geneva, Switzerland, as an example of such a structure. GE-21 developed into a de facto social experiment for fostering inter- and transdisciplinary science and promoting nature-based policies. The results of two ecosystem-based projects carried by GE-21 were rapidly adopted into policy. Here, we provide a post-hoc narrative based on reports, interviews, and observations that collectively assess the merits and drawbacks of such structures for mainstreaming ecosystem services. We contend that the following five characteristics of this boundary organization may have facilitated its role as a conduit between academic research and public agencies: 1) projects proposing timely, relevant, and novel solutions to policy needs; 2) institutional support for applied interdisciplinary research; 3) a 'safe space' to express ideas and develop close relationships over time; 4) availability of high-quality spatial data at a relevant scale, and scientific capacity to treat them; 5) resources dedicated to communication. [ABSTRACT FROM AUTHOR]