The need for spatially explicit thematic data is currently increasing in parallel to the development of observing, storing and processing capabilities. This requires an integrated data management structure in which human and institutional aspects play a key role as part of a Spatial Data Infrastructure (SDI). We focus in this study on the African continent to evaluate the status of its SDI implementation. Because assessing SDI at a continental scale in a traditional way (i.e. following methods developed for national assessments) requires financial resources and mechanisms only affordable to developed countries (e.g. European Union), alternative ways have been explored based on fourteen key SDI indicators that were validated by SDI experts in a previous study. Data was collected for each African country through the African leading SDI institution (UN Economic Commission for Africa) and through Internet searches. We found relatively weak scores of the fourteen SDI indicators for African countries compared to the rest of the World, but with notable differences within Africa. We discuss the implication of the lack of information available on the Internet to assess SDI status in Africa. We conclude that it is necessary to improve statistical information in most African countries. This requires an agreed-on geospatial data structure and organization between concerned institutions that is only achievable through a shared global vision on geospatial data governance. To this end, we suggest a few quick wins and several new mechanisms that would enhance the flow of SDI statistical information and improve data management structure in Africa.

Plausible future scenarios have been created for the Black Sea catchment, focussing on spatially explicit alternatives for land-use changes. Four qualitative storylines (HOT, ALONE, COOP and COOL) were first developed, based on interpretation of the respective global scenarios (A1, A2, B1 and B2) produced by the Intergovernmental Panel on Climate Change. Quantitative statistical downscaling techniques were then used to disaggregate the outputs of global scenarios at a regional level. The resulting land-use maps were spatially allocated at 1 km resolution in the Metronamica model, using a set of factors related to the identified drivers of change. The land-use change model was calibrated on historical trends of land-cover change (MODIS 2001 and 2008) translated into spatial allocation rules, and future land-use projections (IMAGE, 2001) were adopted. Suitability and constraint maps and population trends were used to regulate the modelling process. The calibrated model was validated by statistical procedures, visual evaluation and stakeholder involvement in order to ensure its plausibility and accuracy. This methodology bridged the gap between the global and regional scales. Four simulated future states were produced for the main land-use classes–forest, grassland, cropland and built-up areas, as well as scrublands, crops/natural vegetation and barren land–for 2025 and 2050. The results suggest that the features highlighted in these scenarios are guided by global trends, such as population rise and decreasing agriculture, but with different growth rates and a variety of spatial patterns, with regional variations resulting from local backgrounds and policy objectives. This study aims to provide future land-use data as a potential geographical tool to assist policy makers in addressing environmental emergencies such as water stress and pollution. In particular, the exploration of plausible futures can support future assessments to comply with the EU Water Framework Directive and Integrated Coastal Zone Management policies around the Black Sea.

The environmental status of the Black Sea is obviously closely related to its catchment. Being a closed sea, this large water body drains an area of more than 2 million km(2), encompassing 23 countries inhabited by more than 180 million people. The main environmental issues faced by the Black Sea catchment are the same as elsewhere in Europe. These problems are exacerbated by global changes with drastic changes predicted in temperature and precipitation by the end of the century, as well as land use and demographic changes. These environmental problems are taking place in a complex geopolitical situation. In this particular context, data sharing is essential to inform managers and policy-makers about the state of the environment, which will ultimately influence the state of the Black Sea itself. The enviroGRIDS project was set up in order to promote international data sharing initiatives such as the Global Earth Observation System of Systems and the European INSPIRE directive. The enviroGRIDS project was successful in reaching the following objectives: (a) performing a gap analysis on existing Earth observations systems in the region; (b) developing regional capacities at institutional, infrastructure and human resource levels; (c) creating regional scenarios to set the scene for plausible climatic, demographic and land use futures; (d) building the first hydrological model for the entire Black Sea catchment; (e) developing the Black Sea Catchment Observation System based on interoperability standards and Grid computing technologies; (1) showcasing data sharing in several case studies, addressing important environmental issues while building a network of people with improved capacity on data sharing principles. These relative successes should not, however, hide the difficulties in making the necessary Earth observation data available to scientists, decision makers and the public, as the mind-sets at all levels are changing slowly. Controlling the access to data is still perceived by many as a necessity to guarantee the power of the state on society and as a way to preserve its security. The need to develop national spatial data infrastructures (SDI) is very important to convince all ministries and data owner that publically funded data should be made publically available. The progress in the implementation of SDI seems more limited by political agendas than by technology. It is clear, however, that implementation of the INSPIRE directive in Europe is a prerequisite for the success of many other environmental policies (e.g. Water Framework Directive; Marine Strategy Framework Directive; Biodiversity strategy 2020). (C) 2014 Elsevier Ltd. All rights reserved.

Climate change is a naturally occurring phenomenon that has recently been greatly impacted by anthropogenic greenhouse gas (GHG) emissions. One of the main contributing sectors to GHG emissions is the energy sector, due to its high dependency on fossil fuels. Renewable energy systems, notably solar energy, can be an effective climate change mitigation alternative. Photovoltaic (PV) technology provides an interesting method to produce electricity through a virtually infinite renewable resource at the human time scale: solar radiation.This study evaluates the current and future solar energy potential through the use of grid-connected PV power plants at the scale of countries within the Black Sea catchment. Simulated data are used to determine potential change in climate and land-use according to two different development scenarios. Incident solar radiation flux from re-analyses, spatial interpolation, and the application of the Delta change method are used to assess the current and future solar resource potential within this catchment. Potential sites suitable for PV power plants are selected following a Fuzzy logic approach, and thus the total potential solar energy through PV power generation can be determined. Results show that climate change will have little impact on the solar radiation resource, while land-use change induces more variability. However, regardless of the scenario followed, the solar energy potential is sufficient to provide an interesting contribution to the electricity generation mix of most countries within the Black Sea region. (C) 2014 Elsevier Ltd. All rights reserved.

Abstract. This study aims at identifying historical patterns of meteorological, hydrological, and agricultural (inclusively biophysical) droughts in the Karkheh River Basin (KRB), one of the nine benchmark watersheds of the CGIAR Challenge Program on Water and Food. Standardized precipitation index (SPI), standardized runoff index (SRI), and soil moisture deficit index (SMDI) were used to represent the above three types of droughts, respectively. The three drought indices were compared across temporal and spatial dimensions. Variables required for calculating the indices were obtained from the Soil and Water Assessment Tool (SWAT) constructed for the region. The model was calibrated based on monthly runoff and yearly wheat yield using the Sequential Uncertainty Fitting (SUFI-2) algorithm. Five meteorological drought events were identified in the studied period (1980–2004), of which four corresponded with the hydrological droughts with 1–3 month lag. The meteorological droughts corresponded well with the agricultural droughts during dry months (May–August), while the latter lasted for a longer period of time. Analysis of drought patterns showed that southern parts of the catchment were more prone to agricultural drought, while less influenced by hydrological drought. Our analyses highlighted the necessity for monitoring all three aspects of drought for a more effective watershed management. The analysis on different types of droughts in this study provides a framework for assessing their possible impacts under future climate change in semi-arid areas.

This work analyses the responses of four regression-based interpolation methods for predicting snowpack distribution to changes in the number of data points (sample size) and resolution of the employed digital elevation model (DEM). For this purpose, we used data obtained from intensive and random sampling of snow depth (991 measurements) in a small catchment (6 km(2)) in the Pyrenees, Spain. Linear regression, classification trees, generalized additive models (GAMs), and a recent method based on a correction made by applying tree classification to GAM residuals were used to calculate snow-depth distribution based on terrain characteristics under different combinations of sample size and DEM spatial resolution (grid size).The application of a tree classification to GAM residuals yielded the highest accuracy scores and the most stable models. The other tested methods yielded scores with slightly lower accuracy and varying levels of robustness under different conditions of grid and sample size. The accuracy of the model predictions declined with decreasing resolution of DEMs and sample size; however, the sensitivities of the models to the number of data points showed threshold values, which has implications (when planning fieldwork) for optimizing the relation between the effort expended in gathering data and the quality of the results. Copyright (C) 2009 John Wiley & Sons, Ltd.

This work analyses the response of four regression-based interpolation methods to changes in the number of cases and in the resolution of the digital elevation model (DEM). For this purpose, data obtained from an intensive random snow depth sampling (991 measurements) in a small catchment (6 km) in the Pyrenees, Spain, were used. Linear regression, classification trees, generalized additive models (GAMs), and a new method based on a correction by applying tree classification to residuals of GAMs, were used to calculate snow depth distribution from terrain characteristics under different combinations of sample size (100, 200..., 991 cases) and DEM spatial resolution, (from 5x5m to 95x95m every 10m of grid size). Application of a tree classification to residuals obtained from GAMs yields the best accuracy scores. The other tested methods yield rather similar accuracy scores but different levels of robustness when a cross-validation procedure is applied. Accuracy of the model predictions declines as resolution of DEMs and sample size decreases. However, the sensitivity of the models to the number of cases used shows different thresholds, which has relevant implications to optimise the relation between the effort involved and the quality of the results, when fieldwork is planned.

Questions: Does a reduced nutrient load in open water increase species richness and the importance of regional and local site characteristics for species abundance and spatial distribution? Can we build lake-specific models of macrophyte abundance and distribution based on site characteristics in order to prepare a cost-efficient framework for future surveys?Location: Lake Constance, 47degrees39' N, 9degrees18' E.Methods: Generalized additive models (GAMs) were used to predict the potential distributions of eight species and overall species richness. Submersed macrophyte distribution in 1993 was compared with corresponding data from 1978, when eutrophication was at its maximum.Results: Spatial predictions for eight species and overall species richness were relatively accurate and independent of water chemistry. Depth was confirmed as a main predictor of species distribution, while effective fetch distance was retained in many models. Mineralogical variables of sediment composition represent allogenic and autogenic sediment sources and their east-west gradient in Lake Constance corresponded to east-west gradients of species distribution and richness. GAMs appeared more efficient than generalized linear models (GLMs) for modelling species responses to environmental gradients.Conclusions: Reduced trophic status increases species richness and the importance of regional and local site characteristics for species abundance and distribution. Our models represent a spatio-temporal framework for future lake monitoring purposes and allow the development of effective monitoring; this could be generalized for many ecosystem types and would be particularly efficient for large takes such as Lake Constance.