RePP Africa - a georeferenced and curated database on existing and proposed wind, solar, and hydropower plants.

Promoting a transition to low-carbon energy systems to mitigate climate change requires an optimization of renewable energy (RE) planning. However, curated data for the most promising RE technologies, hydro-, wind and solar power, are missing, which limits data-based decision-making support. Here, a spatially explicit database for existing and proposed renewable power plants is provided: The Renewable Power Plant database for Africa (RePP Africa) encompasses 1074 hydro-, 1128 solar, and 276 wind power plant records. For each power plant, geographic coordinates, country, construction status, and capacity (in megawatt) are reported. The number of RePP Africa records exceeds the respective values in other existing open-access databases and matches available cumulative capacity data reported by international energy organizations best with deviations <13% for hydro-, <23% for wind, and <32% for solar power plants. This contemporary database is the most harmonized open-accessible reference source on RE power plants across Africa for stakeholders from science, (non-)governmental organizations, consulting, and industry; providing a fundamental data basis for the development of an integrated sustainable RE mix.

Geo-referenced simulations of E. coli in a sub-catchment of the Vecht River using a probabilistic approach.

The proportion of wild swimmers at non-official bathing sites has increased during the Covid-19 pandemic. Bathing water quality at designated sites is monitored through analysis of the concentration of fecal indicator bacteria such as E. coli. However, non-official sites are generally not monitored. In a previous work, steady state modelling of E. coli was achieved at catchment scale, enabling a comparison of expected concentrations along an entire catchment for longtime average. However, E. coli concentrations can vary over several orders of magnitude at the same monitoring site throughout the year. To capture the temporal variability of E. coli concentrations on the catchment scale, we extended the existing deterministic E. coli sub-module of the GREAT-ER (Geo-referenced Exposure Assessment tool for European Rivers) model for probabilistic Monte-Carlo simulations. Here, selected model parameters are represented by probability distributions instead of fixed values. Wastewater treatment plant (WWTP) emissions and diffuse emissions were parameterized using selected data from a previous monitoring campaign (calibration data set) and in-stream processes were modeled using literature data. Comparison of simulation results with monitoring data (evaluation data set) indicates that predicted E. coli concentrations well-represent median measured concentrations, although the range of predicted concentrations is slightly larger than the observed concentration variability. The parameters with the largest influence on the range of predicted concentrations are flow rate and E. coli removal efficiency in WWTPs. A comparison of predicted 90th percentiles with the threshold for sufficient bathing water quality (according to the EU Bathing Water Directive) indicates that year-round swimming at sites influenced by WWTP effluents is advisable almost nowhere in the study area. A refinement of the model can be achieved if quantitative relationships between the WWTP removal efficiency and both, the treatment technologies as well as the operating parameters are further established.

Geo-referenced simulation of pharmaceuticals in whole watersheds: application of GREAT-ER 4.1 in Germany.

The geo-referenced regional exposure assessment tool for European rivers (GREAT-ER) is designed to support river basin management or the implementation process within the EU Water Framework Directive by predicting spatially resolved exposure concentrations in whole watersheds. The usefulness of the complimentary application of targeted monitoring and GREAT-ER simulations is demonstrated with case studies for three pharmaceuticals in selected German watersheds. Comparison with monitoring data corroborates the capability of the probabilistic model approach to predict the expected range of spatial surface water concentrations. Explicit consideration of local pharmaceutical emissions from hospitals or private doctor's offices (e.g., for X-ray contrast agents) can improve predictions on the local scale without compromising regional exposure assessment. Pharmaceuticals exhibiting low concentrations hardly detectable with established analytical methods (e.g., EE2) can be evaluated with model simulations. Management scenarios allow for a priori assessment of risk reduction measures. In combination with targeted monitoring approaches, the GREAT-ER model can serve as valuable support tool for exposure and risk assessment of pharmaceuticals in whole watersheds.

Future large hydropower dams impact global freshwater megafauna.

Dam construction comes with severe social, economic and ecological impacts. From an ecological point of view, habitat types are altered and biodiversity is lost. Thus, to identify areas that deserve major attention for conservation, existing and planned locations for (hydropower) dams were overlapped, at global extent, with the contemporary distribution of freshwater megafauna species with consideration of their respective threat status. Hydropower development will disproportionately impact areas of high freshwater megafauna richness in South America, South and East Asia, and the Balkan region. Sub-catchments with a high share of threatened species are considered to be most vulnerable; these are located in Central America, Southeast Asia and in the regions of the Black and Caspian Sea. Based on this approach, planned dam locations are classified according to their potential impact on freshwater megafauna species at different spatial scales, attention to potential conflicts between climate mitigation and biodiversity conservation are highlighted, and priorities for freshwater management are recommended.