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Oligotrophic catchments with short spatey streams, upland lakes and peaty soils characterise northwest European Atlantic coastal regions. These catchments are important biodiversity refuges, particularly for sensitive diadromous fish populations but are subject to changes in land use and land management practices associated with afforestation, agriculture and rural development. Quantification of the degree of catchment degradation resulting from such anthropogenic impacts is often limited by a lack of long-term baseline data in what are generally relatively isolated, poorly studied catchments. This research uses a combination of palaeolimnological (radiometrically-dated variations in sedimentary geochemical elements, pollen, diatoms and remains of cladocera), census, and instrumental data, along with hindcast estimates to quantify environmental changes and their aquatic impacts since the late 19th century. The most likely drivers of any change are also identified. Results confirm an aquatic biotic response (phyto- and zooplankton) to soil erosion and nutrient enrichment associated with the onset of commercial conifer afforestation, effects that were subsequently enhanced as a result of increased overgrazing in the catchment and, possibly, climate warming. The implications for the health of aquatic resources in the catchment are discussed Environmental Protection Agency in Ireland (ILLUMINATE 2005-W-MS-40, P.McGinnity was supported by the Beaufort Marine Research Award in Fish Population Genetics funded by the Irish Government under the Sea Change Programme.

Abstract. The assessment of wind conditions in complex terrain requires the use of Computational Fluid Dynamics (CFD) simulations incorporating an accurate parameterization of forest canopy effects and variable thermal stability effects. This study aims to investigate how incorporating the presence of trees can improve flow predictions. A three-dimensional steady Reynolds-averaged Navier-Stokes (RANS) equations model is set up using OpenFOAM to simulate the flow over a complex terrain site comprising two parallel ridges located near Perdigão, Portugal. A 7.5 km × 7.5 km terrain of the Perdigao site is constructed from elevation data centered around a 100 m met-mast located on the northeast ridge. A 30-min averaged stationary period corresponding to near-neutral conditions on a single met-mast tower is simulated. The impact of incorporating different source terms is studied such as forest canopy, Coriolis forces as well as also buoyancy forces. The prediction capability of the models is analyzed for different groups of towers on the South-West ridge, inside the valley, and on the North-East ridge based on the flow topology. The inclusion of a canopy model is shown to improve predictions close to the ground for most of the towers, while reducing prediction accuracy on top of the ridges, illustrating the need to represent terrain heterogeneity.

Abstract Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are increasingly used to reconstruct mean annual air temperature (MAAT) during the early Paleogene. However, the application of this proxy in coal deposits is limited and brGDGTs have only been detected in immature coals (i.e. lignites). Using samples recovered from Schoningen, Germany (∼48°N palaeolatitude), we provide the first detailed study into the occurrence and distribution of brGDGTs through a sequence of early Eocene lignites and associated interbeds. BrGDGTs are abundant and present in every sample. In comparison to modern studies, changes in vegetation type do not appear to significantly impact brGDGT distributions; however, there are subtle differences between lignites – representing peat-forming environments – and siliciclastic nearshore marine interbed depositional environments. Using the most recent brGDGT temperature calibration (MATmr) developed for soils, we generate the first continental temperature record from central-western continental Europe through the early Eocene. Lignite-derived MAAT estimates range from 23 to 26 °C while those derived from the nearshore marine interbeds exceed 20 °C. These estimates are consistent with other mid-latitude environments and model simulations, indicating enhanced mid-latitude, early Eocene warmth. In the basal part of the section studied, warming is recorded in both the lignites (∼2 °C) and nearshore marine interbeds (∼2–3 °C). This culminates in a long-term temperature maximum, likely including the Early Eocene Climatic Optimum (EECO). Although this long-term warming trend is relatively well established in the marine realm, it has rarely been shown in terrestrial settings. Using a suite of model simulations we show that the magnitude of warming at Schoningen is broadly consistent with a doubling of CO2, in agreement with late Paleocene and early Eocene pCO2 estimates.

Energy demand modelling has been widely applied in various contexts, including power plant generation, building energy simulation and demand-side management. However, it is still an ongoing research topic in terms of the choice of modelling method, feature engineering for data-driven methods, the application contexts and the type of data used. In the residential sector, survey-based and meter-based approaches are categorised according to the type of input data used, i.e. the activity records from the time use survey and energy consumption from meters respectively. These two paradigms are not necessarily easy to combine, which warrants the questions of when one may be preferred over the other and whether they need to be combined despite the significant data requirements. Other details also have a huge impact on the data structure and performance of the energy demand model, including the choice of influential factors, the historical time window of factors selected, the split between training and test data, and the choice of machine learning (ML) algorithm. There is a lack of comparative research to guide researchers and practitioners in developing energy demand modelling capability, specifically as it pertains to these issues. This study analyses three groups of test scenarios in a multi-household residential context based in the UK. Six ML algorithms (LightGBM, Random forest, ANN, SVM, KNN and LSTM), with eight sets of various influential features, at four different historical time window widths and two train-test splits were compared. An appropriate methodology was designed to capture the temporal impact of activities on energy demand and represent the overlap and interaction of activities. The results show that the combination of meter-based and survey-based energy demand models performs better in terms of modelling accuracy and robustness against sudden load variation. Particularly, integrating energy tariffs, household and individual attributes, appliance usage and general activity features can improve the energy demand model. Among the ML algorithms, LightGBM and ANN perform better than other algorithms while LSTM and SVM may not be suitable in this multi-household short monitoring context.

Abstract. This study presents the development of tools for the sustainable thermal management of a shallow unconsolidated urban groundwater body in the city of Basel (Switzerland). The concept of the investigations is based on (1) a characterization of the present thermal state of the urban groundwater body, and (2) the evaluation of potential mitigation measures for the future thermal management of specific regions within the groundwater body. The investigations focus on thermal processes down-gradient of thermal groundwater use, effects of heated buildings in the subsurface as well as the thermal influence of river–groundwater interaction. Investigation methods include (1) short- and long-term data analysis, (2) high-resolution multilevel groundwater temperature monitoring, as well as (3) 3-D numerical groundwater flow and heat transport modeling and scenario development. The combination of these methods allows for the quantifying of the thermal influences on the investigated urban groundwater body, including the influences of thermal groundwater use and heated subsurface constructions. Subsequently, first implications for management strategies are discussed, including minimizing further groundwater temperature increase, targeting "potential natural" groundwater temperatures for specific aquifer regions and exploiting the thermal potential.

Abstract Greenhouse gas emissions from the burning of fossil fuels are one of the main causes of anthropogenic climate change. Large-scale deployment of renewable energy can play an immense role in transforming the global energy system and mitigating the emissions. This paper describes the development of a novel framework called MArket Penetration ModeLing of Renewable Energy Technologies in Electric Power Sector (MAPLET-PS). MAPLET-PS assesses the impacts of policy measures such as carbon price and financial incentives on the adoption of renewable energy technologies. The framework was used to develop a case study for the electric power sector of Alberta, a fossil-dominated province in Western Canada. The results show that implementing a carbon price on fossil fuel electric power sources and incentives for renewable energy, along with the phase-out of coal-fired electricity generation, can mitigate 29% of Alberta's electricity sector 2020 GHG emissions by 2050 and reduce GHG emissions from 46.5 Mt of CO2 eq. in 2020 to 23.6 and 29.1 Mt of CO2 eq. per year in 2030 and 2050, respectively, in Alberta. Moreover, these changes can increase the share of renewable energies from 12.5% in 2018 to 30% in 2050. These rates can be achieved by implementing a carbon price along with a 1000 $ incentive per kW new capacity development and 70 $ incentive per MWh electric power generation from renewable sources, from 2021 to 2025, primarily from wind turbines.

The united nations educational, scientific and cultural organization (UNESCO) considers the historic urban landscapes as the world heritages. Managing historic city centers and maintaining historic cores are the emerging challenges for sustainable urban planning. Today, the historic cores form an important part of the economic, social, environmental, and physical assets and capacities of contemporary cities, and play a strategic role in their development. One of the most important approaches to the development of central textures, especially in historical and cultural cities, is the sustainable urban regeneration approach, which encompasses all aspects of sustainability, such as the economic, social, cultural and environmental aspects. To maintain sustainability and regeneration of historic cores of cities, it is necessary to provide insight into the underlying characteristics of the local urbanization. Furthermore, the fundamental assets are to be investigated as indicators of sustainable regeneration and drivers of urban development. In the meantime, a variety of research and experience has taken place around the world, all of which has provided different criteria and indicators for the development of strategies for the historic cores of cities. The present study, through a meta-analytic and survey method, analyzing the experience and research reported in 139 theoretical and empirical papers in the last twenty years, seeks to provide a comprehensive conceptual model taking into account the criteria and indices of sustainable regeneration in historic cores of cities. The quality of the survey has been ensured using the preferred reporting items for systematic reviews and meta-analysis (PRISMA).