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Advancing water quality model intercomparisons under global change: Perspectives from the new ISIMIP water quality sector
Get PDFWater pollution poses widespread risks to ecosystems, human health, and water users more broadly. Furthermore, the interplay of future hydroclimatic changes and socioeconomic developments will strongly impact the quality status of freshwaters across the globe. Innumerable pollutants are increasingly entering water bodies, potentially creating hotspots at various spatial and temporal scales and with implications for different water-dependent sectors. While it is recognized that proactive solutions to protect and improve water quality are key for the achievement of Sustainable Development Goal 6.3 (clean water for all), deficiencies in our understanding of the current and future quality status pose significant challenges. Water quality models help bridge the gaps in our understanding of water quality due to limited observations, but they vary in terms of pollutants, spatial-temporal resolution, and structure. While such diversity poses various challenges, it also presents an opportunity to design a multi-dimensional framework for water quality model intercomparison projects (WQ-MIPs) that focus on three distinct aspects: multi-pollutant, multi-scale, and multi-sector. The water quality sector has been launched within the ISIMIP initiative to help facilitate these multi-dimensional WQ-MIPs. In this paper, we present community insights on WQ-MIPs. We first synthesize the diversity found among water quality models and then propose an ISIMIP intercomparison framework aimed at enhancing our understanding of uncertainties in pollution levels and identifying robust pollution hotspots, sources, and impacts across multiple sectors, pollutants, and scales. To this end, we use four illustrative examples of WQ-MIPs. Finally, we outline a future agenda for advancing WQ-MIPs that are essential for developing effective solutions to preserve future water quality under global change
Valuing damages and benefits of the altered global nitrogen cycle; lessons for national to global policy support
Get PDFCost-benefit analysis (CBA) is increasingly used to inform environmental policy decisions by identifying interventions with the highest net societal benefits. Here we focus on CBAs for nitrogen (NCBA), explaining its history, presenting results of a recent first global NCBA and discussing opportunities and limitations. NCBAs have been conducted since the late 1990s for various geographic regions in Europe, the US, and China, primarily to support air quality and eutrophication policies. A first valuation of damages and benefits of the full nitrogen (N) cycle was conducted for the European Nitrogen Assessment in 2011, followed by NCBAs for the USA, the Netherlands and Germany. Here we present a first comprehensive global NCBA. Total global damage cost of N pollution in 2010 was estimated at US 2.2 trillion with >95% from increased crop yields. By 2050, global N-related costs will rise faster than N benefits because underlying models project that economic growth (GDP) increases willingness-to-pay to prevent N pollution more than crop prices. The geographical distribution of N-related costs will also shift, with China and India surpassing Europe and North America as regions contributing most to global N-related costs. The estimated N cost range for 2010 was US$ 0.6–2.2 trillion with uncertainty largely in dose-impact and damage cost relations. Given the large uncertainties, when using valuation and NCBA to select a N mitigation option, the net benefits should be substantially higher than the costs and markedly better than for a rejected alternative option. Use of NCBA is discouraged to compare international policy options that involve regions with very different levels of GDP, cultures and political systems
Fast climate impact emulation for global temperature scenarios with the rapid impact model emulator (RIME)
Get PDFClimate model emulation has long been applied to assess the global climate outcomes of integrated assessment model (IAM) emissions scenarios, but is typically limited to first-order climate variables like mean surface air temperatures at limited regional resolution. Here we introduce the rapid impact model emulator (RIME), which uses global warming level interpolation approaches based on inputs of global mean air temperature pathways to calculate a range of climate impact driver (CID) indices and exposure metrics. The emulation is fast and versatile, producing batches of CID indices and exposure metrics to complement IAM scenarios thereby bridging the Intergovernmental Panel on Climate Change (IPCC) Working Groups on impacts (WGII) and mitigation (WGIII) communities. Our lightweight emulator produces both gridded and regionally-aggregated results taking us beyond the computationally-intensive constraints of global earth system and impact models. The approach allows to assess the combined outcome of a wide range of emission and socio-economic scenarios enabling a decomposition of drivers of uncertainty for future climate risks. While climate uncertainties are the primary concern through mid-century, our results indicate that socio-economic factors such as population growth may become the dominant drivers of risk by the end of the century. We demonstrate an application to IPCC scenarios to illustrate its potential utility while acknowledging methodological constraints and delineating a comprehensive roadmap for future development. These rapid climate risk emulation frameworks exhibit significant promise for facilitating cross-disciplinary integration and enhancing scientific inclusivity across diverse research communities
An MILP framework for gas supply chain infrastructure planning with endogenous logistics schemes
Get PDFThis paper presents a mixed-integer linear programming (MILP) framework to minimise the costs of gas supply chains. Distinct from existing approaches in the literature, which often rely on pre-defined logistics schemes and treat storage sizing at receiving terminals in isolation, this framework integrates these into a single optimisation model. By setting these elements as decision variables, the framework allows for simultaneous optimisation of shipping strategies and receiving terminals design. Here, the liquefied natural gas (LNG) supply chain in Indonesia's Maluku Islands was used as a case study. Additionally, the framework was applied to the Finnish coastline and the Caribbean Islands, which differ substantially in terms of demand levels, distances between locations, and geographical contexts, to demonstrate its applicability to problems with differing characteristics. The results show that clustering demands to increase project sizes can lead to significant cost reductions. However, the marginal gains of these economies of scale diminish rapidly as project size grows, especially with longer shipping distances. Finally, the proposed framework was also shown to provide substantially lower-cost solutions compared to methods that rely on pre-determined shipping strategies or optimise shipping and storage capacities separately
Unprecedented UK heatwave harmonised drivers of fuel moisture creating extreme temperate wildfire risk
Get PDFClimate change is resulting in more extreme fire weather during major heatwaves. Across temperate Europe, shrub landscapes dominate the area burned, with the moisture content of fuels during these events determining the threat posed. Current controls on the moisture content of temperate fuel constituents and their response to future extreme heatwaves are not known. We took field measurements of live and dead heather ( Calluna vulgaris ) and organic soil moisture content across the UK over 3 years, including an intensive sampling campaign during the July 2022 heatwave. Here, we show that the fuel moisture content of live fuel is associated significantly with phenological variables, dead fuel only with weather variables, whilst organic-rich ground fuels are more associated with landscape variables. However, during the record 2022 heatwave there was a harmonisation in fuel moisture controls across different fuel constituents, with those controls being driven by weather alone. This caused synchronised extreme dryness outside of current seasonal norms across all fuel constituents at the same time and place. Future intense summer heatwaves can therefore be expected to align the most severe conditions for fire ignition, spread and impact in traditionally non-fire prone regions, producing humid temperate landscapes susceptible to extreme wildfire events
Forming and managing a Farmer Cluster for improved farmland biodiversity in Europe
Get PDF‘Farmer Clusters’ are an English movement where groups of neighbouring farmers have identified and instigated their own conservation initiatives as a collective, providing a ‘bottom‐up’ alternative to the ‘top‐down’, government‐initiated agri‐environment schemes. Although cross‐farm cooperation can be found in mainland Europe, this specific Farmer Cluster approach had not yet been tested before 2020.
FRAMEwork (Farmer clusters for Realising Agrobiodiversity Management across Ecosystems), an EU Horizon 2020 project, aims to identify whether Farmer Clusters could be established in other European countries and improve farmland biodiversity at the landscape scale.
FRAMEwork established 11 Farmer Clusters across nine European countries. The aim of this paper was to describe the different strategies used, the challenges faced and the potential solutions identified to provide future practitioners with guidance.
Forming the Farmer Clusters required a wide range of approaches, from contacting previously known farmers to using advertising campaigns. An integral part of the Farmer Cluster approach is the presence of a ‘facilitator’, someone with farming and environmental knowledge, who can support the group and assist them in their biodiversity‐friendly actions.
Management of the Farmer Clusters required various strategies, and the facilitators were provided with training through the FRAMEwork project. These strategies were applied to unite the farmers within each Farmer Cluster, encouraging them to collaborate and identify their own biodiversity targets.
Expanding the scope of Farmer Cluster activities to enable farmers and local communities and volunteers to observe and monitor biodiversity themselves requires additional effort. However, it provides learning opportunities and capacity development in Farmer Clusters to enhance local collection of information and improved knowledge of local actions and outcomes.
Practical Implication . Farmer Clusters provide a strategy to tackle biodiversity restoration across European farmland at the landscape scale. They also offer tailored and targeted advice from expert facilitators, alleviating the constraints of the current ‘top‐down’ process, allowing farmers more flexibility and ownership of their biodiversity goals. We encourage European policymakers to take up the Farmer Cluster model and provide a facilitation fund similar to that found in England to better aid farmland biodiversity recovery at the landscape scale
Projections of current and future European potential vegetation types
Get PDFThe extent and condition of natural ecosystems is a key factor enabling species populations to thrive. However, the distribution of ecosystems is changing owing to both climatic and anthropogenic factors. Recently negotiated European policy directives, such as the Nature Restoration Regulation, argue for the restoration of natural ecosystems. Yet to determine what is to be restored the range of possible outcomes should be explored, also with regards to future climatic conditions. Here the concept of potential natural vegetation (PNV) is applied and mapped in a data-driven manner at European extent, exploring where PNV transitions are most likely to happen under contemporary and future conditions. Specifically, I predict current and future potential coverage of six natural vegetation types at 1 km2 grain using Bayesian machine learning approaches, relying on a range of contemporary vegetation type records and climate and soil data for prediction. Most current land cover and land use could develop towards no single, but multiple PNV states. Results also indicate that suitable areas for some vegetation types, such as wetlands, might become rarer under future climatic conditions. Furthermore, the challenge of transitioning to PNV was found to be particularly high for current intensively cultivated landscapes. Overall data-driven PNV mapping holds considerable promise for assessing land potentials and supporting restoration assessments. Future work should expand the thematic grain of vegetation maps and also consider feedback with biotic factors
Assessing the impact of urban greenspaces on PM2.5 spatiotemporal variability in Riga, Latvia, via citizen science and low-cost sensors
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Linking social media data with geospatial information to analyse changes in human sentiments in and along surface water environments
Get PDFSocial media data represent a valuable source of information on human activity patterns and emotional responses in relation to natural environments. These data can provide insights into the drivers of human sentiments toward freshwater ecosystems, especially in contexts where traditional survey methods are insufficient or resource intensive. A better understanding of the relationship between human sentiments and the perceived value of freshwater environments can support the integration of public perspectives into ecosystem management and regional development. In this paper, we present a replicable method for acquiring, cleaning, and analysing geolocated Twitter data from 2011 to 2018 from Germany. The method includes multiple data cleaning and filtering steps to prepare the dataset for identifying spatial and temporal trends in sentiments and to determine the primary drivers of emotional responses to water bodies. The demonstrated workflow includes the following steps:
• Geo-located Tweets were collected via the Twitter API, then sorted, indexed, and subjected to filtering and cleaning to ensure data quality.
• Language detection and sentiment analysis using a lexicon-based method (Polyglot), suitable for limited computing power, short-text social media sentiment analysis, particularly in the context of analysing the content posted by individuals spending time in freshwater ecosystems.
• Geospatial enrichment, incorporating contextual data such as weather, population density, and other location-based variables