20253 research outputs found
Sort by
Modeling and managing systemic risk and food - water - (bio)energy security nexus in interdependent land use systems
Full text linkTwo-Phase Approach for Designing Sustainable Biomass Supply Chains for Community-Scale Biomass Power Plants in Thailand
Full text linkThis study proposes a novel two-phase model framework for designing sustainable biomass supply chains of Community-Scale Biomass Power Plants (CSBPPs) by optimization based on geospatial-based Multi-criteria Decision Making (MCDM), the Analytic Hierarchy Process (AHP) method and the Location–Allocation Model. Phase I involved land suitability criteria prioritization and suitable land area analysis. The location–allocation model was the main tool used in Phase II to identify optimal locations, followed by the analysis of the levelized cost of electricity (LCOE). The model optimized site location based on the availability (remaining) of local crop residues, electricity demand, road networks and other key criteria for power plant development, such as the location of substations and the location of existing power plants. The results show that the estimated total remaining crop residue potential in the EEC region was 2403 kt/year, which can generate approximately 34,156 TJ. The location–allocation model identified the top five locations for CSBPPs. The total required installed capacity of these five locations was approximately 100.23 MW in order to serve the district energy demand by the residential sector of 793.82 million (kWh/year). Assuming direct combustion-steam turbine technology with an installed capacity of 6–10 MW, the average LCOE was found to be in a range of 0.081 USD/kWh
Adaptive behavior of farmers under consecutive droughts results in more vulnerable farmers: a large-scale agent-based modeling analysis in the Bhima basin, India
Full text linkConsecutive droughts, becoming more likely, produce impacts beyond the sum of individual events by altering catchment hydrology and influencing farmers' adaptive responses. We use the Geographical, Environmental, and Behavioural (GEB) model, a coupled agent-based hydrological model, and expand it with the subjective expected utility theory (SEUT) to simulate farmer behavior and subsequent hydrological interactions. We apply GEB to analyze the adaptive responses of similar to 1.4 million heterogeneous farmers in India's Bhima basin over consecutive droughts and compare scenarios with and without adaptation. In adaptive scenarios, farmers can either do nothing, switch crops, or dig wells, based on each action's expected utility. Our analysis examines how these adaptations affect profits, yields, and groundwater levels, considering, e.g., farm size, risk aversion, and drought perception. Results indicate that farmers' adaptive responses can decrease drought vulnerability and impact after one drought (6 times the yield loss reduction) but increase them over consecutive periods due to switching to water-intensive crops and homogeneous cultivation (+15 % decline in income). Moreover, adaptive patterns, vulnerability, and impacts vary spatiotemporally and between individuals. Lastly, ecological and social shocks can coincide to plummet farmer incomes. We recommend alternative or additional adaptations to wells to mitigate drought impact and emphasize the importance of coupled socio-hydrological agent-based models (ABMs) for risk analysis or policy testing
Spatiotemporal variability in global lakes turbidity derived from satellite imageries
Full text linkTurbidity is a key indicator of water quality and has significant impacts on underwater light availability of lakes. But the spatiotemporal variability of turbidity, which is important for understanding comprehensive changes in the water quality and status of aquatic ecosystems, remains unclear on a global scale. In this study, the spatial distribution pattern, seasonal variability, spatiotemporal variability, and influencing factors of turbidity in 774 lakes worldwide have been investigated using the turbidity product of Copernicus Global Land Service (CGLS) derived from Sentinel-3 OLCI. We found that 63.4% of lakes show low turbidity (≤ 5 Nephelometric Turbidity Units). The ranking of turbidity by climate zone is as follows: arid climate > tropical climate > temperate climate ∼ polar climate > cold climate. Turbidity decreased significantly in 40% of studied lakes, and increased significantly in 32% lakes. The lake with low turbidity has less seasonal variation, and there is a large seasonal variation in lake turbidity in the tropical and polar climate zones of Northern Hemisphere. Positive covariates to turbidity of global lakes include wind speed of lake, slope, surface runoff, and population in the catchment. Conversely, negative covariates include lake area, volume, discharge, inflow of lake, and GDP. Abundant water volume, favorable flow conditions, and more financial investments in lake management can help to reduce turbidity. These findings highlight the spatiotemporal changes of global lake turbidity and underlying mechanisms in controlling the variability, providing valuable insights for future lake water quality management
Early warning of complex climate risk with integrated artificial intelligence
Full text linkAs climate change accelerates, human societies face growing exposure to disasters and stress, highlighting the urgent need for effective early warning systems (EWS). These systems monitor, assess, and communicate risks to support resilience and sustainable development, but challenges remain in hazard forecasting, risk communication, and decision-making. This perspective explores the transformative potential of integrated Artificial Intelligence (AI) modeling. We highlight the role of AI in developing multi-hazard EWSs that integrate Meteorological and Geospatial foundation models (FMs) for impact prediction. A user-centric approach with intuitive interfaces and community feedback is emphasized to improve crisis management. To address climate risk complexity, we advocate for causal AI models to avoid spurious predictions and stress the need for responsible AI practices. We highlight the FATES (Fairness, Accountability, Transparency, Ethics, and Sustainability) principles as essential for equitable and trustworthy AI-based Early Warning Systems for all. We further advocate for decadal EWSs, leveraging climate ensembles and generative methods to enable long-term, spatially resolved forecasts for proactive climate adaptation
Fair carbon removal obligations under climate response uncertainty
Full text linkDeploying carbon dioxide removal (CDR) is considered unavoidable to meet global climate goals. However, current assessments of the potential role of CDR tend to overlook uncertainty in the Earth System response to our emissions. Here, we assess the level of ‘preventive’ CDR needed to draw warming down to 1.5°C in case of a stronger-than-median Earth System response. Using the ‘1.5°C with no or limited overshoot’ ensemble of pathways assessed by the Intergovernmental Panel on Climate Change (IPCC), we estimate that around 323–787 Gt CO2 (interquartile range) of additional CDR (beyond the 418–763 Gt CO2 (interquartile range) already deployed in these pathways) may be required after net zero CO2 for a very likely (> = 90%) chance of reaching 1.5°C in 2100. We cannot know now whether a net zero society will need to utilize the preventive capacity, but the option must be available to them. Feasibility and sustainability concerns associated with large-scale CDR deployment raise fundamental questions over reducing potential future CDR reliance in light of Earth System uncertainty. Our analysis shows that reducing residual emissions from long-lived (e.g. CO2 and N2O) and short-lived climate forcers (e.g. CH4) can significantly reduce the scale of preventive CDR required. We also explore an illustrative approach to equitably allocate global preventive CDR needs. North America is allocated a per-capita removal responsibility of 13 t CO2/capita annually between 2020 and 2100 in a pathway with limited residual emission cuts, which is more than halved in another with deeper residual emission cuts. Our results underscore the importance of limiting so-called ‘hard-to-abate’ emissions in addition to rapid near-term cuts in emissions as preventive measures to avoid over-reliance on unsustainable levels of preventive CDR
Sampling methods for multi-stage robust optimization problems
Full text linkIn this paper, we consider multi-stage robust optimization problems of the minimax type. We assume that the total uncertainty set is the cartesian product of stagewise compact uncertainty sets and approximate the given problem by a sampled subproblem. Instead of looking for the worst case among the infinite and typically uncountable set of uncertain parameters, we consider only the worst case among a randomly selected subset of parameters. By adopting such a strategy, two main questions arise: (1) Can we quantify the error committed by the random approximation, especially as a function of the sample size? (2) If the sample size tends to infinity, does the optimal value converge to the “true” optimal value? Both questions will be answered in this paper. An explicit bound on the probability of violation is given and chain of lower bounds on the original multi-stage robust optimization problem provided. Numerical results dealing with a multi-stage inventory management problem show that the proposed approach works well for problems with two or three time periods while for larger ones the number of required samples is prohibitively large for computational tractability. Despite this, we believe that our results can be useful for problems with such small number of time periods, and it sheds some light on the challenge for problems with more time periods
Benefits of Calibrating a Global Hydrological Model for Regional Analyses of Flood and Drought Projections: A Case Study of the Yangtze River Basin
Full text linkUncalibrated global hydrological models are primarily used to inform projections of flood and drought changes under global warming and their impacts, but it remains unclear how model calibration might benefit these projections. Using the Yangtze River Basin as a case study, we compare projected changes in flood and drought frequencies and their impacts—area, population, and gross domestic product affected—at various warming levels, from uncalibrated and calibrated simulations with the Community Water Model. These projections are driven by 10 General Circulation Models (GCMs) from Coupled Model Intercomparison Project Phase 6, within the Inter-Sectoral Impact Model Intercomparison Project framework. Calibration significantly improves simulated discharge, yet the impact of calibration under climate change on projected increases in flood frequency and their associated impacts is minor, in contrast to its notable role in drought projections. We further quantify the relative contribution of GCMs, emission scenarios, and calibration approaches to the projected impacts, finding that GCMs primarily drive projected flood changes, while emission scenarios and calibration contribute more significantly to the variance in drought projections after 2050. The differing sensitivities to calibration are attributed to the dominance of extreme precipitation in flood generation and the influence of long-term evapotranspiration trends on drought occurrence. The findings imply that future projections of relative changes in flood frequency and risks based on uncalibrated hydrological models are likely still quite reliable for warm and humid regions. However, careful calibration and model improvement is crucial for enhancing the reliability of future drought impact assessments
Unlocking the global benefits of Earth Observation to address the SDG 6 in situ water quality monitoring gap
Full text linkAchieving Sustainable Development Goal 6 requires innovative and often disruptive approaches to address critical gaps in global water quality monitoring. The most recent SDG Indicator 6.3.2 (Proportion of bodies of water with good ambient water quality) progress report highlights a critical water quality in situ data gap, with an urgent need for countries to strengthen their monitoring capacity and commence state water quality assessments and trend analysis. Earth Observation (EO) technologies hold immense potential to close that gap for SDG Indicator 6.3.2. However, limited awareness, lack of skills and resource inequalities are some of the barriers which hinder widespread adoption of EO. We present insights from a unique workshop held at the University of Stirling in 2024, which convened diverse participants from academia, industry, NGOs, and international agencies and across disciplines, geographies, and sectors. Through creative and collective thinking approaches, they developed four actionable concepts: (1) Space Buzz: a media campaign to raise awareness of EO value; (2) centralised EO access hubs to empower users and improve equality; (3) scalable education strategies for capacity building; and (4) an Intergovernmental Panel for Water Quality to enhance global coordination. Each concept derived from a synoptic creative process, demonstrating the uniqueness of thinking within the teams. To unlock the potential of EO for global water quality monitoring, we invite EO networks, funders, water resource managers and individuals to champion these concepts, and incorporate them into funding calls and proposals
Targeting climate finance for global forests
Full text linkComprehensive data on costs of mitigation are needed to guide the scale and distribution of climate finance to sectors and regions where it will be most cost effective. We estimate the finance required to meet regional forest-based mitigation targets, aggregated from Nationally Determined Contributions (NDCs). Regions accounting for 70% of global forest carbon can meet their forest-based NDCs with carbon prices below 20-72 billion per year by 2030. Under a global coordination scenario, in which the same level of finance is available, but mitigation takes place where it is least costly, we project twice as much mitigation in 2030 as in the upper bound NDC scenario, at the same cost. This highlights potential cost savings from increasing mitigation in regions with low-cost mitigation potential that is not reflected in current national commitments and informs the next generation of NDCs