Liquid organic hydrogen carriers (LOHCs) are a promising approach for long-term, low-carbon energy storage via hydrogen, due to their safety profile and compatibility with existing infrastructure. These properties also make it possible for them to efficiently transport and distribute large-scale hydrogen over long distances. This review explores recent advancements in LOHC technology, focusing on innovations that improve hydrogen storage capacity, techno-economics, and environmental impacts of LOHCs. Additionally, it examines global initiatives aimed at scaling LOHC systems for large-scale hydrogen storage and transport whilst highlighting collaborations, policy frameworks, and investment trends that support the integration of LOHC into renewable energy infrastructure. LOHC has the potential to play a critical role in achieving global decarbonization goals by facilitating the safe and reusable storage of low-carbon hydrogen. However, technological challenges persist, and more research is needed to maximize their overall thermal efficiency and market adoption.
Increasing climatic aridity and growing anthropogenic pressures are expected to alter dissolved organic matter (DOM) dynamics in Mediterranean river basins, with implications for environmental monitoring and drinking water source management. Dissolved organic carbon (DOC), a bulk indicator of DOM and an operational surrogate for disinfection by-products (DBP) precursors, is routinely monitored at drinking water sources but remains difficult to anticipate on seasonal timescales. Here, we present a reproducible open-source workflow that extends conventional monitoring by integrating seasonal climate forecasts (SEAS5) with coupled hydrological and catchment carbon models to generate probabilistic monthly predictions of streamflow and DOC at the inflow of a Mediterranean drinking water reservoir. Seasonal hindcast evaluation using the Continuous Ranked Probability Skill Score (CRPSS) showed limited skill for meteorological variables beyond 1 month, while streamflow exhibited positive skill at short lead times across most seasons. Notably, DOC forecasts achieved the highest and most persistent skill, with CRPSS values exceeding 0.3 for winter initializations and lead times up to 4 months, consistent with catchment system memory. Forecast experiments successfully distinguished contrasting wet and dry hydroclimatic conditions, reproducing higher and more persistent DOC concentrations during the dry period. To support operational use, the forecasts were translated into a co-developed monthly report to provide early warning of periods with elevated organic matter levels in source waters. While DOC alone does not capture the full complexity of DBP formation, the workflow provides a transferable approach for seasonal assessment of source water organic matter dynamics in hydroclimatically variable, human-impacted catchments.
Water temperature plays an important role in physical and biogeochemical processes within lakes, as well as being a key indicator of the impact of climate change on the water body. Thus, analysing lake surface water temperature (LSWT) is essential for comprehending how a lake responds to climate warming. Lake Titicaca, the largest lake in South America, is a critically important water resource in Peru-Bolivia; however, it is also one of the most impacted by climate change. To assess historical and future variability, we analyse LSWT patterns of Lake Titicaca using the Global LAke Surface water Temperature (GLAST) dataset (1981-2020, 2021-2099), and the daily satellite-derived European Space Agency Climate Change Initiative (ESA CCI) Lakes dataset (2000-2020) for validation. Our analysis suggests that: (1) there has been an annual warming trend of + 0.15 K decade-1 in Lake Titicaca over the past 40 years, driven by shortwave radiation and air temperature; and (2) the future warming of Lake Titicaca will vary under different Representative Concentration Pathway (RCP) scenarios, between + 0.02 K decade-1 (RCP 2.6), +0.23 K decade-1 (RCP 6.0), and + 0.44 K decade-1 (RCP 8.5). Additionally, under RCP 8.5, the average intensity of lake heatwaves is projected to increase from 0.5 K (1981-2020) to 3.3 K (2080-2099), with their average duration increasing from 22 days to 365 days. This research provides a comprehensive understanding of LSWT in Lake Titicaca under past and future climatic warming. Our findings will provide valuable information for practitioners and policymakers in tackling the effects of climate change on Lake Titicaca.
Most Artificial intelligence (AI) algorithms are trained on large-scale datasets to learn complex, underlying patterns to make informed decisions. However, due to their data-intensive nature, the performance of these algorithms highly depends on the quality of the training data. Due to strict privacy regulations, large medical datasets are not readily available, which leads to reduced data sizes as well as under-representation of some classes and demographic groups. These data shortcomings, if not handled, are replicated by the AI algorithms, thus compromising their performance. One potential solution to this problem is the augmentation of data by generating synthetic samples that possess the same real-world data properties. Therefore, this study explores the synthetic data generation process and pre-existing research, mainly focusing on statistical, probabilistic, and Machine Learning (ML) based tabular data generation methods. For this purpose, we analysed high-quality peer-reviewed articles extracted from different databases. The findings show that synthetic data not only increases data volume but also improves its quality by increasing diversity and enhancing the representation of various demographic groups within the datasets. Synthetic Minority Oversampling Technique (SMOTE) and its variants are the commonly used techniques with up to 12
South America contains some of the world’s most ecologically and hydrologically diverse freshwater systems, which are increasingly vulnerable to climate change and human pressures. Despite their importance, the diurnal and interannual variability of lake surface water temperature (LSWT) across the continent remains poorly understood. In this study, we analyze thermal patterns in 2,406 South American lakes, spanning both historical (1981–2020) and future (2021–2099) periods. We assess LSWT trends, lake heatwave dynamics, and the influence of key meteorological drivers on lake thermal dynamics. Our results show that 97.0
