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In a world where climate change, population growth, and global diseases threaten economic access to food, policies and contingency plans can strongly benefit from reliable forecasts of agricultural vegetation health. To inform decisions, it is also crucial to quantify the forecasting uncertainty and prove its relevance for food security. Yet, in previous studies both these aspects have been largely overlooked. This paper develops a methodology to anticipate the agricultural Vegetation Health Index (VHI) while making the underlying prediction uncertainty explicit. To achieve this aim, a probabilistic machine learning framework modelling weather and climate determinants is introduced and implemented through Quantile Random Forests. In a second step, a statistical link between VHI forecasts and monthly food price variations is established. As a pilot implementation, the framework is applied to nine countries of South-East Asia (SEA) with consideration of national monthly rice prices. Model benchmarks show satisfactory accuracy metrics, suggesting that the probabilistic VHI predictions can provide decision-makers with reliable information about future cropland health and its impact on food price variation weeks or even months ahead, albeit with increasing uncertainty as the forecasting horizon grows. These results - ultimately allowing to anticipate the impact of weather shocks on household food expenditure - contribute to advancing the multidisciplinary literature linking vegetation health, probabilistic forecasting models, and food security policy.

AbstractVertical land motion (VLM) from past and ongoing glacial changes can amplify or mitigate ongoing relative sea level change. We present a high‐resolution VLM model for the wider Arctic, that includes both present‐day ice loading (PDIL) and glacial isostatic adjustment (GIA). The study shows that the nonlinear elastic uplift from PDIL is significant (0.5–1 mm yr−1) in most of the wider Arctic and exceeds GIA at 15 of 54 Arctic GNSS sites, including sites in nonglaciated areas of the North Sea region and the east coast of North America. Thereby the sea level change from PDIL (1.85 mm yr−1) is significantly mitigated from VLM caused by PDIL. The combined VLM model was consistent with measured VLM at 85% of the GNSS sites ( ) and outperformed a GIA‐only model ( ). Deviations from GNSS‐measured VLM can be attributed to local circumstances causing VLM.

Although rare, temporally and taxonomically highly-resolved palaeoecological studies with high chronological precision are essential to perform detailed comparisons with precisely dated independent evidence such as archaeological findings, historical events, or palaeoclimatic data. Using a new highly-resolved and chronologically precise sedimentary record from Lago di Mezzano (central Italy), we reconstruct decadal-scale vegetation, species diversity, and fire dynamics, aiming to better understand the linkages between climate, land use, fire, and plant communities from the Neolithic to the Copper Age (c. 5100–3100 cal. BC). Closed, mixed beech-oak forests, including evergreen Quercus ilex, dominated the landscape around Lago di Mezzano during the Neolithic and were disturbed by repeated opening phases, with important implications for lake biogeochemistry and mixing regimes. This was in conjunction with increasing fire activity to promote agro-pastoral practices, as inferred from increasing charcoal, Cerealia type, Triticum type, Hordeum type, Plantago lanceolata type, and Urtica pollen. Fires, on their turn, augmented species diversity (richness and evenness). The comparison of the Mediterranean record from Lago di Mezzano with available continuous and high-precision submediterranean and cool-temperate palynological sequences suggests comparable land use pulses across Southern and Central European regions, most likely in connection with climate change. The outcomes of this study are not only of palaeoecological and archaeological interest; they may also help to improve projections of ecosystem dynamics under future global change.

Pyrolysis of the waste organic fraction is expected to be a central element to meet the primary energy demand in future: it increases the impact of renewable energy sources on the power generation sector and allows the amount of waste to be reduced, putting an end to landfills. In the present study, kinetic studies on the pyrolysis of biomass wastes are carried out. Two kinds of industrial organic waste are investigated: brewery spent grain (BSG) and medium-density fiberboard (MDF). The main target of this work is to provide a global equation for the one-step pyrolysis reaction of the investigated materials in an argon atmosphere using isoconversional methods. The conducted analysis allowed to estimate the activation energy as 225.4-253.6 kJ/mol for BSG and 197.9-216.7 kJ/mol for MDF. For both materials nth order reaction was proposed with reaction order of 7.69-8.70 for BSG and 6.32-6.55 for MDF. The developed equation allowed to simulate the theoretical curves of thermal conversion. These curves indicated the highest conversion at the temperature of the degradation of dominant component, which was experimentally verified. By this method, a one-step kinetic model is derived, which can be applied for the reaction kinetics in the CFD modelling of, e.g., pyrolysis and gasification processes.

This article presents the results of research on the changes and variability of snow cover in Kraków in the 100-year period 1921/22–2020/21 and in its two sub-periods covering the years of the slow and rapid territorial, urban and industrial development of Kraków (respectively, 1921/22–1960/61 and 1961/62–2020/21). The long-term variability of the number of days with snow cover, the maximum depth of the snow layer, the dates of the beginning and end of snow cover duration in the winter season, the potential snow cover duration and the index of snow cover stability were analysed. The directions of changes in the snow cover in the last 100 winter seasons in Kraków correspond to the global changes in air temperature presented in the latest IPCC reports: until the end of the 1950s there were no significant trends, or only small trends were observed, whereas from the beginning of the 1960s faster changes in the snow cover duration and maximum seasonal snow depth have been visible. In the last 60 years (1961/62–2020/21), the impact of global changes in Kraków has been joined by the impact of territorial, demographic and industrial development of the city, causing significant negative trends in snow cover with relative values of less than −9% ∙ 10 years−1, both in the case of snow cover duration and its maximum depth in the winter season; these changes are statistically significant. Throughout the whole 100-year period (1921/22–2020/21) and in its second part (1961/62–2020/21), a decrease in snow cover stability has also been observed.

Urban climate is affected by weather, global climate change and urban development. However, climate change and urbanisation take place simultaneously with intertwined impacts. To analyse their relative contribution to the heat load of Zagreb, a modelling approach is applied to two land-use/land-cover (LULC) situations and corresponding climate conditions. The results indicate that the change in total heat load is dominantly affected by climate change (∼88%) with an average increase in the summer days for 35 days. LULC alterations have a weaker impact (∼12%), but they strongly affect heat load spatial variability. The sign of LULC related heat load change depends on the type of the change (e.g. an increase is detected in areas that have turned from green into built-up classes). Generally, LULC effect is limited to the area with the modification, however it can spread to adjacent areas due to the processes like advection and evapotranspiration. In areas with considerable LULC alterations, their impact on the heat load is comparable to that of climate change. These results highlight the potential of change in the city infrastructure for climate adaptation, as well as emphasise the importance of considering future climate conditions when assessing efficiency of climate adaptation measures.