Search Results (26,772)

Accurate, scalable estimation of rice planting dates is essential for climate-adaptive management in multi-cropping regions, yet most models rely on static calendars, which fail to capture climate-driven shifts and bias simulated yield responses. This study aims to develop a climate-driven, spatially explicit framework to simulate dynamic transplanting dates across diverse multi-cropping systems in monsoon Asia. Utilizing daily AgERA5 reanalysis and Monsoon Asia Rice Calendar (MARC) data from 2019 to 2020, we present Geo-ROCKET. The framework integrates an automated K-means clustering workflow to delineate bimodal planting windows and employs random convolutional kernel transforms with adaptive geographic neighborhoods to capture local climate heterogeneity. Evaluated by area-weighted mean absolute error (MAE), the model achieves high accuracy across six seasons (MAE 6.53–12.50 days), outperforming six traditional ROCKET and ensemble baselines while preserving smooth spatial error fields. Sensitivity experiments reveal that a 15-day bias in the previous harvest date can increase transplanting error to 10.8–17.8 days, emphasizing the importance of sequential consistency. By providing dynamic, climate-sensitive inputs, Geo-ROCKET improves the accuracy of crop modeling for climate impact projections. This framework offers a flexible tool for characterizing human management decisions and evaluating adaptation strategies in intensive agricultural systems. Full article

This study examines how the city’s evolving spatial structure shapes its thermal environment. Using Google Earth Engine (GEE) and the Local Climate Zone (LCZ) scheme, we tracked structural changes across Chengdu and its central districts (Jinjiang and Wuhou) in 2017, 2021, and 2025. We then combined the Weather Research and Forecasting (WRF) model with the NSGA-II algorithm. This allowed us to explore links between LCZ patterns and the Universal Thermal Climate Index (UTCI) in the urban center. Results confirm a strong but non-linear relationship between built form and the local climate. Optimized scenarios, respecting practical planning constraints, show that rebalancing LCZ proportions can reduce peak temperatures in the core area by 1.72–2.75 °C. Future plans for Chengdu should therefore limit high-risk compact types (LCZ 1, 3, 8), expand mid-rise and open arrangements (LCZ 4, 5), and preserve or restore natural surfaces (LCZ A–C) to achieve a more thermally equitable urban landscape. Full article

Built heritage is increasingly affected by climate-driven processes, yet its capacity to inform broader understandings of urban environmental change remains insufficiently explored. Here, we synthesize the recent literature (2020–2024) on the application of the Historic Urban Landscape (HUL) approach to the integrated management of cultural heritage under climate risk, reframing the historic built environment as a multi-scale diagnostic medium for climate–urban interactions. We analyze the steps and tools employed to support decision-making across territorial planning, risk assessment, and heritage governance in the papers selected from Web of Science, Science Direct, and Scopus databases. Results show that the approach is a flexible analytical framework that allows the integration of heterogeneous data, multi-criteria evaluations, and diverse stakeholder perspectives across spatial and temporal scales. Information modeling tools are shown to play a central role in structuring territorial knowledge, identifying patterns of vulnerability, and supporting comparative analyses across urban contexts. Nonetheless, significant challenges persist, including limited quantification of climate-induced degradation mechanisms, uncertainties in linking vulnerability assessments to predictive models, structural constraints on participatory implementation, and a tendency to apply the approach as a checklist due to inadequate understanding of its holistic dimensions. Overall, the HUL approach emerges as a scalable and transferable framework for embedding cultural heritage within climate research, advancing the conceptual integration of built heritage into resilience science and sustainability-oriented urban systems. Full article

This study adopts the theory of planned behavior to investigate consumers’ purchasing decisions under extreme weather conditions. Specifically, this paper examines how extreme global weather events motivate consumers to consider purchasing green products and prioritize environmental sustainability in their consumption choices. It further explores whether consumers’ adoption of green products enhances their satisfaction under abnormal global climate conditions, as well as how consumer satisfaction subsequently improves individuals’ mood, anxiety, and cognitive states. Structural equation modeling was employed to test the hypothesized model using data collected from 352 valid respondents in China. As the global community strives to achieve net-zero CO₂ emissions by 2050, numerous firms and manufacturers have incorporated green product concepts to advance sustainable operations. The empirical results reveal that anxiety and cognition are positively related to green purchasing decisions, which in turn exert a positive influence on consumer satisfaction. Based on these findings, this study proposes actionable strategies to promote green consumption behavior by accounting for relevant psychological factors. Full article

The Upper Indus Basin (UIB) plays a crucial role in water security and socio-economic development in Pakistan. Under changing climatic conditions, the sustainable management of the water resources of the UIB needs accurate and reliable projections of hydroclimatic variables. Given the limited assessments of hydroclimatic simulations from CMIP6 models in the region, this study assessed the uncertainties associated with the historical simulations of 16 CMIP6 GCMs in the UIB. The observations of 34 in situ weather stations were used as reference, while the performances of GCMs were assessed based on widely used evaluation indices, including correlation coefficient (CC), bias, relative bias (rBIAS), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), Taylor diagram, and the performance diagram. Results of the evaluation indices indicated that most of the considered GCMs failed to represent the observed precipitation in the UIB. Correlations between the simulations of GCMs and the reference observations were generally low; CCs ranged from −0.24 to 0.16. All GCMs exhibited negative NSE values (ranging between −2.79 and −0.51). The values of RMSE (59.36 to 98.43 mm/month) and rBIAS (9 to 96%) were also very high. Among the considered GCMs, INM-CM4-8 and EC-Earth3-Veg-LR showed comparatively lower RMSE values, moderate rBIAS, and higher CC values. Three GCMs (MRI-ESM2-0, GFDL-ESM4, and CNRM-CM6-1) performed very poorly, with high negative NSE and significant overestimations. Among the 16 GCMs, EC-Earth3-Veg-LR had the highest composite score and better performance across all considered indices. The overall findings of this study indicated that none of the CMIP6-based GCMs (in their raw form) performed satisfactorily in the UIB of Pakistan; therefore, the application of bias-correction techniques is strongly recommended before direct application of these projections for climate impact and adaptation studies in this mountainous region. The results will be useful for the hydroclimatic data users and algorithm developers of global climate models. Full article

Globally, Central Asian wild fruit forests are critical repositories of wild fruit germplasm resources and provide essential ecosystem services. However, their habitats are facing escalating degradation risks driven by climate warming, shifting precipitation regimes, and intensifying anthropogenic disturbances. Accurately quantifying climate-driven spatiotemporal variations in habitat suitability for keystone wild fruit tree species is therefore an essential prerequisite for formulating targeted conservation and management strategies in arid and semi-arid landscapes. In this study, we applied the maximum entropy (MaxEnt) model to simulate the current (2000–2020 baseline) and future (2030s, 2050s, 2070s) potential suitable habitats of two dominant wild fruit tree species, Malus sieversii (Ledeb.) M.Roem. and Prunus cerasifera Ehrh., in the Ili Valley, a core distribution area of Central Asian wild fruit forests in northwestern China. We integrated rigorously screened species occurrence records with key environmental predictors and characterized future climate conditions using three Shared Socioeconomic Pathways (SSPs; SSP126, SSP245, and SSP585) spanning low to high radiative forcing levels. The model exhibited excellent predictive performance (AUC > 0.85), confirming the robustness and reliability of our habitat suitability simulations. Elevation and annual precipitation were identified as the dominant environmental variables governing habitat suitability for both species, highlighting the critical role of terrain–hydroclimate interactions in maintaining viable dryland refugia for wild fruit forests. Under the baseline climate scenario, the total area of suitable habitats reached 24.014 × 10³ km² for Malus sieversii and 18.990 × 10³ km² for Prunus cerasifera. Future climate projections revealed a consistent and significant contraction trend in suitable habitats for both species, with the magnitude of habitat loss escalating with increasing radiative forcing and longer projection time horizons. Specifically, under the high-emission SSP585 scenario by the 2070s, the suitable habitat area is projected to decline by 7.579 × 10³ km² for Malus sieversii and 9.883 × 10³ km² for Prunus cerasifera relative to the baseline. Our findings delineate climate-vulnerable hotspots of wild fruit forests and provide a robust spatial scientific basis for prioritizing in situ conservation, targeted habitat restoration, and anthropogenic disturbance regulation to support the long-term persistence of these irreplaceable wild fruit germplasm resources under accelerating global climate change. Full article

(1) Background: The recent decade appears to be the hottest since the beginning of modern measurements. Changes in climate patterns related to extreme events and disturbances in forest ecosystems are well documented. Six prominent protected areas (PAs), mountainous forest ecosystems in Serbia, were assessed from the perspective of species potential distribution and vulnerability. (2) Methods: Seven different machine learning models were employed, evaluated using AUC, the maximum F-measure, and TSS and joined into an ensemble model for each of the eight tree species/groups taken from the National Forest Inventory. Representatives from four groups of environmental variables were included: 1. climate (Ellenberg’s Climate Quotient), 2. soil (soil organic carbon), 3. topography (elevation), and 4. remotely sensed indices (NDVI). Future climate was derived from four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Stable/gain/loss areas and species vulnerability were calculated with a focus on the end of the 21st century. (3) Results: By the 2090s, generally, contraction of Silver fir, Norway spruce, and European beech is expected, together with the promotion of Downy oak and Sessile oak, in all climate scenarios at all PAs. Two high-mountain PAs expect to see promotions in average forest suitability, one PA both a promotion and a reduction in two scenarios, and three PAs reductions in forest ecosystems in general. (4) Conclusions: National parks “Kopaonik” and “Tara” appear to be the least endangered, followed by “Golija”, while “Stara planina”, “Djerdap”, and “Fruska gora” are expected to experience overall reductions in forest habitats. Full article

Climate change disproportionately threatens low- and middle-income countries, yet integrated assessments combining socio-economic fragility with physical hazards remain limited. This study quantifies multi-dimensional climate vulnerability and derives optimized adaptation policies for six representative nations (Bangladesh, Colombia, Kenya, Morocco, Pakistan, Vietnam) by fusing socio-economic indicators with climate risk data (2000–2024). A computational framework integrating unsupervised learning, dimensionality reduction, and predictive modeling was employed. Principal Component Analysis synthesized eight indicators into a Compound Vulnerability Score (CVS), while K-Means and DBSCAN identified distinct vulnerability regimes. XGBoost quantified driver importance, and Graph Neural Networks captured systemic interconnections. XGBoost identified projected drought risk (31.2%), precipitation change (18.1%), and poverty headcount (14.3%) as primary drivers. Graph networks demonstrated significant risk amplification in African nations (Morocco SRS: 0.728–0.874; Kenya SRS: 0.504–0.641) versus damping in Asian countries. A Reinforcement Learning (RL) agent was trained using Deep Q-Networks with experience replay to optimize intervention portfolios under budget constraints. The RL policy achieved a 23% reduction in systemic risk compared to uniform allocation baselines, generating context-specific priorities: drought management for Morocco (score 50) and Pakistan (40); poverty alleviation for Kenya (40); coastal protection for Bangladesh (40); agricultural resilience for Vietnam (35); and institutional capacity building for Colombia (50). In conclusion, socio-economic fragility non-linearly amplifies climate hazards, with poverty and drought risk constituting critical vulnerability multipliers. The AI-driven framework demonstrates that targeted interventions in high-sensitivity systems maximize systemic risk reduction. This integrated approach provides a replicable, evidence-based foundation for strategic adaptation finance allocation in an increasingly uncertain climate future. Full article

Plants of the genus Opuntia are cacti that grow under natural conditions, with scarce humidity, drastic changes in daytime and nighttime temperatures, and poor soils. Their fruits are a food source in certain regions of the world, and their modified stems (cladodes) have diverse uses, including human consumption—especially when young, tender, and succulent (“nopalitos”) —livestock feed, and raw material for various products. There are approximately 300 species and dozens of variants of this genus, identified as wild, semi-domesticated, or domesticated. The physiological and biochemical responses to abiotic stress in these species are diverse but are related to their Crassulacean acid metabolism and the level of domestication. The morphological modifications in fruits, seeds, and cladodes of the genus Opuntia during domestication appear to be the sum of numerous significant biochemical-physiological changes, but generally of small magnitude. Thus, evaluating wild, semi-domesticated, and domesticated Opuntia species allows us to understand the physiological and biochemical processes along a natural gradient (original and modified by natural and artificial selection and by the cultivation environment) and their alteration by abiotic stress of any kind. This review summarizes our main advances in considering the genus Opuntia as a model for evaluating abiotic stress in the physiology and biochemistry of succulent plants. Furthermore, it shows high relevance, especially in the context of climate change, because Opuntia species are key to food security in arid zones. Full article

Morocco’s recent legalization of industrial and medicinal cannabis has created a rapidly expanding seed-oil sector whose sustainability has yet to be fully assessed. This study applies an environmental life cycle assessment (LCA) in accordance with ISO 14040:2006 and ISO 14044:2006, complemented by a qualitative social responsibility assessment based on ISO 26000:2010, aiming to evaluate the life cycle sustainability of Moroccan cannabis seed oil. Three representative processing chains, traditional artisanal presses, producer cooperatives and regulated industrial plants are compared using a functional unit of 1 kg of cold-pressed oil packaged for local distribution. Inventory data were drawn from field measurements and interviews and were modeled in OpenLCA with background datasets from Ecoinvent 3.8 and Agribalyse v3.1. Impact assessment used the ReCiPe 2016 (H) method at the midpoint level across nine categories (climate change, fossil resource scarcity, water use, freshwater eutrophication, terrestrial acidification, land occupation, carcinogenic, non-carcinogenic human toxicity, and fine particulate matter formation). Sensitivity analyses varied seed yield, electricity mix and transport distances by ±20% to gauge uncertainty. Results show that the cooperative scenario achieves the lowest impacts across nearly all categories because of higher extraction yields (3 kg seed per kg oil), lower energy use (0.54 kWh kg⁻¹ oil) and more effective co-product recovery. In contrast, artisanal extraction requires approximately 1 kg of additional seed input per functional unit compared to optimized scenarios, significantly increasing upstream environmental burdens and causing upstream agricultural burdens to multiply. Industrial facilities perform comparably to cooperatives if powered by renewable electricity. Integrating a semi-quantitative social responsibility assessment reveals that legalization has markedly improved organizational governance, labor conditions, consumer protection and community involvement. Cooperatives display the most balanced social performance, whereas industrial plants excel in governance and quality control. A set of recommendations, including drip irrigation, cultivar improvement, co-product valorisation, renewable energy adoption, eco-designed packaging and cooperative governance, is proposed to enhance the environmental and socio-economic sustainability of Morocco’s emerging cannabis seed-oil industry. Full article

This study introduces a hybrid framework that integrates transient numerical simulations with artificial neural networks (ANNs) to analyze and predict heat transfer in building walls. The framework is applied to ten different material–insulation combinations. Using the Leapfrog–Hopscotch (LH) finite difference scheme, we evaluated dynamic heat transfer and identified optimal insulation thicknesses for buildings in the cold continental climate of Bukhara. An ANN model was trained and validated on a dataset generated from 410 simulated wall configurations. The model achieved high predictive accuracy, with a mean squared error below 0.005. The thickness of the outer material layer ranged from 20 cm to 35 cm, while the inner layer thickness varied from 1 cm to 3 cm. Among the materials analyzed, glass wool + steel and gypsum + brick demonstrated superior insulation performance by minimizing heat loss most effectively, with values as low as 361,234 W/m² and 4,983,441 W/m², respectively, at 35 cm wall thickness. These findings underscore the potential of combining ANN-based predictions with physics-based simulations to design energy-efficient building envelopes in cold climates. Full article

Volatile organic compounds (VOCs) are key precursors of ozone (O₃) and secondary organic aerosols (SOA), among which biogenic VOCs (BVOCs) constitute the dominant natural source. However, large uncertainties remain in the magnitude, spatial distribution, and seasonal variability of BVOC emissions in China under rapidly changing vegetation and climate conditions. In this study, a refined BVOC emission inventory for China in 2023 was developed using the Model of Emissions of Gases and Aerosols from Nature (MEGAN v3.2) driven by WRF meteorological simulations and MODIS vegetation data. The estimated annual BVOC emissions reached 41.70 Tg, including 26.90 Tg isoprene, 4.84 Tg monoterpenes, 0.55 Tg sesquiterpenes, and 9.41 Tg other VOCs. The corresponding ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP) were 346.12 Tg yr⁻¹ and 2137.51 Gg yr⁻¹, respectively. Emissions exhibited a pronounced south–north gradient with hotspots in Guangxi, Guangdong, and Yunnan, and peaked in summer. Broadleaf forests were identified as the dominant emission sources, followed by savannas and shrublands. Isoprene contributed most to OFP, whereas monoterpenes dominated SOAFP. Compared with previous inventories, the updated vegetation data, meteorological inputs, and refined chemical speciation improve the representation of BVOC emissions and their spatial patterns in China. These results highlight the important role of BVOCs in regional O₃ and SOA formation and provide an improved emission basis for atmospheric chemistry modeling and air-quality management. Full article

The rapid growth of cities and expansion of impervious surfaces have intensified surface runoff problems and urban flooding risk. This scenario, exacerbated by the effects of climate change, demands sustainable and integrated solutions. Thus, this study evaluates the pre-feasibility of implementing sustainable urban drainage systems (SUDS) in the Monteverde neighborhood in Montería, Colombia; an area that is critically affected by floods during rainfall events. Using the storm water management model (SWMM) and hydrological simulations based on design hyetographs for different return periods, the performance of a conventional drainage system was compared with five scenarios using SUDS. To determine the modeling scenarios, a decision-making method through the analytic hierarchy process, AHP, was used to select the most appropriate SUDS. The results showed that implementing storage tanks reduces peak flows at outlets 1 and 2 up to 50%, while bioretention zones and rain gardens in isolation showed reduced effectiveness (<6%). Combining strategies slightly improves overall efficiency, although the impact keeps being dominated by tanks. This study demonstrates that the incorporation of SUDS in vulnerable urban areas lessens water risks, strengthens urban resilience, promotes rainwater harvesting, and eases the transition to a more sustainable infrastructure. In addition, it proposes a methodology that can be replicated in other similar Latin American cities. Full article

Background: Global water scarcity, intensified by climate change, demands optimization of irrigation systems consuming 70% of freshwater resources. Despite significant investments in modernizing irrigation infrastructure from open channels to pressurized networks, performance often falls below expectations. Objective: This study develops an integrated diagnostic and simulation framework for evaluating and improving large-scale pressurized irrigation systems by adapting the Mapping System and Services for Pressurized Irrigation (MASSPRES) methodology. Methods: The framework integrates three components: (1) demand flow dynamics determination using stochastic modelling; (2) hydraulic performance simulation incorporating multiple flow regimes; and (3) performance analysis using relative pressure deficit and reliability indicators. The methodology combines deterministic soil water balance calculations with stochastic farmer behaviour modelling. Results: Application to the Sinistra Ofanto irrigation scheme revealed localized pressure deficits during peak demand periods. The rehabilitation strategy restored full hydraulic feasibility of the network, increasing the proportion of hydraulically satisfied operating configurations from 62% to 100% under peak demand conditions and ensuring adequate pressure at all 317 hydrants across the system. Conclusions: The methodology provides robust decision support for cost-effective rehabilitation, ensuring reliable water delivery while promoting water-energy efficiency. Full article

There is increasing interest in delivering greater resilience to climate change through integrated catchment management that includes Nature-based Solutions (NbS) such as riparian buffer strips, tree-planting and wetlands. Governmental organisations also seek to use water quality modelling to understand the mass of different pollutants avoided per feature for appraisal of nutrient-neutrality purposes, but the assessment of efficacy is not yet fully developed, nor is it clear what it implies at the catchment-scale. We introduce three open, freely distributable models to help understanding efficacy and risk-reduction of buffer-strips at the plot (JUMP), waterbody (Fieldmouse), and national (HYPE) scales to help understand risk-reduction and help objectively quantify improvements in catchment resilience. These approaches have been developed across a range of projects but are also being investigated in more detail as part of the modelling element to the NERC Freshwater Quality programme QUANTUM project. Here we report how the particle tracking model predicts the need for very slow velocities, high loss rates or other processes to achieve buffer strip efficacies in common use—slowing the flow alone is unlikely to achieve these results. Upscaling these results to the catchment scale on the Yeo highlights another significant concept, that of the need to define a catchment scale efficacy for a particular Nature-based Solution, given the practicalities of implementation. We demonstrate how HYPE can be used to target and model mitigations and permits both upscaling nationally and through-time source apportionment to help identify when design efficacies may not be achieved in practice. Full article