Search Results (5,426)

Against the backdrop of global climate change and the increasing frequency of extreme weather events, a central scientific question has emerged: how do plants adapt to such “pulsed” stressors? While traditional research has focused on immediate physiological responses and long-term genetic adaptation, this review introduces “ecological memory” as a novel integrative framework. It emphasizes the ability of plants to actively “record” past stress experiences through epigenetic mechanisms, thereby enhancing their adaptability to future adversities. This article systematically elucidates the molecular basis whereby abiotic stressors induce specific epigenetic modifications (e.g., DNA methylation and histone modifications) to form memories. It further discusses how such memories mediate physiological integration mechanisms, such as acclimation and priming-induced resistance at the individual level, and highlights potential pathways for transgenerational epigenetic memory transmission, which may accelerate population-level adaptive evolution. Finally, we evaluate the applications of the ecological memory concept in predicting species distribution, enhancing ecosystem resilience, and guiding the design of “climate smart” crops, aiming to shift the research paradigm from static tolerance studies to dynamic memory and adaptation frameworks. Full article

Aerosol dry deposition is an important sink for particulate matter and a source of uncertainty in air quality modeling. Using the Weather Research and Forecasting model coupled with CUACE (WRF-CUACE), we quantified how three aerosol dry deposition schemes and satellite-based leaf area index (LAI) information affected PM_2.5 dry removal and near-surface PM_2.5 over central and eastern China in January 2022. The schemes were abbreviated as Z01, E20, and PZ10, respectively. A fourth simulation (PZ10_MLAI) used PZ10 but replaced the baseline LAI dataset with a Moderate Resolution Imaging Spectroradiometer (MODIS) constrained LAI field. Hourly PM_2.5 was evaluated with the China National Environmental Monitoring Center network. The schemes produced pronounced, size-dependent differences in deposition velocities, with a pronounced spread in the 0 to 2.5 µm average and more than one order of magnitude spread in the accumulation mode diagnostic, leading to distinct regional mean PM_2.5 dry deposition fluxes. The mean PM_2.5 flux increased by 5.9% in E20 relative to Z01 and decreased by 54.4% in PZ10. The MODIS LAI adjustment changed the PZ10 mean flux by 0.42%. The flux contrasts yielded coherent PM_2.5 responses, with E20 reducing near-surface concentrations by about 10 to 30% and PZ10 increasing them by about 20 to 60%, reaching about 80 to 100% in parts of southern China. Domain mean correlations ranged from 0.61 to 0.65 and PZ10-based simulations exhibited near-zero mean bias. Although MODIS LAI effects were modest for this winter month, local PM_2.5 differences commonly remained within about 4% and approached 6 to 10%, indicating that satellite LAI constraints can be important for multi-year and decadal applications. Full article

As global climate change intensifies and extreme weather events become more frequent, understanding the historical spatial distribution of vegetation is of critical importance. However, most vegetation studies are temporally limited to the post-1980 period due to satellite data constraints. To bridge this gap, we integrated tree-ring width chronologies from the International Tree-Ring Databank with Landsat-derived Enhanced Vegetation Index (EVI) data and evaluated three machine learning models—Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)—to reconstruct annual, spatially explicit EVI for the period 1850–1985 in Diqing, Yunnan, China. RF regression was the best among the three with highest adjusted R² (0.90) and lowest Root Mean Square Error (0.032). The RF-based reconstruction indicated a consistent increase in regional EVI from 1991 to 2005. Breakpoint analysis identified three distinct sub-periods, each with unique spatiotemporal variation patterns. In current times, the EVI value shows a significant positive correlation with average temperatures in June, July, August, and December. In the contemporary period, it also correlates significantly and positively with winter average temperatures, March average precipitation, and spring average precipitation. The spatial pattern for the past 100 years reflects the succession of the local vegetation ecosystem and provides an insight into the influences of natural disturbances (low-temperature damages and droughts) on vegetation growth. This study demonstrates the feasibility of reconstructing high-resolution, long-term vegetation spatial dynamics using tree-ring proxies and machine learning. Full article

Wildfires are environmental hazards with severe ecological, social, and economic impacts. Wildfires devastate ecosystems, communities, and economies worldwide, with rising frequency and intensity driven by climate change, human activity, and environmental shifts. Analyzing wildfire insights such as detection, predictive patterns, and risk assessment enables proactive response and long-term prevention. However, most of the existing approaches have been focused on isolated processing of data, making it challenging to orchestrate cross-modal reasoning and transparency. This study proposed a novel orchestrator-based multi-agent system (MAS), with the aim of transforming multimodal environmental data into actionable intelligence for decision making. We designed a framework to utilize Large Multimodal Models (LMMs) augmented by structured prompt engineering and specialized Retrieval-Augmented Generation (RAG) pipelines to enable transparent and context-aware reasoning, providing a cutting-edge Visual Question Answering (VQA) system. It ingests diverse inputs like satellite imagery, sensor readings, weather data, and ground footage and then answers user queries. Validated by several public datasets, the system achieved a precision of 0.797 and an F1-score of 0.736. Thus, powered by Agentic AI, the proposed, human-centric solution for wildfire management, empowers firefighters, governments, and researchers to mitigate threats effectively. Full article

Against the backdrop of global climate change, the rising frequency and intensity of extreme weather events pose severe challenges to urban transport and commercial systems. As a core capacity for managing uncertainty and risk, urban resilience requires infrastructure to resist shocks, recover rapidly, and adaptively evolve. From a resilience perspective, this study develops a comprehensive evaluation system for spatial accessibility between subway stations and commercial complexes, operationalized by 21 indicators across five dimensions: Connectivity, Redundancy, Robustness, Dynamic adaptability, and Comfort. Spatial accessibility is simulated and measured using sDNA spatial network analysis, while an in-depth questionnaire survey collects, feeds back, and validates users’ subjective perceptions. By constructing a dual evaluation model that integrates spatial configuration and behavioral psychology, we find that the integrated development of subway stations and commercial complexes can maintain stable functional performance and sustained vitality under complex urban conditions by optimizing connectivity, enhancing redundancy, and improving adaptability. This is manifested in the expansion of residents’ pedestrian networks and the spillover of social service functions. In parallel, underground spaces can be transformed into resilient infrastructure to enhance civil air defense performance and provide diversified evacuation routes. The findings offer theoretical support and practical guidance for the construction of resilient cities. Full article

Railway passenger flow forecasting plays a critical role in operational efficiency and resource allocation for transportation systems. However, existing deep learning approaches suffer significant performance degradation when facing rare but high-impact events, primarily due to sample scarcity and their inability to distinguish between routine patterns and disruption regimes. To address these challenges, this study introduces EA-ARIMA-Informer, an adaptive forecasting framework that integrates entropy-augmented ARIMA with Informer through an entropy-guided regime-switching mechanism. The passenger flow series is characterized through a multi-dimensional entropy space comprising four complementary measures: Sample Entropy quantifies local regularity and predictability, Permutation Entropy captures the complexity of ordinal dynamics, Transfer Entropy measures causal information flow from external events (holidays, weather) to passenger demand, and the Conditional Entropy Growth Factor (CEGF)—a novel metric introduced herein—detects regime transitions by tracking the rate of uncertainty change between consecutive time windows. These entropy indicators serve dual roles as feature inputs for representation learning and as state identifiers for segmenting the time series into stable and fluctuating regimes with distinct predictability properties. An adaptive dual-path architecture is then designed accordingly: EA-ARIMA handles low-entropy stable regimes where linear seasonality dominates, while EA-Informer processes high-entropy fluctuating regimes requiring nonlinear residual modeling, with CEGF-guided gating dynamically controlling component weights. Unlike conventional black-box gating mechanisms, this entropy-based switching provides physically interpretable signals that explain when and why different model components dominate the forecast. The framework is validated on a large-scale dataset covering nearly 300 Chinese cities over three years (2017–2019), encompassing normal operations, holiday peaks, and extreme weather disruptions. Experimental results demonstrate that EA-ARIMA-Informer achieves a MAPE of 4.39% for large-scale cities and 7.82% for data-scarce small cities (Tier-3), substantially outperforming standalone ARIMA, XGBoost, and Informer, which yield 15.95%, 13.75%, and 12.87%, respectively, for Tier-3 cities. Ablation studies confirm that both entropy-based feature augmentation and CEGF-guided regime switching contribute significantly to these performance gains, establishing a new paradigm for interpretable and adaptive forecasting in complex transportation systems. Full article

Climate change has led to increasingly frequent and unpredictable droughts and high-temperature events, creating extreme conditions that profoundly impact the productivity of freshwater ecosystems. In this study, we evaluated the effects of extreme temperature and drought events on Nanwan Reservoir, a large, deep body of water in Xinyang, China, by assessing water quality and phytoplankton biomass. Field investigations were conducted during both high-temperature and drought (HTD) conditions in 2019 and normal-temperature and non-drought (NTND) conditions in 2020. HTD conditions significantly disrupted the thermocline and oxycline structures, leading to prolonged stratification during this period. Although phosphorus concentrations remained relatively stable across both periods, nitrogen levels were markedly lower during HTD events, indicating a possible shift in nutrient limitation from phosphorus to nitrogen. Additionally, a complex relationship between environmental variables and phytoplankton biomass was observed under HTD conditions. These findings advance our understanding of primary production responses to extreme weather events in Nanwan Reservoir, highlighting the importance of incorporating this knowledge into water resource management and ecological conservation strategies. Full article

Maritime transport in semi-enclosed seas is increasingly exposed to short-term weather variability, a challenge expected to intensify under climate change and to affect the economic sustainability of shipping operations. This study proposes an integrated probabilistic framework to assess the impact of weather-induced uncertainty on operational costs, using a ferry service in the Baltic Sea as a case study. The approach combines a semi-Markov process, representing transitions between discrete weather hazard states derived from ERA5 reanalysis data (2010–2025), with a state-dependent cost model of key technical subsystems across the vessel’s operational cycle. The results show a strongly disproportionate cost structure, with most expenditures concentrated in open-sea navigation states. Although severe weather conditions occur infrequently, they generate a nonlinear amplification of operational costs, significantly reducing cost predictability and system resilience. The findings indicate that enhancing sustainability in maritime transport requires targeted, state-specific adaptation measures, such as weather-aware routing and condition-based maintenance. The proposed framework supports climate-adaptive decision-making and contributes to sustainability-oriented planning in maritime transport through improved operational robustness and cost resilience under weather uncertainty. Full article

This study quantitatively evaluates the health impacts of urban temperature changes and the potential health benefits of highly reflective roofs as an urban heat island (UHI) mitigation measure. First, empirically derived relationships between ambient temperature and health-related indicators were established for multiple diseases, including both fatal and non-fatal outcomes. Health impacts were assessed using disability-adjusted life years (DALYs), integrating years of life lost (YLLs) and years lived with disability (YLDs). Target diseases included heat- and cold-related mortality, heatstroke, infectious diseases, sleep disturbance, and fatigue. Next, a meteorological simulation was conducted using a Weather Research and Forecasting (WRF) model to estimate outdoor air temperature changes resulting from the implementation of highly reflective building roofs in Osaka Prefecture, Japan. Roof surface reflectance was increased from 0.15 to 0.65 within an urban canopy model, and temperature reductions were evaluated at a 2 km spatial resolution for a one-year period. The results indicate that highly reflective roofs reduced daytime air temperatures by approximately 1.2–1.8 °C, with greater effects observed in high-density urban areas. By integrating the simulated temperature reductions with the temperature–health relationships, annual health impacts were quantified. Although wintertime increases in cold-related health burdens were observed, the annual cumulative DALYs decreased by 1767, corresponding to approximately a 5% reduction in total temperature-related health burdens in Osaka Prefecture. These findings demonstrate that rooftop reflectivity enhancement can contribute to net health improvements while highlighting the importance of accounting for seasonal trade-offs in UHI mitigation strategies. Full article

To address the challenge of forecasting power output from large-scale photovoltaic (PV) bases in desert regions during sand and dust storms, this paper proposes a hybrid data-physics driven prediction method. This approach utilizes satellite remote sensing to obtain regional irradiance data, transforming the traditional one-dimensional time-series forecasting into a two-dimensional spatiotemporal sequence prediction, thereby tracking the dynamic evolution of irradiance intensity under the influence of sand and dust. Firstly, a forecasting model based on a conditional variational autoencoder (CVAE) optimized with a recurrent state-space model (RSSM) is constructed to effectively capture both the deterministic trends and stochastic fluctuations in irradiance variation, providing a reliable input basis for power calculation. Secondly, at the physical modeling level, the model comprehensively considers the isotropic scattering characteristics and changes in sky clarity induced by sand and dust weather, establishing a physical mapping relationship from irradiance to PV output. This mitigates the constraint of scarce historical operational data in desert and sandy regions. This research provides a novel solution for regional-level PV power forecasting under extreme sand and dust weather, contributing to enhanced dispatchability and transmission stability of renewable energy bases during abrupt meteorological changes. Full article

Weather modification has gained significant and growing interest in the United States (US) in recent years. The trend can be largely attributed to the changing climate, persistent droughts, and other extreme weather events that have been experienced across various regions of the US. This paper provides a critical review of weather modification program costs, benefits, policy, and governance to help shed light on policymaking and program management associated with the growing interest in adopting weather modification as a local climate management strategy in the US. Additionally, to deepen our understanding of the widely concerning issues, such as the financial burden on taxpayers and potential environmental risks, the paper explored the local implementation challenges and common environmental and public health concerns related to weather modification activities. A synthesis of the literature and policy debates reached four general conclusions: (1) The need for weather modification programs is expected to keep growing, though regional variations may exist due to regulatory and other local factors; (2) weather modification can bring significant local benefits, ranging from enhanced agricultural yield and recreational economy to extreme weather management and public environmental health benefits; (3) state-level and local support, including financial resources, will be essential for program development in the foreseeable future; and (4) technological advancements will be critical for addressing many of the project operation efficiency challenges and environmental and public health concerns related to weather modification programs. More specifically for program governance and local implementation, aspects such as project planning (including resource pooling), risk and liability management, communication and reporting, outcome measurability, and stakeholder engagement are indispensable for addressing issues related to program legality and oversight, public acceptance, and sustainability. Full article

Accurate photovoltaic (PV) output power forecasting is essential for reliable power system operation, yet rapidly changing meteorological conditions often degrade forecasting accuracy. This study proposes an attention-enhanced bidirectional gated recurrent unit (BiGRU) optimized by an improved Marine Predators Algorithm (IMPA) for PV output power forecasting. Kernel Principal Component Analysis (KPCA) is first employed to extract compact nonlinear representations and suppress redundant features. Then, a dual multi-head self-attention mechanism is integrated before and after the BiGRU layer to strengthen temporal feature learning under fluctuating weather. Finally, the IMPA is designed to improve exploration–exploitation balance and automatically optimize key hyperparameters. Experiments under sunny, cloudy, and rainy conditions demonstrate that IMPA-Att-BiGRU reduces MAE and RMSE by 35.7–58.5% and 22.8–49.1% versus BiGRU, respectively, while increasing R² by 2.2–4.1 percentage points. Against the best benchmark (LSTM), MAE and RMSE are further reduced by 38.1–49.5% and 33.8–52.4%. Moreover, in a cross-day rolling forecasting test with fivefold results, IMPA-Att-BiGRU achieves 62.4% MAE and 49.3% RMSE reductions over BiGRU, confirming robust performance under long-horizon error accumulation. Full article

Bacterial sourcing in urban watersheds is a critical water quality concern because elevated index bacteria concentrations routinely trigger beach advisories and closures in coastal Southern California and elsewhere. This study evaluates diurnal controls on dry-weather bacterial loading in a groundwater-fed storm drain within the Malibu Creek watershed using a 24 h monitoring campaign. Discharge, nutrients, major ions, stable water isotopes, and index bacteria (total coliforms and Escherichia coli) were measured at six time intervals. Storm drain discharge varied by more than an order of magnitude, with rapid nighttime increases of up to +91 L/min during irrigation periods. Total Dissolved Solids ranged from 1276 to 2175 mg/L, peaking during groundwater-dominated low-flow conditions. Nitrate–N ranged from 1.08 to 2.96 mg/L, and orthophosphate from 0.44 to 2.16 mg/L, with nutrient concentrations increasing as irrigation inputs increased. Total coliform concentrations ranged from 13,000 to 670,000 MPN/100 mL, and E. coli ranged from 300 to 120,000 MPN/100 mL, exceeding concentrations in tap water and recycled water runoff by up to two orders of magnitude. End member mixing analysis showed that storm drain flow consisted of approximately 45% groundwater, 23–26% tap water, and 30–33% recycled water during early morning peak flow, shifting to ~56% groundwater and <12% recycled water by mid-morning. The lowest bacterial concentrations occurred during groundwater-only flow, while the largest bacterial increases coincided with the greatest positive changes in discharge rather than with maximum absolute flow. These results support an irrigation-driven biofilm stripping mechanism as the dominant control on dry-weather bacterial loading, with groundwater seepage sustaining biofilm persistence but not peak bacterial release. The findings highlight the importance of internal storm drain processes for managing coastal bacterial exceedances and protecting beach health. Full article

Changes in land use patterns due to urbanisation impact local weather patterns by influencing Land Surface Temperatures (LSTs). Despite rapid urbanisation in the Delhi-NCR (National Capital Region), the peri-urban fringes of Haryana, such as the Central Circle Forest (CCF) region, in the past three decades, a comprehensive 30-year analysis that integrates LST, the Normalised Difference Vegetation Index (NDVI), the Normalised Difference Built-up Index (NDBI), and Land Use/Land Cover (LULC) is lacking. The current study on the decadal analysis covering the 1993 to 2023 time period shows an increase in built-up areas (14.6–38.4%), a decline in NDVI (−0.01 to −0.08), a 6 °C rise in summer LST, and weak correlations between LST and NDVI. A significant increase in summer mean LSTs was observed, with some regions reaching temperatures beyond 35 °C in the selected districts. The LST and LULC zonal statistics revealed that the open fields/agricultural land and floodplains of the Yamuna River have adversely affected the weather pattern with rising LST. The average NDVI declined from −0.01 in 1993 to −0.08 in 2023, indicating a loss of vegetative buffers. Meanwhile, NDBI trends from 2003 to 2023 showed that built-up areas have steadily grown, and LULC data highlighted 38.43% of the built-up area in 2023. Correlation analysis showed a weak negative relationship between LST and NDVI (r = −0.47), suggesting diminishing cooling effects of vegetation, while a weak positive correlation between LST and NDBI indicates that urban expansion is significantly contributing to the urban heat island effect. This study emphasises the need for green infrastructure, afforestation, and water conservation in urban planning frameworks to enhance climate resilience and ecological sustainability. Full article

This study addresses the issue of missing basic data and insufficient accuracy in predicting runoff and non-point-source pollution in the Heilongjiang region of China using the Soil and Water Assessment Tool (SWAT) model. Based on the China Ground Climate Data Daily Dataset (V3.0) and SPAW soil characteristic calculation formula, and assisted by the Python V3.0 language for data processing and computation, new high-precision weather generators and soil attribute databases suitable for the Heilongjiang region of China were established. The weather generator is based on daily data and contains detailed meteorological parameters such as temperature, humidity, wind speed, rainfall, etc., used to characterize the periodic changes in meteorological elements. And the differences and fluctuations outside this change curve were also retained in the basic construction of the weather generator. The soil database covers various parameters, such as soil type, texture, structure, nutrient content, organic matter content, etc., enabling the SWAT model to better simulate hydrological and pollutant transport processes in the soil. Additionally, point-source input data, including various industrial and domestic wastewater discharge situations, were collected and organized to improve data quality. Furthermore, a series of agricultural management measures were developed based on the use of fertilizers and pesticides for simulation, providing an important basis for analyzing non-point-source pollution using the SWAT model. By comparing the different results of the simulation using optimized databases, it is shown that the above work improved the simulation accuracy of the SWAT model in predicting runoff and pollution load in Heilongjiang, China. The NSE of runoff simulation increased from 0.923 to 0.988, and the NSE of ammonia nitrogen and CBOD simulation increased from 0.852 and 0.758 to 0.930 and 0.902, respectively. It is expected that these efforts will provide strong data support for subsequent research and provide a theoretical basis for government decision-makers to build scientifically rigorous and effective pollution control strategies. Full article