Search Results (2,046)

The accuracy of long-term runoff models can be increased through the input of local weather variables and global climate indices. However, existing methods do not effectively extract important information from complex input factors across various temporal and spatial dimensions, thereby contributing to inaccurate predictions of long-term runoff. In this study, local–global–temporal attention mechanisms (LGTA) were proposed for capturing crucial information on global climate indices on monthly, annual, and interannual time scales. The graph attention network (GAT) was employed to extract geographical topological information of meteorological stations, based on remotely sensed elevation data. A long-term runoff prediction model was established based on long-short-term memory (LSTM) integrated with GAT and LGTA, referred to as GAT–LGTA–LSTM. The proposed model was compared to five comparative models (LGTA–LSTM, GAT–GTA–LSTM, GTA–LSTM, GAT–GA–LSTM, GA–LSTM). The models were applied to forecast the long-term runoff at Luning and Pingshan stations in China. The results indicated that the GAT–LGTA–LSTM model demonstrated the best forecasting performance among the comparative models. The Nash–Sutcliffe Efficiency (NSE) of GAT–LGTA–LSTM at the Luning and Pingshan stations reached 0.87 and 0.89, respectively. Compared to the GA–LSTM benchmark model, the GAT–LGTA–LSTM model demonstrated an average increase in NSE of 0.07, an average increase in Kling–Gupta Efficiency (KGE) of 0.08, and an average reduction in mean absolute percent error (MAPE) of 0.12. The excellent performance of the proposed model is attributed to the following: (1) local attention mechanism assigns a higher weight to key global climate indices at a monthly scale, enhancing the ability of global and temporal attention mechanisms to capture the critical information at annual and interannual scales and (2) the global attention mechanism integrated with GAT effectively extracts crucial temporal and spatial information from precipitation and remotely-sensed elevation data. Furthermore, attention visualization reveals that various global climate indices contribute differently to runoff predictions across distinct months. The global climate indices corresponding to specific seasons or months should be selected to forecast the respective monthly runoff. Full article

With China’s rapid economic development in recent years, enhancing the sense of well-being among citizens has become a critical objective. However, the interests of various stakeholder groups are often overlooked in decision-making surrounding land use. In this study, Yongsheng County, Yunnan Province serves as a case study for land use scenario simulations. The equivalent factor method is combined with Participatory Rural Appraisal (PRA) to investigate the relationship between ecosystem multifunctionality (EMF) and the equity index of multiple stakeholder groups in various land use scenarios. We also explore whether an optimal combination of land use types exists. The results indicate that (1) The current ecosystem service value in Yongsheng County is primarily driven by climate regulation and biodiversity conservation, with a relatively high functional value index but a comparatively low equity index; (2) Different stakeholder groups mainly prioritize food production and ecosystem services impacting food production, such as water resource provision and climate regulation; (3) A land use allocation pattern of 20% farmland, 4% water bodies, 21% mixed forest, 20% coniferous forest, and 35% grassland appears to provide the optimal EMF index while simultaneously achieving the optimal equity index across stakeholder groups. This research may offer valuable insights for optimizing land use planning while taking into account the well-being of diverse stakeholder groups. It also may have practical implications for the formulation of innovative land use management strategies. Full article

Ensuring global food security amid mounting challenges, such as population growth, disease infestations, resource limitations, and climate change, is a pressing concern. Anticipated increases in food demand add further complexity to this critical issue. Plant pathogens, responsible for substantial crop losses (up to 41%) in major crops like wheat, rice, maize, soybean, and potato, exacerbate the situation. Timely disease detection is crucial, yet current practices often identify diseases at advanced stages, leading to severe infestations. To address this, remote sensing and Hyperspectral imaging (HSI) have emerged as robust and nondestructive techniques, exhibiting promising results in early disease identification. Integrating machine learning algorithms with image data sets enables precise spatial–temporal disease identification, facilitating timely detection, predictive modeling, and effective disease management without compromising fitness or climate adaptability. By harnessing these cutting-edge technologies and data-driven decision-making, growers can optimize input costs while achieving enhanced yields, making significant strides toward global food security in the face of climate change risks. This review will discuss some of the foundational concepts of remote sensing, several platforms used for remote sensing data collection, successful application of the approach, and its future perspective. Full article

This study investigates the impact of climate change on hydrological dynamics in the Ruzizi River Basin (RRB) by leveraging a combination of observational historical data and downscaled climate model outputs. The primary objective is to evaluate changes in precipitation, temperature, and water balance components under different climate scenarios. We employed a multi-modal ensemble (MME) approach to enhance the accuracy of climate projections, integrating historical climate data spanning from 1950 to 2014 with downscaled projections for the SSP2-4.5 and SSP5-8.5 scenarios, covering future periods from 2040 to 2100. Our methodology involved calibrating and validating the SWAT model against observed hydrological data to ensure reliable simulations of future climate scenarios. The model’s performance was assessed using metrics such as R², NSE, KGE, and PBIAS, which closely aligned with recommended standards. Results reveal a significant decline in mean annual precipitation, with reductions of up to 37.86% by mid-century under the SSP5-8.5 scenario. This decline is projected to lead to substantial reductions in surface runoff, evapotranspiration, and water yield, alongside a marked decrease in mean monthly stream flow, critically impacting agricultural, domestic, and ecological water needs. The study underscores the necessity of adaptive water resource management strategies to address these anticipated changes. Key recommendations include implementing a dynamic reservoir operation system, enhancing forecasting tools, and incorporating green infrastructure to maintain water quality, support ecosystem resilience, and ensure sustainable water use in the RRB. This research emphasizes the need for localized strategies to address climate-driven hydrological changes and protect future water resources. Full article

The Fen River Basin (FRB), an ecologically fragile region in China, exemplifies the intricate interplay between vegetation dynamics and both climatic and human-driven factors. This study leverages a 40-year (1982–2022) dataset, utilizing the kernel-based normalized difference vegetation index (kNDVI) alongside key climatic variables—rainfall (PRE), temperature (TMP), and solar radiation (SRAD)—to investigate vegetation variations and their drivers in the FRB, particularly in relation to the Grain for Green Program (GGP). Our analysis highlights significant greening across the FRB, with the kNDVI slope increasing by 0.0028 yr⁻¹ and green-covered areas expanding by 92.8% over the study period. The GGP facilitated the greening process, resulting in a notable increase in the kNDVI slope from 0.0005 yr⁻¹ to 0.0052 yr⁻¹ and a marked expansion in the area of significant greening from 24.6% to 95.8%. Regional climate shifts, characterized by increased warming, heightened humidity, and a slight rise in SRAD, have further driven vegetation growth, contributing 75%, 58.7%, and 23.6% to vegetation dynamics, respectively. Notably, the GGP has amplified vegetation’s sensitivity to climatic variables, with areas significantly impacted by multiple climate factors expanding from 4.8% to 37.5%. Specially, PRE is the primary climatic influence, impacting 71.3% of the pertinent regions, followed by TMP (60.1%) and SRAD (30%). The integrated effects of climatic and anthropogenic factors, accounting for 47.8% and 52.2% of kNDVI variations, respectively, collectively influence 96% of the region’s vegetation dynamics. These findings underscore the critical role of climate change and human interventions in shaping vegetation patterns and provide a robust foundation for refining ecological conservation strategies, particularly in the context of global warming and land-use policies. Full article

Human leishmaniasis is facing important epidemiological changes in Southern Europe, driven by increased urbanization, climate changes, emerging of new animal reservoirs, shifts in human behavior and a growing population of immunocompromised and elderly individuals. In this evolving epidemiological landscape, we analyzed the clinical and epidemiological characteristics of human leishmaniasis in the Tuscany region of Central Italy. Through a multicentric retrospective analysis, we collected clinical and demographic data about all cases of leishmaniasis recorded between 2018 and 2023. We observed 176 cases of human leishmaniasis, with 128 (72.7%) visceral leishmaniasis (VL) and 47 (26.7%) cutaneous leishmaniasis (CL). Among these, 92.2% of VL and 85.1% of CL cases were autochthonous. The cumulative incidence of autochthonous human leishmaniasis was 0.22 cases per 100,000 inhabitants in 2018, but reached 1.81/100,000 in 2023. We identified three main areas of transmission: around the city of Florence (North-East Tuscany), around Grosseto city (South-West Tuscany) and Elba Island. Our findings confirm that the epidemiology of leishmaniasis is undergoing significant changes in Central Italy. Awareness towards this emerging health threat and surveillance strategies need to be improved in order to reliably assess the disease’s burden. Further research is needed in a “One-Health” perspective, to clarify the epidemiological dynamics at the environmental, reservoir, vector and human levels. The role of climate change and specific climatic factors affecting the epidemiological patterns of human leishmaniasis should be assessed. Further knowledge in these fields would promote targeted control and prevention strategies at regional and national levels. Full article

The use of machine learning models for short-term network flow prediction has become increasingly widespread in recent years. Existing data-driven models are usually able to achieve good accuracy, but machine learning models are usually weakly interpretable and cannot provide clear decision guidance to decision makers in practical applications. Determining the input data shape of the model has an important impact on improving the interpretability of the model and understanding the relationship between the input factors and the application scenarios in the case. In this study, we used an integrated model for urban water demand prediction, which is based on the NeuralProphet model, and introduced the MIC method to screen the model input factors, which led to improvements in the accuracy of the prediction model. The aim of this work is also to improve the interpretability of water demand forecasting methodologies and the applicability of this model in the context of climate change and the complexity of urban water management, in order to help water managers make optimal water resource allocation decisions under different future scenarios. Full article

This study investigates the anatomical characteristics and growth patterns of Salix arctica and Salix herbacea, two prevalent dwarf shrub species in Iceland, to understand their responses to environmental changes. We employed optical and scanning electron microscopy methods and quantitative wood anatomy to analyze the stem and root structures of studied species. Additionally, we developed chronologies and assessed the climatic response of both the stem and root parts for both species. Our results reveal significant differences between the two species, with S. arctica exhibiting larger vessels and fibers compared to S. herbacea, both in stem and root. The growth trends differ between the species: S. arctica shows an overall increase, while S. herbacea exhibits a consistent decline. Both species’ individual parts generally follow these trends, though a recent decline has been observed in the last few years. Climatic responses also differ, highlighting specific climatic parameters influencing each species. S. arctica responds positively to warmer temperatures, while S. herbacea reacts positively to increased precipitation but struggles with rising temperatures, highlighting its role as a drought indicator species. Soil erosion driven by volcanic materials and extreme climates significantly impacts shrub growth, causing rapid changes in growth ring widths and vessel sizes. Understanding these impacts is vital for improving sampling methods in polar environments. This study highlights the importance of integrated wood anatomical studies in comprehending the ecological consequences of climate change on Arctic shrubs, providing new insights into the complexity of shrub expansion both below and above ground. Full article

The Arabian Gulf, a semi-enclosed basin in the Middle East, connects to the Indian Ocean through the Strait of Hormuz and is surrounded by seven arid countries. This study examines the water cycle of the Gulf and its surrounding areas using data from the GRACE and GRACE Follow-On missions, along with ERA5 atmospheric reanalysis data, from 05/2002 to 05/2017 and from 07/2018 to 12/2023. Our findings reveal a persistent water deficit due to high evaporation rates, averaging 370 ± 3 km³/year, greatly surpassing precipitation, which accounts for only 15% of the evaporative loss. Continental runoff provides one-fifth of the needed water, while the remaining deficit, approximately 274 ± 10 km³/year, is balanced by net inflow of saltwater from the Indian Ocean. Seasonal variations show the lowest net inflow of 26 ± 49 km³/year in March and the highest of 586 ± 53 km³/year in November, driven by net evaporation, continental input, and changes in the Gulf’s water budget. This study highlights the complex hydrological dynamics influenced by climate patterns and provides a baseline for future research in the region, which will be needed to quantify the expected changes in the hydrological cycle due to climate change. Full article

The increasing frequency of urban flooding, driven by global climate change, poses significant threats to the safety and resilience of urban rail transit systems. This study systematically examines the cascading failure processes and resilience of these networks under flood conditions, with a specific focus on the Shanghai Metro. A comprehensive resilience evaluation model was developed by integrating geographic information, static network characteristics, and dynamic passenger flow indicators. This study employs an improved Coupled Map Lattice (CML) model to simulate cascading failures by considering the coupling effects of station centrality, geographic elevation, and passenger flow dynamics. The results indicate that stations with higher degrees of centrality are more likely to trigger rapid cascading failures across the network. However, incorporating dynamic passenger flow and geographic elevation data helps mitigate these effects, emphasizing the need for multi-dimensional resilience strategies. The findings provide valuable insights for urban transit management, offering a scientific foundation for developing targeted disaster response strategies to enhance network resilience against floods. This study advances our understanding of the vulnerability of urban rail transit systems and offers practical guidance for improving disaster preparedness in urban transportation infrastructure. Full article

The low-frequency sea level variability in the South China Sea (SCS) is examined using high-resolution regional ocean model simulations that span the last six decades. The analysis reveals interdecadal oscillations with a periodicity of 12–13 years as the dominant mode of sea level variability in the SCS. The fluctuations in the Luzon Strait transport (LST) are identified as primary drivers of interannual to interdecadal sea level variability, rather than atmospheric forcing within the SCS. Fourier spectrum analysis is employed to investigate the association between SCS sea level variability and the Interdecadal Pacific Oscillation (IPO), using principal components of SCS sea surface height anomalies, wind stress curl, wind stress components, net short wave flux, as well as the LST and various climate indices. The variations in the SCS sea level are driven by the IPO, which modifies the LST and ocean heat content, impacting the steric sea level. Full article

Abstract: This study addresses the current difficulties in accurately controlling the indoor temperature of double-skin facades (DSFs), and its optimization, with a focus on the window opening angles of double-skin facades. The Spearman correlation coefficient method was used to select the main meteorological factors, including outdoor temperature, dew point temperature, scattered radiation, direct radiation, and window opening angle. A modified random forest algorithm was used to construct the optimization model and 80% of the data were used for model training. In the experiments, the average accuracy of the optimization model was as high as 93.5% for all window opening angles. This study provides a data-driven method for application to double-skin facades, which can effectively determine and control the window opening angles of double-skin facades to achieve energy saving and emission reduction, reduce indoor temperature, improve comfort, and provide a practical basis for decision-making. Future research will further explore the applicability and accuracy of the model under different climatic conditions. Full article

This study investigates the influence of digital infrastructure on creating an organizational climate conducive to digital transformation. It highlights the critical role of data lakes, network connectivity, and a shared digital language in cultivating an environment that aligns managerial objectives with employee engagement in digital initiatives. Through grounded theory methodology, the research uncovers how robust digital infrastructure can bridge the gap between leadership’s digital aspirations and employees’ practical use of digital tools, promoting data-driven decision-making and improving organizational performance. The findings suggest that a well-developed digital infrastructure is essential for fostering a digital climate that supports strategic business goals and enhances competitive advantage. Full article

The Vallparadís Section encompasses various geological layers that span a significant chronological range, extending from the latest Early Pleistocene to the early Middle Pleistocene, covering a timeframe from approximately 1.2 to 0.6 Ma. This period holds particular importance, as it coincides with a significant climatic transition known as the Early–Middle Pleistocene Transition, a pivotal phase in Quaternary climatic history. This transition, marked by the shift from a 41,000-year obliquity-driven climatic cycle to a 100,000-year precession-forced cyclicity, had profound effects on the Calabrian carnivorous mammal communities. Notably, the once diverse carnivore guild began to decline across Europe during this period, with their last documented occurrences coinciding with those found within the Vallparadís Section (e.g., Megantereon or Xenocyon). Concurrently, this period witnessed the initial dispersals of African carnivorans into the European landscape (e.g., steppe lions), marking a significant shift in the composition and dynamics of the region’s carnivorous fauna. Full article

The Pinus halepensis (Aleppo pine) forests prevailing in the western part of the Mediterranean Basin are amongst the most severely affected by fire due to their inherent flammability. Our understanding of the environmental factors driving post-fire community dynamics is currently limited by the lack of time-series data at temporal scales. In this present study, we analyzed a chronosequence of Greek Aleppo pine forests spanning a post-fire period of 65 years. Our goal is to explore the role of post-fire age, altitude, exposure, slope level, parent-rock material, rock cover, and cover of evergreen sclerophyllous shrubs (maquis) on plant assemblage diversity (species richness and Menhinick’s diversity index) and composition. Post-fire age had a significant effect on taxonomic distinctness and community turnover but not on species richness. Taxonomic distinctness increased with post-fire age due to a higher prevalence of the families Fabaceae, Asteraceae, and Poaceae during the early post-fire period. Maquis cover was significantly associated with Menhinick’s diversity index, taxonomic distinctness, and community turnover. Exposure and slope influenced only Menhinick’s diversity index. The turnover in species composition was primarily driven by the geographical proximity of the forests and secondarily by post-fire age and the maquis cover. This highlights the importance of the initial floristic composition in the process of autosuccession after a fire in Mediterranean-climate ecosystems. Full article