Search Results (2,239)

Integrated hydrological modeling plays a crucial role in advancing sustainable water resource management, particularly in regions facing seasonal and extreme precipitation events. However, comprehensive studies that assess hydrological variability in temperate river basins remain limited. This study addresses this gap by evaluating the performance of the Soil and Water Assessment Tool (SWAT) in simulating streamflow, water balance, and seasonal hydrological dynamics in the Chikugo River Basin, Kyushu Island, Japan. The basin, originating from Mount Aso and draining into the Ariake Sea, is subject to frequent typhoons and intense rainfall, making it a critical case for sustainable water governance. Using the Sequential Uncertainty Fitting Version 2 (SUFI-2) approach, we calibrated the SWAT model over the period 20072–2021. Water balance analysis revealed that baseflow plays dominant roles in basin hydrology which is essential for agricultural and domestic water needs by providing a stable groundwater contribution despite increasing precipitation and varying water demand. These findings contribute to a deeper understanding of hydrological behavior in temperate catchments and offer a scientific foundation for sustainable water allocation, planning, and climate resilience strategies. Full article

Vegetation dynamics are complexly influenced by multiple factors such as climate, human activities, and topography. In recent years, the frequency, intensity, and diversity of human activities have increased, placing substantial pressure on the growth of vegetation. Arid and semi-arid regions are particularly sensitive to climate change, and climate change and large-scale ecological restoration have led to significant changes in the dynamic of dryland vegetation. However, few studies have explored the nonlinear relationships between these factors and vegetation dynamic. In this study, we integrated trend analysis (using the Mann–Kendall test and Theil–Sen estimation) and machine learning algorithms (XGBoost-SHAP model) based on long time-series remote sensing data from 2001 to 2020 to quantify the nonlinear response patterns and threshold effects of bioclimatic variables, topographic features, soil attributes, and anthropogenic factors on vegetation dynamic. The results revealed the following key findings: (1) The kNDVI in the study area showed an overall significant increasing trend (p < 0.01) during the observation period, of which 26.7% of the area showed a significant increase. (2) The water content index (Bio 23, 19.6%), the change in land use (15.2%), multi-year average precipitation (pre, 15.0%), population density (13.2%), and rainfall seasonality (Bio 15, 10.9%) were the key factors driving the dynamic change of vegetation, with the combined contribution of natural factors amounting to 64.3%. (3) Among the topographic factors, altitude had a more significant effect on vegetation dynamics, with higher altitude regions less likely to experience vegetation greening. Both natural and anthropogenic factors exhibited nonlinear responses and interactive effects, contributing to the observed dynamic trends. This study provides valuable insights into the driving mechanisms behind the condition of vegetation in arid and semi-arid regions of China and, by extension, in other arid regions globally. Full article

In early May 2025, extreme wildfires swept across Manitoba, Canada, fueled by unseasonably warm temperatures, prolonged drought, and stressed vegetation. We explore how multi-source satellite indicators—such as anomalies in snow cover, precipitation, temperature, vegetation indices, and soil moisture in April–May—jointly signal landscape preconditioning for fire, highlighting the potential of these compound anomalies to inform fire risk awareness in boreal regions. Results indicate that rainfall deficits and diminished snowpack significantly reduced soil moisture, which subsequently decreased vegetative greenness and created a flammable environment prior to ignition. This concept captures how multiple moderate anomalies, when occurring simultaneously, can converge to create high-impact fire conditions that would not be flagged by individual thresholds alone. These findings underscore the importance of integrating climate and biosphere anomalies into wildfire risk monitoring to enhance preparedness in boreal regions under accelerating climate change. Full article

Karst groundwater systems are critical to water supply and ecological sustainability in northern China, yet their heterogeneity poses challenges for flow characterization. The Yuquan Mountain (YM) Spring, historically a major karst spring in western Beijing, has experienced persistent drying, raising concerns about its recharge and flow mechanisms. This study integrates published isotope data, spatial distributions of Na⁺ and Cl⁻ as hydrochemical tracers, groundwater age estimates, and geophysical survey results to assess the recharge sources and flow pathways within the YM Spring catchment area. The analysis identifies two major recharge zones: the Tanzhesi area, primarily recharged by direct infiltration of precipitation through exposed carbonate rocks, and the Junzhuang area, which receives mixed recharge from rainfall and Yongding River seepage. Three potential flow pathways are proposed, including shallow flow along faults and strata, and a deeper, speculative route through the Jiulongshan-Xiangyu syncline. The synthesis of multiple lines of evidence leads to a refined conceptual model that illustrates how geological structures govern recharge, flow, and discharge processes in this karst system. These findings not only enhance the understanding of subsurface hydrodynamics in complex geological settings but also provide a scientific basis for future spring restoration planning and groundwater management strategies in the regions. Full article

The cement sheath is critical for ensuring the long-term safety and operational efficiency of oil and gas wells. However, complex geological conditions and operational stresses during production can induce cement sheath deterioration and cracking, leading to reduced zonal isolation, diminished hydrocarbon recovery, and elevated operational expenditures. This study investigates the development of a novel microbial self-healing well cement slurry system, employing fly ash as microbial carriers and sustained-release microcapsules encapsulating calcium sources and nutrients. Systematic evaluations were conducted, encompassing microbial viability, cement slurry rheology, fluid loss control, anti-channeling capability, and the mechanical strength, permeability, and microstructural characteristics of set cement stones. Results demonstrated that fly ash outperformed blast furnace slag and nano-silica as a carrier, exhibiting superior microbial loading capacity and viability. Optimal performance was observed with additions of 3% microorganisms and 3% microcapsules to the cement slurry. Microscopic analysis further revealed effective calcium carbonate precipitation within and around micro-pores, indicating a self-healing mechanism. These findings highlight the significant potential of the proposed system to enhance cement sheath integrity through localized self-healing, offering valuable insights for the development of advanced, durable well-cementing materials tailored for challenging downhole environments. Full article

Corydalis yanhusuo W. T. Wang, commonly known as Yanhusuo, is an important and rare medicinal plant resource in China. Its habitat integrity is facing severe challenges due to climate change and human activities. Establishing an integrative quality zoning system for this species is of significant practical importance for resource conservation and adaptive management. This study integrates multiple data sources, including 121 valid distribution points, 37 environmental factors, future climate scenarios (SSP126 and SSP585 pathways for the 2050s and 2090s), and measured content of tetrahydropalmatine (THP) from 22 sampling sites. A predictive framework for habitat suitability and spatial distribution of effective components was constructed using a multi-model coupling approach (MaxEnt, ArcGIS spatial analysis, and co-kriging method). The results indicate that the MaxEnt model exhibits high prediction accuracy (AUC > 0.9), with the dominant environmental factors being the precipitation of the wettest quarter (404.8~654.5 mm) and the annual average temperature (11.8~17.4 °C). Under current climatic conditions, areas of high suitability are concentrated in parts of Central and Eastern China, including the Sichuan Basin, the middle–lower Yangtze plains, and coastal areas of Shandong and Liaoning. In future climate scenarios, the center of suitable areas is predicted to shift northwestward. The content of THP is significantly correlated with the mean diurnal temperature range, temperature seasonality, and the mean temperature of the wettest quarter (p < 0.01). A comprehensive assessment identifies the Yangtze River Delta region, Central China, and parts of the Loess Plateau as the optimal integrative quality zones. This research provides a scientific basis and decision-making support for the sustainable utilization of C. yanhusuo and other rare medicinal plants in China. Full article

The division of the flood season is of great significance for the precise operation of water conservancy projects, flood control and disaster reduction, and the rational allocation of water resources, alleviating the contradiction of the uneven spatial and temporal distribution of water resources. The single weighting method can only determine the weight of the flood season division indicators from a certain perspective and cannot comprehensively reflect the time-series attributes of the indicators. This study proposes a Flood Season Division Model based on the Goose Optimization Algorithm and Minimum Deviation Combined Weighting (FSDGOAMDCW). The model uses the Goose Optimization Algorithm (GOA) to solve the Minimum Deviation Combination model, integrating weights from two subjective methods (Expert Scoring and G1) and three objective methods (Entropy Weight, CV, and CRITIC). Combined with the Set Pair Analysis Method (SPAM), it realizes comprehensive flood season division. Based on daily precipitation data of the Nandujiang River (1961–2022), the study determines its flood season from 1 May to 30 October. Comparisons show that: ① GOA converges faster than the Genetic Algorithm, stabilizing at T = 5 and achieving full convergence at T = 24; and ② The model’s division results have the smallest Intra-Class Differences, avoiding indistinguishability between flood and non-flood seasons under special conditions. This research aims to support flood season division studies in tropical islands. Full article

The development of the Great Wall National Cultural Park has unleashed the potential for integrating cultural and tourism development along the Great Wall. However, ICH along the Great Wall, a key part of its cultural identity, suffers from low recognition and a mismatch between protection and development efforts. This study analyzes provincial-level and above ICH along Hebei’s Great Wall using geospatial tools and the Geographical Detector model to explore distribution patterns and influencing factors, while Geographically Weighted Regression is utilized to reveal spatial heterogeneity. It tests two hypotheses: (H1) ICH shows a clustered pattern; (H2) economic factors have a greater impact than cultural and natural factors. Key findings show: (1) ICH distribution is numerically balanced north–south but spatially uneven, with dense clusters in the south and scattered patterns in the north. (2) ICH and crafts cluster significantly, while dramatic balladry spreads evenly, and other categories are random. (3) Average annual temperature and precipitation have the greatest impact on ICH distribution, with the factors ranked as: natural > cultural > economic. Multidimensional interactions show significant enhancement effects. (4) Influencing factors vary spatially. Population density, transport, temperature, and traditional villages are positively related to ICH. Elevation, precipitation, tourism, and cultural institutions show mixed effects across regions. These insights support targeted ICH conservation and sustainable development in the Great Wall cultural corridor. Full article

Transmission towers are particularly vulnerable to extreme wind events, which can lead to structural damage or collapse, thereby compromising the stability of power transmission systems. Enhancing the wind-resistant capacity of these towers is therefore critical for improving the reliability and resilience of electrical infrastructure. This study utilizes finite element analysis (FEA) to evaluate the structural response of a 220 kV transmission tower subjected to fluctuating wind loads, effectively capturing the dynamic characteristics of wind-induced forces. A comprehensive dynamic analysis is conducted to account for uncertainties in wind loading and variations in wind direction. Through this approach, this study identifies the most critical wind angle and local structural weaknesses, as well as determines the threshold wind speed that precipitates structural collapse. To improve structural resilience, a concurrent multi-scale modeling strategy is adopted. This allows for localized analysis of vulnerable components while maintaining a holistic understanding of the tower’s global behavior. To mitigate failure risks, the traditional perforated plate reinforcement technique is implemented. The reinforcement’s effectiveness is evaluated based on its impact on load-bearing capacity, displacement control, and stress redistribution. Results reveal that the critical wind direction is 45°, with failure predominantly initiating from instability in the third section of the tower leg. Post-reinforcement analysis demonstrates a marked improvement in structural performance, evidenced by a significant reduction in top displacement and stress intensity in the critical leg section. Overall, these findings contribute to a deeper understanding of the wind-induced fragility of transmission towers and offer practical reinforcement strategies that can be applied to enhance their structural integrity under extreme wind conditions. Full article

Theranostic materials, which combine therapeutic and diagnostic capabilities, represent a promising advancement in cancer treatment by improving both the precision and personalization of therapies. Recently, metal peroxides (MePOs) have attracted significant interest from researchers for their potential use in both cancer diagnosis and therapy. This review provides an overview of recent developments in the application of MePOs for innovative cancer treatment strategies. The unique properties of MePOs, such as oxygen generation, are highlighted for their potential to improve therapeutic outcomes, especially in hypoxic tumor microenvironments. Initially, methods for MePO synthesis are briefly discussed, including hydrolyzation–precipitation, reversed-phase microemulsion, and sonochemical techniques, emphasizing the role of surfactants in regulating the particle size and enhancing bioactivity. Next, we discuss the main therapeutic approaches where MePOs have shown promise. These applications include chemotherapy, photodynamic therapy (PDT), immunotherapy, and radiation therapy. Overall, we focus on integrating MePOs into theranostic platforms to enhance cancer treatment and enable diagnostic imaging for improved clinical outcomes. Finally, we discuss potential future research directions that could lead to clinical translation and the development of advanced medicines. Full article

For effective groundwater resource management, it is essential to model the dynamic behaviour of aquifers in response to rainfall. Here, a methodological approach using a recurrent neural network, specifically a Long Short-Term Memory (LSTM) network, is used to model groundwater levels of the shallow porous aquifer in Southern Italy. This aquifer is recharged by local rainfall, which exhibits minimal variation across the catchment in terms of volume and temporal distribution. To gain a deeper understanding of the complex interactions between precipitation and groundwater levels within the aquifer, we used water level data from six wells. Although these wells were not directly correlated in terms of individual measurements, they were geographically located within the same shallow aquifer and exhibited a similar hydrogeological response. The trained model uses two variables, rainfall and groundwater levels, which are usually easily available. This approach allowed the model, during the training phase, to capture the general relationships and common dynamics present across the different time series of wells. This methodology was employed despite the geographical distinctions between the wells within the aquifer and the variable duration of their observed time series (ranging from 27 to 45 years). The results obtained were significant: the global model, trained with the simultaneous integration of data from all six wells, not only led to superior performance metrics but also highlighted its remarkable generalization capability in representing the hydrogeological system. Full article

This study applies the Weighted Overlay Analysis (WOA) method integrated with GIS to assess landslide susceptibility along Al Figrah Road in Al-Madinah Al-Munawarah, western Saudi Arabia. Seven key conditioning factors, elevation, slope, aspect, drainage density, lithology, soil type, and precipitation were integrated using high-resolution remote sensing data and expert-assigned weights. The output susceptibility map categorized the region into three zones: low (93.5 million m²), moderate (271.2 million m²), and high risk (33.1 million m²). Approximately 29% of the road corridor lies within the low-risk zone, 48% in the moderate zone, and 23% in the high-risk zone. Ten critical sites with potential landslide activity were detected along the road, correlating well with the high-risk zones on the map. Structural weaknesses in the area, such as faults, joints, foliation planes, and shear zones in both igneous and metamorphic rock units, were key contributors to slope instability. The findings offer practical guidance for infrastructure planning and geohazard mitigation in arid, mountainous environments and demonstrate the applicability of WOA in data-scarce regions. Full article

Stable oxygen isotopes in tree rings (δ¹⁸O) serve as important proxies for climate change and offer unique advantages for climate reconstruction in arid and semi-arid regions. We established an annual δ¹⁸O chronology spanning 1964–2023 using Juniperus excelsa tree-ring samples collected from the Alborz Mountains in Iran. We analyzed relationships between δ¹⁸O and key climate variables: precipitation, temperature, Palmer Drought Severity Index (PDSI), vapor pressure (VP), and potential evapotranspiration (PET). Correlation analysis reveals that tree-ring δ¹⁸O is highly sensitive to hydroclimatic variations. Tree-ring cellulose δ¹⁸O shows significant negative correlations with annual total precipitation and spring PDSI, and significant positive correlations with spring temperature (particularly maximum temperature), April VP, and spring PET. The strongest correlation occurs with spring PET. These results indicate that δ¹⁸O responds strongly to the balance between springtime moisture supply (precipitation and soil moisture) and atmospheric evaporative demand (temperature, VP, and PET), reflecting an integrated signal of both regional moisture availability and energy input. The pronounced response of δ¹⁸O to spring evaporative conditions highlights its potential for capturing high-resolution changes in spring climatic conditions. Our δ¹⁸O series remained stable from the 1960s to the 1990s, but showed greater interannual variability after 2000, likely linked to regional warming and climate instability. A comparison with the δ¹⁸O variations from the eastern Alborz Mountains indicates that, despite some differences in magnitude, δ¹⁸O records from the western and eastern Alborz Mountains show broadly similar variability patterns. On a larger climatic scale, δ¹⁸O correlates significantly and positively with the Niño 3.4 index but shows no significant correlation with the Arctic Oscillation (AO) or the North Atlantic Oscillation (NAO). This suggests that ENSO-driven interannual variability in the tropical Pacific plays a key role in regulating regional hydroclimatic processes. This study confirms the strong potential of tree-ring oxygen isotopes from the Alborz Mountains for reconstructing hydroclimatic conditions and high-frequency climate variability. Full article

Multi-source fusion techniques have emerged as cutting-edge approaches for spatial precipitation estimation, yet they face persistent accuracy limitations, particularly under extreme conditions. Machine learning offers new opportunities to improve the precision of these estimates. To bridge this gap, we propose a hybrid artificial neural network–geographically weighted regression (ANN–GWR) model that synergizes event recognition and quantitative estimation. The ANN module dynamically identifies precipitation events through nonlinear pattern learning, while the GWR module captures location-specific relationships between multi-source data for calibrated rainfall quantification. Validated against 60-year historical data (1960–2020) from China’s Yongding River Basin, the model demonstrates superior performance through multi-criteria evaluation. Key results reveal the following: (1) the ANN-driven event detection achieves 10% higher accuracy than GWR, with a 15% enhancement for heavy precipitation events (>50 mm/day) during summer monsoons; (2) the integrated framework improves overall fusion accuracy by more than 10% compared to conventional GWR. This study advances precipitation estimation by introducing an artificial neural network into the event recognition period. Full article

Rapid socio-economic development and the impact of human activities have exerted tremendous pressure on the groundwater system of the Dawen River Basin (DRB), the largest tributary in the middle and lower reaches of the Yellow River. Hydrochemical studies on the DRB have largely centered on the upstream Muwen River catchment and downstream Dongping Lake, with some focusing solely on karst groundwater. Basin-wide evaluations suggest good overall groundwater quality, but moderate to severe contamination is confined to the lower Dongping Lake area. The hydrogeologically complex mid-reach, where the Muwen and Chaiwen rivers merge, warrants specific focus. This region, adjacent to populous areas and industrial/agricultural zones, features diverse aquifer systems, necessitating a thorough analysis of its hydrochemistry and origins. This study presents an integrated hydrochemical, isotopic investigation and EWQI evaluation of groundwater quality and formation mechanisms within the multiple groundwater types of the central DRB. Central DRB groundwater has a pH of 7.5–8.2 (avg. 7.8) and TDSs at 450–2420 mg/L (avg. 1075.4 mg/L) and is mainly brackish, with Ca²⁺ as the primary cation (68.3% of total cations) and SO₄²⁻ (33.6%) and NO₃⁻ (28.4%) as key anions. The Piper diagram reveals complex hydrochemical types, primarily HCO₃·SO₄-Ca and SO₄·Cl-Ca. Isotopic analysis (δ²H, δ¹⁸O) confirms atmospheric precipitation as the principal recharge source, with pore water showing evaporative enrichment due to shallow depths. The Gibbs diagram and ion ratios demonstrate that hydrochemistry is primarily controlled by silicate and carbonate weathering (especially calcite dissolution), active cation exchange, and anthropogenic influences. EWQI assessment (avg. 156.2) indicates generally “good” overall quality but significant spatial variability. Pore water exhibits the highest exceedance rates (50% > Class III), driven by nitrate pollution from intensive vegetable cultivation in eastern areas (Xiyangzhuang–Liangzhuang) and sulfate contamination from gypsum mining (Guojialou–Nanxiyao). Karst water (26.7% > Class III) shows localized pollution belts (Huafeng–Dongzhuang) linked to coal mining and industrial discharges. Compared to basin-wide studies suggesting good quality in mid-upper reaches, this intensive mid-reach sampling identifies critical localized pollution zones within an overall low-EWQI background. The findings highlight the necessity for aquifer-specific and land-use-targeted groundwater protection strategies in this hydrogeologically complex region. Full article