Search Results (1,387)

Major floods following prolonged drought can substantially alter ground cover, soil stability and pasture condition, yet longer-term trajectories of rangeland recovery remain poorly understood. This study assessed land condition at 62 monitoring sites in the Flinders River catchment, north-west Queensland, following the 2019 flood, with re-assessment in 2024. Land condition was evaluated using the A–B–C–D framework alongside rainfall, satellite-derived bare ground, land type, distance to drainage and flood-extent data. In March 2019, 79% of sites were classified as C or D, reflecting the combined influence of prolonged drought and flood disturbance. By 2024, 30 of 62 sites (48%) had improved by at least one class, and median condition shifted from class C to B, though change was spatially variable. Initial land condition was negatively correlated with net change (ρ = −0.47, p < 0.001, n = 62). Rainfall showed no significant association with land condition or net change among sites, whereas dry-season bare ground was significantly associated across multiple temporal windows. These findings indicate that land condition change following drought–flood disturbance is likely associated with site-level factors, particularly residual pasture structure and soil surface condition, and highlight the value of combining field and satellite data for rangeland monitoring following extreme climate events. Full article

In carbonate petroleum reservoirs, permeability impairment caused by asphaltene precipitation and deposition remains a major challenge that limits long-term productivity. This study introduces a green polymeric nanocomposite (KCl/SiO₂/Xanthan/Origanum vulgare, NCs) designed to control interfacial dynamics and preserve flow capacity in carbonate formations. Using a multi-technique approach—interfacial tension (IFT) analysis, atomic force microscopy (AFM), and rock-core, fluid-flooding experiments at simulated subsurface conditions—the NCs’ abilities were evaluated in terms of their potential to modify properties at fluid–fluid and fluid–rock interfaces. The NCs increased the CO₂–brine/oil IFT slope in certain pressure regions by up to 40.77%. These results indicate competitive adsorption that stabilizes interfaces. Adsorption isotherms confirmed a monolayer mechanism with a high capacity of 294.12 mg/g. AFM topographic mapping revealed order-of-magnitude changes in surface roughness (reductions in average roughness by ~75%, root-mean-square by ~83%, peak-to-valley by ~93%). These results directly link nanoscale smoothing to reduced capillary pinning. Core flooding tests demonstrated that NCs treatment decreased formation damage by up to 67.45% at 4000 psi, maintaining a high permeability ratio (k/k_i = 0.87) and preserving porosity (φ/φ_i = 0.887, representing 88.7% porosity retention). These results establish that the studied NCs coherently manipulate fluid physics in relation to molecular adsorption and macroscopic permeability. Consequently, these NCs offer a sustainable, high-performance strategy for flow assurance and formation damage control in geological and geothermal reservoirs. Full article

Agricultural nonpoint source pollution mainly causes lake eutrophication in China, largely affected by variations in crop distribution. To analyze the multiscale relationships between the long-term evolution of cropping patterns and lake water quality at the macro scale, this study analyzed nationwide datasets for 2000 and 2020 covering 420 relatively large lakes. We systematically examined the spatial restructuring of six major food and cash crops—wheat, rice, maize, soybean, peanut, and rapeseed—and evaluated their multiscale associations with lake total nitrogen (TN) and total phosphorus (TP) concentrations and how these associations changed over time. The results showed the following: (1) From 2000 to 2020, the spatial distributions of the six major crops underwent substantial restructuring. The dominant production areas of rice, wheat, and maize were maintained or further reinforced, whereas soybean, rapeseed, and peanut exhibited varying degrees of regional redistribution and localized concentration. (2) Lake water quality differed between the flood and non-flood seasons. TN exhibited pronounced seasonal differences between the two study periods, whereas temporal changes in TP were generally limited; both nutrients nevertheless showed marked regional heterogeneity among the five major lake regions. (3) The crop–water quality relationship exhibits significant scale dependence and crop-specific variations. The XGBoost model demonstrated a certain degree of out-of-field (OOF) predictive capability for both TN and TP, with OOF R² values of 0.448 and 0.447, respectively. For TN, the highest OOF R² values were observed in the 1000–2000 m buffer zone in both 2000 and 2020; the optimal prediction scale for TP shifted from 1000–2000 m in 2000 to 2000–5000 m in 2020. SHAP results showed that corn maintained a high and relatively stable predictive importance in the TN model, followed by wheat, peanuts, and rice; in the TP model, corn and rapeseed were the crop predictors with the highest relative SHAP importance. PDP results further indicate that there are generally nonlinear or non-monotonic relationships between different crop coverage proportions and TN and TP. (4) Pronounced spatial heterogeneity was observed across the five lake regions. The Eastern Plain Lake Region was characterized by associations involving multiple crops, whereas maize was the most prominent crop in the Northeast Plain and Mountain Lake Region. In the Inner Mongolia–Xinjiang Plateau Lake Region, maize predominated, with wheat and rapeseed also showing notable importance. In the Tibetan Plateau Lake Region, TN was associated with multiple crops, whereas TP was primarily related to maize and rapeseed. The Yunnan–Guizhou Plateau Lake Region exhibited particularly strong scale-dependent differences. This study provides a nationwide analytical framework for comparing the scale differences and regional variations in the statistical associations between the spatial distribution of crops and lake water quality at the specific crop level. The findings can provide a scientific basis for formulating differentiated agricultural nonpoint source pollution control strategies that are adapted to the evolving characteristics of crop planting structures. Full article

Urban resilience is a critical component of disaster risk governance, particularly in regions exposed to multiple natural, technological, and environmental hazards. This study proposes an integrated spatial decision-support framework that combines disaster risk, resilience capacity, and institutional responsibilities to identify intervention priorities. Using Zonguldak Province, Türkiye, as a case study, seven risk and six resilience indicators were evaluated across eight districts. The Analytic Hierarchy Process (AHP) was used to determine criterion weights, while VIKOR was applied to rank districts according to their relative intervention priorities. Institutional responsibility mapping was additionally used to relate major risk types to responsible institutions and priority districts. The results identified landslide, flood, and earthquake as the most influential risk criteria, while emergency response capacity and healthcare accessibility were the leading resilience criteria. VIKOR ranked Gökçebey and Devrek as the two highest-priority districts, reflecting the combined effect of substantial hazard exposure and limited resilience capacity. Sensitivity analyses showed that these leading priorities remained stable under alternative VIKOR parameter values and reasonable changes in criterion weights. Overall, the findings demonstrate that intervention priorities cannot be explained by hazard levels alone but should be evaluated together with local resilience capacities and institutional responsibilities. The proposed framework provides a modular and transferable approach for supporting spatially differentiated disaster risk reduction and resource allocation in multi-hazard contexts. Full article

Against the backdrop of global climate change, drought–flood abrupt alternation (DFAA) has become a major compound climate extreme threatening ecosystem stability and sustainable watershed management. This study investigated the spatiotemporal characteristics and ecological responses of DFAA in the Jialing River Basin, China, using meteorological and hydrological observations from 1971 to 2020. DFAA events were identified using the Standardized Weighted Average Precipitation Index (SWAP) and run theory, and their spatiotemporal heterogeneity was characterized using spatial autocorrelation analysis. The Long-duration DFAA Index (LDFAI) was derived using the WEP-L distributed hydrological model. Ecological responses during 2000–2020 were evaluated by integrating the Remote Sensing Ecological Index (RSEI), grey relational analysis, and a Copula-based conditional probability model. The results showed that drought-to-flood events exhibited stronger spatial clustering than flood-to-drought events. Ecosystem responses showed significant lag effects, averaging 6.9 months for spring–summer events and 5 months for summer–autumn events, with greater sensitivity during the summer–autumn period. Under DTF events, the probability of maintaining relatively high ecological quality was significantly higher than under FTD events, whereas FTD events were associated with a higher probability of ecological degradation. Under compound scenarios, consecutive same-type events were more conducive to ecosystem stability, while alternating sequences of different event types significantly amplified negative ecological stress and represented high-risk scenarios for ecological degradation. These findings provide scientific support for adaptive watershed management, ecological restoration, and climate change adaptation in drought–flood-prone regions. Full article

Geochemical proxies in coastal sediments are widely applied to reconstruct past climatic and environmental changes, yet their sensitivity to climate versus localized depositional processes remains debated in dynamic land–sea interfaces. Here, we present major, trace, and rare earth element (REE) data from two Holocene sediment cores (QX01 and QX02) from the western coast of Bohai Bay to decouple provenance, weathering, and hydrodynamic controls. Provenance-sensitive trace element ratios (La/Sc and Th/Sc) and REE fractionation patterns indicate exceptional source stability dominated by the Yellow River and adjacent cratonic catchments throughout the Holocene, eliminating provenance shifts as a confounding variable. Since ~8 ka, an overarching upward increase in raw chemical index of alteration (CIA), accompanied by increasing Al/Si ratios and grain sizes, broadly aligned with the warm and humid Holocene Climate Optimum. Crucially, a highly significant linear relationship between grain size, Al/Si and CIA (R² > 0.78) demonstrates that raw CIA variations were decisively modulated by sea-level-driven hydrodynamic sorting during the Holocene transgression, which shifted the depositional setting from high-energy fluvial regimes to low-energy marine settings, preferentially trapping fine-grained, clay-hosted aluminosilicates with inherently high CIA values. During the late Holocene (~5 ka to present), stabilizing sea-level conditions shifted the raw records into an elevated plateau punctuated by high-frequency fluctuations and localized geochemical anomalies, reflecting a dynamic interface sensitive to episodic fluvial floods or tidal/storm reworking that periodically introduced coarser, quartz-rich detritus. The resulting sorting-corrected CIA_C removes the transgressive clay-trapping artifact and reveals a broad, subdued weathering plateau between ~8 ka and 4 ka BP, consisting with previous Chinese Loess Plateau weathering intensity. These findings highlight that in dynamic marginal-marine sinks, raw silicate weathering indices reflect physical sorting overprints, and hydrodynamic detrending is essential to extract genuine continental paleoclimate signals. Full article

Rice is a globally important staple crop and a major dietary source of inorganic arsenic (As). Compared with upland crops, flooded rice cultivation profoundly alters soil redox conditions, making paddy soils one of the most active agricultural interfaces for As mobilization, transformation, and food-chain transfer. While previous research has primarily focused on total As and inorganic As [As(III)/As(V)], methylated and thiolated As species also carry critical agronomic and health implications. Dimethylarsinic acid (DMA) can accumulate in grain and induce straighthead disease, whereas dimethylmonothioarsenate (DMMTA) shows substantially higher toxicity and uptake potential; DMMTA root uptake can be approximately 10 times higher than DMA, and its straighthead-inducing potency can exceed DMA by more than fivefold. This review synthesizes the sources, biogeochemical transformations, plant uptake, grain accumulation, safety assessment, and management implications of As along the paddy soil–rice–grain continuum. Particular emphasis is placed on how water regimes, redox potential, Fe/Mn/Al oxides, sulfur cycling, dissolved organic matter (DOM), microbial functional genes, and crop genotypes regulate diverse As species. Quantitative evidence indicates that alternate wetting and drying (AWD) can reduce grain total As and inorganic As by medians of 32% and 22%, respectively, but may increase grain cadmium (Cd) by a median of 58%; meanwhile, DMA and DMMTA can account for approximately 10–90% and 1–21% of total grain As, respectively, emphasizing that grain-As risk cannot be evaluated using inorganic As alone. Future research should establish speciation-based monitoring systems for inorganic, methylated, and thiolated As; develop process models linking water regime, Fe/S cycling, microbial transformations, and plant transport; and translate these mechanisms into field decision tools that balance As–Cd risk reduction, crop yield, and rice safety under changing environmental conditions. Full article

Digital infrastructures are increasingly exposed to diverse and evolving cyber threats, highlighting the need for robust intrusion detection systems (IDSs). Although machine learning (ML)-based IDSs have achieved strong performance, most existing frameworks are still developed and evaluated mainly under intra-dataset settings, providing limited evidence of their ability to generalize across unseen environments. Moreover, few studies go beyond simply reporting cross-dataset performance to propose dedicated mechanisms for improving generalization. To address this limitation, we propose BHM-IDS, a three-stage hierarchical intrusion detection framework that combines behavior-driven hierarchy with multi-dataset training to improve generalization. The first stage performs binary detection of benign versus malicious traffic, while the second stage classifies malicious traffic into two behaviorally distinct groups: the first corresponding to flood and exhaustion attacks and the second to infiltration and exploitation attacks. The final stage performs fine-grained attack classification through two specialized multi-class classifiers. To expose the framework to more diverse attacks, CIC-IDS2017 is enriched with CIC-DDoS2019 during training, while CSE-CIC-IDS2018 is used as an external test dataset to evaluate generalization. The cross-dataset validation results yielded stage-wise accuracies of 0.93, 0.96, and 0.99, respectively, while the complete end-to-end framework achieved a weighted recall of 0.93. Recall values ranging from 0.76 to 1.00 were obtained for several major classes, including benign traffic, Patator, DoS, and DDoS, although limitations remained for certain attack categories, particularly Web Attack. Overall, the proposed framework demonstrated promising and competitive performance compared with simpler frameworks and existing state-of-the-art approaches. These findings highlight the potential of combining behavior-driven hierarchical classification with multi-dataset training to improve cross-dataset generalization in IDSs. Full article

Compound flooding increasingly threatens developed coastal deltas. High risk pressure does not necessarily produce a resilience deficit where capacity is sufficient, whereas moderate-pressure areas may remain vulnerable when capacity is weak. Recent assessments increasingly integrate hazard, exposure, vulnerability, and adaptive capacity within unified risk frameworks such as the IPCC AR5 risk model. However, by collapsing these dimensions into a single composite risk score, such formulations cannot explicitly diagnose where—and by how much—compound flood risk pressure exceeds intrinsic resilience capacity, which is the information most directly needed for prioritizing resilience investment. This study diagnoses compound flood resilience deficits across Jiangsu Province, China, at county scale from 2000 to 2020. We introduce a diagnostic approach that separates risk pressure from intrinsic resilience capacity and quantifies their spatial mismatch. The risk pressure index is evaluated for consistency with observed disaster-loss indicators—direct economic loss and flood-affected area—over 2010–2020, and spatial statistics, time-series clustering, and explainable machine learning identify deficit patterns, pathways, and associated factors. Both the risk-pressure index and the derived deficit index are positively associated with observed losses, confirming that the framework captures major flood impacts. The resilience deficit index reveals persistent risk–resilience mismatch across Jiangsu. Three pathways emerge: capacity-buffered exposure, inland adaptive adjustment, and coastal resilience-deficit lock-in. Land-system conditions, communication access, transport connectivity, and economic recovery capacity are jointly associated with resilience deficits. The framework offers a transferable approach for prioritizing differentiated flood-risk management in coastal deltas. Full article

Flooding remains a major hydro-meteorological hazard in KwaZulu-Natal, yet province-wide susceptibility assessments incorporating modelling uncertainty are limited. This study develops an ensemble multi-criteria flood susceptibility framework integrating Analytical Hierarchy Process (AHP), fuzzy logic transformation, and frequency ratio (FR) modelling within a cloud-based geospatial environment. Twelve hydro-geomorphological and environmental conditioning factors, including topography, rainfall, land cover, hydrology, and soil proxies, were normalized using percentile scaling. Three independent flood susceptibility models were generated and combined using ensemble mean aggregation, while pixel-wise standard deviation quantified spatial uncertainty. Model validation employed a 10-year historical flood inventory (2015–2025) comprising 65 documented flood locations. The ensemble flood susceptibility index (FSI) ranged from 0.05 to 1.00, with moderate susceptibility zones covering 51.08% of the province. High and very high susceptibility classes occupied 8.36%, indicating spatially concentrated but hydrologically significant risk hotspots. Uncertainty analysis showed low inter-model variability (0.00–0.11), demonstrating strong methodological stability. Validation results confirmed that 73.85% of historical flood points were located within high susceptibility zones, with over 90% captured within overall susceptible classes. The study introduces a hybrid deterministic–fuzzy–probabilistic ensemble modelling approach combined with pixel-level uncertainty mapping and scalable cloud computation. Findings support disaster risk reduction, urban and catchment planning, and early warning system optimization in flood-prone regions. The framework provides a transferable methodology for data-limited environments requiring reliable and uncertainty-aware flood hazard assessment. Full article

Climate change is progressing most rapidly in the high-latitude regions of the Northern Hemisphere, where boreal and Arctic ecosystems are experiencing significant environmental changes and transformations. These ecosystems are being altered by rising temperatures, changing precipitation patterns characterized by more intense rainfall events, decreasing snow and ice cover, and increasing floods, heat waves and fires. These changes pose significant risks to ecosystems, human health, and animal health, especially in the boreal zone. A narrative literature review was conducted across major scientific databases, including Scopus, Web of Science, PubMed, and Google Scholar, covering publications up to 2026. The evidence was combined thematically across key climate hazards and health domains, including infectious and non-communicable diseases, and mental health outcomes. The review shows that accelerating warming in northern regions is changing species distribution, increasing the risk of zoonoses and vector-borne diseases, and increasing cardiovascular, respiratory, and mental health issues. Extreme heat and cold events are important drivers of morbidity and mortality, while floods and wildfires contribute to long-term psychological distress. Vulnerable populations, including elderly adults and socioeconomically disadvantaged groups, are mostly affected. This review identifies key knowledge gaps that could be filled by developing evidence-based public health adaptation plans in high-latitude regions. Full article

Deep learning surrogate models trained with mean-squared-error loss produce statistically accurate but physically unconstrained flood predictions: water may flow uphill, appear spontaneously, or smooth over street-level corridors. In this work, a physics-informed training framework is developed for CNN-LSTM models that predict urban flood depths at 15 min intervals over a $128\times 128$ spatial grid. Three differentiable penalty terms are embedded directly into the loss function: (i) a gravity loss that penalizes depth increases against the water-surface-elevation gradient, (ii) a continuity loss enforcing local mass conservation with rainfall-adaptive thresholds, and (iii) a topography-aware false-alarm penalty modulated by the topographic wetness index (TWI). The framework is evaluated on the Norfolk, Virginia, flood dataset spanning two major storm events (August 2017 and September 2022) comprising 300 samples, with all variants trained on identical splits and robustness assessed over repeated random splits and leave-one-storm-out tests. A road-proximal evaluation restricted to a TWI-derived street mask quantifies street-level skill. The physics-constrained model achieves near-zero gravity violations (∼10⁻⁶) and the highest street-channel recall (0.77 ± 0.09 versus 0.44 ± 0.10 for the unconstrained baseline), the capability most relevant to downstream traffic routing, and its recall advantage more than doubles on a held-out storm, while a uniform false-alarm variant attains 16% lower mean absolute error but suppresses street recall to 0.25. The proposed TWI-modulated penalty reconciles this trade-off: it improves upon the uniform variant on every metric measured, recovering 60% higher street recall at the lowest MAE among all constrained variants and the best street-level F1 score. These results expose a fundamental tension between aggregate pixel-level error metrics and application-specific physical plausibility, and demonstrate that terrain-aware loss modulation offers a principled resolution. Full article

The Mississippi River Delta region is of national and international relevance in terms of agriculture, energy, river navigation, and fisheries. Being one of the most engineered rivers in the world, the Mississippi River has been intensively altered over the past 150 years. The river alterations included the construction of dams, levees, diversions, channelization, spillway flood control systems, and many others. These engineering practices have significantly modified the natural hydrology and sediment dynamics of the river and its deltaic region. While these interventions have provided critical benefits such as flood protection, improved navigation, and economic development, they have also led to profound environmental and ecological consequences. The reduction in sediment delivery to the Mississippi River Delta has accelerated land loss, contributing to the disappearance of coastal wetlands at an alarming rate. The land loss has diminished critical habitats for wildlife, reduced storm surge protection for coastal communities, and disrupted the delta’s natural ability to adapt to fast subsidence. The long-term sustainability of the delta is further threatened by the compounding effects of climate change, including rising sea levels, increased storm intensity, and extreme precipitation and drought conditions. This paper examines the effects, consequences, and future risks of the major river engineering practices on the Mississippi River Delta and provides strategic recommendations that balance human needs with changing natural conditions to ensure sustainability. Specific recommendations include river diversion upstream of New Orleans, better strategies to deal with floods and droughts, strategic maintenance or removal of portions of levees, hybrid coastal-inland human migration, improved transportation connections between coast and inland, and better preparation for future ecosystem shifts. This review is needed because river engineering has made the Mississippi River Delta economically vital yet increasingly vulnerable to sediment loss, wetland collapse, saltwater intrusion, flooding, and population decline. By synthesizing these linked natural and human consequences, it provides a timely framework for rethinking delta sustainability under climate change. Full article

Global climate change significantly alters environmental and health dynamics, posing a major 21st-century public health threat. Diseases commonly transmitted through water and/or food (diarrhea, cholera, dysentery) are particularly sensitive to these changes. While numerous studies have investigated the climate impacts on waterborne diseases, a critical assessment of the applied methodologies and the consistencies of their outcomes is lacking. This systematic review synthesizes the impact of climate change on waterborne diseases, focusing on analytical methods and predictive models used to explain disease presence or abundance. We further evaluate the strengths and limitations of each approach, identifying key gaps for future research. We analyzed 78 indexed research articles published between 1996 and 2023. Our findings demonstrate that diverse climate-related extreme events, including precipitation, flooding, and drought, substantially impact waterborne disease incidence. Additionally, temperature, salinity, humidity, sunshine duration, wind patterns, and specific water quality parameters play significant roles, with regional variations observed. Sensitivity analysis methods and mathematical models are employed to assess sensitivities, quantify correlations, examine impacts, and predict risks. Based on these findings, we urge future research to prioritize elucidating mechanisms and developing models that leverage climate data to predict disease presence or abundance accurately and quantitatively. Full article

Quantifying the net hydrological impact of large dams amidst climatic and anthropogenic influences remains a major challenge. This study isolates the effect of Three Gorges Dam (TGD) regulation on discharge at Jiujiang Station in the middle Yangtze River (2009–2016) using a novel scenario-based framework. A Long Short-Term Memory (LSTM) network, optimized by the Sparrow Search Algorithm (SSA), simulated daily discharge with high accuracy (Nash–Sutcliffe Efficiency coefficient > 0.97). By comparing a “with-TGD” simulation against a “without-TGD” scenario—generated by replacing the dam’s regulated outflow with its reconstructed natural inflow—we quantified the net impact ($\Delta Q$). Results show that $\Delta Q$ is substantially modulated by river–lake interactions. For example, in December, the backwater effect from Poyang Lake amplified the direct flow reduction by an additional −82.5 m³/s. The “peak-shaving” effect was context dependent: TGD regulation increased high flows (>30,870 m³/s) by an average of +372 m³/s while slightly decreasing low flows (<12,711 m³/s) by −31 m³/s. The impact exhibits strong seasonality alongside considerable intra-seasonal variability, reflecting multi-objective operations (flood control, power generation, water supply). This framework provides a transferable approach for attributing hydrological change in large regulated rivers and supports integrated water resources management. Full article