Search Results (1,750)

Climate change increasingly affects the sustainability and reliability of urban water and wastewater infrastructure. This study analyzes the relationship between climatic variables and the frequency of failures in water and sewage networks in northeastern Poland, using operational data from the Mrągowo system (2020–2023) and meteorological records from 1966 to 2023. Statistical analyses and trend assessments were employed to identify climate-related failure patterns and infrastructure vulnerabilities. Climatic parameters—including temperature extremes, precipitation, snow cover, and sunshine duration—were analyzed in relation to infrastructure reliability. The results indicate rising temperatures, reduced snowfall, and altered precipitation regimes. Although extreme cold corresponded with increased sewage network failures, no significant association was found for high temperatures. Precipitation and snow cover showed weak correlations, except during heavy rainfall events. The study highlights the need to integrate climate resilience into water infrastructure management through preventive maintenance, smart monitoring, and nature-based solutions. Findings contribute to sustainable urban development strategies by demonstrating how climate variability directly affects service reliability. By identifying climate-sensitive failure thresholds, the study supports sustainable infrastructure management by enabling risk-informed adaptation strategies that reduce service disruptions, resource losses, and environmental impacts. This case study offers methodological insights and empirical evidence that may support the assessment of climate-related vulnerability of water and wastewater infrastructure in similar urban contexts. Full article

Frequent extreme rainfall events in northwestern China have made loess–mudstone composite slopes highly susceptible to progressive failure, posing serious threats to infrastructure and public safety. This study investigates the deformation–failure mechanisms and evolutionary characteristics of such slopes under rainfall infiltration by integrating indoor physical model tests with long-term SBAS-InSAR time-series deformation monitoring. The physical model experiments reveal pronounced hydro-mechanical heterogeneity within the composite slope: surface fissures act as preferential flow paths, the mudstone interface exerts a significant water-blocking effect, and hydrological responses differ markedly between shallow and deep layers. The wetting front exhibits a distinct dual-layer migration pattern, characterized by rapid lateral expansion in the shallow layer and delayed advancement in the deep layer. Rainfall infiltration induces a progressive failure process, evolving from toe infiltration softening and mid-slope local erosion to differential crest erosion and ultimately overall sliding, forming a typical failure pattern of frontal creeping, central shearing, and rear tensile deformation. SBAS-InSAR results indicate that the natural landslide experienced a similar long-term progressive evolution, developing from shallow, localized deformation to deep-seated and slope-wide acceleration under multi-year rainfall. Despite differences in spatial deformation patterns influenced by natural microtopography, the failure stages and dominant deformation zones identified by both approaches show strong consistency. The combined results demonstrate that rainfall-induced suction decay, interface softening, pore water pressure accumulation, and stress redistribution jointly control the progressive instability of loess–mudstone slopes. This study highlights the effectiveness of integrating physical modeling and InSAR monitoring for elucidating rainfall-induced landslide mechanisms and provides scientific insights for hazard assessment and mitigation in composite-structure slopes. Full article

Landslide susceptibility maps (LSMs) are crucial for risk mitigation, but integrating Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data is often hampered by a lack of physical interpretation. To address this issue, this study proposes an enhanced modeling framework that integrates multi-source monitoring data by coupling dynamic deformation features. Ground deformation velocity is obtained using MT-InSAR and embedded as dynamic physical constraints into the loss function of a Multi-Layer Perceptron (MLP) model. This approach enables the joint optimization of static geological factors and dynamic deformation characteristics in landslide susceptibility prediction. The proposed framework was applied to Zunyi City, Guizhou Province, China, utilizing an inventory of landslide hazard sites and a dataset of 16 susceptibility factors for model training and evaluation. The results demonstrated that the dynamically constrained model significantly improved predictive performance (AUC = 0.976, an increase of 0.032 compared to the baseline model), and enhanced spatial consistency, reflected by an average increase of 0.0184 in predicted susceptibility for inventoried landslide hazard sites. The framework also outperformed other conventional machine learning models across multiple evaluation metrics. Furthermore, SHAP (SHapley Additive exPlanations) analysis revealed that slope (18.68%), DEM (13.26%), rainfall (11.57%), and mining activities (8.79%) were the primary contributing factors in high-susceptibility areas. This study offers a physically interpretable and robust methodology that advances landslide risk assessment and contributes to disaster prevention strategies. Full article

The tropospheric zenith wet delay (ZWD) serves as a pivotal parameter for atmospheric water vapour inversion. By converting it into precipitable water vapour, high-temporal-resolution atmospheric humidity monitoring becomes feasible, providing crucial support for enhancing short-term rainfall forecast accuracy. However, ZWD exhibits significant non-stationarity due to complex influencing factors, and traditional models struggle to achieve precise predictions across all scenarios owing to limitations in local feature extraction. This article employs a ZWD prediction method based on the dynamic temporal decomposition module of TimesNet, re-constructing one-dimensional high-frequency ZWD time series into two-dimensional tensors to overcome the technical limitations of conventional models. Comprehensively considering topographical characteristics, climatic features, and seasonal factors, experiments were conducted using 30 s ZWD data from 20 IGS stations. This dataset comprised four consecutive days of PPP solutions for each season in 2023. Through comparative experiments with CNN-ATT and Informer models, the global prediction accuracy, seasonal adaptability, and topographical robustness of TimesNet were systematically evaluated. Results demonstrate that under the input-prediction window configuration where each can achieve the optimal accuracy, TimesNet achieves an average seasonal Root Mean Square Error (RMSE) of 5.73 mm across all seasonal station samples, outperforming Informer (7.89 mm) and CNN-ATT (10.02 mm) by 27.4% and 42.8%, respectively. It maintains robust performance under the most challenging conditions—including summer severe convection, high-altitude terrain, and climatically variable maritime zones—while achieving sub-5 mm precision in stable environments. This provides a reliable algorithmic foundation for short-term precipitation forecasting in Global Navigation Satellite System (GNSS) real-time meteorology. Full article

Climate change has emerged as one of the defining risks in recent years. These risks are associated with economic losses and, ultimately, the stability of the financial system. This study examines the impact of climate change on financial stability in Namibia using quarterly data spanning from the period 2009 to 2023. The Nonlinear Autoregressive Distributed Lag (NARDL) approach is employed to assess how climate change asymmetrically affects the stability of Namibia’s financial system. The findings reveal that both increases and decreases in rainfall, as well as higher temperatures, exert negative long-term asymmetric effects on financial stability, while rises in CO₂ emissions appear to enhance it. Accordingly, this study recommends the integration of climate-related risks into financial institutions’ risk assessment frameworks, together with the adoption of long-term monitoring and mitigation strategies. Finally, regulators are also encouraged to conduct climate stress tests to assess the resilience of the financial system under varying climate scenarios. Full article

Tropical cyclones can rapidly alter watershed chemistry by shifting hydrologic pathways and mobilizing stored nutrients, yet these disturbances often remain undetected when storms cause little visible flooding or geomorphic damage. During Hurricane Helene 2024, intense rainfall across the Oconee River watershed in Georgia generated sharp increases in discharge that triggered substantial nutrient export despite minimal physical alteration to the landscape. High-frequency measurements of nitrate, phosphate, and sulfate in urban, forested, and recreational settings revealed pronounced and synchronous post-storm increases in all three solutes. Nitrate showed the strongest and most persistent response, with mean concentrations increasing from approximately 1–3 mg/L during pre-storm conditions to 6–14 mg/L post-storm across sites, and remaining elevated for several months after hydrologic conditions returned to baseline. Phosphate concentrations increased sharply during the post-storm period, rising from pre-storm means of ≤0.3 mg/L to a post-storm average of 1.5 mg/L, but declined more rapidly during recovery, consistent with sediment-associated mobilization and subsequent attenuation. Sulfate concentrations also increased substantially across the watershed, with post-storm mean values commonly exceeding 20 mg/L and maximum concentrations reaching 41 mg/L, indicating sustained dissolved-phase release and enhanced temporal variability. Recovery trajectories differed by solute: phosphate returned to baseline within weeks, nitrate declined gradually, and sulfate remained elevated throughout the winter. These findings demonstrate that substantial chemical perturbations can occur even in the absence of visible storm impacts, underscoring the importance of event-based, high-resolution monitoring to detect transient but consequential shifts in watershed biogeochemistry. They also highlight the need to better resolve solute-specific pathways that govern nutrient mobilization during extreme rainfall in mixed-use watersheds with legacy nutrient stores and engineered drainage networks. Full article

Current satellite-based active microwave observations lack the temporal resolution needed to accurately capture rapid Earth system dynamics such as soil–plant–atmosphere interactions, rainfall interception, snowfall and rain-on-snow events. Ground-based radar systems can resolve these processes but typically rely on high-end VNAs, limiting their affordability and deployment scale. This work presents a low-cost SFCW radar system built around a compact, SDR-based VNA with an enhanced RF front end supported by remote-access firmware and a cloud-based back end with automatic backup. Calibration experiments and preliminary measurements demonstrate that the system achieves stable performance and is capable of capturing high-temporal-resolution microwave signatures relevant for climate monitoring. Full article

Estimating runoff in ungauged catchments remains a major challenge in hydrology, particularly in remote Andean headwaters where limited accessibility and budgetary constraints hinder the long-term operation of monitoring networks. This study integrates satellite-derived rainfall data, hydrological modeling, and benthic macroinvertebrate diversity analysis to explore how short-term antecedent flow conditions relate to temporal variation in community structure. The research was conducted in a pristine 0.26 km² micro-catchment of the upper Collay basin (southern Ecuador). Daily simulated discharge was used to compute antecedent flow descriptors representing short-term variability and cumulative changes in stream conditions, which were related to taxonomic (i.e., H = Shannon diversity, E = Pielou evenness, and D = Simpson dominance) and functional indices (i.e., Rao = Rao’s quadratic entropy, FAD1 = Functional Attribute Diversity, and wFDc = weighted functional dendrogram-based diversity) using Generalized Additive Models. Results showed progressively higher hydrology–biology associations with increasing antecedent flow integration length, suggesting that biological variability responds more strongly to cumulative than to instantaneous flow conditions. Among hydrological descriptors, the cumulative magnitude of negative flow changes was consistently associated with taxonomic diversity. H and E showed more coherent and robust patterns than functional metrics, indicating a faster response of community composition to short-term hydrological variability, whereas functional diversity integrates slower ecological processes. While based on modeled discharge under severe hydrometeorological data limitations, this study provides a practical ecohydrological starting point for identifying short-term hydrological memory signals potentially relevant to aquatic biodiversity in ungauged headwater systems. Full article

Against the backdrop of the increasing frequency of extreme hydrological events and persistent over-extraction of groundwater, the North China Plain (NCP) is facing significant land subsidence. This study systematically analyzed the surface subsidence response patterns and mechanisms of the NCP during extreme rainfall events by integrating Gravity Recovery and Climate Experiment (GRACE) data, Global Navigation Satellite System (GNSS) observations, environmental load models, well data, and precipitation records. The main findings are as follows: (1) From 2002 to 2020, the groundwater storage change (GWSC) in most of the study area declined at an average rate of trend about 5 cm/yr, while from 2021 to 2024, influenced by heavy rainfall recharge, GWSC recovered with a mean rate of trend about 7 cm/yr; (2) During the extreme rainfall event from 1 July to 31 August 2023, the environmental loading model effectively captured the vertical deformation caused by hydrological loading, showing general consistency with GNSS monitoring results in spatial distribution. Most GNSS stations experienced rapid subsidence during the event (GNSS: 5 mm, model: 2 mm), followed by a gradual rebound after the extreme rainfall, consistent with elastic theory; (3) The deformation at the TJBH station exhibited anomalies attributable to porous elastic effects; (4) Integrated well data confirmed that rainfall recharge primarily influences shallow groundwater. This study reveals the multiple mechanisms underlying extreme hydrological induced land subsidence in the NCP. Full article

Climate change is steadily reshaping hydrological regimes, and one of its clearest consequences is the growing disruption of the biogeochemical pathways that govern water quality across river basins. More frequent high-intensity rainfall events, prolonged dry spells, and shifts in seasonal runoff patterns are altering the timing and magnitude of nutrient, organic matter, sediment, and contaminant fluxes. These pulses of material often originate from short-lived episodes of enhanced connectivity between soils, groundwater, and surface waters, making water-quality responses more variable and harder to anticipate than in previous decades. This review describes the ecohydrological mechanisms underlying these changes, focusing on threshold behaviors, the functioning of transitional zones such as riparian corridors and floodplains, and the cumulative effects of legacy pollution. We also discuss the capacity of nature-based solutions (NbS) to buffer climatic pressures. Although NbS can improve retention and moderate peak flows, their performance proves highly sensitive to hydrological variability and landscape context. In the final part, we describe tools that can strengthen adaptive water-quality management, including high-frequency monitoring, event-focused early-warning systems, and modeling approaches that integrate hydrology with biogeochemical processing. This article addresses ecohydrological pathways for water quality under climate change and presents nature-based solutions for regulating pollutant flows within a general framework. Data from North America and Europe, among other areas, are used as primary examples. However, it is important to remember that the issues and proposed solutions vary depending on landscape conditions and climatic zones, which vary across the globe. This article provides an overview of the most common solutions. Full article

Accurate snowmelt runoff prediction is critical for water resource management in mountainous regions where seasonal snowpack constitutes the dominant water supply. This study demonstrates operational application of the degree-day-based Snowmelt Runoff Model (SRM) integrated with Geographic Information Systems (GIS) and multi-platform remote sensing for discharge forecasting in the Kirkgoze Basin (242.7 km², 1823–3140 m elevation), Eastern Anatolia, Turkey. Three automatic weather stations spanning 872 m elevation gradient provided meteorological forcing, while MODIS MOD10A2 8-day composite products supplied operational snow cover observations validated against Landsat-5/7 (30 m resolution, 87.3% agreement, Kappa = 0.73) and synthetic aperture radar imagery (RADARSAT-1 C-band, ALOS-PALSAR L-band). Uncalibrated model performance was modest (R² = 0.384, volumetric difference = 29.78%), demonstrating necessity of site-specific calibration. Systematic adjustment of snowmelt and rainfall runoff coefficients yielded excellent calibrated performance for 2009 melt season: R² = 0.8606, correlation coefficient R = 0.927, Nash–Sutcliffe efficiency = 0.854, and volumetric difference = 3.35%. Enhanced temperature lapse rate (0.75 °C/100 m vs. standard 0.65 °C/100 m) reflected severe continental climate. Multiple linear regression analysis identified temperature, snow-covered area, snow water equivalent, and calibrated runoff coefficients as significant discharge predictors (R² = 0.881). Results confirm SRM’s operational feasibility for seasonal forecasting and flood warning in data-scarce snow-dominated basins, with modest requirements (daily temperature, precipitation, and satellite snow cover) aligning with operational monitoring capabilities. The methodology provides a transferable framework for regional water resource management in climatically vulnerable mountain environments where snowmelt supports agriculture, hydropower, and municipal supply. Full article

Yersinia enterocolitica is a psychrotrophic zoonotic pathogen that causes diarrhea in animals and enteritis in humans, mainly transmitted through the food chain. This systematic review and meta-analysis estimated the prevalence, geographical distribution, and related risk factors of Y. enterocolitica in livestock throughout the Chinese Mainland. Comprehensive searches were conducted in PubMed, ScienceDirect, CNKI, Wanfang, and VIP databases for studies between 1 January 2000 and 1 August 2025. Out of 1092 identified studies, 28 met the inclusion criteria. The estimated overall prevalence of Y. enterocolitica was 9.37%. Prior to 2015, the prevalence peaked at 9.69% but declined in subsequent years. The highest prevalence was found in Southern China (25.00%). Among livestock species, pigs showed higher susceptibility (9.93%) compared to cattle (4.67%). Meat samples exhibited the highest prevalence (15.47%), while qPCR yielded the highest detection rate (10.79%). Geographical factors such as longitude, latitude, altitude, climate, temperature, rainfall, and humidity also influenced prevalence patterns. Y. enterocolitica remains widely distributed in livestock and meat products. Variability was linked to regional, species-specific, and methodological aspects, highlighting the need for One-Health-based monitoring, stricter hygiene regulations, and standardized diagnostics to protect food safety. Full article

Non-point source pollution poses a severe threat to the water quality of the Li River. This study conducted field monitoring of pollution loads from different land-use types on Maozhou Island in the Li River during the 2023 rainy season. Runoff water quality from vegetable plots, orchards, and bamboo forests consistently exceeded standards, with vegetable plots being the primary source of pollution. Their total phosphorus (TP) concentration exceeded standards by nearly 25 times, contributing the highest annual load. The transport of pollutants (TP, total nitrogen(TN), chemical oxygen demand(COD_Cr)) was closely correlated with suspended solids (SS), with the finest particles (<5 μm) identified as the primary carrier exhibiting the strongest pollutant enrichment capacity (e.g., in vegetable fields, the correlation coefficient r between < 5 μm particles and TP was >0.85, p < 0.01). Rainfall patterns significantly influenced pollutant concentrations; TN and TP levels increased with preceding dry days, while phosphorus output from vegetable plots decreased with rising average rainfall temperature. Compared to bamboo forests, vegetable plots and orchards exhibited lower soil adsorption capacity. This study recommends a connectivity-based strategy prioritizing the interception of heavily enriched fine particulate matter (<5 μm) through runoff control and enhanced wetland retention functions. These findings underscore the importance of controlling fine particulate matter for reducing non-point source pollution and maintaining ecological health in the Lijiang River basin. Full article

Farmland N₂O emissions exhibit significant fluctuations in subtropical regions due to notable seasonal rainfall and temperature variations. The dominant factors influencing N₂O emissions in red-soil sloping farmland, which is widely distributed and actively cultivated in the region, remain uncertain. To investigate N₂O emission characteristics of red-soil sloping farmland and responses to meteorological and soil environmental variables and tillage practices, a typical planting system (summer peanut-winter rapeseed rotation system) in southern China was selected. Two common soil micro-environments (conventional tillage, CT, n = 6; and conventional tillage with straw mulching, MT, n = 4) were established within this system, and in situ N₂O emissions were monitored over two consecutive years using the static chamber–gas chromatography method. The N₂O emission peaks across various growing seasons occurred primarily within 1 to 16 days after fertilization. The N₂O emission hotspot periods were observed during the first month following fertilization, accounting for 74.13–91.01% of the total emissions during each growing season. Significant interannual variations in seasonal N₂O cumulative emissions were observed, whereas no significant difference in cumulative N₂O emissions was observed between MT and CT. Changes in weather and soil environment jointly drive the dynamics of N₂O emissions from red soil sloping farmland. Rapeseed-season N₂O emissions were driven mainly by rainfall and air temperature, whereas peanut-season N₂O emissions were also influenced by soil temperature and NO₃⁻-N content at 0–10 cm depths. These findings provide a sound basis for developing eco-agricultural mitigation pathways in subtropical red-soil hilly regions. Full article

Frequent heavy rainfalls due to climate change in recent years have led to an increasing incidence of severe damage, such as levee breaches. However, the integrity of levees is currently assessed by visual inspection, relying on the skill and experience of the overseeing engineers. Future work requires close monitoring of the external shape of levees and the implementation of quantitative assessments if abnormalities such as deformation are discovered. Therefore, the mobile mapping system (MMS), which uses a vehicle-mounted laser scanner to conduct surveys while moving, has attracted attention as a method for conducting high-precision surveys. However, the presence of blind spots in the laser irradiation indicates that there is no practical method for identifying areas that require countermeasures for the entire levee. In this paper, we discuss the appropriate position of laser irradiation that allows data acquisition down to the toe of the slope, and then propose a method of laser irradiation from a high altitude. Compared to previous laser surveys using vehicles, this method was able to obtain a high-density laser point cloud over the entire levee, demonstrating that it is possible to detect detailed deformations not only on the crest of the levee but also on the slope. Full article