Search Results (823)

As climate change intensifies, cities are experiencing more severe rainfall and frequent waterlogging. When rainfall exceeds the carrying capacity of urban drainage networks, it poses a significant risk to urban facilities and public safety, seriously affecting sustainable urban development. Compared with general urban built-up areas, they demonstrate greater vulnerability to rainfall-induced waterlogging due to their obsolete infrastructure and high heritage value, making it imperative to comprehensively enhance their waterlogging resilience. In this study, Qingdao’s historic urban area is selected as a sample case to analyze the interaction between rainfall intensity, the built environment, and population and business characteristics and the mechanism of waterlogging disaster in the historic urban area by combining with the concept of resilience; then construct a resilience assessment system for waterlogging in the historic urban area in terms of dangerousness, vulnerability, and adaptability; and carry out a measurement study. Specifically, the CA model is used as the basic model for simulating the possibility of waterlogging, and the waterlogging resilience index is quantified by combining the traditional research data and the emerging open-source geographic data. Furthermore, the waterlogging resilience and obstacle factors of the 293 evaluation units were quantitatively evaluated by varying the rainfall characteristics. The study shows that the low flooding resilience in the historic city is found in the densely built-up areas within the historic districts, which are difficult to penetrate, because of the high vulnerability of the buildings themselves, their adaptive capacity to meet the high intensity of tourism and commercial activities, and the relatively weak resilience of the built environment to disasters. Based on the measurement results, targeted spatial optimization strategies and planning adjustments are proposed. Full article

Climate change and extreme rainfall events pose great pressures on a city’s resilience to flooding and waterlogging. Designed as a kind of green infrastructure to manage stormwater, urban sponge parks (USPs) in China have been demonstrated to have ecological and societal benefits, while their landscape economic values lack evaluation. Taking the real-estate choices surrounding six USPs in China as an example, an evaluation framework integrating text mining with housing introduction documents and hedonic price model (HPM) regression with housing prices was constructed to combine the stated preferences and revealed preferences of citizens when purchasing properties. The main findings include the following: (1) USPs do contribute to property appreciation, especially in newer urban areas, although they are not as strong as location and property characteristic factors; (2) the extent of the influence of USPs on houses decreases as the distance increases, with a maximum radius of 3 km; (3) a USP’s effects vary according to the urban and environmental context, as HPM with GWR (R² ranges from 0.203 to 0.679) outperforms the OLS method (R² ranges from 0.149 to 0.491), which evokes the need for more affluent and detailed analyses in the future. This study demonstrates the economic benefits of USPs and provides an evaluation approach based on citizen science data, which could contribute to the policy-making of USPs in China and promote the implementation of Nature-based Solutions. Full article

Time-series anomaly detection is a critical task in various domains, including industrial control systems, where the early detection of unusual patterns can prevent system failures and ensure operational reliability. This paper introduces ChaMTeC (CHAnnel Mixing and TEmporal Convolution Network), a novel deep learning framework designed for time-series anomaly detection. ChaMTeC integrates an inverted embedding strategy, multi-layer temporal encoding, and a Mean Squared Error (MSE)-based feedback mechanism with dynamic thresholding to enhance anomaly detection performance. The framework is particularly tailored for industrial environments, where anomalies are rare and often subtle, making detection challenging. We evaluate ChaMTeC on six publicly available datasets and a newly introduced dataset, WaterLog, which is specifically designed to reflect real-world industrial control system scenarios with reduced anomaly rates. The experimental results demonstrate that ChaMTeC outperforms state-of-the-art models, achieving superior performance in terms of F1-CPA (Coverage-based Point-Adjusted F1) scores. The WaterLog dataset, which has been made publicly available, provides a more realistic benchmark for evaluating anomaly detection systems in industrial settings, addressing the limitations of existing datasets that often contain frequent and densely packed anomalies. Our findings highlight the effectiveness of combining channel-mixing techniques with temporal convolutional networks and dynamic thresholding for detecting anomalies in complex industrial environments. The proposed framework offers a robust solution for real-time anomaly detection, contributing to the reliability and sustainability of critical infrastructure systems. Full article

Forest management plans offer valuable data on forest species composition and structure, useful for large-scale bird conservation. We examined the relationship between bird community diversity and five vegetation characteristics from management plans in Krkonoše Mts. National Park. Bird communities were surveyed from 2012 to 2014 using the point method on 285 plots (radius 100 m). We analyzed songbirds, woodpeckers, and pigeons. The vegetation characteristics were divided into composition (tree species proportion, soil-based phytocoenosis, and target vegetation type) and structure (vertical tree layering and remotely sensed heights). Bird species richness was used as a diversity measure. Redundancy analysis (RDA) tested the impact of vegetation characteristics on bird community composition. Higher bird diversity was linked to deciduous forests, particularly beech, in multi-layered stands (20–40 m height) on rich soils. In contrast, lower diversity occurred in spruce-dominated stands with Scots pine, waterlogged soils, and low vegetation (<0.5 m). All vegetation characteristics correlated significantly with bird community diversity and composition. Our findings demonstrate that forest management data can help identify key variability sources in bird communities, aiding in large-scale monitoring and landscape planning. Beyond tree composition and structure, phytocoenological characteristics provide useful insights for conservation. Full article

Sesbania cannabina (Retz.) Pers., as a legume, has strong waterlogging tolerance, but the lack of genomic information limits the exploration of key genes and molecular mechanisms. In this study, single-molecule real-time technology was used to sequence stems RNA of two Sesbania varieties at five time points under waterlogging stress through the PacBio Iso-Seq platform. The full-length transcriptome information contained 42 Gb raw data, 32,503 transcripts with an average length of 1912.28 nt, N50 length of 2059 nt and GC content of 42.69%. A total of 32,143 coding sequences (CDSs), 1745 transcription factors (TFs), 282 long non-coding RNAs (LncRNAs), 7497 simple sequence repeats (SSRs) and 202 alternative splicing (AS) events were identified through sequence alignment and software analysis. The analysis showed that 10,075 transcripts were enriched in 137 KEGG pathways, and 519 transcripts were included in plant hormone signal transduction, of which 103 and 123 transcripts were, respectively, involved in the ethylene and auxin pathways. The assembly and annotation of full-length transcriptome data of Sesbania provided reliable and accurate genomic information for the exploration of key genes and the study of molecular mechanisms in stem response to waterlogging stress. Full article

With the acceleration of urbanization and the increased frequency of extreme rainfall events, flooding has emerged as one of the most serious natural disaster problems, particularly affecting riparian cities. This study conducted a flooding risk assessment and an analysis of the driving factors behind flood disasters in the Songhua River Basin utilizing an improved Soil Conservation Service Curve Number (SCS-CN) model. First, the model was improved by slope adjustments and effective precipitation coefficient correction, with its performance evaluated using the Nash–Sutcliffe efficiency coefficient (NSE) and the Root Mean Square Error (RMSE). Second, flood risk mapping was performed based on the improved model, and the distribution characteristics of the flooding risk were analyzed. Additionally, the Geographical Detector (GD), a spatial statistical method for detecting factor interactions, was employed to explore the influence of natural, economic, and social factors on flooding risk using factor detection and interaction detection methods. The results demonstrated that the improvements to the SCS-CN model encompassed two key aspects: (1) the optimization of the CN value through slope correction, resulting in an optimized CN value of 50.13, and (2) the introduction of a new parameter, the effective precipitation coefficient, calculated based on rainfall intensity and the static infiltration rate, with a value of 0.67. Compared to the original model (NSE = 0.71, rRMSE = 19.96), the improved model exhibited a higher prediction accuracy (NSE = 0.82, rRMSE = 15.88). The flood risk was categorized into five levels based on submersion depth: waterlogged areas, low-risk areas, medium-risk areas, high-risk areas, and extreme-risk areas. In terms of land use, the proportions of high-risk and extreme-risk areas were ranked as follows: water > wetland > cropland > grassland > shrub > forests, with man-made surfaces exacerbating flood risks. Yilan (39.41%) and Fangzheng (31.12%) faced higher flood risks, whereas the A-cheng district (6.4%) and Shuangcheng city (9.4%) had lower flood risks. Factor detection results from the GD revealed that river networks (0.404) were the most significant driver of flooding, followed by the Digital Elevation Model (DEM) (0.35) and the Normalized Difference Vegetation Index (NDVI) (0.327). The explanatory power of natural factors was found to be greater than that of economic and social factors. Interaction detection indicated that interactions between factors had a more significant impact on flooding than individual factors alone, with the highest explanatory power for flood risk observed in the interaction between annual precipitation and DEM (q = 0.762). These findings provide critical insights for understanding the spatial drivers of flood disasters and offer valuable references for disaster prevention and mitigation strategies. Full article

Under the influence of climate change, extreme rainfall events (EREs) have become increasingly frequent. The urban waterlogging caused by these events has a particularly significant impact on cities with flat terrain and inadequate surface runoff dynamics. This study proposes a Coordinated Regulation of Storage and Discharge Mode (CRSD) tailored for plain cities. It establishes an evaluation system for CRSD based on regional rainwater flood carrying capacity, drainage capacity, and regional value, thereby assigning customized storage and drainage strategies to different urban areas. The model optimizes the relationship between storage and drainage across regions based on the fundamental principles of CRSD and establishes dynamic cross-regional water distribution rules according to optimization objectives. Finally, CRSD is validated using the MIKE models. The results indicate that as the rainfall return period increases, the area affected by urban waterlogging expands, though the proportion of waterlogging across various severity levels remains stable. CRSD can effectively alleviate urban waterlogging caused by EREs, with waterlogging reduction percentages ranging from 12.21% to 18.50%. Among the optimization schemes, Safe Consumption (SC) delivers the best overall performance, reducing waterlogging by up to 1.80 km² under 500 yr. The Average Pressure (AP) performs best in high-value areas, reducing waterlogging by up to 1.96 km² under the same return period. This study advances urban flood management by integrating cross-regional coordination mechanisms with blue–green–grey infrastructure, providing a replicable strategy for flatland cities to cope with the increasing challenges of EREs. Full article

The rapid acceleration of urbanization, combined with the proliferation of impervious surfaces and the inherently low permeability of soil layers, has worsened urban waterlogging. This study explores the layout of filter element seepage wells within a sponge city framework to enhance rainwater infiltration and reduce surface water accumulation, proposing an optimized method for determining well spacing and depth. The optimization uses a multi-objective genetic algorithm to target the construction cost, seepage velocity, total head, and pore water pressure. A combined weighting method assigns weights to each aim, while the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) determines the perfect spacing and depth. The results show that the optimal spacing and depth of the filter element seepage wells are 1.572 m and 2.794 m, respectively. Compared to the initial plan, the optimized scheme reduces construction costs by 21.31%, increases the rainwater infiltration efficiency by approximately 200%, raises the total hydraulic head by 17.23%, and decreases the pore water pressure by 5.73%. Sensitivity analysis shows that the optimized scheme remains stable across different weight combinations. This optimized layout significantly improves both the infiltration capacity and cost-effectiveness. Full article

Aphis gossypii Glover (Homoptera: Aphidinae), a major pest of Chinese pepper (Zanthoxylum bungeanum Maxim), causes significant agricultural damage. Ginger (Zingiber officinale Roscoe) has shown potential as a source for developing botanical pesticides due to its strong bacteriostatic and insecticidal properties; however, the underlying mechanisms remain poorly understood. This study evaluated the repellent activity of ginger shoot extract (GSE) across four solvent phases—petroleum ether, trichloromethane, ethyl acetate, and methanol—against A. gossypii. The results demonstrated that GSE exhibited significant repellent effects, with the methanol phase showing the most pronounced activity. Twelve fractions were chromatographically separated from the methanol phase, and electroantennography (EAG) analysis revealed that fraction 4 induced strong EAG responses in both winged and wingless aphids. Further identification of active compounds in fraction 4 by gas chromatography–mass spectrometry (GC–MS) indicated the presence of terpenes, aromatics, alkanes, esters, and phenols as major constituents. Subsequent EAG analysis identified several key compounds—octahydro-pentalene (C1), (Z)-cyclooctene (C2), dimethylstyrene (C3), tetramethyl-heptadecane (C5), tetrahydro-naphthalene (C6), and heptacosane (C9)—as responsible for eliciting EAG responses in both aphid forms. Additionally, results from Y-tube olfactometer assays showed that (Z)-cyclooctene and heptacosane were significantly attractive, while octahydro-pentalene acted as a strong repellent to both winged and wingless aphids. These findings offer valuable insights for the development of synthetic attractants and repellents for A. gossypii and provide a theoretical foundation for utilizing ginger in the creation of botanical pesticides targeting this pest. Full article

Rapeseed (Brassica napus) is highly susceptible to waterlogging during the seedling stage; however, most of the studies on its gene expression under waterlogging stress have focused on transcriptional regulation, with little work conducted on post-transcriptional regulation to date. To elucidate this regulatory network, comparative transcriptome and miRNA analyses in the leaves and roots of rapeseed Zhongshuang11 (ZS11) were performed. Differentially expressed genes (DEGs) and miRNAs (DEmiRNAs) were identified by comparing the normal planting condition (the control group, CKT) with waterlogging treatment (WLT). DEGs identified in leaves and roots were enriched in different metabolic pathways, indicating their distinct mechanisms in response to waterlogging stress. In total, 68 and 82 DEmiRNAs were identified in leaves and roots, respectively, predicted to target 543 and 2122 DEGs in each tissue. Among these, 12 and 9 transcription factors (TFs) were exclusively targeted by DEmiRNAs in leaves and roots, respectively. Notably, six upregulated TFs in leaves were associated with the ethylene response and were predicted targets of bna-miR172 family members, and four TFs in roots participated in the ethylene response pathway. Furthermore, bna-miR169, along with novel-miR-23108 and novel-miR-42624 family members, played crucial roles in waterlogging response of rapeseed. Combining with the determination results of ethylene and jasmonic acid content, a preliminary model of miRNA-mediated gene expression regulation in rapeseed response to waterlogging stress was developed. These findings advance our understanding of transcriptional regulation under waterlogging and lay a theoretical foundation for improving rapeseed waterlogging tolerance. Full article

With the acceleration of climate change and the increase of extreme rainfall, the risk of flooding has intensified in the Huang-Huai region, which is often hit by floods. Urban water accumulation is a complicated process, and the hydrological simulation analysis is highly accurate, but it is time-consuming and laborious. Machine learning is becoming an important new method because of its ability to analyze large areas with high precision. In this paper, a simulation analysis method based on machine learning is constructed by selecting 13 disaster factors, and the waterlogging point in Xuzhou city is predicted successfully. The following conclusions are found: (1) Among the five machine learning models, CatBoost has the highest accuracy rate, reaching 81.67%. (2) Temperature, elevation, and rainfall are relatively important influencing factors of waterlogging. (3) Machine learning can discover water accumulation areas that are easily overlooked except for the built-up areas. (4) The results of the coupling analysis show that the exposure risk of the population exposed to rainwater in the old urban area, the southern area, and the northwestern area is relatively high. This research is of great significance for reducing the risk of exposure to rain and flooding and promoting the safety and sustainable development of cities. Full article

Developing crop varieties with combined salinity and waterlogging tolerance is essential for sustainable agriculture and food security in regions affected by these stresses. This process requires an efficient method to rapidly and accurately assess the tolerance of multiple genotypes to these stresses. Our study examined the use of a pot trial in combination with the assessment of multiple traits to assess the tolerance of 100 wheat (Triticum aestivum L.) genotypes sourced from around the world to these combined stresses. The stresses were imposed on the plants using 100 mM NaCl and by submerging the root systems of the plants in their bathing solutions. The data gathered were subjected to principal component analysis (PCA), and an integrated score (IS) for each genotype was calculated based on multiple morpho-physiological traits; the score was used to rank the genotypes with respect to tolerance or susceptibility. There were significant differences among the 100 wheat genotypes in terms of the relative reductions in their growth parameters and chlorophyll contents, suggesting a rich, genetic diversity. To assess the accuracy of this methodology and to gain insight into the causes of tolerance or susceptibility, the five most tolerant (Misr4 (W85), Corack (W41), Kzyl-Sark (W94), Hofed (W57), BAW-1157 (W14)), and two least tolerant (Livingstong (W60) and Sunvale (W73)) genotypes were selected based on their IS and PCA analysis. These genotypes were then grown hydroponically with and without salinity stress. The data from this second trial were again subjected to PCA, and their IS were calculated; there was reasonable agreement in the ranking of the genotypes between the two trials. The most tolerant genotype (W85; Misr4 from Egypt) and most susceptible genotype (W73; Sunvale from Australia) were then examined in further detail in a third trial. Plants of Misr4 (W85) had lower Na⁺/K⁺ ratios, higher superoxide dismutase, peroxidase, catalase, and ascorbate peroxidase activities, and higher glutathione concentrations. As a result, plants of Misr4 (W85) had lower concentrations of reactive oxygen species (H₂O₂ and O₂^•−) and malondialdehyde than those of Sunvale (W73). This study offers an efficient methodology for the assessment of multiple sources of germplasm for stress tolerance. It has also identified germplasm that can be used for future breeding work and for further research on the mechanisms of tolerance and susceptibility to combined salinity and waterlogging stresses. Full article

With the frequent occurrence of extreme weather events worldwide, the compound frequency of drought and flood events has significantly increased, imposing multidimensional pressures on agricultural water resource management. Agricultural water consumption accounts for approximately 70%, with severe waste, as a large amount of water is lost during transmission and distribution. Faced with increasingly severe and frequent extreme weather, traditional drainage systems may become unsustainable. Identifying the factors influencing drainage time is crucial for efficient drainage. The MIKE URBAN model has significant potential in farmland waterlogging simulation and drainage network optimization. This study validated the model’s accuracy based on infiltration well overflow capacity experiments, with Average Relative Error (ARE) values of 2.29%, 6.52%, 4.41%, 3.17%, 4.37%, and 5.69%, demonstrating good simulation accuracy. The MIKE URBAN model was used to simulate drainage networks, explore factors affecting drainage time, establish an annual cost system for the drainage network, and optimize the network using a genetic algorithm with the objective of minimizing annual costs. Research findings: There is a clear negative correlation between the maximum inflow of infiltration wells and drainage time. As inflow increases, drainage becomes faster, but beyond 0.0075 m³/s (27 m³/h), the efficiency gains level off. This indicates that selecting infiltration wells with at least a 20% opening ratio is essential. Similarly, increasing the collector’s diameter enhances drainage efficiency significantly, though the effect follows a diminishing return pattern. While smaller lateral spacing improves local water collection, it may lead to flow congestion if the collector is undersized; conversely, larger spacing increases drainage paths and delays, even if the collector is large. An optimal spacing range of 100–150 m is suggested alongside the collector diameter. Lateral diameter also affects performance: increasing it reduces drainage time, but the benefit plateaus around 200 mm, making further enlargement cost-ineffective. The genetic algorithm helped to optimize the drainage network design. Utilizing the genetic algorithm, the drainage network was optimized in just 15 iterations. The fitness function value rapidly decreased from 351,000 CNY to 55,000 CNY and then stabilized, reducing the annual cost from 59,640.67 CNY to 45,337.86 CNY—a 24% savings—highlighting the approach’s effectiveness in designing efficient and economical farmland drainage systems. This study has shown that the simulation-based optimization of drainage networks provides a more rational and cost-effective approach to planning drainage infrastructure. Full article

Soil degradation is a critical challenge to global agricultural sustainability, driven by intensive land use, unsustainable farming practices, and climate change. Conventional soil monitoring techniques often rely on invasive sampling methods, which can be labor-intensive, disruptive, and limited in spatial coverage. In contrast, non-invasive geophysical techniques, particularly ground-penetrating radar, have gained attention as tools for assessing soil properties. However, an assessment of ground-penetrating radar’s applications in agricultural soil research—particularly for detecting soil structural changes related to degradation—remains undetermined. To address this issue, a systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. A search was conducted across Scopus and Web of Science databases, as well as relevant review articles and study reference lists, up to 31 December 2024. This process resulted in 86 potentially relevant studies, of which 24 met the eligibility criteria and were included in the final review. The analysis revealed that the ground-penetrating radar allows the detection of structural changes associated with tillage practices and heavy machinery traffic in agricultural lands, namely topsoil disintegration and soil compaction, both of which are important indicators of soil degradation. These variations are reflected in changes in electrical permittivity and reflectivity, particularly above the tillage horizon. These shifts are associated with lower soil water content, increased soil homogeneity, and heightened wave reflectivity at the upper boundary of compacted soil. The latter is linked to density contrasts and waterlogging above this layer. Additionally, ground-penetrating radar has demonstrated its potential in mapping alterations in electrical permittivity related to preferential water flow pathways, detecting shifts in soil organic carbon distribution, identifying disruptions in root systems due to tillage, and assessing soil conditions potentially affected by excessive fertilization in iron oxide-rich soils. Future research should focus on refining methodologies to improve the ground-penetrating radar’s ability to quantify soil degradation processes with greater accuracy. In particular, there is a need for standardized experimental protocols to evaluate the effects of monocultures on soil fertility, assess the impact of excessive fertilization effects on soil acidity, and integrate ground-penetrating radar with complementary geophysical and remote sensing techniques for a holistic approach to soil health monitoring. Full article

In Northeast China, waterlogging has emerged as a significant challenge due to climate change, particularly during the June–August period when spring maize (Zea mays L.), at the post-tasseling phase, impedes a comprehensive understanding of responses and the development of resistance technologies. 2-(3,4-dichlorophenoxy) triethylamine (DCPTA) is suitable for the entire lifecycle of various economic and food crops, improving crop quality and enhancing stress resistance. The study investigated the ear leaf photosynthesis in relation to the root antioxidant systems’ differential responses of spring maize to waterlogging among the tasseling (VT), vesicle (R2) and dough (R4) stages, and the exogenous DCPTA regulating effect. Results revealed that waterlogging inhibited root physiological activity due to oxidative damage. Consequently, the stomatal restriction and non-stomatal restriction on photosynthesis appeared successively, and R4 was the most sensitive stage. Pretreatment with DCPTA reduced stomatal restriction by maintaining water transfer to the leaf through maintaining root physiological activity via enhanced ascorbate–glutathione cycle. Delayed non-stomatal restriction appeared due to relatively stable chlorophyll content and photosynthetic activities, and VT stage exhibited the highest susceptibility to DCPTA. The study provides a necessary theoretical foundation for comprehending the physiological mechanisms underlying yield formation of spring maize under waterlogging stress in Northeast China, and offers valuable insights for the development of chemical regulation technology. Full article