Flood Season Research Articles (Page 1)

Enhanced spatiotemporal prediction of rainfall erosivity through flood season data integration

Embankment piping and leakage are primary causes of flood control infrastructure failure, accounting for more than 90% of embankment failures worldwide and posing significant threats to public safety and economic stability. Current manual inspection methods are labor-intensive, hazardous, and inadequate for emergency flood season monitoring, while existing automated approaches using thermal infrared imaging face limitations in cost, weather dependency, and deployment flexibility. This study addresses the critical scientific challenge of developing reliable, cost-effective automated detection systems for embankment safety monitoring using Unmanned Aerial Vehicle (UAV)-based visible light imagery. The fundamental problem lies in extracting subtle textural signatures of piping and leakage from complex embankment surface patterns under varying environmental conditions. To solve this challenge, we propose the Embankment-Frequency Network (EmbFreq-Net), a frequency-enhanced deep learning framework that leverages frequency-domain analysis to amplify hazard-related features while suppressing environmental noise. The architecture integrates dynamic frequency-domain feature extraction, multi-scale attention mechanisms, and lightweight design principles to achieve real-time detection capabilities suitable for emergency deployment and edge computing applications. This approach transforms traditional post-processing workflows into an efficient real-time edge computing solution, significantly improving computational efficiency and enabling immediate on-site hazard assessment. Comprehensive evaluations on a specialized embankment hazard dataset demonstrate that EmbFreq-Net achieves 77.68% mAP@0.5, representing a 4.19 percentage point improvement over state-of-the-art methods, while reducing computational requirements by 27.0% (4.6 vs. 6.3 Giga Floating-Point Operations (GFLOPs)) and model parameters by 21.7% (2.02M vs. 2.58M). These results demonstrate the method’s potential for transforming embankment safety monitoring from reactive manual inspection to proactive automated surveillance, thereby contributing to enhanced flood risk management and infrastructure resilience.

Mountain catchments in Central Europe are highly susceptible to flash floods. To inform local adaptation, this study quantifies the future flood response of a Polish Carpathian catchment (Upper Skawa, 240.4 km2) to Intergovernmental Panel on Climate Change (IPCC) scenarios. A semi-distributed HEC-HMS model was calibrated and validated using observed flood events (2014–2019). Future hydrographs were then simulated using the delta change method for RCP4.5 and RCP8.5 (near- and long-term horizons). The validated model showed high predictive accuracy. Results indicate a consistent trend towards a polarized hydrological regime, with increased spring/autumn flood peaks and decreased summer flows. This trend is significantly amplified under the RCP8.5 scenario, with long-term peak flood increases approximately double those of RCP4.5. The catchment’s non-linear response further compounds these impacts. These findings suggest a future of heightened seasonal flood risk and emerging summer water scarcity, implying that existing infrastructure, designed for historical stationarity, may be insufficient. This study provides a quantitative evidence base for re-evaluating regional flood risk policies and developing integrated adaptation strategies.

ABSTRACT Environmental filtering and ecological interactions can shape the phylogenetic structure of coexisting species. Given the high mortality rates early in plant development, studying the relative roles of these processes in structuring seedling communities across habitats highlights the importance of specialization in the regeneration niche. Additionally, because seedlings interact at fine spatial scales, focusing on smaller assemblages and source pools is essential. We analyzed the phylogenetic structure of seedling communities in a white‐sand tropical forest mosaic on the southeastern coast of Brazil, influenced by seasonal flooding and varying soil conditions. We tested whether the occurrence‐ and abundance‐based mean phylogenetic distance of seedling assemblages in three forest types differed from null expectation at both habitat and neighborhood scales and evaluated the influence of dispersal limitation by quantifying the proportion of seedling species also present as adults nearby. At the habitat scale, poorer sandy soils filtered closely related species, while the flooded habitat favored distantly related species, likely due to the effects of natural enemies. However, we cannot rule out the role of neutral processes and dispersal limitation, given the random phylogenetic patterns when species abundance was considered. At the neighborhood scale, assemblages with very closely related species were rarer than expected in all three forest types, emphasizing the role of ecological interactions at short distances. As seedling species occurrence within neighborhoods was not strongly associated with the presence of adults nearby, we argue that seedling phylogenetic structure results from processes acting on the regeneration niche rather than merely reflecting adult patterns.

Sluice operations significantly influence pollutant migration and transformation by altering river hydrological regimes. However, the mechanisms and ecological effects remain unclear in the high-sediment, strongly erosive environment of China's Loess Plateau during flood seasons. This study systematically investigated the multiscale impacts of hydrological fluctuations on degradation coefficients of COD, BOD5, NH4+-N, and TP through integrated field monitoring and laboratory simulations in the Dan River, a typical sluice-controlled watercourse. Key findings include: The river's physicochemical properties exhibited diurnal stability between 11:00 and 16:30, providing an optimal timeframe for determining pollutant degradation coefficients in the Dan River; Flow velocity exhibited the strongest positive correlations with degradation coefficients of all four pollutants (R = 0.681-0.909, p < 0.01), showing highest sensitivity for NH4+-N; Straight river reaches demonstrated greater pollutant removal efficiency than curved river reaches. Sluice-opening enhanced degradation coefficients of all four pollutants in straight river reaches but inhibited BOD5and TP degradation in curved river reaches; Under controlled laboratory conditions (20°C), the upstream Niucun section showed higher degradation coefficients for COD, BOD5, and NH4+-N, compared to downstream sections, while TP degradation was lower. Natural conditions generally yielded higher degradation coefficients than laboratory settings, though negative coefficients occurred in curved river reaches due to pollutant accumulation. This study provides fundamental data and insights for optimizing sluice management and improving water quality in flood-prone river networks of the Loess Plateau.

Effective watershed pollution management is often hampered by temporal variations in hydrological conditions. However, most existing studies rely on static scenario assumptions facilitated by nucleic-acid sequencing source tracking tools, limiting their applicability to dynamic pollution events. Here, we developed a time-series pollution source tracing framework by leveraging high-resolution environmental DNA (eDNA) data obtained through a field-compatible eDNA system, incorporating both public and local DNA source libraries. During the 2024 flood season (June 13-July 15) in the Yan Tietang watershed, we deployed an eDNA autosampler to collect 65 semidaily water samples, quantifying temporal contributions from six major pollution sources. The eDNA autosampling system was validated, demonstrating microbial community richness and structure remained stable over 21 days across different storage temperatures (4 °C, 25 °C, 35 °C). Using the public-source library, wastewater treatment plants (WWTPs) were the dominant source, contributing 60.9% (spatial) and 54.8% (temporal) of total loads. These contributions were temporally correlated with key water quality parameters, such as NH3-N and TP. Further validation with the local-source library confirmed source-specific signals (Pearson R = 0.83), and strengthened the association between WWTP effluents and regulatory exceedances. This study highlights the potential of high-frequency eDNA monitoring for real-time pollution diagnostics and adaptive watershed management.

The Pantanal, the largest continuous flooded plain in the world, faces preservation challenges due to the seasonal flooding cycle and human interventions. To better understand and preserve this biome, monitoring systems are essential, and the use of remote sensing techniques combined with advanced machine learning emerges as a promising strategy. This study investigated deep learning models for water body segmentation in UAV (unmanned aerial vehicle) images of the Pantanal. The images were captured using the MAVIC 2 Air camera, with a spatial resolution of 3 cm. Deep learning models such as InterImage, DeepLabv3+, and SegFormer were compared to evaluate their segmentation capabilities. A protocol was established for evaluation, considering metrics such as Intersection over Union (IoU) and Dice. SegFormer showed the best results, with an IoU of 96.16%, Recall of 97.85%, Precision of 99.46%, and an F1 Score of 98.04%. Although DeepLabv3+ and InterImage presented lower metrics, they also demonstrated robust performance. All models produced satisfactory results, but some difficulties were observed in accurately identifying water bodies.

This study systematically investigates the spatiotemporal variation patterns and pollution driving mechanisms of water quality in transboundary rivers, utilizing monthly monitoring data (2020-2023) from 63 cross-boundary sections in the Haihe River Basin. The results demonstrate: (1) Significant spatiotemporal heterogeneity in water quality, with the annual proportion of "good/excellent" water bodies increasing by 17%, yet decreasing by16.8-18.4% during flood seasons (June-September) due to combined urban/agricultural non-point source pollution. (2) Cluster analysis categorized sections into three types, and established flood/non-flood season-specific WQImin models (R2 ≥ 0.789), reducing monitoring indicators to 6-9 parameters while maintaining accuracy. Furthermore, to address practical governance needs, we developed a unified model and a unified parameters model. (3) The APCS-MLR(Absolute Principal Component Score-Multiple Linear Regression) was employed to identify the dominant influencing factors and quantify their impact magnitude on the key parameters of each model. (4) The proposed dual-mode monitoring network ("classified models-unified parameters") and dynamic traceability management strategy provide a scientific foundation for transboundary river governance. Building upon a comprehensive understanding of transboundary river water quality dynamics, this study developed WQImin for distinct cross-boundary sections to reduce monitoring and management costs. By prioritizing key parameters, we systematically identified dominant influencing factors across models and hydrological periods using APCS-MLR. This dual approach-combining model simplification with source apportionment-provides actionable insights for precision management of transboundary water environments.

The reuse of reclaimed water causes a large number of toxic and harmful substances （e.g.， antibiotics） to enter the rivers receiving the reclaimed water. However， study of the occurrence， sources， and influencing factors of antibiotics in the water and sediment of receiving rivers remains limited. In consideration of this fact， the Fuhe River Basin was selected as the study area， and 15 water and sediment samples were collected in the non-flood season （May） and flood season （August） of 2023， respectively. Ultra-high performance liquid chromatography-mass spectrometry （HPLC-MS/MS） was used to analyze the spatiotemporal distribution of 38 antibiotics in the water and sediment samples. In addition， the Unmix model was applied to analyze the sources of antibiotics. Spearman correlation analysis and multiple regression models were used to identify the main factors that influence the sources of antibiotics in the river. The results were as follows： ① Among the target antibiotics， 27 and 19 antibiotics were detected in water （ND-165.5 ng·L-1） and sediment （ND-31.5 ng·g-1）， respectively. ② The spatial distribution of the antibiotics concentration in water was in the order Xinjinxian River &gt; Huanghuagou River &gt; the main stream &gt; Huandi River &gt; Yimuquan River. In sediments， the antibiotics content ranking was Huanghuagou River &gt; Yimuquan River &gt; the main stream &gt; Huandi River &gt; Xinjinxian River in May and Yimuquan River &gt; the main stream &gt; Xinjinxian River &gt; Huandi River &gt; Huanghuagou River in August. ③ In May， aquaculture and livestock activities was the major source of antibiotics in both water （36.3%） and sediment （67.0%）； in August， surface runoff was the major source in water （49.3%）， while wastewater treatment plants were the major source for sediment （40.2%）. ④ The main influencing factors for the source of medical wastewater in the water were urban population and gross regional product， the main influencing factor for the source of sewage treatment plants in the water was permanent resident population， and the main influencing factor for the source of sewage treatment plants in the sediment was NO3--N. This study quantitatively analyzed the sources of antibiotics in receiving rivers and identified their main influencing factors， which provides theoretical and data support for precise control of antibiotics in the receiving rivers.

This study utilizes the Standard k-ε turbulence model and ANSYS CFX software to tackle silt erosion in the top cover clearances of guide vane of the Francis turbine at Genda Power Station (Minjiang River Basin section, 103°17′ E and 31°06′ N) under sediment-laden flow conditions. A numerical simulation of a solid–liquid two-phase flow along the whole flow route was performed under rated operating circumstances to examine the impact of varying guide vane end clearance heights (0.3 mm, 0.5 mm, and 1.0 mm) on internal flow patterns and sediment erosion characteristics. The simulation parameters employed an average sediment concentration of 2.9 kg/m3 and a median particle size of 0.058 mm, indicative of the flood season. The findings demonstrate that augmenting the clearance height intensifies leaky flow and secondary flow, resulting in a 0.49% reduction in efficiency. As the gap expanded from 0.3 mm to 1.0 mm, the leakage flow velocity notably increased to 40 m/s, exacerbating flow separation, enlarging the vortex structures in the vaneless space, and augmenting the sediment velocity gradient and concentration, consequently heightening the risk of erosion. An experimental setup was devised based on the numerical results, and the dynamic resemblance between the constructed test section and the prototype turbine was confirmed for flow velocity, concentration, and Reynolds number. Tests on sediment erosion revealed that the erosion resistance of the anti-sediment erosion material 04Cr13Ni5Mo markedly exceeded that of the base cast steel, especially in high-velocity areas. This study delivers a systematic, quantitative analysis of clearance effects on flow and erosion, along with an experimental wear model specifically for the Gengda Power Station, thereby providing direct theoretical support and engineering guidance for its wear protection strategy and maintenance planning.

ABSTRACT Lateral hydrological connectivity critically regulates river–floodplain ecosystems, yet current investigations into optimal connectivity thresholds remain insufficient. This study integrated remote sensing interpretation with an improved graph theory model to quantitatively assess lateral hydrological connectivity and determine the optimal connectivity thresholds favorable for vegetation growth in the Huayuankou and Jiahetan river–floodplain systems (Lower Yellow River, China) from 1986 to 2021. The operations of the Xiaolangdi Dam enhanced the flood-season lateral hydrological connectivity (4.26–27.27%) and increased vegetation coverage in both flood and non-flood seasons (17.73–164.82%), but reduced non-flood seasons connectivity by over 20%. The influence pattern of connectivity on vegetation growth was similar at the same water level. The identified optimal connectivity thresholds for vegetation revealed that higher connectivity (0–0.5) is beneficial at low water levels, whereas lower connectivity is preferred at high water levels. At medium water levels, the optimal values exhibited regional and seasonal variation, ranging from 0.4–0.5 (flood season) and 0.6–0.7 (non-flood season) at Huayuankou, and from 0.45–0.5 (flood season) and 0.2–0.3 (non-flood season) at Jiahetan. The overall optimal hydrological connectivity threshold for vegetation growth was 0.3–0.4. These findings offer important insights for conserving and managing river–floodplain systems.

Background: Malaria is a major cause of illness and death in sub-Saharan Africa, and Somalia is one of the countries that is hit the hardest. The floodplains of the Shabelle River, together with irrigated farming and seasonal flooding in Hirshabelle State, make it easy for malaria to spread. Despite the use of insecticide-treated nets (ITNs), indoor residual spraying (IRS), and rapid diagnostic tests (RDTs), malaria is still a major public health problem in the area. Objective: The objective of this study was to analyse the epidemiological trends of malaria in Hirshabelle State, Somalia, from 2022 to 2025, focusing on annual incidence patterns by sex and age groups. Approaches: A retrospective analysis employed secondary data obtained from the Health Management Information System (HMIS) using the DHIS2 platform. The data consisted of confirmed malaria cases from healthcare facilities around Hirshabelle. A descriptive analysis was conducted in Stata 16.0 to examine the annual incidence, test positivity rate, and demographic distribution. Findings: From 2022 to 2025, there were 4,401 cases of malaria. The incidence rates fluctuated throughout time, recording the minimum rate in 2022 (0.38 per 1,000) and the maximum rate in 2023 (0.60 per 1,000). In 2024 (0.48 per 1,000) and 2025 (0.46 per 1,000), the rate experienced a slight decline. Females accounted for 67.4% of the cases, while children under five years accounted for 53.5% of the overall burden. The 5–14-year age cohort, albeit numerically inferior, exhibited the highest average annual incidence (2.17 per 1,000). Conclusion: Malaria in Hirshabelle continues to follow a cyclical pattern that is highly linked to how much rain occurs in a certain season. The continued high rates of illness among women and children, and other vulnerable groups, show that current treatments are not good enough. We need improved ways to stop the spread of malaria and aid in long-term control in Somalia. This includes better ways to prevent it, quicker access to diagnostic and treatment, and the use of climate-sensitive forecasting tools.

Abstract Urban flooding is one of the most frequent and impactful natural disasters that significantly impacts communities in major cities of West Africa. Understanding the spatial and temporal variations of floods is essential for effective disaster management associated with them. This study presents the first comprehensive evaluation of satellite rainfall estimates (SREs) across the District of Abidjan, utilising an enhanced urban rain gauge network from 2019 to 2022. Using multiple SRE products, namely IMERG (EARLY and FINAL runs) and CHIRPS, we analyse the dynamics of urban flooding from 1990 to 2022, examine flood-generating processes, and assess seasonal and interannual flood variability through a geo-historical approach. In terms of SRE product performance, IMERG-EARLY stands out at the daily scale, whereas CHIRPS clearly performs better in detecting extreme rainfall values. Moreover, the findings reveal that approximately 210 flood events occurred in the District of Abidjan, with the majority (146, representing 70%) occurring in June. These events resulted in more than 350 casualties, including 170 deaths and 180 injuries, as well as considerable material and economic damages. Heavy rainfall events (&gt; 30 mm day–1) constitute the most significant share across all datasets and municipalities, underscoring their critical role in triggering floods. Moreover, these flood events are classified to identify the different flood drivers: excess rainfall, prolonged rainfall, and short rainfall. Thus, the results revealed that nearly 75% of the identified flood events were linked to excessive or short-duration rainfall episodes, driven by soil saturation and intense precipitation occurring before and/or during the flood events. These findings could contribute to the development of effective flood risk management strategies, including early warning systems and policy measures that enhance urban resilience.

Seasonal design floods and operational water levels are critical for high-efficient water resource utilization. In this study, statistical and rational analyses methods were applied to divide the flood season based on seasonal rainfall patterns. The Mann–Kendall test and Theil–Sen analysis were used to detect trend changes in the observed flow series. Both stationary and nonstationary flood frequency analysis methods were conducted to estimate seasonal design floods. The Three Gorges Reservoir (TGR) in the Yangtze River, China, was selected as the case study. Results show that the TGR flood season could be divided into four periods: the reservoir drawdown period (1 May–20 June), the Meiyu flood period (21 June–31 July), the transition period (1 August–10 September), and the Autumn Rain refill period (11 September–31 October). Trend analyses indicate that the flow series at the TGR dam site exhibited a decreasing trend in recent decades. Upstream reservoir regulation has significantly reduced inflow discharges of TGR, and the nonstationary seasonal 1000-year design floods in the transition period are decreased by about 20%, and the flood control water level could rise from 145 m to 157 m, which can generate 2.288 billion kW h more hydropower (16.57% increase) while maintaining unchanged flood prevention standards. This study provides valuable insights into the TGR operational water level in the flood season and highlights the necessity of considering the regulation impact of upstream reservoirs for design floods and reservoir operational water levels.

The Relationship between Water Quality in the Niyang River During the Flood Season and Land Use at Different Spatial Scales

Flooding has become a major threat to soybean [Glycine max (L.) Merr.] production as the frequency and intensity of extreme precipitations have been increasing due to climate change. While advances have been made in identifying soybean genetic resources and genomic regions associated with mid‐season flood tolerance, there is limited understanding of early season flood tolerance at the vegetative growth stages V2/V4. This study aimed to identify genomic regions associated with early season flood tolerance using a diverse panel of 254 soybean accessions, as well as investigate the viability of implementing genomic prediction models for flood tolerance. Field trials were conducted over 2 years, with flooding imposed at the V2/V4 vegetative growth stages. Genome‐wide association studies were performed using the Bayesian‐information and linkage‐disequilibrium iteratively nested keyway and the multiple locus mixed linear model. Forward stepwise genomic prediction models using random forest (RF) were developed to identify the set of single nucleotide polymorphisms (SNPs) yielding the highest prediction accuracy while assessing the negative impacts of multicollinearity and overfitting on prediction accuracy. Genomic regions on chromosomes 4, 17, and 20 associated with early season flood tolerance were identified, all distinct from regions previously identified for mid‐season tolerance. The RF model achieved a prediction accuracy of 0.64 with 29 selected SNPs, significantly improving over RF and ridge regression best linear unbiased prediction models with higher SNP counts. These findings provide genomic tools for improving the efficiency of breeding for early season flood tolerance, supporting the need to develop season‐long flood‐tolerant soybean genotypes.

The presence and accumulation of microplastics (MPs) in riverine waters have been widely documented. The sustained operation of cascade reservoirs has altered the retention characteristics of MPs in the Han River basin. In this study, the composition, sources, and ecological risks of MPs in the water column and sediments of the Han River mainstream across different periods were investigated. Results showed that the MP abundances in the water column and sediments were higher during the flood season than in the non-flood season. Additionally, MPs in the water column exhibited an increasing trend along the operational sequence of cascade reservoirs. During the flood season, polyamide (PA), polyethylene (PE), and polypropylene (PP) were the dominant MP types in the water column, while polycarbonate (PC) and PP prevailed in sediments. In the non-flood season, polyethylene terephthalate (PET) was the dominant MP type in the water column, whereas PC and PET dominated in sediments. Overall, the distribution characteristics of MPs conformed to the “upstream input-reservoir accumulation-downstream output” pattern. The pollution risk of MPs in both the water column and sediments ranged from low to moderate. These findings provide a basis for exploring the impacts of cascade reservoir operation on the characteristics of MP in water and sediments. Future research will focus on migration mechanisms of MP under the joint operation of cascade reservoirs.

The Jialing River is a representative and typical canalized tributary in the upper Yangtze River basin. In the context of the 10-year fishing ban, it is imperative to elucidate the dynamics and assembly of fish communities in the Jialing River. In this study, field surveys were conducted during the flood and dry seasons from 2021 to 2025 at the Cangxi section of the Jialing River. Fifty fish species belonging to three orders and six families were collected in the study area, with Chanodichthys oxycephaloides, Hemibarbus labeo, Xenocypris davidi, and Siniperca chuatsi identified as dominant species. In addition, fish communities in this region exhibited significant temporal variations, clustering into flood season and dry season groups (p < 0.05). The result suggested that diversity indices were consistently higher in the flood season than the dry season and an increasing trend was observed during the sampling periods. Community assembly analysis revealed that the fish communities were mainly characterized by phylogenetic clustered structure, indicating environmental filtering as the deterministic process in the study area. Despite the steady recovery of fish biodiversity and resource following the fishing ban in the study area, further attention must be directed to other anthropogenic disturbances, especially habitat fragmentation. This study provides a scientific reference for fisheries management after the fishing ban in the Jialing River.

Regional pollution source apportionment and emission thresholds determination in Zunyi's major watersheds: Implications for environmental management.

Gas bubble trauma of Schizothorax prenanti at various life stages induced by total dissolved gas supersaturation.