Search Results (1,845)

Floods are among the most destructive natural hazards worldwide, causing significant environmental degradation, economic losses, and social disruption. This study aims to delineate flood-susceptible zones within the Mekerra Wadi Watershed in northwestern Algeria by applying an integrated approach combining remote sensing techniques, Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP), a widely used multi-criteria decision analysis method. Ten conditioning factors influencing flood occurrence were considered, including elevation, slope, precipitation, Topographic Wetness Index (TWI), Normalized Difference Vegetation Index (NDVI), drainage density, distance from streams, Stream Power Index (SPI), soil type, and Land Use/Land Cover (LULC). The AHP method was employed to determine the relative importance of each factor, and the resulting weights were integrated through GIS-based weighted overlay analysis to generate a spatial flood susceptibility map. The findings classified the basin into five susceptibility categories, ranging from very low to very high flood susceptibility. The highest susceptibility areas were mainly associated with low-lying terrain, gentle slopes, high drainage accumulation, and proximity to river networks, whereas elevated regions with steeper slopes exhibited lower flood susceptibility. Model performance assessment using the Receiver Operating Characteristic (ROC) curve yielded an Area Under the Curve (AUC) value of 0.915, indicating a high predictive capability. Furthermore, the reliability of the model was confirmed through validation using historical flood events recorded between 2017 and 2019 and in 2022, showing a strong spatial correspondence between predicted susceptibility zones and observed flood occurrences. The resulting flood susceptibility map provides an effective decision-support tool for flood hazard assessment, sustainable land-use planning, and the development of targeted disaster risk reduction strategies in the Mekerra Wadi Watershed. Full article

Owing to the projected rise in sea level, saltwater flooding and seawater infiltration are expected to affect coastal agricultural fields and young seedlings germinating in them more frequently. We therefore studied the effects of short-term salt shock treatment with 600 mM NaCl:KCl (1:1) and, in some cases, isosmotic polyethylene glycol 6000 (PEG) treatment on leaf segments of 8–11-day-old dark-grown, then greened or light-grown wheat (Triticum aestivum L. cv. Mv Béres) seedlings. Our transmission electron microscopic, flame photometric, infrared gas exchange analyses, fast chlorophyll a fluorescence induction (OJIP), pulse-amplitude modulated (PAM) fluorescence imaging and ultra-performance liquid chromatography (UPLC) measurements revealed moderate differences in photosynthesis-related parameters in salt-stressed light-grown and greened seedlings, with no important alterations in photosynthetic pigments with the exception of a significantly increased xanthophyll de-epoxidation ratio in stressed samples. The slightly lower Na⁺ and K⁺ contents of the dark-grown leaves exposed to the same salt stress did not correspond to the pronounced ultrastructural sensitivity of the dark-grown tissues, the observed peripheral segmentation and exclusion of stroma regions of etioplasts and photosynthetically inactive etio-chloroplasts under salt stress. This piecemeal degradation of the plastid indicates that seedlings germinating in the soil under no or limited autotrophy are the most sensitive to salt stress. Full article

Arsenic (As) contamination in paddy soils poses a persistent threat to food safety and human health through rice consumption. The environmental fate of As in flooded paddy soils is governed by complex physicochemical and microbially transformations, yet the role of biologically active interfaces remains poorly understood. Periphyton, a ubiquitous and metabolically active biofilm at the paddy soil–water interface, may play a critical role in regulating As speciation and behavior. Herein, we integrated biofilm development, microscopic and elemental characterization, bacterial community profiling, and As-transforming functional gene analysis to elucidate the role of periphyton in As transformation potential. Results showed that periphyton formed a heterogeneous three-dimensional matrix composed of microbial aggregates, extracellular matrix-like materials, and mineral particles. Periphyton-associated As was 2.37 mg kg⁻¹ in the irradiated-soil sample and 1.16 mg kg⁻¹ in the natural-soil sample, whereas dissolved As in the overlying water was 2.06 and 5.85 μg L⁻¹, respectively. The natural-soil composite showed higher bacterial richness and diversity indices than the irradiated-soil composite, with 2615 versus 1124 observed ASVs and Shannon indices of 8.63 versus 7.22. Proteobacteria and Cyanobacteria dominated both communities. The coexistence of aioA, arrA, arsC, and arsM indicated the potential for concurrent As(III) oxidation, As(V) reduction, detoxification, and methylation. In the natural-soil composite, aioA, arrA, and arsM reached 1.84 × 10⁶, 1.99 × 10⁶, and 2.63 × 10⁶ copies g⁻¹ sample, respectively. These values were 2.4-, 9.8-, and 7.2-fold higher than those in the irradiated-soil composite, whereas arsC remained relatively stable. These findings indicate that periphyton provides a structured microbial interface with the genetic potential for multiple As transformation pathways. We propose that paddy periphyton represents a structurally and microbiologically complex interface linking microbial assembly with As-transforming functional potential. Full article

Nitrogen fertilization in flooded rice systems can promote nitrate leaching and groundwater contamination, particularly where groundwater is used for human consumption. This study aimed to assess soil susceptibility to nitrate leaching toward groundwater in rice-growing areas of Rioja, San Martín, Peru. Sampling was based on a semidetailed soil map of the Rioja sector, from which 14 soil series were identified; one georeferenced point was established per series, with soil collected at two depths (0–20 and 20–40 cm). From these points, four representative soil profiles were selected through a Delphi consensus process and classified as Typic Endoaquepts (P1 and P2), a Typic Endoaquoll (P3), and a Fluventic Endoaquept (P4). The 28 saturated hydraulic conductivity (Ks) measurements were used to develop a local pedotransfer function, in which the inverse of bulk density was the main predictor of Ks. Profile-specific Ks estimates were incorporated into HYDRUS-1D to simulate nitrate transport under an identical fertilization regime over 10 years. Ks averaged 0.21 cm·h⁻¹ with high variability (CV = 84.12%), and the pedotransfer function explained 42.9% of this variability (R²adj = 0.383, RMSE = 0.127 cm·h⁻¹). Despite receiving identical fertilizer inputs, the four profiles showed simulated nitrate transport differing by up to 19.9%, driven by differences in soil hydraulic properties and profile structure rather than by management practices. Flow-weighted mean nitrate concentrations at the lower model boundary ranged from 48.33 to 57.96 mg·L⁻¹; two profiles (P4 and P1) exceeded the 50 mg·L⁻¹ WHO guideline, reflecting a greater leaching risk in soils that are more stratified and have higher hydraulic conductivity, while the profile with the highest organic carbon content (P3) showed lower susceptibility, likely due to greater denitrification. These findings indicate that local soil properties are a key control on nitrate leaching susceptibility, supporting the need for soil-type-specific nitrogen management to protect groundwater resources. Full article

The rhizosphere of Fabaceae contains plant growth-promoting rhizobacteria (PGPR) that regulate phytohormone balance, nutrient acquisition, and biofilm formation, thereby contributing to plant development and stress tolerance. Although Indole-3-acetic acid (IAA) and exopolysaccharides (EPS) are well-established PGPR-associated factors, their combined contributions to waterlogging tolerance in soybean remain unclear. In this study, Bacillus-like PGPR were isolated from the rhizosphere of soybean plants cultivated in field soil, and Priestia megaterium and P. aryabhattai were identified among Bacillus-like bacteria based on strong EPS production, a PGPR-associated trait. The P. megaterium isolate S135-A1-2 was subjected to gamma-ray irradiation to generate mutants, from which mutants #6 (increased EPS), #8 (increased IAA), and #12 (increased EPS and IAA) were selected for further analysis. Mutant #8 produced 4.48 μg/mL IAA, a 3.6-fold increase over the parental strain, and protease and siderophore activities were also evaluated as additional PGPR-associated traits. Their plant growth-promoting traits were evaluated using pot assays under waterlogging conditions. Inoculation with mutants #6 and #8 increased lateral root development and root biomass relative to the parental strain, whereas mutant #12 showed a distinct response pattern associated with dual-trait enhancement under waterlogging stress. After 7 days of waterlogging, mutant #12 showed higher chlorophyll-related values than mutants #6 and #8. These results indicate that gamma-irradiated P. megaterium mutants can differentially modify soybean root system responses to flooding stress, supporting gamma-ray irradiation as a useful approach for developing PGPR with improved functions for agricultural applications under waterlogging stresses. Full article

Flooded rice paddies emit large quantities of greenhouse gases, and biochar amendment serves as a promising strategy to conserve water, sequester carbon, mitigate emissions and stabilize crop yields. Nevertheless, the DNDC (DeNitrification-DeComposition) model has no specific biochar module and fails to precisely quantify coupled water–carbon–nitrogen cycles in biochar-amended paddies. Using two-season field observations from the Jianghan Plain, this study constructs the Biochar-DNDC model by introducing biochar pH and substrate adsorption parameters into the original organic fertilizer module and coupling a double-tank exponential decay model. Five biochar application rates (0, 1.5, 3, 4.5, and 6 kg∙m⁻²; CK, BC1.5, BC3, BC4.5, BC6) were set. Combined with the 40-year historical precipitation series of the study area, five hydrological year types were classified to carry out multi-scenario simulations. The results indicated that, compared with DNDC, Biochar-DNDC improved the simulation accuracy by an average of 28.4%. In terms of RRMSE, Biochar-DNDC improved the simulation accuracy of yield, SOC, CH₄, and N₂O by 37.0%, 28.7%, 28.6%, and 42.1%, respectively. Environmental temperature, precipitation, soil bulk density, optimal rice yield, and the proportion of straw returned to the field were key sensitive factors regulating water–carbon–nitrogen fluxes. Biochar reduced irrigation water use by 6.3~28.5% while promoting the accumulation of soil carbon stocks, optimizing nitrogen-use efficiency, and suppressing greenhouse gas emissions. There was a significant interaction between rainfall regime and biochar: DOC and NH₄⁺-N were higher in wet years, while NO₃⁻-N tended to accumulate in arid conditions. Biochar weakened the positive driving effect of precipitation on net primary productivity (NPP). Rice yield, NPP, and net ecosystem exchange (NEE) first increased and then decreased with increasing biochar dosage. Overall, the optimal dosage of biochar for water conservation, carbon sequestration, yield increases, and emission reductions in paddy fields on the Jianghan Plain was 4.5 kg∙m⁻². This study develops a modeling tool for the carbon and nitrogen cycles in biochar-amended paddy fields, which can provide quantitative support for low-carbon water and fertilizer management in the rice-growing regions of the middle Yangtze River Basin. Full article

Flooding is the most recurrent and economically devastating natural hazard in Nigeria, yet no standard or consistent nationwide assessment method exists. Spatially explicit flood susceptibility information also remains scarce, particularly given limited hydrometric monitoring. This study comparatively evaluates national-scale flood susceptibility in Nigeria using frequency ratio (FR), logistic regression (LR), Random Forest (RF), and gradient boosting (XGBoost), while assessing conditioning-factor importance within a satellite-informed framework for data-sparse environments. Six conditioning factors were evaluated—elevation, TWI, HAND, LULC, slope, and soil type—with elevation, TWI, HAND, and LULC retained following multicollinearity and sensitivity assessments. The flood inventory was derived from a HEC-RAS 100-year floodplain simulation driven by Dartmouth Flood Observatory (DFO) satellite-derived discharge, yielding 1,048,575 binary flood/non-flood observations for supervised model development and FR computation. The HEC-RAS floodplain was qualitatively checked for spatial plausibility against documented DFO historical flood reports. On the held-out HEC-RAS-derived test subset, XGBoost achieved the highest AUC (0.956) and overall accuracy (0.892). As an internal spatial-reproduction diagnostic, LR, RF, and XGBoost showed substantial agreement with the HEC-RAS reference (Kappa = 0.662–0.700), whereas FR showed moderate agreement (Kappa = 0.410). External evaluation against the independent 2022 Sentinel-1 SAR flood extent showed consistently high flood-class detection across all four susceptibility models (93.81–94.50%). At the national scale, HAND ranked highest overall across the evaluated importance analyses. The results demonstrate the value of combining statistical and machine learning approaches with satellite-informed hydrodynamic data for national-scale flood susceptibility assessment. XGBoost showed the strongest overall predictive performance, while the resulting maps provide spatial information to support disaster risk management, land-use planning, and early warning in Nigeria and other data-sparse regions. Full article

Microforests (also called mini forests or tiny forests) are increasingly recognized as a promising nature-based solution for mitigating urban challenges such as flooding, biodiversity loss, air pollution, and the heat island effect. Despite their rapid global adoption, practitioners have limited access to practical, evidence-based guidance for planning, implementing, and monitoring these projects. This article presents an adaptable framework for community-based microforest establishment and long-term monitoring based on lessons learned from two contrasting case studies in the northeastern United States: a demonstration microforest at the New York State Department of Environmental Conservation’s Five Rivers Environmental Education Center and a series of urban microforests established by Groundwork Elizabeth in New Jersey. We describe practical approaches to site selection, soil assessment and preparation, native species selection, volunteer engagement, planting, and maintenance that can be adapted to diverse environmental and social contexts. We also introduce a standardized yet flexible monitoring framework encompassing five complementary components: forest development, soil recovery, biodiversity recovery, microclimate regulation, and community stewardship. The framework incorporates monitoring methods ranging from accessible citizen science protocols to more advanced ecological techniques, enabling organizations with varying levels of technical capacity to document ecological change and compare outcomes across projects. Rather than prescribing a single implementation model, we provide communities, nonprofit organizations, government agencies, and researchers with practical guidance to establish, evaluate, and adapt microforests while promoting standardized monitoring, long-term stewardship, and evidence-based urban ecological restoration. Full article

The growing worldwide demand for food and fiber is met with limited agricultural land for expansion, climate challenges, and rising production costs. Resilient management strategies are needed to address dynamic biotic and abiotic variables in forage-livestock systems. The objective of this web-based survey was to assess perceptions of climate-resilient management strategies among agricultural educators and livestock farmers in the Southeastern United States of America (USA). The 16-question survey was distributed through Qualtrics^@ from 29 July to 29 August 2025. Participants were 38 farmers and 29 agricultural educators. Cow-calf operations were predominant (53%) among farmer participants. Land-grant university management recommendations were the primary resource used by participants, including for the adoption of new management technology. Flooding, drought, and rising temperatures were the primary weather-related issues identified as impacting agricultural production. Most respondents affirmed that financial incentives are essential to support the implementation of sustainable management practices in operations. Integrated pest and weed management, soil health, livestock management, and climate-resilient landscapes were the knowledge gaps identified. Future efforts should target emergent needs of forage-livestock ecosystems due to the changing climate conditions and increasing input costs. Thus, educational resources should address research-based recommendations to support the sustainability and feasibility of operations. Full article

Water management during grain filling plays a critical role in determining rice quality, but its coordinated regulation of eating and aroma quality in aromatic rice remains unclear. In this study, two indica cultivars, Nongxiang 32 and Yuzhenxiang, were subjected to three irrigation regimes: continuous flooding (CF), alternate wetting and drying (AWD), and saturated soil irrigation (SSI). Our results showed that 2-acetyl-1-pyrroline (2AP) content exhibited the highest level under AWD and the lowest under SSI. Starch pasting properties were improved under AWD and SSI, as reflected by higher peak viscosity and breakdown and lower setback values. Further analysis of Nongxiang 32 showed that AWD enhanced amylopectin and total starch contents, elevated proline and putrescine levels, and differentially regulated enzyme activities, maintaining higher proline dehydrogenase activity at early filling stage and favorable Δ¹-pyrroline-5-carboxylate synthetase, ornithine δ-aminotransferase, and diamine oxidase activities at late filling. Meanwhile, AWD was associated with reduced γ-aminobutyric acid accumulation, consistent with greater precursor availability for 2AP biosynthesis. Antioxidant enzyme activities followed the order AWD > SSI > CF, whereas malondialdehyde and free fatty acids showed SSI > AWD > CF. Hormonal responses differed: gibberellic acid (SSI > AWD > CF), abscisic acid (AWD > SSI > CF), and indole-3-acetic acid (AWD > CF > SSI). Collectively, in Nongxiang 32, AWD optimally coordinates precursor metabolism, enzyme activities, antioxidant defense, and hormone regulation, thus improving both the aroma and starch quality of aromatic rice. This work provides a mechanistic understanding of water-driven quality formation in aromatic rice and offers a feasible irrigation strategy for high-quality aromatic rice cultivation. Full article

Predicting flood dynamics in data-scarce urban catchments remains a major hydrological challenge, yet physically-based modeling is essential for design mitigation infrastructure. This study assessed the impact of land cover change on the hydrological response and hydraulic performance of the ungauged Kinalumsan River in Cebu City, Philippines, which frequently overflows during intense rainfall. To overcome the absence of historical gauge records, a short-term field monitoring campaign was conducted to capture discrete storm events for event-based calibration. Land cover maps from 1996 to 2023 were analyzed to evaluate spatiotemporal trends. A physically-based, coupled modeling framework was deployed: a HEC-HMS model utilized the Soil Conservation Service Curve Number (SCS-CN) method to simulate peak discharges under 10-, 25-, 50- and 100-year return periods, which subsequently drove a HEC-RAS (Version 6.6) 2D model to assess flood characteristics. The event-based calibration demonstrated good predictive performance, achieving Nash–Sutcliffe Efficiency (NSE) values of 0.96 (HEC-HMS) and 0.88 (HEC-RAS), indicating reliable simulation of the catchment’s response. Results show a steady increase in built-up areas, which contributed to higher peak discharges and expanded flood extents. Extreme rainfall events further amplified flood hazards, with the 100-year return period producing the widest inundation. The districts of Tisa and Punta Princesa located near the downstream portion of the Kinalumsan River consistently experienced the greatest exposure in both depth and velocity terms. Overall, the study demonstrates that integrating short-duration monitoring campaigns with coupled hydrologic–hydraulic modeling provides a scientifically defensible, highly transferable template for rapid flood hazard mapping and stormwater management in data-constrained developing regions. This cost-effective method of flood modelling can be upscaled to other cities where data is scarce including selection of nature-based solutions for the improvement of urban drainage and stormwater management for resilient cities. Full article

Earthen levees are crucial flood defense systems but are susceptible to failure due to internal erosion and saturation-induced instability, potentially causing breaches and endangering lives, infrastructure, and ecosystems in protected areas. Understanding soil saturation dynamics is vital for improved monitoring and resilience. This study presents a novel method of levee health monitoring by deploying a wireless sensor network consisting of nine small sensing spike packages with long-term UAV-deployability potential. In-package pressure, temperature, humidity, and—most importantly—soil conductivity are all measured by the suite of environmental sensors integrated into each spike package. This integrated sensing capability gives spatiotemporal information about the embankment’s subsurface moisture conditions. A 2 m long, 1 m wide, and 0.45 m tall sand-filled embankment replica was built for a controlled flume experiment to verify the system, enabling close examination of moisture permeability and propagation. The deployment of cutting-edge analytical methods for data interpretation is one of this study’s main contributions. Discrete conductivity measurements were converted into continuous, two-dimensional maps of moisture propagation using interpolation techniques, namely radial basis function (RBF). This makes it possible to see the changing moisture front inside the embankment structure in detail. Important information about early warning signs of possible instability (piping failures) is provided by this combined analytical framework. Using a network of nine wireless sensing spike packages, the proposed framework monitored moisture propagation within a 2 m long earthen embankment over a 105 min lab experiment and identified resistance changes associated with piping-like seepage behavior at approximately 19 min and 34 min, which exited approximately 10 min after detection. However, only seven of the nine packages produced viable readings. Improving flood risk forecasting models, improving infrastructure health monitoring applications, and guiding the development of more resilient levee systems are all directly impacted by this. This study demonstrates the potential in autonomous levee monitoring and management given that it combines robust spatial analytics and an interpolation method with inexpensive, quickly deployable sensing technology. Full article

Intensifying global climate change poses a severe threat to agricultural production, and strengthening China’s food security resilience is therefore of great practical significance for safeguarding food security in China. This study constructs an evaluation system comprising 17 indicators across four dimensions—supply availability, consumption access, food utilization, and system stability—and applies the entropy–TOPSIS method, the Dagum Gini coefficient, kernel density estimation, Markov chains, an obstacle degree model, and the GM(1,1) grey prediction model to measure the level and potential of food security resilience across 31 provinces in China. The results show that China’s food security resilience exhibits a steady upward trend, with the composite score rising from 0.2240 in 2010 to 0.3504 in 2024. Regional disparities are dominated by inter-group differences, whose average contribution rate reaches 70.13%, and the overall Gini coefficient declines gradually. Resilience shows a significant positive spatial autocorrelation throughout, with the global Moran’s I remaining significant at the 1% level in all years. Transitions among resilience levels are characterized by “club convergence”, with the high-level club being the most stable, and neighborhood states significantly condition local transitions. The grain self-sufficiency rate, rural broadband access per capita, and the soil erosion control rate are the leading obstacle factors; at the dimension level, the obstacle degree of supply availability resilience rises and ranks highest by 2024. Panel econometric results further confirm that flood shocks and temperature variability significantly undermine food security resilience, providing direct evidence for the climate change context. Forecasts indicate that national food security resilience will rise to around 0.42 by 2029, with the eastern region maintaining its lead; Tianjin and Beijing rank at the top, while Tibet is the only province projected to decline. The findings of this study provide a reference for the Chinese government in formulating policies to enhance food security resilience and offer a Chinese solution for promoting global food security. Full article

Over the past decade, the Punjab plains of Northern India have experienced recurrent flooding driven by hydroclimatic variability, specifically shifts in western disturbances that have intensified monsoon precipitation. Following a devastating flood in 2025, the region remains highly vulnerable to hydrological extremes, a risk further exacerbated by shifting land use and agricultural patterns, geomorphic parameters, and complex fluvial systems. This study assesses flood susceptibility by integrating multi-sensor satellite observations, multi-temporal Sentinel-1 backscatter signals, and refined runoff potential estimates derived from local climate zones, accounting for land cover, soil type, and infiltration characteristics, into machine learning frameworks. The model is trained using a 2025 flood inventory generated from a synthetic aperture radar backscatter threshold ratio. The calibrated frameworks are applied to the 2023 flood events to test independent transferability. The temporal consistency and predictive performance of the models are evaluated using the precision–recall trade-offs, threshold-dependent predicted probability distribution, and Shapley Additive exPlanations (SHAP). Results indicate more balanced classification performance of Random Forest and Extreme Gradient Boosting in comparison to Artificial Neural Network performance that exhibits higher recall with lower precision. The model performance for 2023 models is considered more robust, with greater class separability of 2023 flood events than for 2025. Probability distributions for both events further demonstrate model-dependent threshold behavior, highlighting a trade-off between flood detection sensitivity. SHAP identifies rainfall, soil moisture, runoff, and elevation as the dominant contributors. The analysis further indicates that all model frameworks effectively capture the physical control of hydrological and topographical variability on the temporal flood events. The consistent contribution of hydrological and topographical factors across the two events supports model transferability, while threshold sensitivity, uncertainty, and spatial dependence are important considerations for flood susceptibility modelling. The results reflect a balanced interaction between extreme rainfall, runoff potential, and topographic control in causing periodic floods in the Punjab plains. Full article

Nitrogen cycling in freshwater lakes is mediated by diverse microbial communities, yet how nitrogen-transforming communities respond to flood perturbations across multiple habitats remains poorly characterized. In this study, we investigated nitrogen forms and associated microbial communities in Baiyangdian Lake before (Phase 1, May) and after (Phase 2, August) a major summer flood, using 16S rRNA gene sequencing and Phase 1 metagenomics. Flooding was associated with increased water temperature and dissolved oxygen, and moderate declines in nitrogen and phosphorus concentrations, consistent with a dilution-driven effect. Key nitrogen-cycling taxa showed apparent abundance shifts between phases. Baseline metagenomics revealed comprehensive nitrogen cycling functional potential across water, sediment, and soil habitats, with denitrification genes most abundant in sediments, nitrification genes in the water column, and nitrogen fixation genes in soils. Temperature, dissolved oxygen, and nitrogen forms were identified as primary environmental drivers of community variation. Co-occurrence network analysis identified modular organization of nitrogen-cycling taxa. These findings demonstrate that flood events can alter lake nitrogen regimes through dilution, with implications for shallow lake nutrient management under changing hydrological regimes. Full article