Search Results (5,316)

Prolonged inundations are altering coastal forest ecosystems of the southeastern US, causing extensive tree die-offs and the development of ghost forests. This hydrological stressor also alters carbon fluxes, threatening the stability of coastal carbon sinks. This study was conducted to investigate the interactions between hydrological drivers and ecosystem responses by analyzing daily eddy covariance flux data from a wetland forest in North Carolina, USA, spanning 2009–2019. We analyzed temporal patterns of net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RE) under both flooded and non-flooded conditions and evaluated their relationships with observed tree mortality. Generalized Additive Modeling (GAM) revealed that groundwater table depth (GWT), leaf area index (LAI), NEE, and net radiation (Rn) were key predictors of mortality transitions (R² = 0.98). Elevated GWT induces root anoxia; declining LAI reduces productivity; elevated NEE signals physiological breakdown; and higher Rn may amplify evapotranspiration stress. Receiver Operating Characteristic (ROC) analysis revealed critical early warning thresholds for tree mortality: GWT = 2.23 cm, LAI = 2.99, NEE = 1.27 g C m⁻² d⁻¹, and Rn = 167.54 W m⁻². These values offer a basis for forecasting forest mortality risk and guiding early warning systems. Our findings highlight the dominant role of hydrological variability in ecosystem degradation and offer a threshold-based framework for early detection of mortality risks. This approach provides insights into managing coastal forest resilience amid accelerating sea level rise. Full article

Recent years have seen frequent heavy rainfall events in Japan, often linked to Baiu fronts and typhoons. These events are exacerbated by global warming, leading to an increased frequency and intensity. As floods represent a serious threat to sustainable urban development and community resilience, this study contributes to sustainability-focused risk reduction through integrated analysis. This study focuses on the 2 June 2023 heavy rain disaster in Toyohashi City, Japan, which caused extensive damage due to flooding from the Yagyu and Umeda Rivers. Using numerical models, this study accurately reproduces flooding patterns, revealing that high tides amplified the inundation area by 1.5 times at the Yagyu River. A resident questionnaire conducted in collaboration with Toyohashi City identifies key trends in evacuation behavior and disaster information usage. Traditional media such as TV remain dominant, but younger generations leverage electronic devices for disaster updates. These insights emphasize the need for targeted information dissemination and enhanced disaster preparedness strategies, including online materials and flexible training programs. The methods and findings presented in this study can inform local and regional governments in building adaptive disaster management policies, which contribute to a more sustainable society. Full article

This study introduces a novel methodology for assessing ice-jam flood hazards along river channels. It employs empirical equations that relate non-dimensional ice-jam stage to discharge, enabling the generation of an ensemble of longitudinal profiles of ice-jam backwater levels through Monte-Carlo simulations. These simulations produce non-exceedance probability profiles, which indicate the likelihood of various flood levels occurring due to ice jams. The flood levels associated with specific return periods were validated using historical gauge records. The empirical equations require input parameters such as channel width, slope, and thalweg elevation, which were obtained from bathymetric surveys. This approach is applied to assess ice-jam flood hazards by extrapolating data from a gauged reach at Fort Simpson to an ungauged reach at Jean Marie River along the Mackenzie River in Canada’s Northwest Territories. The analysis further suggests that climate change is likely to increase the severity of ice-jam flood hazards in both reaches by the end of the century. This methodology is applicable to other cold-region rivers in Canada and northern Europe, provided similar fluvial geomorphological and hydro-meteorological data are available, making it a valuable tool for ice-jam flood risk assessment in other ungauged areas. Full article

Floods are natural hazards that have the greatest socioeconomic impact worldwide, given that 23% of the global population live in urban areas at risk of flooding. In this field of research, the analysis of flood risk has traditionally been studied based mainly on approaches specific to civil engineering such as hydraulics and hydrology. However, these patterns of approaching the problem in research seem to be changing in recent years. During the last few years, a growing trend has been observed towards the use of methodology-based approaches oriented towards urban planning and land use management. In this context, this study analyzes the evolution of these research patterns in the field by developing a bibliometric meta-analysis of 2694 scientific publications on this topic published in recent decades. Evaluating keyword co-occurrence using VOSviewer software version 1.6.20, we analyzed how phenomena such as climate change have modified the way of addressing the study of this problem, giving growing weight to the use of integrated approaches improving territorial planning or implementing adaptive strategies, as opposed to the more traditional vision of previous decades, which only focused on the construction of hydraulic infrastructures for flood control. Full article

In the irrigation districts of Northern China, the flood resources utilization for deep storage irrigation, which is essentially characterized by active excessive irrigation, aims to have the potential to mitigate freshwater shortages, and long-term groundwater overexploitation. It is crucial to detect the effects of irrigation amounts on agricultural yield and the mechanisms under deep storage irrigation. A three-year field experiment (2020–2023) was conducted in the Guanzhong Plain, according to five soil wetting layer depths (RF: 0 cm; W1: control, 120 cm; W2: 140 cm; W3: 160 cm; W4: 180 cm) with soil saturation water content as the irrigation upper limit. Results exhibited that, compared to W1, the W2, W3, and W4 treatments led to the increased plant height, leaf area index, and dry matter accumulation. Meanwhile, the W2, W3, and W4 treatments improved kernel weight increment achieving maximum grain-filling rate (W_max), maximum grain-filling rate (G_max), and average grain-filling rate (G_ave), thereby enhancing the effective spikes (ES) and grain number per spike (GS), and thus increased wheat grain yield (GY). In relative to W1, the W2, W3, and W4 treatments increased the ES, GS, and GY by 11.89–19.81%, 8.61–14.36%, and 8.17–13.62% across the three years. Notably, no significant difference was observed in GS and GY between W3 and W4 treatments, but W4 treatment displayed significant decreases in ES by 3.04%, 3.06%, and 2.98% in the respective years. The application of a structural equation modeling (SEM) revealed that deep storage irrigation improved ES and GS by positively regulating W_max, G_max, and G_ave, thus significantly increasing GY. Overall, this study identified the optimal threshold (W3 treatment) to maximize wheat yields by optimizing both the vegetative growth and grain-filling dynamics. This study provides essential support for the feasibility assessment of deep storage irrigation before flood seasons, which is vital for the balance and coordination of food security and water security. Full article

Recent climate changes have led to an increase in flood intensity, often resulting in frequent levee overtopping, which causes significant human and property damage. High vulnerability to such breaches is expected in general, especially at river curves. This study aims to predict the occurrence of levee overtopping at these critical points and to suggest a curve, the levee overtopping risk curve, to assess overtopping probabilities. For this purpose, several dimensionless parameters, such as superelevation relative to levee height ($∆$y/H) and the channel’s Froude number, were examined. Based on dimensional analysis, a relationship was developed, and the levee overtopping curve was finally proposed. The accuracy of this curve was validated through numerical analysis using a selected levee case, which clearly distinguished between safe and risky conditions for levee overtopping. The curve is designed for immediate integration into the hydraulic design processes, providing engineers with a reliable method for optimizing levee design to mitigate overtopping risks. It also serves as a critical decision-making tool in flood risk management, particularly for urban planning and infrastructure development in areas prone to flooding. Full article

(1) Background: Wetlands are ecologically significant ecosystems that support biodiversity and contribute to essential environmental functions such as water purification, carbon storage and flood regulation. However, these ecosystems face increasing pressures from land-use change and degradation, prompting the need for scalable and accurate classification methods to support conservation and policy efforts. In this research, our motivation was to test whether high-spatial-resolution PlanetScope imagery can be used with pixel-based machine learning to support the mapping and monitoring of wetlands at a national scale. (2) Methods: This study compared four machine learning classification models—Random Forest (RF), XGBoost (XGB), Histogram-Based Gradient Boosting (HGB) and a Multi-Layer Perceptron Classifier (MLPC)—to detect and map wetland areas across New Zealand. All models were trained using eight-band SuperDove satellite imagery from PlanetScope, with a spatial resolution of ~3 m, and ancillary geospatial datasets representing topography and soil drainage characteristics, each of which is available globally. (3) Results: All four machine learning models performed well in detecting wetlands from SuperDove imagery and environmental covariates, with varying strengths. The highest accuracy was achieved using all eight image bands alongside features created from supporting geospatial data. For binary wetland classification, the highest F1 scores were recorded by XGB (0.73) and RF/HGB (both 0.72) when including all covariates. MLPC also showed competitive performance (wetland F1 score of 0.71), despite its relatively lower spatial consistency. However, each model over-predicts total wetland area at a national level, an issue which was able to be reduced by increasing the classification probability threshold and spatial filtering. (4) Conclusions: The comparative analysis highlights the strengths and trade-offs of RF, XGB, HGB and MLPC models for wetland classification. While all four methods are viable, RF offers some key advantages, including ease of deployment and transferability, positioning it as a promising candidate for scalable, high-resolution wetland monitoring across diverse ecological settings. Further work is required for verification of small-scale wetlands (<~0.5 ha) and the addition of fine-spatial-scale covariates. Full article

Flooding due to dam failures is a critical issue with significant impacts on human safety, infrastructure, and the environment. This study assessed the potential flood hazard that could be generated from breaching of the Alacranes dam in Villa Clara, Cuba. Thirteen reservoir breaching scenarios were simulated under several criteria for modeling the flood wave through the 2D Saint Venant equations using the Hydrologic Engineering Center’s River Analysis System (HEC-RAS). A sensitivity analysis was performed on Manning’s roughness coefficient, demonstrating a low variability of the model outputs for these events. The results show that, for all modeled scenarios, the terrain topography of the coastal plain expands the flood wave, reaching a maximum width of up to 105,057 km. The most critical scenario included a 350 m breach in just 0.67 h. Flood, velocity, and hazard maps were generated, identifying populated areas potentially affected by the flooding events. The reported depths, velocities, and maximum flows could pose extreme danger to infrastructure and populated areas downstream. These types of studies are crucial for both risk assessment and emergency planning in the event of a potential dam breach. Full article

Flood susceptibility mapping is essential for urban planning and disaster risk management, especially in rapidly urbanizing areas exposed to extreme rainfall events. This study applies an integrated approach combining Geographic Information Systems (GIS), map algebra, and the Analytic Hierarchy Process (AHP) to assess flood-prone zones in Ananindeua, Pará, Brazil. Five geoenvironmental criteria—rainfall, land use and land cover (LULC), slope, soil type, and drainage density—were selected and weighted using AHP to generate a composite flood susceptibility index. The results identified rainfall and slope as the most influential criteria, with both contributing to over 184 km² of high-susceptibility area. Spatial patterns showed that flood-prone zones are concentrated in flat urban areas with high drainage density and extensive impermeable surfaces. CHIRPS rainfall data were validated using Pearson’s correlation (r = 0.83) and the Nash–Sutcliffe efficiency (NS = 0.97), confirming the reliability of the precipitation input. The final susceptibility map, categorized into low, medium, and high classes, was validated using flood events derived from Sentinel-1 SAR data (2019–2025), of which 97.2% occurred in medium- or high-susceptibility zones. These findings demonstrate the model’s strong predictive performance and highlight the role of unplanned urban expansion, land cover changes, and inadequate drainage in increasing flood risk. Although specific to Ananindeua, the proposed methodology can be adapted to other urban areas in Brazil, provided local conditions and data availability are considered. Full article

Small reservoirs have important functions, such as water resource guarantee, flood control and drought resistance, biological habitat and maintaining regional economic development. In order to better clarify the impact of agricultural activities on the nutritional status of water bodies in small reservoirs, zooplankton were quantitatively collected from four small reservoirs in the Jiuxianshan agricultural area of Qufu, Shandong Province, in March and October 2023, respectively. The physical and chemical parameters in sampling points were determined simultaneously. Meanwhile, water samples were collected for nutrient salt analysis, and the eutrophication of water bodies in four reservoirs was evaluated using the comprehensive nutrient status index method. The research found that the species richness of zooplankton after farming (100 species) was significantly higher than that before farming (81 species) (p < 0.05). On the contrary, the dominant species of zooplankton after farming (7 species) were significantly fewer than those before farming (11 species). The estimation results of the standing stock of zooplankton indicated that the abundance and biomass of zooplankton after farming (92.72 ind./L, 0.13 mg/L) were significantly higher than those before farming (32.51 ind./L, 0.40 mg/L) (p < 0.05). Community similarity analysis based on zooplankton abundance (ANOSIM) indicated that there were significant differences in zooplankton communities before and after farming (R = 0.329, p = 0.001). The results of multi-dimensional non-metric sorting (NMDS) showed that the communities of zooplankton could be clearly divided into two: pre-farming communities and after farming communities. The Monte Carlo test results are as follows (p < 0.05). Transparency (Trans), pH, permanganate index (COD_Mn), electrical conductivity (Cond) and chlorophyll a (Chl-a) had significant effects on the community structure of zooplankton before farming. Total nitrogen (TN), total phosphorus (TP) and electrical conductivity (Cond) had significant effects on the community structure of zooplankton after farming. The co-linearity network analysis based on zooplankton abundance showed that the zooplankton community before farming was more stable than that after farming. The water evaluation results based on the comprehensive nutritional status index method indicated that the water conditions of the reservoirs before farming were mostly in a mild eutrophic state, while the water conditions of the reservoirs after farming were all in a moderate eutrophic state. The results show that the nutritional status of small reservoirs in agricultural areas is significantly affected by agricultural activities. The zooplankton communities in small reservoirs underwent significant changes driven by alterations in the reservoir water environment and nutritional status. Based on the main results of this study, we suggested that the use of fertilizers and pesticides should be appropriately reduced in future agricultural activities. In order to better protect the water quality and aquatic ecology of the water reservoirs in the agricultural area. Full article

Climate change is projected to significantly alter hydrological conditions across the Northern Hemisphere, with increased precipitation variability, more intense rainfall events, and earlier, rain-driven spring floods in regions like northern Sweden. These changes will affect both natural ecosystems and hydropower-regulated rivers, particularly during ecologically sensitive periods such as the grayling spawning season in late spring. This study examines the impact of extreme spring flow conditions on grayling spawning habitats by analyzing historical runoff data and simulating high-flow events using a 2D hydraulic model in Delft3D FM. Results show that previously suitable spawning areas became too deep or experienced flow velocities beyond ecological thresholds, rendering them unsuitable. These hydrodynamic shifts could have cascading effects on aquatic vegetation and food availability, ultimately threatening the survival and reproductive success of grayling populations. The findings underscore the importance of integrating ecological considerations into future water management and hydropower operation strategies in the face of climate-driven flow variability. Full article

Quantifying future changes in extreme events and associated flooding is challenging yet fundamental for stormwater managers. Along the U.S. Atlantic Coast, Eastern North Carolina (ENC) is frequently exposed to catastrophic floods from extreme rainfall that is typically associated with tropical cyclones. This study presents a novel approach that uses rainfall data from five dynamically and statistically downscaled (DD and SD) global climate models under two scenarios to visualize a potential future extent of flooding in ENC. Here, we use DD data (at 36-km grid spacing) to compute future changes in precipitation intensity–duration–frequency (PIDF) curves at the end of the 21st century. These PIDF curves are further applied to observed rainfall from Hurricane Matthew—a landfalling storm that created widespread flooding across ENC in 2016—to project versions of “Matthew 2100” that reflect changes in extreme precipitation under those scenarios. Each Matthew-2100 rainfall distribution was then used in hydrologic models (HEC-HMS and HEC-RAS) to simulate “2100” discharges and flooding extents in the Neuse River Basin (4686 km²) in ENC. The results show that DD datasets better represented historical changes in extreme rainfall than SD datasets. The projected changes in ENC rainfall (up to 112%) exceed values published for the U.S. but do not exceed historical values. The peak discharges for Matthew-2100 could increase by 23–69%, with 0.4–3 m increases in water surface elevation and 8–57% increases in flooded area. The projected increases in flooding would threaten people, ecosystems, agriculture, infrastructure, and the economy throughout ENC. Full article

Flood control infrastructure is essential for the development of cities and the population’s well-being. The goal is to protect human and economic resources by reducing the inundation area and controlling the flood level and peak discharges. Detention basins can do this by storing a large volume of water to be released after the peak discharge. By doing this, a large amount of energy is stored, which can be recovered via micro-hydropower. In addition, as the release flow is controlled and almost constant, Pumps as Turbines (PAT) could be a feasible and economic option in these cases. Thus, this study investigates the feasibility of micro-hydropower (MHP) in urban detention basins, using the Santa Lúcia detention basin in Belo Horizonte as a case study. The methodology involved hydrological modeling, hydraulic analysis, and economic and environmental assessment. The results demonstrated that PAT selection has a crucial role in the feasibility of the MHP, and exploiting rainfall with lower intensities but higher frequencies is more attractive. Using multiple PATs with different operating points also showed promising results in improving energy production. In addition to the economic benefits, the MHP in the detention basin produces minimal environmental impact and, as it exploits a wasted energy source, it also reduces the carbon footprint in the urban water cycle. Full article

The hydrological response of a watershed is strongly influenced by the spatiotemporal dynamics of rainfall. Rainfall events of similar magnitude can produce markedly different flood processes due to variations in the spatiotemporal patterns of rainfall, posing significant challenges for flood forecasting under complex rainfall scenarios. Traditional methods typically rely on high-resolution or synthetic rainfall data to characterize the scale, direction and velocity of rainstorms, in order to analyze their impact on the flood process. These studies have shown that storms traveling along the main river channel tend to exert the greatest impact on flood processes. Therefore, tracking the movement of the rainfall center along the flow direction, especially when only rain gauge data are available, can reduce model complexity while maintaining forecast accuracy and improving model applicability. This study proposes a machine learning-based time-variable instantaneous unit hydrograph that integrates rainfall spatiotemporal dynamics using quantitative spatial indicators. To overcome limitations of traditional variable unit hydrograph methods, a pre-training and fine-tuning strategy is employed to link the unit hydrograph S-curve with rainfall spatial distribution. First, synthetic pre-training data were used to enable the machine learning model to learn the shape of the S-curve and its general pattern of variation with rainfall spatial distribution. Then, real flood data were employed to learn the actual runoff routing characteristics of the study area. The improved model allows the unit hydrograph to adapt dynamically to rainfall evolution during the flood event, effectively capturing hydrological responses under varying spatiotemporal patterns. The case study shows that the improved model exhibits superior performance across all runoff routing metrics under spatiotemporal rainfall variability. The improved model increased the simulation qualified rate for historical flood events, with significant rainfall center movement during the event from 63% to 90%. This study deepens the understanding of how rainfall dynamics influence watershed response and enhances hourly-scale flood forecasting, providing support for disaster early warning with strong theoretical and practical significance. Full article

Accurate rainfall quantile estimation is critical for urban flood management, particularly given the escalating climate change impacts. This study comprehensively compared at-site frequency analysis and regional frequency analysis for sub-hourly rainfall quantile estimation, using data from 27 sites across Seoul. The analysis focused on Seoul’s disaster prevention framework (30-year and 100-year return periods). Employing L-moment statistics and Monte Carlo simulations, the rainfall quantiles were estimated, the methodological performance was evaluated, and Seoul’s current disaster prevention standards were assessed. The analysis revealed significant spatio-temporal variability in Seoul’s precipitation, causing considerable uncertainty in individual site estimates. A performance evaluation, including the relative root mean square error and confidence interval, consistently showed regional frequency analysis superiority over at-site frequency analysis. While at-site frequency analysis demonstrated better performance only for short return periods (e.g., 2 years), regional frequency analysis exhibited a substantially lower relative root mean square error and significantly narrower confidence intervals for larger return periods (e.g., 10, 30, 100 years). This methodology reduced the average 95% confidence interval width by a factor of approximately 2.7 (26.98 mm versus 73.99 mm). This enhanced reliability stems from the information-pooling capabilities of regional frequency analysis, mitigating uncertainties due to limited record lengths and localized variabilities. Critically, regionally derived 100-year rainfall estimates consistently exceeded Seoul’s 100 mm disaster prevention threshold across most areas, suggesting that the current infrastructure may be substantially under-designed. The use of minute-scale data underscored its necessity for urban hydrological modeling, highlighting the inadequacy of conventional daily rainfall analyses. Full article