Search Results (216)

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

A systems-oriented review of machine learning (ML) over the entire flood management spectrum, encompassing fluvial flood control, pluvial flood management, and resiliency-risk characterization was undertaken. Deep learners like long short-term memory (LSTM) networks perform well in predicting reservoir inflows and outflows. Convolution neural networks (CNNs) and other object identification algorithms are being explored in assessing levee and flood wall failures. The use of ML methods in pump station operations is limited due to lack of public-domain datasets. Reinforcement learning (RL) has shown promise in controlling low-impact development (LID) systems for pluvial flood management. Resiliency is defined in terms of the vulnerability of a community to floods. Multi-criteria decision making (MCDM) and unsupervised ML methods are used to capture vulnerability. Supervised learning is used to model flooding hazards. Conventional approaches perform better than deep learners and ensemble methods for modeling flood hazards due to paucity of data and large inter-model predictive variability. Advances in satellite-based, drone-facilitated data collection and Internet of Things (IoT)-based low-cost sensors offer new research avenues to explore. Transfer learning at ungauged basins holds promise but is largely unexplored. Explainable artificial intelligence (XAI) is seeing increased use and helps the transition of ML models from black-box forecasters to knowledge-enhancing predictors. Full article

Geo-historical information deduced from geo-iconographical resources, derived from extensive research and the selection of cartographies and historical documents, enabled the investigation of the natural and anthropic transformations of the perifluvial area of the Po River in the Emilia-Romagna region (Italy). This territory, significant in terms of its historical, cultural, and environmental contexts, for centuries has been the scene of flood events. These have characterised the morphological and dynamic variability in the riverbed and relative floodplain. The close relationship between man and river is well documented: the interference induced by anthropic activity has alternated with the sometimes-damaging effects of river dynamics. The attention given to the fluvial region of the Po River and its main tributaries, in a peculiar lowland sector near Parma, is critical for understanding spatial–temporal changes contributing to current geo-hydrological risks. A GIS project outlined the geomorphological aspects that define the considerable variations in the course of the Po River (involving width reductions of up to 66% and length changes of up to 14%) and its confluences from the 16th to the 21st century. Knowledge of anthropic modifications is essential as a tool within land-use planning and enhancing community awareness in risk-mitigation activities and strategic management. This study highlights the importance of interdisciplinary geo-historical studies that are complementary in order to decode river dynamics in damaging flood events and latent hazards in an altered river environment. Full article

The Chacopampean Plain is a major physiographic unit in Argentina, bounded by the Colorado River to the south, the Sierras Pampeanas and Subandinas to the west, and the Paraná River, Río de la Plata Estuary, and the Argentine Sea to the east. Its subsurface preserves sediments from the Miocene marine transgression, while the surface hosts some of the country’s most productive soils. Two main geomorphological domains are recognized: fluvial systems dominated by alluvial megafans in the north, and aeolian systems characterized by loess accumulation and wind erosion in the south. The southern sector exhibits diverse landforms such as deflation basins, ridges, dune corridors, lunettes, and mantiform loess deposits. Despite their regional extent, the origin and chronology of many aeolian features remain poorly constrained, as previous studies have primarily focused on depositional units rather than wind-sculpted erosional features. This study integrates remote sensing data, field observations, and a synthesis of published chronometric and sedimentological information to characterize these aeolian landforms and elucidate their genesis. Our findings confirm wind as the dominant morphogenetic agent during Late Quaternary glacial stadials. These aeolian morphologies significantly influence the region’s hydrology, as many permanent and ephemeral water bodies occupy deflation basins or intermediate low-lying sectors prone to flooding under modern climatic conditions, which are considerably wetter than during their original formation. Full article

Flooding is the most frequent natural hazard that accompanies hardships for millions of civilians and substantial economic losses. In Sri Lanka, fluvial floods cause the highest damage to lives and properties. Ratnapura, which is in the Kalu River Basin, is the area most vulnerable to frequent flood events in Sri Lanka due to inherent weather patterns and its geographical location. However, flood-related studies conducted based on the Kalu River Basin and its most vulnerable cities are given minimal attention by researchers. Therefore, it is crucial to develop a robust and reliable dynamic flood forecasting system to issue accurate and timely early flood warnings to vulnerable victims. Modeling the water level at the initial stage and then classifying the results of this into pre-defined flood risk levels facilitates more accurate forecasts for upcoming susceptibilities, since direct flood classification often produces less accurate predictions due to the heavily imbalanced nature of the data. Thus, this study introduces a novel hybrid model that combines a deep leaning technique with a traditional Linear Regression model to first forecast water levels and then detect rare but destructive flood events (i.e., major and critical floods) with high accuracy, from 1 to 3 days ahead. Initially, the water level of the Kalu River at Ratnapura was forecasted 1 to 3 days ahead by employing a Vanilla Bi-LSTM model. Similarly to water level modeling, rainfall at the same location was forecasted 1 to 3 days ahead by applying another Bi-LSTM model. To further improve the forecasting accuracy of the water level, the forecasted water level at day t was combined with the forecasted rainfall for the same day by applying a Time Series Regression model, thereby resulting in a hybrid model. This improvement is imperative mainly because the water level forecasts obtained for a longer lead time may change with the real-time appearance of heavy rainfall. Nevertheless, this important phenomenon has often been neglected in past studies related to modeling water levels. The performances of the models were compared by examining their ability to accurately forecast flood risks, especially at critical levels. The combined model with Bi-LSTM and Time Series Regression outperformed the single Vanilla Bi-LSTM model by forecasting actionable flood events (minor and critical) occurring in the testing period with accuracies of 80%, 80%, and 100% for 1- to 3-day-ahead forecasting, respectively. Moreover, overall, the results evidenced lower RMSE and MAE values (<0.4 m MSL) for three-days-ahead water level forecasts. Therefore, this enhanced approach enables more trustworthy, impact-based flood forecasting for the Rathnapura area in the Kalu River Basin. The same modeling approach could be applied to obtain flood risk levels caused by rivers across the globe. Full article

As a critical intervention for enhancing inland navigation efficiency, waterway regulation projects profoundly modify riverine hydrodynamic conditions while optimizing navigability. This study employs the MIKE21 hydrodynamic model to establish a two-dimensional numerical framework for assessing hydrological alterations induced by channel regulation in the Hui River, China. Through comparative simulations of pre- and post-project scenarios across dry, normal, and wet hydrological years, the research quantifies impacts on water levels, flow velocity distribution, and geomorphic stability. Results reveal that channel dredging and realignment reduced upstream water levels by up to 0.26 m during drought conditions, while concentrating flow velocities in the main channel by 0.5 m/s. However, localized hydrodynamic restructuring triggered bank erosion risks at cut-off bends and sedimentation in anchorage basins. The integrated analysis demonstrates that although regulation measures enhance flood conveyance and navigation capacity, they disrupt sediment transport equilibrium, destabilize riparian ecosystems, and compromise hydrological monitoring consistency. To mitigate these trade-offs, the study proposes design optimizations—including ecological revetments and adaptive dredging strategies—coupled with enhanced hydrodynamic monitoring and riparian habitat restoration. These findings provide a scientific foundation for balancing navigation improvements with the sustainable management of fluvial systems. Full article

Urban flooding is a growing concern, particularly in coastal lowland cities where climate change exacerbates hazards through rising sea levels and intense rainfall. Traditional flood defenses like fluvial polders often exacerbate urban fragmentation and maintenance costs if poorly integrated into planning. This study proposes a multifunctional assessment design framework to evaluate polder design effectiveness considering both the hydraulic and social–environmental dimensions, emphasizing blue–green infrastructure (BGI) for flood control, leisure, and landscape integration. Three design scenarios for Rio de Janeiro’s Jardim Maravilha neighborhood were modeled hydrodynamically: S1 (dike near urban areas, pump-dependent) and S2/S3 (dikes along the riverbank, gravity-driven). Results show S2/S3 outperformed S1 in storage capacity (2.7× larger volume), freeboard resilience (0.42–0.43 m vs. 0.25 m), and urban integration (floodable parks accessible to communities), though S1 had faster reservoir emptying. Under climate change, all scenarios sustained functionality, but S1’s freeboard reduced by 86%, nearing its limit. The framework’s standardized scoring system balanced quantitative and qualitative criteria, revealing trade-offs between hydraulic efficiency and urban adaptability. The optimized S3 design, incorporating external storage and dredging, achieved the best compromise. This approach aids decision-making by systematically evaluating resilience, operational feasibility, and long-term climate adaptation, supporting sustainable flood infrastructure in coastal cities. Full article

Flooding is a prevalent and growing problem involving significant economic losses worldwide. Traditional flood mitigation measures are based on the use of levees, dams, dredging, and river channelization, which can distort the perception of risk, leading to a false sense of security that can induce an increase in the occupation of flood-prone areas. An undisturbed watershed and its fluvial system provide regulating services that contribute to flood mitigation. However, anthropogenic activities can degrade and diminish such services, impacting the magnitude of floods by changing the runoff patterns, erosion, sedimentation, channel conveyance capacity, and floodplain connectivity. Restoration and natural flood management (NFM) seek to recover and improve their watershed regulation services. The bibliographic review performed here aimed to assess the degradation of the natural regulation services of watersheds, which allowed us to identify significant alterations to runoff and streamflow. Also, the review studies of NMF allowed us to identify the restoration actions oriented to recover or enhance the flow regulation capacity of catchments and their fluvial systems. A current challenge is to accumulate more empirical evidence for the effectiveness of such flood mitigation solutions. Currently, the results for large catchments have been obtained mainly by the application of hydrologic and hydraulic models. Also, the adequacy of the different NFM actions to catchments with different physiographic and climatological settings needs to be addressed. Full article

When rivers impinge on the steep bluffs of valley walls, dynamic changes stem from a combination of fluvial and mass wasting processes. This study identifies the geomorphic changes, drivers, and timing of a landslide adjacent to the Apalachicola River at Alum Bluff, the tallest natural geological exposure in Florida at ~40 m, comprising horizontal sediments of mixed lithology. We used hydrographic surveys from 1960 and 2010, two sets of LiDAR from 2007 and 2018, historical aerial, drone, and ground photography, and satellite imagery to interpret changes at this bluff and river bottom. Evidence of slope failure includes a recessed upper section with concave scarps and debris fans in the lower section with subaqueous features including two occlusions and a small island exposed from the channel bottom at lower water levels. Aerial photos and satellite images indicate that the failure occurred in at least two phases in early 2013 and 2015. The loss in volume in the 11-year interval, dominantly from the upper portion of the bluff, was ~72,750 m³ and was offset by gains of ~14,760 m³ at the lower portion of the bluff, suggesting that nearly 80% of the material traveled into the river, causing changes in riverbed morphology from the runout. Despite being along a cutbank and next to the scour pool of a large meandering river, this failure was not driven by floods and the associated lateral erosion, but instead by rainfall in noncohesive sediments at the upper portion of the bluff. This medium-magnitude landslide is now the second documented landslide in Florida. Full article

Flood models based on high-resolution digital elevation models (DEMs) are important for identifying urban land inundation during extreme rainfall events. Urban pluvial and fluvial floods are influenced by distinct processes that are interconnected; thus, they can transform into one another. Conventional flood models struggle to delineate inundation caused by drainage system overflow (urban pluvial flood) and that caused by rivers (urban fluvial flood). In this study, we proposed a novel method for identifying urban pluvial–fluvial floods using a high-resolution DEM. We developed a DEM-based surface pluvial and fluvial inundation tracking model (DEM-SPFITM) that incorporated flood development and mutual transformation processes. When combined with a surface flood control model (SFCM), this approach enabled tracking of the flow paths and exchanged water volume associated with both flood types. The case study results indicate that the proposed method effectively captures the interplay between pluvial and fluvial flooding, enabling the separate identification of flood extent, depth, and velocity under extreme rainfall conditions for both pluvial and fluvial flooding. Compared to the conventional approach, which independently simulates pluvial and fluvial flooding using the SFCM and subsequently overlays the results to estimate pluvial–fluvial flooding inundation, the proposed method demonstrates superior accuracy and computational efficiency. Simulations of three extreme rainstorms indicated that pluvial flooding primarily contributed to extensive land inundation, characterised by shallower depths and lower velocities, with a limited influence of flood depth on velocity. Meanwhile, fluvial flooding further exacerbated land inundation, leading to significant pluvial–fluvial coexistence. In areas adjacent to these flood zones, fluvial flooding predominated, resulting in greater inundation depths and a more pronounced effect of flood depth on velocity. As rainfall intensity and total rainfall increased, the area of fluvial inundation diminished significantly, whereas pluvial–fluvial coexistence intensified and was distributed in zones with relatively large inundation depths and higher flow velocities. This research presented a novel method for distinguishing between urban pluvial–fluvial floods, providing valuable insights for integrated urban flood management and joint flood risk zoning. Full article

This paper presents a novel methodology to generate Digital Elevation Models (DEMs) in flat areas, incorporating river channels from relatively coarse initial data. The technique primarily utilizes filtered dense point clouds derived from SfM-MVS (Structure from Motion-Multi-View Stereo) photogrammetry of available crewed aerial imagery datasets. The methodology operates under the assumption that the aerial survey was carried out during low-flow or drought conditions so that the dry (or almost dry) riverbed is detected, although in an imprecise way. Direct interpolation of the detected elevation points yields unacceptable river channel bottom profiles (often exhibiting unrealistic artifacts) and even distorts the floodplain. In our Fluvial Domain Method, channel bottoms are represented like “highways”, perhaps overlooking their (unknown) detailed morphology but gaining in general topographic consistency. For instance, we observed an 11.7% discrepancy in the river channel long profile (with respect to the measured cross-sections) and a 0.38 m RMSE in the floodplain (with respect to the GNSS-RTK measurements). Unlike conventional methods that utilize active sensors (satellite and airborne LiDAR) or classic topographic surveys—each with precision, cost, or labor limitations—the proposed approach offers a more accessible, cost-effective, and flexible solution that is particularly well suited to cases with scarce base information and financial resources. However, the method’s performance is inherently limited by the quality of input data and the simplification of complex channel morphologies; it is most suitable for cases where high-resolution geomorphological detail is not critical or where direct data acquisition is not feasible. The resulting DEM, incorporating a generalized channel representation, is well suited for flood hazard modeling. A case study of the Ranchería river delta in the Northern Colombian Caribbean demonstrates the methodology. Full article

The identification of ongoing evolutionary trajectories, the prediction of future changes in the functioning of riverine habitats, and the assessment of flood-related risks to human populations all depend on regular hydro-morphological monitoring of fluvial settings. This paper focuses on the satellite monitoring of river macro-morphological units (assemblages of water, sediment, and vegetation units) and their temporal evolution. In particular, we develop a deep-learning semantic segmentation method using Synthetic Aperture Radar (SAR) Sentinel-1 dual-polarized data. The methodology is executed and tested on the Po River, located in Italy. The training of a relatively deep convolutional neural network requires a large amount of ground-truth data, which is often limited and challenging to acquire. To address this limitation, the dataset is augmented using a random forest (RF) classification algorithm. RF parameters are trained with both Sentinel-1 (S1) and Sentinel-2 (S2) data. The RF classification algorithm is very robust and achieves excellent performance. To overcome the limitation linked with the scarce availability of contemporary acquisition by S1 and S2 sensors, the deep learning (DL) model is trained by using only the Sentinel-1 input data and the ground truth from the RF result. The proposed approach achieves promising results in the classification of water, sediments, and vegetation along rivers such as the Italian Po River with low computational costs and no concurrency constraints between S1 and S2. Full article

Narrow Fluvial Sand Bodies are primarily developed along the river center, with horizontal wells for injection and production in some Bohai waterflooded oilfields. This results in a rapid increase in water cut due to a single injection–production direction. Over time, dominant water breakthrough channels form between wells, and the remaining oil moves to deeper regions, which makes conventional profile control measures less effective. We developed a quantitative method based on integrated dynamic and static big data to identify these breakthrough channels and measure the flow intensity between injection and production wells. To address deep remaining oil mobilization, we performed micro-analysis and physical simulations with heterogeneous core models, which led to the development of a deep profile control system using emulsion polymer gel and self-assembling particle flooding. Experiments show that the combined technology can reduce oil saturation in low-permeability layers to 45.3% and improve recovery by 30.2% compared to water flooding. Field trials proved to be completely effective, with a cumulative oil increase of over 23,200 cubic meters and a 12% reduction in water cut per well. This deep profile control technology offers significant water cut reduction and enhanced oil recovery. It can provide technical support for efficient water control and profile management in similar reservoirs. Full article

Despite substantial research, the spatio-temporal dynamics of microplastic fluxes remain underexplored, especially in lower-order rivers. This study aims to quantify microplastic loads using a spatio-temporal sampling approach in a single cross-section of the Lahn River, a typical low-mountain river in Central Germany, over a sampling period from July 2020 to April 2021, covering varying discharge conditions, from low to high flow. A total of 198 plastic particles were detected, averaging 3.67 particles per hour, with a mean microplastic load of 0.03 ± 0.027 particles per cubic metre. Microplastic abundance varied spatially within the river cross-section, with lower concentrations found at deeper sampling positions. The data indicate that higher discharge conditions correlate with increased microplastic loads, predominantly at the water surface, suggesting that hydrological conditions significantly influence plastic transport dynamics. However, it remains unclear whether the microplastics observed at higher discharges originate from additional sources or are reactivated from river sediments. This research highlights the need for further studies to validate model assumptions and better understand the reactivation and transport mechanisms of microplastics in river systems. Full article

Demak is highly vulnerable to flooding from both fluvial and coastal storms, facing increasing pressures on its sustainability and resilience due to multiple compounding flood hazards. This study assesses the inundation hazards in Demak coastal areas by modeling the impacts of compound flooding. We modeled eight scenarios incorporating long-term forces, such as sea level rise (SLR) and land subsidence (LS), as well as immediate forces, like storm surges, wind waves, and river discharge. Our findings reveal that immediate forces primarily increase inundation depth, while long-term forces expand the inundation area. Combined effects from storm tides and other factors resulted in a 10–20% increase in flood extent compared to individual forces. Fluvial flooding mostly impacts areas near river outlets, but the combination of river discharge and storm tides produces flood extents similar to those caused by SLR. Land subsidence emerged as the primary driver of coastal flooding, while other factors, adding just 25% to area increase, significantly impacted inundation depth. These findings underscore the effectiveness of mangroves in mitigating floods in low-lying areas against immediate forces. However, the resilience and sustainability of the Demak region are challenged by SLR, LS, and the need to integrate these factors into a comprehensive flood mitigation strategy. Full article