Search Results (86)

The radial sand-ridge field off the Jiangsu coast is a distinctive landform in a strongly tide-dominated environment, where sediment supply and geomorphic patterns have been profoundly altered by Yellow River course changes, reduced Yangtze-derived sediment, and large-scale reclamation. Focusing on a typical nearshore sector off Dongtai, this study integrates multi-source data from 1979 to 2025, including historical nautical charts, high-precision engineering bathymetry, full-tide hydro-sediment observations, and surficial sediment samples, to quantify seabed erosion–deposition over 46 years and clarify linkages among tidal currents, suspended-sediment transport, and surface grain-size patterns. Surficial sediments from Maozhusha to Jiangjiasha channel systematically fine from north to south: sand-ridge crests are dominated by sandy silt, whereas tidal channels and transition zones are characterized by silty sand and clayey silt. From 1979 to 2025, Zhugensha and its outer flank underwent multi-meter accretion and a marked accretion belt formed between Gaoni and Tiaozini, while the Jiangjiasha channel and adjacent deep troughs experienced persistent scour (local mean rates up to ~0.25 m/a), forming a striped “ridge accretion–trough erosion” pattern. Residual and potential maximum currents in the main channels enhance scour and offshore export of fines, whereas relatively strong depth-averaged flow and near-bed shear on inner sand-ridge flanks favor frequent mobilization and short-range trapping of coarser particles. Suspended-sediment concentration and median grain size are generally positively correlated, with suspension coarsening in high-energy channels but dominated by fine grains on nearshore flats and in deep troughs. These findings refine understanding of muddy-coast geomorphology under strong tides and may inform offshore wind-farm foundation design, navigation-channel maintenance, and coastal-zone management. Full article

Torrential flows are hazardous hydro-geomorphological phenomena characterized by sudden water discharge and intense sediment transport. They occur in mountainous areas where hydrometeorological monitoring is often limited or absent. The lack of such data hinders the identification of flow types and sediment transport conditions, reducing the effectiveness of mitigation measures. To address this issue, the current study focuses on geomorphic characteristics of torrential watersheds and identifies indirect indicators of torrential activity. The sedimentological and geomorphic signatures of torrential flows in the lower Damdere River catchment (Eastern Rhodopes Mountains, southern Bulgaria) were characterized. To capture inter-annual variability in torrential activity and differences between the Damdere and its tributary the Duandere, we sampled riverbed deposits. We also sampled areas upstream and downstream of the check dam to assess its influence. Samples were analyzed for grain size distribution, petrography, and mineralogy (X-ray diffraction). Results show contrasting controls on sediment supply and transport: the Duandere delivers relatively coarse material, whereas the Damdere attains higher transport capacity during torrential events. The check dam is largely infilled and exerts only local effects by trapping finer sediments upstream. Downstream, the channel retains its torrential character. Inter-annual comparison upstream of the structure shows sediment fining linked to lower flows. Petrographic and XRD data point to mechanically driven erosion and rapid sediment transfer. The results underline the importance of geological–geomorphological indicators in the lack of long-term monitoring in similar mountain catchments and can support flood risk management. Full article

Coastal erosion poses a significant risk to the Romanian Black Sea coast, a region characterized by the interaction of natural geomorphological processes and anthropogenic pressures. The research focuses on developing a tool to quantify the cumulative impact of hydro-geo-morphological factors and to assess the vulnerability of the coastal zone to these influences. The approach involves adapting the Coastal Vulnerability Index (CVI)—previously applied in various methodologies—to the specific characteristics of this semi-enclosed basin, which included the exclusion of the tidal range variable due to the Black Sea’s negligible tidal amplitude. The selection of key variables, including coastal geology and geomorphology, shoreline change rates, coastal slope, sea level, and wave regime, was conducted with consideration for the specific characteristics of the Romanian coastal zone. By classifying these variables on a semi-quantitative scale and integrating them into a CVI, the study identifies and maps areas of high vulnerability. The analysis, based on a 1 × 1 km grid resolution, identified sectors of very high vulnerability in the northern Danube Delta unit, particularly along the coastlines of South Sulina–Câşla Vădanei, Sahalin, and Periboina-Edighiol-Vadu. These findings are validated through a comparison with long-term, multidecadal shoreline evolution data, confirming the model’s predictive accuracy. While the 1 × 1 km grid is effective for a macro-scale assessment, the study highlights the need for a finer resolution (e.g., 100 × 100 m) for detailed analysis in the southern region, due to localized geodynamic conditions and the significant influence of coastal infrastructure. Full article

A combined Differential InSAR (D-InSAR) and Digital Elevation Model (DEM)-based analysis revealed that earthquake-triggered landslides significantly altered river morphology and intensified flooding in the Kota Belud catchment, Sabah, Malaysia. This 1386 km² catchment, home to about 120,000 people, has experienced a marked rise in flood events following the 4 June 2015 and 8 March 2018 earthquakes. Multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data and a 30 m Shuttle Radar Topography Mission (SRTM) DEM, complemented by river network information from HydroBASINS, were integrated to map sediment redistribution and model flood extent. Upstream zones exhibited extensive coseismic landslides and pronounced geomorphic disruption. Interferometric analysis showed that coherence was well preserved over stable terrain but rapidly degraded in vegetated and steep areas. Sediment aggradation, interpreted qualitatively from patterns of coherence loss and increased backscatter intensity, highlights slope failure initiation zones and depositional build-up along channels. Conversely, downstream, similar sedimentary adjustments were detected immediately upstream of areas with repeated flood incidents. Between 2015 and 2018, flood occurrences increased over fivefold, and after 2018, they increased by more than thirteenfold relative to pre-2015 conditions. DEM-based inundation simulations demonstrated that channel shallowing substantially reduced conveyance capacity and expanded flood extent. Collectively, these results confirm that earthquake-induced landslides have contributed to reshaping the geomorphology and amplified flooding in the area. 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

Coastal wetlands play important environmental roles. However, their hydrogeomorphological dynamics remain poorly understood under scenarios of extreme climate events. The aim of this study was to characterize the temporal and spatial variability of hydrogeomorphological attributes (vegetation, water, and soil) in the wetlands of Lagoa do Peixe National Park, Brazil. The methodology involved applying Principal Component Analysis (PCA) in both temporal (T) and spatial (S) modes, decomposing spectral indices for each attribute to identify variability patterns. The results revealed that vegetation and water are strongly correlated with seasonal dynamics influenced by ENSO (El Niño/La Niña) events. Soils reflected their textural characteristics, with a distinct temporal response to the water balance. PCA proved to be a useful tool for synthesizing large volumes of multitemporal data and detecting dominant variability patterns. It highlighted the Lagoon Terraces and the Lagoon Fringe, where low slopes amplified hydrological variations. Temporal variability was more responsive to climate extremes, with implications for ecosystem conservation, while spatial variability was modulated by geomorphology. Full article

Three-dimensional hydro-acoustic imaging is a research hot spot in the underwater acoustic signal processing field, which has a wide range of application prospects in marine environmental resource surveying, seabed topography and geomorphological mapping, and underwater early warning and monitoring. To solve the problem that the resolution of the current imaging sonar reduces rapidly with increase in distance and a scanning gap exists in side-scan sonar, we designed a down-looking 3D-imaging sonar with a linear array structure. The imaging scheme adopts a time-domain spatial beam-forming method with the Back Projection (BP) algorithm as the core, and the formation of a virtual plane array can effectively improve the along-track resolution. To cope with the interference of the carrier motion error on the imaging, we proposed a high-precision sub-wavelength motion compensation method based on a real-time acoustic calibration system. Simulation and real data experiments show that the motion compensation method can effectively eliminate the influence of motion error and make the imaging energy more focused, leading to higher-quality acoustic images. Under equal average energy, the maximum superimposed sound intensity values in the imaging results increased by 20.75 dB and 6.57 dB, respectively, for simulation and real data. After motion compensation, the resolution of this imaging system reached 3 cm × 3 cm × 2.5 cm @ Depth = 17 m, TBP = 30 s · Hz. Full article

In recent decades, understanding how climate variability and human activities drive long-term changes in river discharge and sediment load has become a crucial field of research in fluvial geomorphology, particularly for South Asia’s densely populated and environmentally sensitive regions. This study analyses spatio-temporal trends in water discharge (Q_d) and sediment load (Q_s) in the Krishna basin, Peninsular India’s second-largest catchment. Using nearly 50 years of daily discharge, sediment concentration, and rainfall data from the Central Water Commission (CWC) and India Meteorological Department (IMD), we applied Mann–Kendall, Pettitt tests, and double mass equations to detect long-term trends, abrupt changes, and the relative influence of climate and anthropogenic effects. Results showed a notable decline in the annual discharge, with 15 of 20 stations showing decreasing trends, especially along the Bhima, Ghataprabha, and lower Krishna rivers. The annual stream flow data showed a 53% decline in the mean Q_d from 26.01 × 10⁹ m³ year⁻¹ before 2000 to 12.32 × 10⁹ m³ year⁻¹ after 2000 at the terminal station. Eight of ten gauging stations showed a significant decrease (p-value < 0.05) in their annual sediment load, with a 76% reduction across the Krishna basin after its changepoint in 1983. The Pettitt test identified a statistically significant downward shift in discharge at seven stations. Double mass plots indicate that anthropogenic factors, such as large-scale reservoirs and water diversion, are the main contributors, accounting for 82.7% of sediment decline, with climatic factors contributing 17.1%. The combined trend analysis and double mass plots confirm these findings, underscoring the need for further study of human impacts on the basin’s hydro-geomorphology. This study offers a clear and robust approach to quantifying the relative effects of climate and human activities, providing a versatile framework that can enhance understanding in similar studies worldwide. Full article

Soil piping erosion is an underground soil erosion process that is significantly underestimated or overlooked. It can lead to intense soil erosion and trigger surface processes such as landslides, collapses, and channel erosion. Conducting susceptibility mapping is a vital way to identify the potential for soil piping erosion, which is of enormous significance for soil and water conservation as well as geological disaster prevention. This study utilized airborne radar drones to survey and map 1194 sinkholes in Sunjiacha basin, Huining County, on the Loess Plateau in Northwest China. We identified seventeen key hydrogeomorphological factors that influence sinkhole susceptibility and used six machine learning models—support vector machine (SVM), logistic regression (LR), Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), random forest (RF), and gradient boosting decision tree (GBDT)—for the susceptibility assessment and mapping of loess sinkholes. We then evaluated and validated the prediction results of various models using the area under curve (AUC) of the Receiver Operating Characteristic Curve (ROC). The results showed that all six of these machine learning algorithms had an AUC of more than 0.85. The GBDT model had the best predictive accuracy (AUC = 0.94) and model migration performance (AUC = 0.93), and it could find sinkholes with high and very high susceptibility levels in loess areas. This suggests that the GBDT model is well suited for the fine-scale susceptibility mapping of sinkholes in loess regions. Full article

This study focuses on a complex Brazilian Neoproterozoic karst (hydro)geological and geomorphological area, consisting of metapelitic–carbonate sedimentary rocks of ~740–590 Ma, forming the largest carbonate sequence in the country. At the center of the area lies the Lagoa Santa Karst Environmental Protection Area (LSKEPA), located near the Minas Gerais’ state capital, Belo Horizonte, and presents a series of lakes associated with the large fluvial system of the Velhas river under the influence, locally, of carbonate rocks. The hydrodynamics of carbonate lakes remain enigmatic, and various factors can influence the behavior of these water bodies. This work analyzed the hydrological behavior of 129 lakes within the LSKEPA to understand potential connections with the main karst aquifer, karst-fissure aquifer, and porous aquifer, as well as their evolution patterns in the physical environment. Pluviometric surveys and satellite image analysis were conducted from 1984 to 2020 to observe how the lakes’ shorelines behaved in response to meteorological variations. The temporal assessment for understanding landscape evolution proves to be an effective tool and provides important information about the interaction between groundwater and surface water. The 129 lakes were grouped into eight classes representing the hydrological connection patterns with the aquifers in the region, with classes defined for perennial lakes: (1) constantly connected, (2) seasonally disconnected, and (3) disconnected; for intermittent lakes: (4) disconnected during the analyzed time interval, (5) seasonally connected, (6) disconnected, (7) extremely disconnected, and (8) intermittent lakes that connected and stopped drying up. The patterns observed in the variation of lakes’ shorelines under the influence of different pluviometric moments showed a positive correlation, especially in dry periods, where these water bodies may be functioning as recharge or discharge zones of the karst aquifer. These inputs and outputs are conditioned to the well-developed karst tertiary porosity, where water flow in the epikarst moves according to the direction of enlarged karstified fractures, rock foliation planes, and lithological contacts. Other factors may condition the hydrological behavior of the lakes, such as rates of evapotranspiration, intensity of rainfall during rainy periods, and excessive exploitation of water. Full article

Natural risks comprise a whole range of disasters and dangers, requiring comprehensive management through advanced assessment, forecasting, and warning systems. Our specific focus is on landslides in difficult terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum GIS approach, which integrates qualitative parameters. Despite the challenges posed by the rugged terrain in Northern Algeria, it is paradoxically home to a dense population attracted by valuable hydro-agricultural resources. The goal of our research is to study landslide risks in these areas, particularly in the Mila region, with the aim of constructing a mathematical model that integrates both hazard and vulnerability considerations. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. We developed two algorithms, the analytic hierarchy process (AHP) and the fuzzy analytic hierarchy process (FAHP), to prioritize criteria and sub-criteria by assigning weights to them, aiming to find the optimal solution. By integrating multi-source data, including satellite images and in situ measurements, into a GIS and applying the two algorithms, we successfully generated landslide susceptibility maps. The FAHP method demonstrated a higher capacity to manage uncertainty and specialist assessment errors. Finally, a comparison between the developed risk map and the observed risk inventory map revealed a strong correlation between the thematic datasets. Full article

This study presents a comprehensive multi-model machine learning (ML) approach to predict river bed load, addressing the challenge of quantifying predictive uncertainty in fluvial geomorphology. Six ML models—random forest (RF), categorical boosting (CAT), extra tree regression (ETR), gradient boosting machine (GBM), Bayesian regression model (BRM), and K-nearest neighbors (KNNs)—were thoroughly evaluated across several performance metrics like root mean square error (RMSE), and correlation coefficient (R). To enhance model training and optimize performance, particle swarm optimization (PSO) was employed for hyperparameter tuning across all the models, leveraging its capability to efficiently explore complex hyperparameter spaces. Our findings indicated that RF, GBM, CAT, and ETR demonstrate superior predictive performance (R score > 0.936), benefiting significantly from PSO. In contrast, BRM displayed lower performance (0.838), indicating challenges with Bayesian approaches. The feature importance analysis, including permutation feature and SHAP values, highlighted the non-linear interdependencies between the variables, with river discharge (Q), bed slope (S), and flow width (W) being the most influential. This study also examined the specific impact of individual variables on model performance by adding and excluding individual variables, which is particularly meaningful when choosing input variables for the model, especially in limited data conditions. Uncertainty quantification through Monte Carlo simulations highlighted the enhanced predictability and reliability of models with larger datasets. The correlation between increased training data and improved model precision was evident in the consistent rise in mean R scores and reduction in standard deviations as the sample size increased. This research underscored the potential of advanced ensemble methods and PSO to mitigate the limitations of single-predictor models and exploit collective model strengths, thereby improving the reliability of predictions in river bed load estimation. The insights from this study provide a valuable framework for future research directions focused on optimizing ensemble configurations for hydro-dynamic modeling. Full article

The Precambrian hard rock topography of the Manbhum-Singhbhum plateau, which is well known for its semi-arid climates prone to drought, is often seen in Purulia district in West Bengal, India. Despite the district’s middling groundwater capacity, 17 out of 20 blocks have exorbitant fluoride pollution in the groundwater that negatively impacts the health of local residents. Approximately 13% of the whole area suffers from severe erosion. It is evident that the river Kangsabati and its tributaries are not well fed by rainwater and thereby there is always a dearth of ground water. The aim of this study was to identify and prioritize integral watersheds in the Purulia area using Multi-Criteria Decision Making (MCDM) and Geographic Information Systems (GIS). The evaluation was carried out in the Bandu sub-watershed, which contains five micro watersheds: 2A2B5m, 2A2B5k, 2A2B5h, 2A2B5b, and 2A2B5j. The analysis considered five major factors: lithological properties, land use and land cover, soil erosion, groundwater recharge, and hydrogeomorphology. The weights of these criteria were determined by the Analytical Hierarchy Process (AHP) model, which was then prioritized using the Techniques for Order of Preference by Similarity to the Ideal Solution (TOPSIS) technique. This study emphasized an integrated approach to assess watershed hazards and to establish rational conservation goals. The Central Ground Water Board (CGWB) of India report was referred during data analysis. As a result of this study, the 2A2B5k watershed emerged as the most critical due to its susceptibility across the analyzed parameters. This thorough plan demonstrated the usefulness of identifying watershed threads and prioritizing conservation efforts. Full article

The hydro-geomorphological background in microplastic (MP) deposition and mobilization is often neglected, though the sampling environment is the key point in a monitoring scheme. The aim of the study was to analyze the environmental driving factors of MP transport over three years (2020–2022) along a 750 km-long section of the Tisza River, Central Europe. The mean MP content of the fresh clayey sediments was 1291 ± 618 items/kg in 2020, and then it decreased (2021: 730 ± 568 items/kg; 2022: 766 ± 437 items/kg). The upstream and downstream sections were the most polluted due to improper local sewage treatment. In 2020, 63% of the sites were hotspot (≥2000 items/kg), but their number decreased to one-third in 2021 and 2022. MP pollution is influenced by highly variable environmental factors. (1) The geomorphological setting of a site is important, as most of the hotspots are on side bars. (2) The tributaries convey MP pollution to the Tisza River. (3) The bankfull or higher flood waves effectively rearrange the MP pollution. (4) The dams and their operation influence the downstream trend of MP pollution in the reservoir. (5) Downstream of a dam, the clear-water erosion increases the proportion of the pristine sediments; thus, the MP concentration decreases. Full article

The increasing availability and quality of remote sensing data are changing the methods used in fluvial geomorphology applications, allowing the observation of hydro-morpho-biodynamics processes and their spatial and temporal variations at broader and more refined scales. With the advent of cloud-based computing, it is nowadays possible to reduce data processing time and increase code sharing, facilitating the development of reproducible analyses at regional and global scales. The consolidation of Earth Observation mission data into a single repository such as Google Earth Engine (GEE) offers the opportunity to standardize various methods found in literature, in particular those related to the identification of key geomorphological parameters. This work investigates different computational techniques and timeframes (e.g., seasonal, annual) for the automatic detection of the active river channel and its multi-temporal aggregation, proposing a rational integration of remote sensing tools into river monitoring and management. In particular, we propose a quantitative analysis of different approaches to obtain a synthetic representative image of river corridors, where each pixel is computed as a percentile of the bands (or a combination of bands) of all available images in a given time span. Synthetic images have the advantage of limiting the variability of individual images, thus providing more robust results in terms of the classification of the main components of the riverine ecosystem (sediments, water, and riparian vegetation). We apply the analysis to a set of rivers with analogous bioclimatic conditions and different levels of anthropic pressure, using a combination of Landsat and Sentinel-2 data. The results show that synthetic images derived from multispectral indexes (such as NDVI and MDWI) are more accurate than synthetic images derived from single bands. In addition, different temporal reduction statistics affect the detection of the active channel, and we suggest using the 90th percentile instead of the median to improve the detection of vegetated areas. Individual representative images are then aggregated into multitemporal maps to define a systematic and easily replicable approach for extracting active river corridors and their inherent spatial and temporal dynamics. Finally, the proposed procedure has the potential to be easily implemented and automated as a tool to provide relevant data to river managers. Full article