Search Results (316)

The description and assessment of braided river architecture are usually limited by the paucity of real geological datasets from field observations; due to the complexity and diversity of rivers, traditional evaluation models are difficult to apply to braided river systems in different climatic and tectonic settings. This study aims to establish an architectural model suitable for the study area setting by introducing a hierarchical analysis approach through well-exposed three-dimensional outcrops along the Second Songhua River. A micro–macro four-level hierarchical framework is adopted to obtain a detailed anatomy of sedimentary outcrops: lithofacies, elements, element associations, and archetypes. Fourteen lithofacies are identified: three conglomerates, seven sandstones, and four mudstones. Five elements provide the basic components of the river system framework: fluvial channel, laterally accreting bar, downstream accreting bar, abandoned channel, and floodplain. Four combinations of adjacent elements are determined: fluvial channel and downstream accreting bar, fluvial channel and laterally accreting bar, erosionally based fluvial channel and laterally accreting bar, and abandoned channel and floodplain. Considering the sedimentary evolution process, the braided river prototype, which is an element-based channel filling unit, is established by documenting three contact combinations between different elements and six types of fine-grained deposits’ preservation positions in the elements. Empirical relationships are developed among the bankfull channel depth, mean bankfull channel depth, and bankfull channel width. For the braided river systems, the establishment of the model promotes understanding of the architecture and evolution, and the application of the hierarchical analysis approach provides a basis for outcrop, underground reservoir, and tank experiments. Full article

African hydrological systems are incredibly complex and highly sensitive to climate variability. This review synthesizes observational data, remote sensing, and climate modeling to understand the interactions between fluvial processes, water cycle dynamics, and anthropogenic pressures. Currently, these systems are experiencing accelerating warming (+0.3 °C/decade), leading to more intense hydrological extremes and regionally varied responses. For example, East Africa has shown reversed temperature–moisture correlations since the Holocene onset, while West African rivers demonstrate nonlinear runoff sensitivity (a threefold reduction per unit decline in rainfall). Land-use and land-cover changes (LULCC) are as impactful as climate change, with analysis from 1959–2014 revealing extensive conversion of primary non-forest land and a more than sixfold increase in the intensity of pastureland expansion by the early 21st century. Future projections, exemplified by studies in basins like Ethiopia’s Gilgel Gibe and Ghana’s Vea, indicate escalating aridity with significant reductions in surface runoff and groundwater recharge, increasing aquifer stress. These findings underscore the need for integrated adaptation strategies that leverage remote sensing, nature-based solutions, and transboundary governance to build resilient water futures across Africa’s diverse basins. 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

Riverbed dynamics are natural processes that are strongly driven by human and climatic factors. In the last two centuries, the anthropogenic influence and impact of climate change on European rivers has resulted in significant degradation of riverbeds. This research paper aims to determine the historical evolution (1856–2021) and future trends of the Ialomița riverbed (Romania) under the influence of anthropogenic impact and climate change. The case study is a reach of 66 km between the confluences with the Ialomicioara and Pâscov rivers. The localisation in a contact zone between the Curvature Subcarpathians and the Târgoviște Plain, the active recent tectonic uplift of the area, and the intense anthropogenic intervention gives to this river reach favourable conditions for pronounced riverbed dynamics over time. To achieve the aim of the study, we developed a complex methodology which involves the use of Geographical Information System (GIS) techniques, hierarchical cluster analysis (HCA), the Mann–Kendall test (MK), and R programming. The results indicate that the evolution of the Ialomița River aligns with the general trends observed across Europe and within Romania, characterised by a reduction in riverbed geomorphological complexity and a general transition from a braided, multi-thread into a sinuous, single-thread fluvial style. The main processes consist of channel narrowing and incision alternating with intense meandering. However, specific temporal and spatial evolution patterns were identified, mainly influenced by the increasingly anthropogenic local influences and confirmed climate changes in the study area since the second half of the 20th century. Future evolutionary trends suggest that, in the absence of river restoration interventions, the Ialomița riverbed is expected to continue degrading on a short-term horizon, following both climatic and anthropogenic signals. The findings of this study may contribute to a better understanding of recent river behaviours and serve as a valuable tool for the management of the Ialomița River. Full article

Given the increasing challenges posed by frequent extreme climatic events, understanding the climate–human connection between the climate system and the transitions of ancient civilizations is crucial for addressing future climatic challenges, especially when examining the relationship between the abrupt events of the Holocene and the Neolithic culture development. Compared with the globally recognized “4.2 ka collapse” of ancient cultures, the initial start time and the cultural significance of the 5.5 ka climatic fluctuation are more complex and ambiguous. The Hongshan culture (6.5–5.0 ka) is characterized by a complicated society evident in its grand public architecture and elaborate high-status tombs. However, the driving mechanisms behind cultural changes remain complex and subject to ongoing debate. This paper delves into the role of climatic change in Hongshan cultural shifts, presenting an integrated dataset that combines climatic proxy records with archaeological data from the Hongshan culture period. Based on synthesized aeolian, fluvial-lacustrine, loess, and stalagmite deposits, the study indicates a relatively cold and dry climatic fluctuation occurred during ~6.0–5.5 ka, which is widespread in the Horqin dune field and adjacent areas. Combining spatial analysis with ArcGis 10.8 on archaeological sites, we propose that the climatic fluctuation between ~6.0–5.5 ka likely triggered the migration of the Hongshan settlements and adjustment of survival strategies. 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

The Lower–Middle Jurassic of the Kuqa Depression consists of terrestrial clastic deposits containing coal seams and thick lacustrine mudstones, and is of great significance for oil and gas exploration. Based on the comprehensive analysis of core, well-logging, outcrop, and seismic data, the sequence stratigraphy, depositional systems, and the controlling factors of the basin filling in the depression are systematically documented. Four primary depositional systems, including braided river delta, meandering river delta, lacustrine, and swamp deposits, are identified within the Ahe, Yangxia, and Kezilenuer Formations of the Lower–Middle Jurassic. The basin fills can be classified into two second-order and nine third-order sequences (SQ1–SQ9) confined by regional or local unconformities and their correlative conformities. This study shows that the sedimentary evolution has undergone the following three stages: Stage I (SQ1–SQ2) primarily developed braided river, braided river delta, and shallow lacustrine deposits; Stage II (SQ3–SQ5) primarily developed meandering river, meandering river delta, and extensive deep and semi-deep lacustrine deposits; Stage III (SQ6–SQ9) primarily developed swamp (SQ6–SQ7), meandering river delta, and shore–shallow lacustrine deposits (SQ8–SQ9). The uplift of the Tianshan Orogenic Belt in the Early Jurassic (Stage I) may have facilitated the development of braided fluvial–deltaic deposits. The subsequential expansion of the sedimentary area and the weakened sediment supply can be attributed to the planation of the source area and widespread basin subsidence, with the transition of the depositional environments from braided river delta deposits to meandering river delta and swamp deposits. The regional expansion or rise of the lake during Stage II was likely triggered by the hot and humid climate conditions, possibly associated with the Early Jurassic Toarcian Oceanic Anoxic Event. The thick swamp deposits formed during Stage III may be controlled by the interplay of rational accommodation, warm and humid climatic conditions, and limited sediment supply. Milankovitch cycles identified in Stage III further reveal that coal accumulation was primarily modulated by long-period eccentricity forcing. Full article

This article focuses on forecasting morphological changes in small rivers, using the Ulken Almaty River, located on the northern slope of the Ile Alatau range in the Tien Shan mountain system, as a case study. One of the key components of river morphology is the dynamics of channel processes, including erosion, accretion, and the shifting of channel forms. Understanding these processes in rivers flowing through urbanized areas is essential for mitigating environmental and infrastructural risks. Despite their importance, studies of this nature in Kazakhstan remain at a formative stage and are largely fragmentary, underscoring the need for modern approaches to river morphology analysis. Three representative sections of the Ulken Almaty River (upstream, midstream, and downstream) were selected for analysis. Satellite imagery from 2012 to 2021 was used for manual digitisation of river channel outlines. Annual erosion and accretion areas were calculated based on these data. The DSAS 5.1 module, integrated into ArcGIS 10.8.1, was applied to determine the rates of erosion and accretion over the ten-year period. To forecast future channel changes, the Kalman filter model was employed, enabling projections for 10 and 20 years into the future. A comparative analysis of the intensity of the erosion and accretion processes was conducted for each river section. Spatial and temporal variations in bank dynamics were identified, with the most significant changes occurring in the middle and lower reaches. Forecasted scenarios indicate the possible deformation pathways of the river channel influenced by both natural and anthropogenic factors. The results provide valuable insights into the spatiotemporal dynamics of fluvial processes in small mountain rivers under the pressure of urban development and climatic variability. The methodology employed in this study offers practical applications for urban planning, river management, and the mitigation of geomorphological hazards. Full article

Microplastics (MPs) have emerged as a critical environmental challenge in China’s aquatic ecosystems, driven by rapid industrialization and population growth. This review synthesizes recent findings on the abundance, morphology, and polymer types of MPs in China’s freshwater systems (rivers, lakes, reservoirs) and coastal marine environments. Spatial analysis reveals significant variability in MP abundance, ranging from 0.1 items/L in Tibet’s Lalu Wetland to 30.8 items/L in Beijing’s Qinghe River, with polypropylene (PP) and polyethylene (PE) dominating polymer profiles. Coastal regions exhibit distinct contamination patterns, with the Yellow Sea (5.3 ± 2.0 items/L) and the South China Sea (180 ± 80 items/m³) showing the highest MP loads, primarily as fibers and fragments. Fluvial transport, atmospheric deposition, and coastal anthropogenic activities (e.g., fisheries, tourism) are identified as major pathways for marine MP influx. Secondary MPs from degraded plastics and primary MPs from industrial/domestic effluents pose synergistic risks through the adsorption of heavy metals and organic pollutants. Human exposure routes—ingestion, inhalation, and dermal contact—are linked to inflammatory, metabolic, and carcinogenic health outcomes. Policy interventions, including bans on microbeads and non-degradable plastics, demonstrate progress in pollution mitigation. This work underscores the urgency of integrated source control, advanced wastewater treatment, and transboundary monitoring to address MP contamination in aquatic ecosystems. Full article

The Upper Carboniferous strata in the eastern Qaidam Basin, comprising several hundred meters of thick, mixed clastic–carbonate successions that have been little reported or explained, provide an excellent geological record of paleoenvironmental and paleo-sea level changes during the Late Carboniferous icehouse period. This tropical carbonate–clastic system offers critical constraints for correlating equatorial sea level responses with high-latitude glacial cycles during the Late Paleozoic Ice Age. Based on detailed outcrop observations and interpretations, five facies assemblages, including fluvial channel, tide-dominated estuary, wave-dominated shoreface, tide-influenced delta, and carbonate-dominated marine, have been identified and organized into cyclical stacking patterns. Correspondingly, four third-order sequences were recognized, each composed of lowstand, transgressive, and highstand system tracts (LST, TST, and HST). LST is generally dominated by fluvial channels as a result of river juvenation when the sea level falls. The TST is characterized by tide-dominated estuaries, followed by retrogradational, carbonated-dominated marine deposits formed during a period of sea level rise. The HST is dominated by aggradational marine deposits, wave-dominated shoreface environments, or tide-influenced deltas, caused by subsequent sea level falls and increased debris supply. The sequence stratigraphic evolution and geochemical records, based on carbon and oxygen isotopes and trace elements, suggest that during the Late Carboniferous period, the eastern Qaidam Basin experienced at least four significant sea level fluctuation events, and an overall long-term sea level rise. These were primarily driven by the Gondwana glacio-eustasy and regionally ascribed to the Paleo-Tethys Ocean expansion induced by the late Hercynian movement. Assessing the history of glacio-eustasy-driven sea level changes in the eastern Qaidam Basin is useful for predicting the distribution and evolution of mixed cyclic succession in and around the Tibetan Plateau. Full article

As the world’s third-longest river supporting 40% of China’s population, the Yangtze River Basin exemplifies the critical challenges of balancing riparian development and ecological resilience for major fluvial systems globally. This study analyzed the spatiotemporal evolution, proximity dynamics to the Yangtze River, and driving mechanisms of the “three types of spaces” (urban, agricultural, and ecological) in 130 counties along the Yangtze River mainstem from 2000 to 2020, utilizing an integrated approach incorporating land use transfer matrices, centroid-based distance metrics and GeoDetector models. Key findings reveal: (1) Urban space exhibited significant irreversible expansion while agricultural space continued to shrink, with ecological space maintaining overall stability but showing high-frequency bidirectional conversion with agricultural areas in localized zones. (2) Spatial proximity analysis demonstrated contrasting patterns—eastern riparian counties showed urban spatial agglomeration towards the river, whereas most mid-western regions experienced urban expansion away from the watercourse, with marked regional disparities in agricultural and ecological spatial changes. (3) Driving mechanism analysis identified topography as the dominant natural factor influencing ecological space evolution, while socioeconomic factors exerted stronger impacts on proximity variations of agricultural and urban spaces, with natural–socioeconomic interactive effects showing the most significant explanatory power. These spatial dynamics reflect universal trade-offs between economic development and ecosystem conservation in large river basins worldwide. We advocate differentiated spatial governance strategies, including rigorous riparian ecological redlines, eco-agricultural models in agricultural retreat zones, and proximity-based real-time monitoring for ecological early warning. The integrated methodology and spatial governance framework offer transferable solutions for sustainable management of major fluvial systems under rapid urbanization pressure. These findings provide scientific evidence and implementable pathways for coordinating socioeconomic development with ecosystem resilience in the Yangtze River Economic Belt. Full article

As a legacy of historical metal mining and the processing and smelting of metalliferous ores, metal pollution is a serious environmental problem in many areas around the globe. This review summarizes the history, technical development and environmental hazards of historic metal mining and metallurgical activities in the Harz Region (Germany), one of the oldest and most productive mining landscapes in Central Europe. The release of large amounts of metal-containing waste into rivers during historic ore processing and the ongoing leaching of metals from slag heaps, tailings dumps and contaminated soils and sediments are the main sources of metal pollution in the Harz Mountains and its foreland. This pollution extends along river systems with tributaries from the Harz Mountains and can even be detected in mudflats of the North Sea. In addition to fluvial discharges, atmospheric pollution by smelter smoke has led to long-term damage to soils and vegetation in the Harz Region. Currently, the ecological hazards caused by the legacy pollution from historical metal mining and metallurgy in the Harz Region are only partially known, particularly regarding the effects of changes in river ecosystems as a consequence of climate change. This review discusses the complexity and dynamics of human–environment interactions in the Harz Mountains and its surroundings, with a focus on lead (Pb) pollution. The paper also identifies future research directions with respect to metal contamination. 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

Understanding the structural concordance between taxonomic and functional diversity (FD) metrics is essential for improving the ecological interpretation of community patterns in biomonitoring programs. This study evaluated the concordance between taxonomic and FD metrics of benthic macroinvertebrates along a fluvial habitat quality gradient in the Paute River Basin, Ecuador. Macroinvertebrate communities were sampled over six years at twelve sampling points and assessed using four taxonomic metrics: Shannon diversity (H), the Margalef index (D_Mg), family richness (N), and the Andean Biotic Index (ABI). Functional diversity was evaluated using four metrics: weighted functional dendrogram-based diversity (wFDc), Rao’s quadratic entropy (Rao), functional dispersion (FDis), and functional richness (FRic). The fluvial habitat index (FHI) was used as an environmental reference to evaluate diversity metric responses. K-means clustering was independently applied to each metric, and pairwise concordance was quantified using the Measure of Concordance (MoC) and overlap in sampling points groupings across replicates. Most metrics (except FRic and N) showed clear responsiveness to the FHI gradient, confirming their ecological relevance. Strong structural concordance was observed between H and D_Mg and the FD metrics Rao, FDis, and wFDc, showing that these metrics captured similar yet complementary aspects of community organization. In contrast, ABI showed marked sensitivity to the FHI gradient but low concordance with functional metrics, suggesting distinct dimensions of biological integrity not encompassed by trait-based metrics. These findings highlight the value of combining taxonomic and functional metrics to detect both broad and subtle ecological changes. Integrating metrics with differing structural properties and environmental sensitivities can enhance the robustness of freshwater biomonitoring frameworks, especially in systems undergoing ecological transition or habitat degradation. Full article

The High Andean region of Peru, characterized by a complex orography, has unique and highly biodiverse ecosystems. This region has several headwater basins that play a critical role in the hydrological cycle, providing diverse ecosystem services essential to sustain biodiversity and supply water to human communities. Despite the importance of this region, it faces significant human intervention, particularly mining activities, which affect basin headwaters and jeopardize water security. This study aimed to calibrate the Biological Monitoring Working Party (BMWP) index to evaluate water quality in High Andean rivers in Peru affected by mining activities, using aquatic macroinvertebrates as bioindicators. We used a 15-year dataset (2008 to 2023) from three headwater basins in the High Andean region; this dataset included physicochemical water quality parameters, trace metals, and aquatic macroinvertebrates. The BMWP was calibrated for the High Andean region of Peru with this dataset (BMWP/PeIAZIM); afterward, it was validated to assess water quality in an area influenced by mining activities in this region. The results allowed us to differentiate between aquatic macroinvertebrate families tolerant to mining pollution and highly sensitive families. The sites heavily affected by mining activity returned very low BMWP/PeIAZIM scores; sites with no mining impact had the highest scores. These findings indicate that the calibrated index can be used for water resource management in the High Andean region, contributing to the conservation of its ecosystems. Full article