Search Results (1,265)

The Upper Blue Nile Basin contributes about 60% of the Nile River’s annual streamflow but faces severe sediment-related challenges driven by intense monsoonal erosion and reservoir siltation. Accurate estimation of suspended sediment concentration (SSC) and sediment load in large, data-scarce watersheds remains difficult due to sparse monitoring and complex supply-limited transport dynamics. This study develops a hybrid machine learning (ML) and process-based approach for the Kessie watershed (65,784 km²), a major sediment source upstream of the GERD. The approach combines Random Forest (RF) based SSC reconstruction from 251 intermittent samples spanning 1995–2011, approximately 70% collected during the wet season (June–October) and 94% concentrated in 2008–2011, covering a wide range of observed streamflow conditions at the time of sampling (120–5897 m³/s), with a two-stage calibration of the WASA-SED model. Using hydrologically informed predictors, the RF algorithm substantially outperformed the bias-corrected traditional sediment rating curve and other ML algorithms, increasing the validation coefficient of determination (R²) from 0.274 to 0.693. The reconstructed daily SSC yielded a mean annual sediment load of 180.7 Mt/yr. The model performed well, particularly at monthly scales for 1995–2011, achieving good to very good performance (NSE up to 0.83/0.71 for streamflow and 0.86/0.63 for sediment load, calibration/validation, respectively) and reproducing dominant hydrological and sediment regimes using duration curves. Mann–Kendall trend analysis (α = 0.05) indicated no statistically significant monotonic trends in annual rainfall (p = 0.90), mean annual streamflow (p = 0.24 observed; p = 0.84 simulated), or mean annual sediment load (p = 0.66 simulated); the reconstructed sediment load series showed a near-significant increasing tendency (p = 0.06) that falls below the accepted significance threshold and should be interpreted with caution given the short 17-year record. This hybrid approach effectively captures monsoon-driven sediment fluxes and provides model-based daily-to-monthly sediment load estimates with quantified uncertainty. It supports improved reservoir sedimentation assessment, erosion-risk evaluation, and transboundary water-resources planning in data-scarce tropical highlands. Full article

The middle–lower Huaihe River Basin faces persistent flood and waterlogging threats, with river–lake interactions being a critical yet underexplored factor constraining flood discharge capacity. This study investigates the flood discharge capacity and erosion–deposition dynamics in the Bengbu–Hongze Lake reach and proposes sustainable management solutions. By analyzing long-term hydrological data (1954–2020) and cross-sectional measurements (1971–2025), we quantified changes in channel morphology and flood behavior. The results reveal that while upstream inflow has remained stable (annual runoff 20.5–33.3 billion m³), sediment concentration has continuously declined by approximately 80%—from 0.474 kg/m³ in the 1950s to 0.094 kg/m³ in the 2020s. The main channel exhibits persistent incision totaling 135.7 × 10⁶ m³, while floodplains have undergone progressive aggradation of 35.1 × 10⁶ m³, reflecting a sediment-starved river system in geomorphic disequilibrium. Critically, the riverbed leading to Hongze Lake exhibits an adverse slope, rising from –10 m at Fushan to over +9 m at Laozishan, while the lake’s sedimentation has reduced its storage capacity by 29% since the 1980s (from 31.27 × 10⁸ m³ to 22.15 × 10⁸ m³). Despite extensive engineering interventions, significant issues persist—including the backwater effect of Hongze Lake, prolonged high water levels during moderate floods (in 2020, water level at Fushan reached 18.34 m at only 61% of the design discharge), and exacerbated waterlogging in riparian lowlands. Therefore, we advocate for a paradigm shift towards a river–lake separation scheme, specifically, an inner-lake embankment approach. This nature-based solution aims to restore the river’s physical structure and harness its self-shaping morphological function for long-term flood management and ecological sustainability. Our findings provide a quantitative basis for re-evaluating the river–lake relationship and offer a strategic direction for sustainable flood management in highly altered alluvial river systems. Full article

This study experimentally examines turbulent flow structures induced by the coupled interaction of transverse bank slope, rigid vegetation, and bed roughness heterogeneity in a compound channel. Three-dimensional velocity components were measured using Acoustic Doppler Velocimetry (ADV) in a 13 m long flume under three transverse bank slopes (0°, 10°, and 25°), both with and without submerged rigid vegetation. Quantitatively, the presence of vegetation on the sloped bank reduced local flow velocity by 40–50% due to drag caused by vegetation canopy, while the accelerating flow in the main channel reduced by 25–35%. The combined effect of a steep 25° slope and vegetation amplified the turbulent kinetic energy (TKE) by ~55% and maximum Reynolds shear stress (RSS) by 50–70% at the sand–gravel interface compared to bare-bed conditions, generating a rigorous lateral shear layer. These quantitative insights provide critical design guidance for river restoration, bank protection, and flood management. The identified interactions between bank slope and vegetation establish a predictive framework for mitigating localized scour and bank erosion while optimizing channel conveyance capacity in ecologically managed river systems. Full article

This study presents an integrated, data-driven framework for satellite-derived bathymetry and morphodynamic assessment in large, regulated rivers, providing a spatial database to support reach-scale hydromorphological monitoring and river management. Satellite-derived bathymetry (SDB) was developed using 24,768 in situ depth measurements and Sentinel-2 multispectral data to train Random Forest (RF) and Artificial Neural Network (ANN) models. Under turbid water conditions, the Random Forest model outperformed the Artificial Neural Network model in simulating the non-linear relationship between the water spectrum and water depth; the RF model achieved an R² of 0.828 and an RMSE of 0.93 m, while the ANN model produced an R² of 0.608 and an RMSE of 1.40 m. Depth-dependent errors were smallest at intermediate depths and larger in shallow and deep water. Morphometric parameters, including the Sinuosity Index (SI) and Braiding Index (BI), were calculated for 2017, 2019, and 2021 using the NDWI-based water mask to define channel boundaries. The reach exhibited moderate sinuosity (SI ≈ 1.16), and an increase in braiding was observed (BI ranging from 1.33 to 1.36). From 2017 to 2019, erosion (3.51 km²) exceeded deposition (1.25 km²). In contrast, the 2019–2021 period showed approximately equal areas of erosion and deposition (1.63 km² each). The analysis is constrained by a single 2015 calibration survey, the optical penetration limit of Sentinel-2, and the reliance on three morphometric snapshots (2017, 2019, 2021), which may not capture short-term adjustments. The novelty of this study lies in integrating ML-based Sentinel-2 bathymetry with multi-temporal morphometric indicators to characterize the vertical and horizontal dynamics of regulated rivers jointly. Full article

Flow in fluid machinery can include multiphase phenomena such as cavitation due to energy exchange between the machinery and flow of a liquid, which, in turn, produces local drops in pressure and, therefore, evaporation. When vapor structures are subjected to increased pressure, they collapse during the process of condensation. These collapses are driven by high ambient pressure and can, over time, induce damage to the fluid machinery solid walls—a process termed cavitation erosion. In addition, fluid machinery such as water turbines and pumps often operate in water that is not pure. In this water, solid particles can be present, for example, river sediment, which can also produce erosion. In the past, these causes of erosion were mostly investigated separately; however, as they sometimes occur simultaneously, studies that replicate these conditions are necessary. In the present work, we investigated the erosion of a Venturi channel surface with several different divergent angles using CFD simulations, which were validated experimentally using the soft paint method. Results showing the cavitation and particle erosion were obtained for different flow conditions (specifically with different vortical structures) that are expected to occur in fluid machinery. The results suggest that cavitation modifies the flow field in a manner that affects particle-induced erosion, leading to different macroscopic erosion patterns. Full article

The middle reaches of the West Liaohe River Basin, a typical semi-arid to semi-humid transition and agro-pastoral ecotone in northern China, exhibit high ecological sensitivity, low resilience, and pronounced fragility. Despite growing concerns, existing studies in this region lack a comprehensive assessment paradigm that effectively couples inherent ecological attributes with nonlinear predictive modeling. To fill this gap, we developed an integrative framework that innovatively combined the SRP conceptual model with a stacking ensemble learning technique. This coupling is methodologically novel because it moves beyond linear assumptions, enables the detection of complex nonlinear response surfaces, and establishes a seamless analytical chain from historical evaluation to future projection. By selecting 13 indicators, including topography, climate, soil, vegetation, and socio-economic factors, the weight was determined by the comprehensive application of the analytic hierarchy process and entropy weight method, and the ecological fragility of the middle reaches of the West Liaohe River Basin from 2000 to 2020 was evaluated at multiple scales. The spatial differentiation driving factors were analyzed using a geographic detector. Therefore, an Ensemble Learning Regression model was used to simulate and predict the ecological fragility pattern in 2030. The results show that from 2000 to 2020, the ecological fragility of the study area showed a decreasing trend overall, with the Ecological Vulnerability Synthetical Index (EVSI) decreasing from 3.48 to 2.68, and the spatial pattern gradually shifting from “high in the northwest, low in the southeast” to “overall stability, local optimization.” The spatial agglomeration of ecological fragility gradually weakened, indicating that high-fragility areas tend to disperse and low-fragility areas expand in contiguous areas, and the ecosystem structure tends to develop towards equilibrium. The driving mechanism shows an evolution characteristic from “soil erosion dominated” to “biological abundance dominated,” with the impact of climate factors first increasing and then stabilizing, and the direct pressure from human activities continuously weakening. Under the assumption that historical trends continue, the ensemble learning model projects that by 2030, the ecological vulnerability pattern will be dominated by Mild and Moderate levels, with the area of extremely vulnerable regions significantly reduced to 0.36%. This study verified the applicability of the SRP model in transitional river basins, and the constructed “evaluation-driving mechanism-prediction” framework can provide a scientific basis for the ecological protection and adaptive management of the West Liaohe River Basin and provide a methodological reference for ecological fragility research in similar areas. However, limitations persist: the indicator system and weight assignment are subject to inherent subjectivity, and the 2030 scenario projection based on the Stacking ensemble learning model relies on the BAU (Business-As-Usual) assumption, which fails to account for abrupt climate extremes or major policy shifts. Future studies should incorporate multi-scenario constraints to reduce predictive uncertainty. Full article

In engineering applications such as filling and slope protection, natural river sand suffers from high compressibility and poor erosion resistance. Microbially induced calcium carbonate precipitation (MICP) can mitigate these issues by sand solidification, but the optimal volumetric ratio of bacterial solution to cementation solution ($r_v$) for natural river sand remains unclear. This study used natural river sand (0.063–1.6 mm), Bacillus subtilis, and a cementation solution (2 M urea + 2 M $\mathrm{C}\mathrm{a}{\mathrm{C}\mathrm{l}}_2$, 1:1). Eight $r_v$ values from 2:5 to 3:1 were tested. Compressibility was evaluated via one-dimensional consolidation tests, and erosion resistance via a slope model. Results show a non-linear “U-shaped” relationship between $r_v$ and compression index ($C_c$). The optimal $r_v$ = 3:2 yields the lowest $C_c$ (0.044). Higher or lower ratios increase $C_c$ to ≥0.064. Microscopy reveals that at $r_v$ = 3:2, a dense, continuous $\mathrm{C}\mathrm{a}{\mathrm{C}\mathrm{O}}_3$ network fills pores, whereas excess bacteria cause sparse cementation and too few cause local agglomeration. The optimal ratio reduces erosion modulus by 55.0–57.5% compared to untreated slopes. This work provides a quantitative, eco-friendly optimization strategy for MICP-treated natural river sand, balancing mechanical performance with ecological adaptability (pH within vegetation tolerance). Full article

Earthen archeological sites may be damaged by rain-induced erosion processes. Huaca Fortaleza (HF; 600–750 AD) is an originally four-level truncated pyramid in the semi-arid Lambayeque region of northern Peru, an area affected by seasonal intense rain due to El Niño Southern Oscillation (ENSO). We use data from a UAV-based photogrammetric survey and generate a Digital Surface Model from which we extract selected geomorphometric parameters and apply a hillslope diffusion model. The obtained data show that HF steep flanks exhibit a marked erosion expressed by a drainage network of parallel rills and gullies with architectural structures controlling pathways for concentrated flow. The southwestern flank is affected by gravity instability. Localized pits at the top of HF cause infiltration of rainwater. The erosion by ENSO rainfall is responsible for extensive architectural loss, with the HF lower platforms now entirely obliterated. We calculate vertical erosion rates of 0.28–0.38 m/century, a range of values comparable with that estimated for river incision. Erosion due to diffusion processes is estimated in the order of ~0.015 m/century. Our approach represents a transferable methodology applicable to other earthen archeological sites affected by erosion worldwide. Full article

While terrestrial carbon storage is vital for achieving global carbon neutrality, its spatiotemporal evolution in ecologically fragile regions—such as the Pisha sandstone area—is complicated by intense erosion and complex environmental drivers. Widely known as the Pisha sandstone area, often referred to as the “Earth’s ecological cancer” due to its unique geological instability (“hard as rock when dry, soft as mud when wet”), this area is a critical but vulnerable carbon sink in the Yellow River Basin. This study aims to clarify these dynamics and identify their non-linear driving mechanisms by integrating a coupled PLUS–InVEST model with an XGBoost–SHAP framework to simulate land-use cover change and quantify carbon sequestration potential from 1990 to 2040. Our results reveal: (1) a robust path dependence in land use, where grassland remained the dominant landscape matrix (>75%), which partly explains the stable regional carbon-stock structure and the moderate FoM value of the PLUS validation; (2) carbon storage followed a fluctuating but overall increasing trajectory, projected to reach a peak of 3.19 × 10⁵ tC by 2040 under the Ecological Conservation Scenario (ECS), which significantly outperforms the economic-driven and natural growth modes; (3) hot spot analysis showed that statistically notable low-carbon cold spots were concentrated mainly along valley corridors, marginal transition zones, and locally disturbed patches, whereas high-carbon hot spots were spatially limited; and, (4) crucially, XGBoost–SHAP results should be interpreted as model-based associations rather than direct causal proof; the whole-region model and the regional models jointly suggest that topography, water availability, socioeconomic pressure, and erosion-related factors contribute differently across bare, loess-covered, and sand-covered Pisha sandstone units. These findings support differentiated land-use and restoration strategies rather than uniform regional management. The findings suggest that future management in the Pisha sandstone area should transition from general restoration toward targeted and differentiated regulation to improve regional ecosystem services. Full article

The formation and development of gullies are pervasive drivers of hillslope degradation, yet forecasting where and at what elevation gullies begin remains challenging. This study proposes a morphometric–energetic framework to anticipate gully-initiation zones in catchments developed on low-permeability lithologies and limited tectonic control across contrasting climatic and geomorphic settings. Using GIS analyses and morphometric parameters, with some derived from hypsometric curves, our objective is to link basin-scale morphology and energy distribution to the propensity for linear incision, thereby defining a statistically representative initiation belt and stream network positions most susceptible to gully initiation. The study results show that the altitudinal range most susceptible to gully development is at the mean basin’s elevation, and that this range can be associated with an energy potential (Şen’s “Energy Index”) similar to those used to calculate hydroelectric potential in a river basin. Furthermore, the study highlights that the contributing area required to activate these erosive processes varies within fairly narrow limits, between 1 and 3 ha. The framework is designed to be quantitative, transferable among landscapes, and parsimonious in data requirements, even if applicable, as mentioned, in basins with low-permeability lithology and limited tectonic control, and as a first-level predictive tool. By prioritizing diagnostics that can be computed from standard topographic datasets, the approach aims to support land-use planning and sediment-risk mitigation, offering a practical pathway for early identification and management of areas vulnerable to gullying. Full article

Tropical mid-size cities on alluvial floodplains face compounded flood challenges combining pluvial accumulation from intense convective storms, regulated river overflow, and aging drainage networks. This study presents an integrated framework for Monteria, Colombia (~450,000 inhabitants; Sinu River, Caribbean lowlands), within Colombian Decree 1807/2014 and structured in four phases. (1) Hazard: A Rain-on-Grid 2D HEC-RAS 6.6 model covering 4090 ha, calibrated against four gauged events, identifies three dominant pluvial mechanisms (poor hydraulic connectivity, limited evacuation capacity, downstream channel overflow), plus 17 critical fluvial erosion points affecting ~289 properties at 100-year return period. (2) Vulnerability: Depth-damage functions from 1465 household surveys yield 36.36% of 3015 assets in high risk and 57.77% in medium risk. (3) Measures: Scenario M2 (channel widening plus dikes, land-raising, retention lagoons) removes 80 ha of flooding while displacing 28 ha at COP 845 million pre-design cost. Non-structural measures include a Sustainable Urban Drainage Master Plan, IoT-based Early Warning System, minimum construction-elevation map, and land-management instruments. A Monte Carlo residual-risk model reduces baseline risk to 19.9% under full implementation. (4) Emergency: A February 2026 cold-front event was addressed with a 4300 m perimeter dike and six pump stations deployed jointly by the Regional Environmental Authority (CVS) and Municipal Administration. Full article

Pisha sandstone (PS) is a weakly cemented soft rock widely distributed in the middle reaches of the Yellow River, China. PS disintegrates rapidly upon contact with water and has poor erosion resistance, making it a major source of coarse sediment in the Yellow River. However, PS is rich in aluminosilicate minerals and clay fractions, offering great potential as a sustainable precursor for geopolymer cementitious materials and as an amendment for degraded soils. The sustainable resource utilization of PS provides a new pathway for coordinated ecological and economic development in the PS areas. This paper first reviews the mineralogical and chemical characteristics of PS, clarifying that low diagenetic degree and high montmorillonite content cause poor erosion resistance, and that compound erosion from freeze–thaw, water, wind, and gravity erosion creates a superimposed amplification effect, which is the primary driver of severe soil erosion. Subsequently, three major control measures for soil erosion in the PS areas are summarized, namely biological measures using sea-buckthorn (Hippophae rhamnoides), chemical solidification, and microbially induced calcium carbonate precipitation (MICP), with analyses of their mechanisms, efficiency, and limitations. Furthermore, the research progress on the sustainable resource utilization of PS in the preparation of geopolymer cementitious materials and the amelioration of degraded soils is elaborated. Finally, future research directions are discussed to support the control of soil erosion and the green, sustainable resource utilization of PS. Full article

Coastal zones are facing rising exposure to climate-related hazards alongside intensifying human pressures, which highlights the need for robust tools to assess vulnerability. This study uses a GIS-based Coastal Vulnerability Index (CVI) to quantify and map relative vulnerability along ~13 km of shoreline in Al Hoceima Bay (northern Morocco). The proposed CVI integrates eight geological and physical indicators, including geomorphology, shoreline erosion and accretion rates, coastal slope, elevation, natural habitats, relative sea-level rise, significant wave height, and tidal range. Spatial analyses were performed using remote sensing data, historical records, field measurements, and Geographic Information Systems (GIS). The analysis reveals that 37% of the shoreline is categorized as high vulnerability, 44% is moderate, and 19% is low. Highly vulnerable sectors are primarily associated with low elevations, gentle coastal slopes, sandy beach systems, limited natural habitat protection, and proximity to river mouths. These findings demonstrate that the applied CVI provides a rapid and cost-effective framework for identifying priority areas for coastal management and climate adaptation. The proposed approach offers valuable decision-support insights for sustainable coastal planning in Al Hoceima Bay and other Mediterranean coastal environments characterized by limited data availability. Full article

The Amazon Basin and its rivers play a vital role in regional biodiversity, the carbon cycle, and socio-economic security. Through erosion and deposition, river planforms change over time, affecting local infrastructure, food security, and changes to ecosystems. Long-term monitoring is essential for observing these dynamics. Synthetic Aperture Radar (SAR) provides a method to consistently map river planform dynamics across large areas because it is largely independent of atmospheric conditions. This study presents an approach for deriving river planform metrics across the entire Amazon Basin using Sentinel-1 C-band SAR data. This approach followed three main steps: water mask generation, validation of the data and river metrics extraction. Sentinel-1 imagery from 2017 to 2025 was composited into quarterly mean images, after which Otsu thresholding was applied to derive water classifications. Additional post-processing steps were applied to reduce terrain- and seasonal effects. The final water masks were divided into water-change classes, validated using stratified sampling and achieved an overall accuracy of 98.5%. Quarterly river planform metrics, including sinuosity, mean channel width and migration rate, were derived using channel centerline extraction, but due to a lack of in situ validation data the river metric values have not been validated. The resulting time series provide insights into how river planform changes across all Amazon sub-basins from 2017 to 2025 can be monitored using SAR-based methods. The results reveal spatial differences in river dynamics between tributaries, mostly depending on flow pattern, up- or downstream path and location in the upper, middle or lower Amazon Basin. These findings demonstrate the potential of SAR time series for monitoring large-scale river planform dynamics. Full article

Agricultural intensification in Eastern Africa has raised concerns about the transport of pesticides and nutrients from farmland into surface waters, posing risks to ecosystems and human health. This study systematically reviews the peer-reviewed literature published between 2010 and 2024 to assess the extent, patterns, and drivers of agrochemical contamination in rivers, lakes, and reservoirs across the region. Reported pesticide concentrations ranged from <0.01 to 0.55 μg L⁻¹, with several studies indicating exceedances of drinking-water or environmental guideline values, particularly for organophosphate and carbamate compounds. Nutrient enrichment was widespread, with nitrate concentrations of 0.99–5.6 mg L⁻¹ and phosphate levels of 0.16–2.0 mg L⁻¹, frequently linked to eutrophication. Many studies showed strong seasonal variability, with higher concentrations during rainy periods due to increased runoff and erosion. Variability among findings reflected differences in land use, catchment characteristics, sampling design, and analytical approaches. Where evaluated, mitigation measures such as vegetated buffer strips, cover cropping, and improved nutrient management were associated with reductions in agrochemical runoff of approximately 50–80%. Overall, agrochemical contamination is widespread across Eastern African basins and influenced by agricultural practices and hydrological dynamics, highlighting the need for strengthened monitoring, improved stewardship, and broader adoption of mitigation strategies. Full article