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Soil surface roughness (RR) and its spatiotemporal variations are important for understanding soil erosion process, but how the erosion‐induced evolution of RR affects flow pathways and sediment connectivity patterns remains unclear. This study used high‐resolution topographic data sets from a long‐term rainfall simulation experiment in semi‐arid environments. A total of approximately 1,500–2,400 mm of rainfall was applied on 2.0 m by 6.1 m plots with stony soil at three slope treatments (5%, 12%, and 20%). The temporal variations in RR, flow pathway length (FL), sinuosity (SS), and longitudinal profile roughness (PR) were investigated. The ratio of RR to slope relief, λ, was incorporated into a newly proposed sediment connectivity index (RIC). The results showed: (a) RR and PR continuously increased by 73.9% and 34.6% as compared to the initial surfaces, respectively, and steeper slopes developed greater RR and PR; (b) FL and SS increased due to the combined effects of increased RR and surficial rock fragment coverage; (c) RIC decreased because of the increases in FL, RR, and PR; (d) λ, SS and RIC were found to be appropriate predictors for sediment yield rates with determination coefficients of 0.50, 0.72 and 0.38, respectively, signifying that SS would be a promising parameter for modeling sediment yield. This study also highlighted the important roles of RR and λ in regulating flow and sediment connectivity, and suggested that stony hillslopes might evolve in a way wherein sediment connectivity and the effects of slope gradient on connectivity will decrease as erosion progresses.

The prediction of river planimetric evolution and related interactions with anthropic activities and public safety is one of the most critical aspects in the planning of a sustainable land‐use. Since the beginning of the past century, a large number of theoretical and experimental studies have focused on the investigation of river meandering dynamics, coming to sometimes contrasting conclusions in the forecast of the associated bend sequence pattern. Drawing inspiration from the phenomenological equivalence between fluid‐dynamic and morpho‐dynamic dispersion within the river floodplain, the present contribution proposes an explicit analytical solution in terms of scale‐dependent and equilibrium sinuosity. Such analytical solution, which reveals the strong dependence of river equilibrium planform on valley bank‐full velocity distribution, is successfully validated on the basis of a field data set provided via a restoration pilot project by Basilicata Region Environment and Energy Department (Italy), and further discussed by related lagrangian simulations. Moreover, the governing equation from which the equilibrium solution originates is shown to be compatible with the interpretation of near‐equilibrium dynamics highlighted by stochastic numerical experiments documented in the literature.

In nature, rivers are always connected in various forms to constitute a specific type of river network. The identification and classification of river network types in watersheds is the premise of hydrological research. In this study, the Yellow River Basin, Huaihe River Basin, Haihe River Basin and Yangtze River Basin are divided into 71 sub-basins. According to the definition of river network types, the sub-basin river networks are qualitatively divided into 7 types. By comparing and analysing three river network characteristic parameters, which are river network density, river flow direction and river sinuosity, this study found that the types of river networks can be preliminarily determined according to the statistical data distribution of river sinuosity. The Cauchy distribution is used to fit the distribution characteristics of river sinuosity to further accurately determine the types of river networks. Except for the average R2 of the rectangular river network, which is 0.66, the R2 values of the fitting curves of the other river network types all range from 0.86 to 0.97. This method is applied to the four major watersheds, and the results are consistent with the hierarchical clustering analysis, with an accuracy of 82.86%. The method proposed in this study has application potential and can be applied to the automatic classification of river network types with high accuracy and efficiency.

Recently, rainfall‐runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics‐NN models—particularly, a genre called differentiable modeling that intermingles NNs with physics to learn relationships between variables. However, hydrologic routing simulations, necessary for simulating floods in stem rivers downstream of large heterogeneous basins, had not yet benefited from these advances and it was unclear if the routing process could be improved via coupled NNs. We present a novel differentiable routing method (δMC‐Juniata‐hydroDL2) that mimics the classical Muskingum‐Cunge routing model over a river network but embeds an NN to infer parameterizations for Manning's roughness (n) and channel geometries from raw reach‐scale attributes like catchment areas and sinuosity. The NN was trained solely on downstream hydrographs. Synthetic experiments show that while the channel geometry parameter was unidentifiable, n can be identified with moderate precision. With real‐world data, the trained differentiable routing model produced more accurate long‐term routing results for both the training gage and untrained inner gages for larger subbasins (>2,000 km2) than either a machine learning model assuming homogeneity, or simply using the sum of runoff from subbasins. The n parameterization trained on short periods gave high performance in other periods, despite significant errors in runoff inputs. The learned n pattern was consistent with literature expectations, demonstrating the framework's potential for knowledge discovery, but the absolute values can vary depending on training periods. The trained n parameterization can be coupled with traditional models to improve national‐scale hydrologic flood simulations. Key Points A novel differentiable routing model can learn effective river routing parameterization, recovering channel roughness in synthetic runs With short periods of real training data, we can improve streamflow in large rivers compared to models not considering routing For basins >2,000 km2, our framework outperformed deep learning models that assume homogeneity, despite bias in the runoff forcings

Stabilization of riverbanks by vegetation has long been considered necessary to sustain single-thread meandering rivers. However, observation of active meandering in modern barren landscapes challenges this assumption. Here, we investigate a globally distributed set of modern meandering rivers with varying riparian vegetation densities, using satellite imagery and statistical analyses of meander-form descriptors and migration rates. We show that vegetation enhances the coefficient of proportionality between channel curvature and migration rates at low curvatures, and that this effect wanes in curvier channels irrespective of vegetation density. By stabilizing low-curvature reaches and allowing meanders to gain sinuosity as channels migrate laterally, vegetation quantifiably affects river morphodynamics. Any causality between denser vegetation and higher meander sinuosity, however, cannot be inferred owing to more frequent avulsions in modern non-vegetated environments. By illustrating how vegetation affects channel mobility and floodplain reworking, our findings have implications for assessing carbon stocks and fluxes in river floodplains. Riparian vegetation densities critically mediate the morphodynamics of meandering rivers: plants slow the rate at which channels move laterally and reinforce the key, first-order control that curvature exerts on meander planform evolution.

Accurate identification and characterization of meander loops are essential for understanding river evolution, managing water resources, planning hydraulic projects, and preventing geological disasters. Traditional methods for identifying river meanders rely on detecting inflection points, where channel curvature reverses, or measuring directional changes at fixed intervals along the channel. The former approach lacks reproducibility, while the latter requires careful interval selection and intervals complicated procedures. Consequently, these methods face limitation when applied to more complex channel geometries, such as asymmetrical or compound meanders. This study aimed to develop a novel method for automatically identifying and characterizing compound meander loops using channel centerline data, and primarily through measurements of three channel planform parameters: local maximum sinuosity (LMS), maximum rotation angle (MRA), and simple subloop numbers. The key technical steps in this method include (1) detecting bends with LMS value exceeding a defined threshold using a top-down iterative search algorithm, (2) identifying meander loops based on MRA, (3) identifying compound meander loops using subloop counts and neck length, and (4) measuring the geometric parameters of compound meander loops. The proposed method was tested on the Yavari, Tarauaca, Purus, and Jurua rivers in the Amazon Basin, which are characterized by high water and sediment discharge and are among the world’s fastest-migrating meandering rivers. Results indicate that this method provides a simple yet efficient approach for identifying and characterizing compound meander loops in complex river channels. Additionally, it offers a potential solution for detecting loops in other types of linear features, such as roads, contour lines, and coastlines.

As one of the important characteristics of river morphology, river sinuosity has a direct impact on the river water quality and self-purification capacity. In the present study, 4 physical river channel simulation models using circulating water with a sinuosity of 2.2, 1.8, 1.4, and 1.0, respectively, were established in our laboratory. Related hydraulic tests and detection were performed, including the detection of microbial communities in overlying water, monitoring of the river flow velocity and depth, and observation of the river flow line and bank scouring. The results show that the TN reduction rate at a sinuosity of 2.2 was 1.09, 1.20, and 1.75 times that at a sinuosity of 1.8, 1.4, and 1.0, respectively. And the total plate count for the set of tests with a sinuosity of 2.2 was 3.32 times that for the set of tests with a straight channel. The sinuous rivers have more complex flow regimes, more suitable hydraulic conditions, larger hyporheic zone areas, better microbial environments, and longer river flow paths, giving them a higher purification capacity against pollution. These findings can provide a theoretical basis for the optimization of water system layout and the restoration of river environments in the process of urbanization in China.

Geological facies modeling has long been studied to predict subsurface resources. In recent years, generative adversarial networks (GANs) have been used as a new method for geological facies modeling with surprisingly good results. However, in conventional GANs, all layers are trained concurrently, and the scales of the geological features are not considered. In this study, we propose to train GANs for facies modeling based on a new training process, namely progressive growing of GANs or a progressive training process. In the progressive training process, GANs are trained layer by layer, and geological features are learned from coarse scales to fine scales. We also train a GAN in the conventional training process, and compare the conventionally trained generator with the progressively trained generator based on visual inspection, multi-scale sliced Wasserstein distance (MS-SWD), multi-dimensional scaling (MDS) plot visualization, facies proportion, variogram, and channel sinuosity, width, and length metrics. The MS-SWD reveals realism and diversity of the generated facies models, and is combined with MDS to visualize the relationship between the distributions of the generated and training facies models. The conventionally and progressively trained generators both have very good performances on all metrics. The progressively trained generator behaves especially better than the conventionally trained generator on the MS-SWD, MDS plots, and the necessary training time. The training time for the progressively trained generator can be as small as 39% of that for the conventionally trained generator. This study demonstrates the superiority of the progressive training process over the conventional one in geological facies modeling, and provides a better option for future GAN-related researches.

The complex channel planforms dynamics of river systems have attracted a lot of attention worldwide because of the tremendous effects that morphological changes have on nearby ecosystems and human populations. The present research aims at understanding intricate changes in the Rel river's channel as well as the erosion and deposition taking place over the past 48 years (1975–2023) through the application of Geographic Information System (GIS) and remote sensing. Spatial data within GIS were scrutinized to identify alterations in sinuosity, centreline migration, and large-scale dynamics of the river. A synergetic approach employing earth observation data, topographic mapping, and GIS processing, the research underscores the pivotal role of geospatial analysis in providing actionable spatiotemporal variations insights in the length of the river varying from 49.61 to 71 km, sinuosity index ranging from 1.25 to 1.79 and the maximum erosion and deposition were observed in year 1990 and 2015, respectively. This study's relevance extends to the broader context of river management and sustainable development, emphasizing the need for a holistic understanding of river systems to address contemporary challenges. In essence, the research contributes valuable insights for both scientific understanding and practical applications in the field of river dynamics, flood, drought, and environmental sustainability.

This study focused on quantifying changes in river morphology using a remote sensing approach. Historical river planforms were derived from Landsat images processed through Google Earth Engine platform. Five water indices were used: the MBWI, NDWI, MNDWI, AWEIshadow and AWEInoshadow. To automatically extract surface image, combined OTSU-thresholding and Edge-Detection Approached was employed. The analysis covered the years 1990 to 2023 at 5-year interval. Baseline river planform was established using a 1970 topographic map from NAMRIA. For the sinuosity analysis, the entire stretch of Pagsanjan River was divided into 10 1-kilometer segments, starting from the first river junction of the main river to the outlet. Results show that the most significant change is observed near the outlet between the baseline year 1970 and 1990. An eastward shift was also observed during this year. Aside from these observed changes, no substantial changes in sinuosity or channel migrations were detected from 1990 onward. This stability can be attributed to the implementation of riverbank stabilization in the early 1990s. These findings demonstrate the effectiveness of remote sensing in morphological analysis and emphasize the long-term importance of river stabilization measures in maintaining channel. This is particularly important in a country, such as the Philippines, which is frequently visited by Typhoon which often lead to increase in channel discharge and subsequent changes in channel planforms.