Runoff Characteristics and Soil Loss Mechanism in the Weathered Granite Area under Simulated Rainfall

Soils developed from the parent materials of highly weathered granite are particularly susceptible to soil erosion. Therefore, it is of great significance to conduct in-depth research on runoff characteristics and soil loss mechanisms in weathered granite areas. Using the weathered granite area in the hilly region of southeastern China as the research object, we conducted indoor artificial rainfall simulation experiments involving three slope steepnesses (SSs), 8°, 15°, and 25°, and five rainfall intensities (RIs), 0.5, 1.0, 1.5, 2.0, and 2.5 mm/min. The results showed that sediment load (SL) has positively linear relationships with mean runoff velocity (V), Reynolds number (Re), Froude number (Fr), shear stress (τ), and stream power (w). The eroded sediment was principally composed of silt and clay that accounted for 65.41–73.41% of the total SL. There was a boundary point at 0.02 mm for the particle size distribution (PSD) of the eroded sediment. The enrichment ratio (Er) of sand-grained particles (0.02–2 mm) ranged from approximately 0.45 to 0.65, while the Er of fine-grained particles (<0.02 mm) ranged from approximately 1.37 to 1.80. These results increase our understanding of the relationships among RI, SS, runoff, and soil losses from weathered granite hillslopes, particularly the relationships between different hydraulic parameters and sediment size characteristics.

Effects of Spur Dyke’s Orientation on Bed Variation in Channel Bend

Spur dykes also known as Groynes are often used to either divert or attract the flow from the main structure to safeguard their life. Those structures may be bridge piers, abutments or any similar hydraulics structures. Spur dykes are also used to save the cutting of banks on concave side of stream. Lots of work have been done in recent past on spur dykes by many investigators in which various hydraulic and geometrical parameters of spur dykes such as discharge, sediment size, flow velocity, shear stress, spur dykes shape, size and submergence etc. are studied in detail. But mostly all the studies were pointed out in straight open channels. Very few studies were done in curved channel and only their similar effects were studied. In present thesis main emphasis is given to study the effect of orientation and location of spur dykes in meandering channel on the bed of downstream side. In the present study experimental work has been carried out in 80° bend and constant discharge (Q = 4.5 l/s) is allowed to pass in channel without spur dyke. It is found that maximum scouring occurs at angular displacement θ = 60° to 80° in the vicinity of outer bank. To minimize this scouring, spur dyke has been installed at angular displacement θ = 20°, 40° & 60° by changing the dyke angle α = 60°, 90° & 120° respectively. It is found that scouring at θ = 60° is reduced by installing spur dyke at angular displacement θ = 40° which is oriented at α = 60° and scouring at θ = 80° is reduced by installing spur dyke at angular displacement θ = 60° which is oriented at α = 60°.

Analytical Chemistry of Plastic Debris: Sampling, Methods, and Instrumentation

Approaches for the collection and analysis of plastic debris in environmental matrices are rapidly evolving. Such plastics span a continuum of sizes, encompassing large (macro-), medium (micro-, typically defined as particles between 1 μm and 5 mm), and smaller (nano-) plastics. All are of environmental relevance. Particle sizes are dynamic. Large plastics may fragment over time, while smaller particles may agglomerate in the field. The diverse morphologies (fragment, fiber, sphere) and chemical compositions of microplastics further complicate their characterization. Fibers are of growing interest and present particular analytical challenges due to their narrow profiles. Compositional classes of emerging concern include tire wear, paint chips, semisynthetics (e.g., rayon), and bioplastics. Plastics commonly contain chemical additives and fillers, which may alter their toxicological potency, behavior (e.g., buoyancy), or detector response (e.g., yield fluorescence) during analysis. Field sampling methods often focus on >20 μm and even >300 μm sized particles and will thus not capture smaller microplastics (which may be most abundant and bioavailable). Analysis of a limited subgroup (selected polymer types, particle sizes, or shapes) of microplastics, while often operationally necessary, can result in an underestimation of actual sample content. These shortcomings complicate calls for toxicological studies of microplastics to be based on “environmentally relevant concentrations.” Sample matrices of interest include water (including wastewater, ice, snow), sediment (soil, dust, wastewater sludge), air, and biota. Properties of the environment, and of the particles themselves, may concentrate plastic debris in select zones (e.g., gyres, shorelines, polar ice, wastewater sludge). Sampling designs should consider such patchy distributions. Episodic releases due to weather and anthropogenic discharges should also be considered. While water grab samples and sieving are commonplace, novel techniques for microplastic isolation, such as continuous flow centrifugation, show promise. The abundance of nonplastic particulates (e.g., clay, detritus, biological material) in samples interferes with microplastic detection and characterization. Their removal is typically accomplished using a combination of gravity separation and oxidative digestion (including strong bases, peroxide, enzymes); unfortunately, aggressive treatments may damage more labile plastics. Microscope-based infrared or Raman detection is often applied to provide polymer chemistry and morphological data for individual microplastic particles. However, the sheer number of particles in many samples presents logistical hurdles. In response, instruments have been developed that employ detector arrays and rapid scanning lasers. The addition of dyes to stain particulates may facilitate spectroscopic detection of some polymer types. Most researchers provide microplastic data in the form of the abundances of polymer types within particle size, polymer, and morphology classes. Polymer mass data in samples remain rare but are essential to elucidating fate. Rather than characterizing individual particles in samples, solvent extraction (following initial sample prep, such as sediment size class sorting), combined with techniques such as thermoanalysis (e.g., pyrolysis), has been used to generate microplastic mass data. However, this may obviate the acquisition of individual particle morphology and compositional information. Alternatively, some techniques (e.g., electron and atomic force microscopy and matrix-assisted laser desorption mass spectrometry) are adept at providing highly detailed data on the size, morphology, composition, and surface chemistry of select particles. Ultimately, the analyst must select the approach best suited for their study goals. Robust quality control elements are also critical to evaluate the accuracy and precision of the sampling and analysis techniques. Further, improved efforts are required to assess and control possible sample contamination due to the ubiquitous distribution of microplastics, especially in indoor environments where samples are processed.

Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms

To reduce the problem of sedimentation in open channels, calculating flow velocity is critical. Undesirable operating costs arise due to sedimentation problems. To overcome these problems, the development of machine learning based models may provide reliable results. Recently, numerous studies have been conducted to model sediment transport in non-deposition condition however, the main deficiency of the existing studies is utilization of a limited range of data in model development. To tackle this drawback, six data sets with wide ranges of pipe size, volumetric sediment concentration, channel bed slope, sediment size and flow depth are used for the model development in this study. Moreover, two tree-based algorithms, namely M5 rule tree (M5RT) and M5 regression tree (M5RGT) are implemented, and results are compared to the traditional regression equations available in the literature. The results show that machine learning approaches outperform traditional regression models. The tree-based algorithms, M5RT and M5RGT, provided satisfactory results in contrast to their regression-based alternatives with RMSE = 1.184 and RMSE = 1.071, respectively. In order to recommend a practical solution, the tree structure algorithms are supplied to compute sediment transport in an open channel flow.

Mapping riverbed sediment size from Sentinel 2 satellite data

A comprehensive understanding of river dynamics requires quantitative knowledge of the grain size distribution of bed sediments and its variation across different temporal and spatial scales. Several techniques are already available for grain size assessment based on field and remotely sensed data. However, the existing methods are only applicable on small spatial scales and on short time scales. Thus, the operational measurement of grain size distribution of river bed sediments at the catchment scale remains an open problem. A solution could be the use of satellite images as the main imaging platform. However, this would entail retrieving information at sub-pixel scales. In this study, we propose a new approach to retrieve sub-pixel scale grain size class information from Copernicus Sentinel-2 imagery building upon a new image-based grain size mapping procedure. Three Italian gravel-bed rivers featuring different morphologies were selected for Unmanned Aerial Vehicle (UAV) acquisitions coupled to field surveys and lab analysis meant to serve as ground truth grain size data. Grain size maps on river bars were generated in each study site by exploiting image texture measurements, upscaled and co-registered with Sentinel-2 data resolution. Relationships between the grain sizes measured and the reflectance values in Sentinel-2 imagery were analyzed by using a machine learning framework. Results show statistically significant predictive models (MAE of ±8.34 mm and R2=0.92). The trained model was applied on 300 km of the Po river in Italy and allows to detect grain size longitudinal variation and to identify the gravel-sand transition occurring along this river length.Our proposed approach based on freely available satellite data calibrated by low-cost automated drone technology can provide reasonably accurate estimates of surface grain size classes, in the range of sand to gravel, for bar sediments in medium to large river channels, over lengths of hundreds of kilometers.

Retrofitting of Pressurized Sand Traps in Hydropower Plants

Unlined pressure tunnels in sound rock, combined with pressurized sand traps at the downstream end, allow for low-cost construction of hydropower tunnel systems. This design concept is utilized in hydropower plants across the world. Currently, many such power plants are being upgraded with higher installed capacity, which may result in challenges with the sand trap efficiency. A physical scale model test, accompanied by 3D CFD simulations of a case study pressurized sand trap, has been studied for economic retrofitting. The geometric model scale is 1:36.67 while the velocity scale and sediment scale are 1:1 (same average flow velocity and sediment size in model and prototype). This is currently an uncommon scaling approach but with several advantages, as presented in this paper. Various options for retrofitting were investigated. A combined structure of ramp and ribs was found to significantly improve the sediment trap efficiency. The main novelties from this work are the proposed design of the combined ramp and rib structure. Secondary results include an efficient setup for physical scale models of pressurized sand traps and a methodology that combines the benefits of 3D CFD simulations with physical scale models testing for sand trap engineering and design.

Sediment size on talus slopes correlates with fracture spacing on bedrock cliffs: implications for predicting initial sediment size distributions on hillslopes

The detachment of rock fragments from fractured bedrock on hillslopes creates sediment with an initial size distribution that sets the upper limits on particle size for all subsequent stages in the evolution of sediment in landscapes. We hypothesize that the initial size distribution should depend on the size distribution of latent sediment (i.e., fracture-bound blocks in unweathered bedrock) and weathering of blocks both before and during detachment (e.g., disintegration along crystal grain boundaries). However, the initial size distribution is difficult to measure because the interface across which sediment is produced is often shielded from view by overlying soil. Here we overcome this limitation by comparing fracture spacings measured from exposed bedrock on cliff faces with particle size distributions in adjacent talus deposits at 15 talus–cliff pairs spanning a wide range of climates and lithologies in California. Median fracture spacing and particle size vary by more than 10-fold and correlate strongly with lithology. Fracture spacing and talus size distributions are also closely correlated in central tendency, spread, and shape, with b-axis diameters showing the closest correspondence with fracture spacing at most sites. This suggests that weathering has not modified latent sediment either before or during detachment from the cliff face. In addition, talus at our sites has not undergone much weathering after deposition and is slightly coarser than the latent sizes because it contains unexploited fractures inherited from bedrock. We introduce a new conceptual framework for understanding the relative importance of latent size and weathering in setting initial sediment size distributions in mountain landscapes. In this framework, hillslopes exist on a spectrum defined by the ratio of two characteristic timescales: the residence time in saprolite and weathered bedrock and the time required to detach a particle of a characteristic size. At one end of the spectrum, where weathering residence times are negligible, the latent size distribution can be used to predict the initial size distribution. At the other end of the spectrum, where weathering residence times are long, the latent size distribution can be erased by weathering in the critical zone.

Mega riverbed-patterns: linear and weakly nonlinear perspectives

In this paper, we explore the mega riverbed-patterns, whose longitudinal and vertical length dimensions scale with a few channel widths and the flow depth, respectively. We perform the stability analyses from both linear and weakly nonlinear perspectives by considering a steady-uniform flow in an erodible straight channel comprising a uniform sediment size. The mathematical framework stands on the dynamic coupling between the depth-averaged flow model and the particle transport model including both bedload and suspended load via the Exner equation, which drives the pattern formation. From the linear perspective, we employ the standard linearization technique by superimposing the periodic perturbations on the undisturbed system to find the dispersion relationship. From the weakly nonlinear perspective, we apply the centre–manifold-projection technique, where the fast dynamics of stable modes is projected on the slow dynamics of weakly unstable modes to obtain the Stuart–Landau equation for the amplitude dynamics. We examine the marginal stability, growth rate and amplitude of patterns for a given quintet formed by the channel aspect ratio, wavenumber of patterns, shear Reynolds number, Shields number and relative roughness number. This study highlights the sensitivity of pattern formation to the key parameters and shows how the classical results can be reconstructed on the parameter space.