Coherent flow structures in a depth-limited flow over a gravel surface : the role of near-bed turbulance and influence of Reynolds number

In gravel bed rivers, the microtopography of the bed exerts a significant effect on the generation of turbulent flow structures. Although field and laboratory measurements have indicated that flows over gravel beds contain coherent macroturbulent flow structures, the origin of these phenomena, and their relationship to the ensemble of individual roughness elements forming the bed, is not quantitatively well understood. Here we report upon a flume experiment in which flow over a gravel surface is quantified through the application of digital particle imaging velocimetry, which allows study of the downstream and vertical components of velocity over the entire flow field. The results indicate that as the Reynolds number increases (1) the visual distinctiveness of the coherent flow structures becomes more defined, (2) the upstream slope of the structures increases, and (3) the turbulence intensity of the structures increases. Analysis of the mean velocity components, the turbulence intensity, and the flow structure using quadrant analysis demonstrates that these large-scale turbulent structures originate from flow interactions with the bed topography. Detection of the dominant temporal length scales through wavelet analysis enables calculation of mean separation zone lengths associated with the gravel roughness through standard scaling laws. The calculated separation zone lengths demonstrate that wake flapping is a dominant mechanism in the production of large-scale coherent flow structures in gravel bed rivers. Thus, we show that coherent flow structures over gravels owe their origin to bed-generated turbulence and that large-scale outer layer structures are the result of flow-topography interactions in the near-bed region associated with wake flapping

Population density controls on microbial pollution across the Ganga catchment

For millions of people worldwide, sewage-polluted surface waters threaten water security, food security and human health. Yet the extent of the problem and its causes are poorly understood. Given rapid widespread global urbanisation, the impact of urban versus rural populations is particularly important but unknown. Exploiting previously unpublished archival data for the Ganga (Ganges) catchment, we find a strong non-linear relationship between upstream population density and microbial pollution, and predict that these river systems would fail faecal coliform standards for irrigation waters available to 79% of the catchment’s 500 million inhabitants. Overall, this work shows that microbial pollution is conditioned by the continental-scale network structure of rivers, compounded by the location of cities whose growing populations contribute c. 100 times more microbial pollutants per capita than their rural counterparts

Investigating the effects of DEM error in scaling analysis

Digital elevation models (DEMs) are prone to error that, as they can never be entirely eliminated, must be managed effectively. Thus, it is important to understand the nature of error and their sources, especially in the context of the intended use of a DEM. This paper investigates the effects that can be expected when common DEM errors propagate through a scaling analysis. The errors investigated include those associated with perturbation of camera exterior orientation parameters, focal length, and DEM image coordinates, which were simulated numerically. The role of detrending was also investigated. Scaling analysis, by way of the fractal dimension, using a new two-dimensional approach was carried out on a variety of surfaces before and after the introduction of error and the application of detrending. The results reveal some serious procedural implications on scaling analysis and cast doubt on the authenticity of some scaling analysis results in the absence of robust quality assessment and of independent supporting evidence

Feature based image processing methods applied to bathymetric measurements from airborne remote sensing in fluvial environments

Bathymetric maps produced from remotely sensed imagery are increasingly common. However, when this method is applied to fluvial environments, changing scenes and illumination variations severely hinder the application of well established empirical calibration methods used to obtain predictive depth-colour relationships. In this paper, illumination variations are corrected with feature based image processing, which is used to identify areas in an image with a near-zero water depth. This information can then be included in the depth-colour calibration process, which results in an improved prediction quality. The end product is an automated bathymetric mapping method capable of a 4 m2 spatial resolution with a precision of ±15 cm, which allows for a more widespread application of bathymetric mapping

Aerial photosieving of exposed gravel bars for the rapid calibration of airborne grain size maps.

In recent years, fluvial remote sensing has seen considerable progress in terms of methods capable of system scale characterisation of river catchments. One key development is automated grain size mapping. It has been shown that high resolution aerial photography can be used to automatically produce grain size maps over entire rivers. However, current aerial grain size mapping procedures all require field calibration data. The collection of such data can be costly and problematic in the case of remote areas. This paper presents a method developed to remove the need for field based calibration data. Called ‘aerial photosieving’, this method consists of using the same very high resolution aerial imagery intended for grain size map production to visually measure particle sizes on‐screen in order to provide calibration data. The paper presents a rigorous comparison of field‐based photosieving calibration data and aerial photosieving calibration data. Statistical tests are used to demonstrate that aerial photosieving gives similar results when compared with field‐based data with only a slight systematic overprediction. The new aerial photosieving method therefore simplifies the overall procedure required for the production of grain size maps and thus improves the cost‐effectiveness and potential availability of this new fluvial remote sensing technology

Image classification of marine-terminating outlet glaciers in Greenland using deep learning methods

A wealth of research has focused on elucidating the key controls on mass loss from the Greenland and Antarctic ice sheets in response to climate forcing, specifically in relation to the drivers of marine-terminating outlet glacier change. The manual methods traditionally used to monitor change in satellite imagery of marine-terminating outlet glaciers are time-consuming and can be subjective, especially where mélange exists at the terminus. Recent advances in deep learning applied to image processing have created a new frontier in the field of automated delineation of glacier calving fronts. However, there remains a paucity of research on the use of deep learning for pixel-level semantic image classification of outlet glacier environments. Here, we apply and test a two-phase deep learning approach based on a well-established convolutional neural network (CNN) for automated classification of Sentinel-2 satellite imagery. The novel workflow, termed CNN-Supervised Classification (CSC) is adapted to produce multi-class outputs for unseen test imagery of glacial environments containing marine-terminating outlet glaciers in Greenland. Different CNN input parameters and training techniques are tested, with overall F1 scores for resulting classifications reaching up to 94 % for in-sample test data (Helheim Glacier) and 96 % for out-of-sample test data (Jakobshavn Isbrae and Store Glacier), establishing a state of the art in classification of marine-terminating glaciers in Greenland. Predicted calving fronts derived using optimal CSC input parameters have a mean deviation of 56.17 m (5.6 px) and median deviation of 24.7 m (2.5 px) from manually digitised fronts. This demonstrates the transferability and robustness of the deep learning workflow despite complex and seasonally variable imagery. Future research could focus on the integration of deep learning classification workflows with free cloud-based platforms, to efficiently classify imagery and produce datasets for a range of glacial applications without the need for substantial prior experience in coding or deep learning