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Mud plays a pivotal role in estuarine ecology and morphology. However, field data on the lateral and vertical depositional record of mud are rare. Furthermore, numerical morphodynamic models often ignore mud due to long computational times and simplifications of mixed depositional processes. This study aims to understand the spatial distribution, formative conditions and preservation of mud deposits in the intertidal zone of bars in high-energy sand-dominated estuaries, and to elucidate the effects of mud on morphology, ecology and stratigraphic architecture. To meet these objectives, field data (historic bathymetry, bio-morphological maps and sediment cores of the shoal of Walsoorden, Western Scheldt estuary, the Netherlands) were combined with complementary hydro-morphodynamic numerical modelling (Delft3D). Based on the field observations, two types of mud deposits were distinguished: (1) mudflat deposits, which are thick (>10 cm) mud beds at the surface associated with high elevations and low accumulation rates; and (2) mud drapes, which are thin (millimetre to centimetre) buried laminae that form and preserve at a wide range of elevations and energy conditions. Model results show that deposition on mudflats occurs just after high-tide slack water in areas shielded from high flood velocities, suggesting that mud accumulation is mostly controlled by elevation, flow velocity and flow direction. Mud accumulation increases shoal elevation, sometimes to supratidal levels. This reduces flow over the shoal, which in turn reduces chute channel formation, stabilises bar morphology and decreases local tidal prism. These effects further promote mud deposition and vegetation settling. Although observations show that mud cover at the surface is relatively high (20%–40% of the intertidal area), mud constitutes only a small percentage of the total estuary volume (ca 5%) revealing that only a small fraction is preserved in the stratigraphy. Due to this mismatch between sur

Channels in rivers and estuaries are the main paths of fluvial and tidal currents that transport sediment through the system. While network representations of multi-channel systems and their connectivity are quite useful for characterisation of braiding patterns and dynamics, the recognition of channels and their properties is complicated because of the large bed elevation variations, such as shallow shoals and bed steps that render channels visually disconnected. We present and analyse two mathematically rigorous methods to identify channel networks from a terrain model of the river bed. Both methods construct a dense network of locally steepest-descent channels from saddle points on the terrain, and select a subset of channels with a certain minimum sediment volume between them. This is closely linked to the main mechanism of channel formation and change by displacement of sediment volume. The two methods differ in how they compute these sediment volumes: either globally through the entire length of the river, or locally. We compare the methods for the measured bathymetry of the Western Scheldt estuary, The Netherlands, over the past decades. The global method is overly sensitive to small changes elsewhere in the network compared to the local method. We conclude that the local method works best conceptually and for stability reasons. The associated concept of alluvial connectivity between channels in a network is thus the inverse of the volume of sediment that must be displaced to merge the channels. Our method opens up possibilities for new analyses as shown in two examples. First, it shows a clear pattern of scale dependence on volume of the total network length and of the number of nodes by a power law relation, showing that the smaller channels are relatively much shorter. Second, channel bifurcations were found to be predominantly mildly asymmetrical, which is unexpected from fluvial bifurcation theory.

Unsustainable exploitation of groundwater in northwestern India has led to extreme but spatially variable depletion of the alluvial aquifer system in the region. Mitigation and management of groundwater resources require an understanding of the drivers behind the pattern and magnitude of groundwater depletion, but a regional perspective on these drivers has been lacking. The objectives of this study are to (1) understand the extent to which the observed pattern of groundwater level change can be explained by the drivers of precipitation, potential evapotranspiration, abstraction, and canal irrigation, and (2) understand how the impacts of these drivers may vary depending on the underlying geological heterogeneity of the system. We used a transfer function-noise (TFN) time series approach to quantify the effect of the various driver components in the period 1974–2010, based on predefined impulse response functions (θ). The dynamic response to abstraction, summarized by the zeroth moment of the response M0, is spatially variable but is generally large across the proximal and middle parts of the study area, particularly where abstraction is high but alluvial aquifer bodies are less abundant. In contrast, the precipitation response is rapid and fairly uniform across the study area. At larger distances from the Himalayan front, observed groundwater level rise can be explained predominantly by canal irrigation. We conclude that the geological heterogeneity of the aquifer system, which is imposed by the geomorphic setting, affects the response of the aquifer system to the imposed drivers. This heterogeneity thus provides a useful framework that can guide mitigation efforts; for example, efforts to decrease abstraction rates should be focused on areas with thinner and less abundant aquifer bodies.

This data set contains supplementary material and the raw data for the Esurf paper Weisscher et al (2020). The supplementary material includes four supplementary figures to the paper and a movie for visual comparison between measured and modelled flow. In addition, there is a supplementary manual that explains how to remodel the hydrodynamics of scale experiments (i.e., the iRIC manual). The raw data of the meandering and braided river scale experiments was collected by Van Dijk et al (2013a). The raw data include for each river type both a DEM file (CSV format) and a .mat data file for that contains the measured and analysed data as presented in Van Dijk et al (2013a). Consult the iRIC manual on how to use the CSV files to remodel the results in Weisscher et al (2020). The raw data of the estuary scale experiments was collected by Leuven et al (2018). The raw data include a DEM file (CSV format), a hydrograph file (CSV format), and a .mat data file that contains the flow velocity components in the x and y direction for 16 equally spaced moments over 1 tidal cycle. Consult the iRIC manual and the online manuals on i-ric.org on how to upload the CSV files in the iRIC environment to remodel the results as presented in Weisscher et al (2020). Finally, the raw data also contains 16 images in the Lab colour space (at equal phases as the flow velocity component measurements). The blueness in these images is indicative of water depth. Consult Leuven et al (2018) and Weisscher et al (2020) to estimate water depths from these blueness values.

Physical scale experiments enhance our understanding of fluvial, tidal and coastal processes. However, it has proven challenging to acquire accurate and continuous data on water depth and flow velocity due to limitations of the measuring equipment and necessary simplifications during post-processing. A novel means to augment measurements is to numerically model flow over the experimental digital elevation models. We investigated to what extent the numerical hydrodynamic model Nays2D can reproduce unsteady, nonuniform shallow flow in scale experiments and under which conditions a model is preferred to measurements. To this end, we tested Nays2D for one tidal and two fluvial scale experiments and extended Nays2D to allow for flume tilting, which is necessary to steer tidal flow. The modelled water depth and flow velocity closely resembled the measured data for locations where the quality of the measured data was most reliable, and model results may be improved by applying a spatially varying roughness. The implication of the experimental data-model integration is that conducting experiments requires fewer measurements and less post-processing in a simple, affordable and labour-inexpensive manner that results in continuous spatio-temporal data of better overall quality. Also, this integration will aid experimental design..