Sand mining is a growing environmental and socioeconomic concern worldwide. As urbanisation and infrastructure development continue to increase, the demand for sand has skyrocketed. When mined on or near rivers, it alters the river's pathway, eroding riverbanks, damaging housing, infrastructure and livelihoods. This thesis examines the role of sand mining in the river-delta system, by examining the influence of dredging duration, dredging intensity, location and pit size on the river delta system.

A 2-dimensional depth averaged Delft3D model is made. Here a river-delta system is modelled and run for 600 years. Over the last 100 years, different sand mining scenarios have been modelled. With varying duration, intensity, locations, and pit geometry, each of these scenarios is then analysed using various method of analyses. Though changing the dredging scenarios, changes the downstream morphology and hypothe-
sised trends—such as pit migration, increased erosion, and reduced delta growth—were partially
observed. Furthermore, in the five scenarios, dredging influenced the river-delta system in complex, non-linear ways.

Some configurations (e.g., 30-year duration, 5.0x intensity, 200 m width) led to pronounced short-term changes, but long-term outcomes returned toward control-like conditions. In general, the results highlight high internal variability and limited predictability based solely on single dredging parameters. It is recommended to include a cluster of slightly varied control scenarios in future research to distinguish the effect of dredging from the natural variability of the river.

Marine plastic debris has become an established concern as a threat to marine and coastal ecosystems. Despite progress in understanding plastic transport dynamics under deep-water conditions, the characterisation of these processes in the nearshore environment remains incomplete. This poses significant challenges in their parametrisation, essential for the accurate representation of coastal transport dynamics in predictive models.

In this study, experimental measurements of the plastic particles wave-induced transport in intermediate to shallow water depths are presented. The focus is put on the influence of wave steepness as a key parameter affecting the transport of marine plastic debris in the transition from deep water to the shoreline. Its potential as a predictive parameter is investigated through controlled laboratory experiments involving the generation of seven regular breaking wave conditions, characterised by varying offshore steepness, propagating in shallow water depth over a sloped bathymetry.

The results reveal a consistent increase in particle drift speed with increasing offshore wave steepness. While the exact functional nature of the observed positive relationship could not be definitively concluded, the trend appears more likely linear than quadratic, aligning with previous findings for particles deviating from perfect tracers undergoing deep water breaking conditions. Furthermore, wave breaking was observed to play an important role in enhancing particle drift speed. Finally, particle drift speeds were consistently underestimated by the Stokes drift and only partially captured by the wave crest speed estimates, progressively diverging from the former and approaching the latter as offshore steepness increased, though remaining consistently lower than crest speeds. This trend was most recognisable in the breaking zone across all the tested wave conditions.

Overall, the findings suggest offshore wave steepness as a robust predictor for marine plastic debris transport in the nearshore environment, proving its value as a classification parameter for future modelling efforts. By investigating how plastic particles respond to changing wave conditions in the nearshore environment, this study aims to contribute to a better understanding of their dynamics.

Floating marine plastic debris has emerged as a major global environmental threat in recent years due to its persistence, long-distance transport, and harmful impacts on marine ecosystems. Understanding the key processes affecting plastic beaching is essential for accurately modelling plastic transport and predicting accumulation zones in nearshore marine environments. So far, research on plastic transport in shallow, nearshore waters is limited compared to deep ocean studies, resulting in significant uncertainties about wave-driven transport in these zones. While it is established that the impact of plastic density on the movement of floating plastic debris varies across wave zones, the dynamics in shallow water remain poorly understood. This research investigates how the density of finite-sized plastic particles influences the beaching dynamics under controlled, regular wave conditions in a laboratory flume simulating a nearshore environment using a sloped bathymetry. The densities relative to water of idealized spherical particles were systematically varied ranging from 0.09 to 0.93. Particles were released in the shoaling zone and tracked through the wave flume until beaching, allowing drift speeds to be analysed across different wave zones. It is observed that prior to breaking, in the shoaling zone, particles travel onshore with a speed close to the locally estimated Stokes drift regardless of the particles' relative density. In the breaking zone, density significantly affects particle drift speed: low-density particles accelerate strongly, nearing crest and phase speeds, while higher-density particles show only modest acceleration. Extending these findings to real-world coastal environments indicates that low density plastics tend to beach quickly, while denser particles remain suspended longer and thus may be affected more by lateral currents. While further research is needed to fully understand the role of density in plastic transport near the shore, this study clearly demonstrates that density significantly influences beaching dynamics—underscoring its importance in accurately modelling plastic transport in the nearshore environment.

This project explores how landscape design can contribute to rebalancing flood prevention, ecological resilience, and productive land use within the Jing River Flood Storage Area (JRFSA), a key node in China’s middle Yangtze River flood control system. Located in the densely cultivated Jianghan Plain, the area faces a growing mismatch between large-scale flood management strategies and the everyday realities of agricultural production, ecological degradation, and rural development. Through the concept of Cascading Floodspace, the design introduces a multi-layered spatial framework that divides the floodplain into adaptive zones with differentiated flooding frequencies, land use patterns, and ecological functions. These zones support flood-responsive agriculture, habitat diversity, and seasonal tourism, while allowing for long-term landscape transformation without immediate disruption. The project operates across multiple scales—from regional water networks to site-specific interventions—and repositions the role of the landscape architect as both a spatial strategist and a mediator between competing values: risk and livelihood, ecology and economy, permanence and change.

Understanding morphodynamic processes and structures is essential for effective river management and enhancing our knowledge of river systems. River bars can be investigated through theoretical analyses, field measurements, experimental studies, or numerical modelling. While numerical modelling offers accuracy, it is computationally intensive, making multiple simulations or parameter calibrations both time-consuming and impractical. The numerical simulations follow physical laws and equations which make the runtime significantly high making instantaneous results in times of emergencies unfeasible.
Convolutional neural networks (CNNs), a type of data-driven modelling, have been employed to study the physical parameters defined by linear stability analysis. This study utilizes Delft3D simulations to generate diverse datasets, facilitating easier access and variability. A specific type of riverbar pattern, and alternate bars were chosen for simplicity. The CNN model takes initial bed levels as inputs and provides predictions for the next step of bed level or a time series, with velocity included an additional parameter to assess its influences on the model performance. The model is able to predict the bar behaviour with R2 being 0.99. The model can predict bar suppression or migration solely based on the initial bed levels provided. The model performance did not improve with an additional input parameter although this possibility can be explored with other architectures. However, the model currently lacks accuracy in making one-step-ahead predictions, potentially due to boundary issues within the numerical model or the CNN itself. Further optimization and exploration of additional methods are necessary. The integration of physical parameters into the training process may improve prediction accuracy. Strong conclusions cannot be drawn until additional research is conducted.