Dunes commonly dominate the bed of sandy rivers and they are of central importance in predicting flow resistance and water levels. In the present study, we show that by using light-weight polystyrene particles as substrate in a laboratory setting, promising morphodynamic similarity is obtained between dunes in shallow flow (flume) and deep flow (field) conditions. In particular, results from our flume experiments show that dune lee-side angles, which are crucial in turbulence production and energy dissipation, better approximate dune lee-side angles observed in natural channels. Furthermore, dune height evolution towards upper stage plane bed observed in the present experimental study, closely follows dune height evolution as observed in world’s large rivers.
<p>We address the need for improved forecasts of saltwater intrusion in estuaries. Estuaries worldwide face problems with saltwater intrusion, which threatens the freshwater supply for drinking, agriculture and industry. The Rhine-Meuse delta is taken as a case study. This is a complex multi-branched system that is highly influenced by hydraulic management structures. Problems with saltwater intrusion occur regularly in this delta (e.g. 2003, 2005, 2006, 2011, 2013, 2018). These problems are most likely to occur when high sea levels due to storm swell coincide with low river discharge. We aim to provide water managers with better forecasts, so they can take mitigating measures in a timely fashion. Two modelling approaches will be investigated on how they can be applied to forecast salt intrusion on a timescale of days to weeks. These approaches are a machine learning model and several improvements (e.g. parameters, data assimilation, postprocessing) to the existing hydrodynamic SOBEK 1D model forecasts. In both approaches, the probabilistic nature of the input data will be processed to yield a probabilistic forecast of salt intrusion. Finally, we will test the developed models, or a combination thereof, in a scenario analysis of several water management decisions. The aim of this presentation is to exchange ideas on the various methods of (salt intrusion) forecasting, their advantages and limitations, and their application for deriving actionable forecasts.</p>
<p>Estuarine salt intrusion causes problems with freshwater availability in many deltas. For water managers to mitigate and adapt to salt intrusion, they require timely and accurate forecasts. Data-driven models derived with machine learning can help with this, as they can mimic complex non-linear systems and are computationally very efficient. We set up such a model for salt intrusion in the Rhine-Meuse delta. The model predicts chloride concentrations at Krimpen aan den IJssel, an important location for freshwater provision. As input features, we selected observations of water level, discharge, chloride concentration and wind speed. We then used the Boruta algorithm to select a subset of relevant features. We set up a Long Short-Term Memory network (LSTM) to make predictions of chloride concentrations one day ahead and ran the resulting model multiple times to simulate a multi-day forecast. This model predicts baseline concentrations and peak timing well, but peak height is underestimated, a problem that gets worse with increasing lead time. Because this model is reasonably successful, we aim to extend it to other locations in the delta. We also expect a similar setup can work in other deltas, especially those with a similar or simpler geometry. A more complete version of this model should finally be made suitable for use in an operational forecasting system.</p>
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