Understanding plastic mobility in rivers is crucial in estimating plastic emissions into the oceans. Most studies have so far considered fluvial plastic transport as a uniform process, with stream discharge and plastic concentrations as the main variables necessary to quantify plastic transport. Decelerating (e.g.: trapping effects) and accelerating effects (e.g.: increased water flows) on plastic transport are poorly understood, despite growing evidence that such mechanisms affect riverine plastic mobility. In this observation-based study, we explored the roles of an invasive floating plant species (i.e. water hyacinths) as a major disruptor of plastic transport. The different functions of aquatic vegetation in trapping and transporting plastics play a key part in our evolving understanding of how plastic moves in rivers. We collected a one-year dataset on plastic transport, densities and hyacinth abundance in the Saigon river, Vietnam, using both a visual counting method and UAV imagery analysis. We found that hyacinths trap the majority of floating plastic observed (~60%), and plastic densities within patches are ten times higher than otherwise found at the river surface. At a monthly and seasonal scale, high hyacinth coverage coincides with peaks in both plastic transport and densities over the dry season (Dec-May) in the Saigon river. We also investigated the large-scale mechanisms governing plant-plastic-water interactions through a conceptual model based on our observations and available literature. Distinguishing total and net plastic transport is crucial to consider fluctuations in freshwater discharge, tidal dynamics and trapping effects caused by the interactions with aquatic vegetation and/or other sinks.

The importance of soil moisture is recognized globally because it controls hydrological processes that are relevant to agriculture and climate studies. Currently, estimation of root zone soil moisture is largely accomplished using physical models, which are based on flow and transport equations. However, with the complexity of the processes operating in the vadose zone as well as their interactions with each other, parameterizing all the relevant processes is quite a challenge. This complexity is further enhanced by spatio-temporal variability in soil and vegetation properties which demand model parameters to be dynamic. Alternatively, purely data-based methods for root zone soil moisture estimation are still limited despite the growing availability of datasets from networks established within the last decade. Currently, these datasets are used largely for calibration and validation of physical models and retrieval methods from satellites. In this study, we explored the utility of Random Forest (RF) as an approach for predicting and forecasting daily root zone soil moisture from selected stations in the Raam and Twente network. We trained a single RF using meteorological datasets, soil type, land cover type, and LAI as predictor variables. The model was also tuned in order to obtain the optimal hyperparameters (mtry and ntree) and number of training samples. A comparison with model simulation results using Hydrus-1D was also performed. Our results show that RF can accurately predict and forecast root zone soil moisture at the study sites based on RMSE of 0.02 – 0.12 m3m-3, in comparison with Hydrus-1D simulations having RMSE of 0.05-0.22 m3m-3. However, poor results were obtained for saturated water conditions. In addition, 5-95% RF prediction intervals become wider at saturated water conditions for some sites, which indicates higher prediction and forecast uncertainties. RF can be used for root zone soil moisture estimation, especially at data poor regions where information on soil hydraulic parameters are sparse or lacking. It can also be used for estimating missing values at gaps in time series datasets.