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.

To assess the potential of radar rainfall nowcasting for early warning, nowcasts for 659 events were used to construct discharge forecasts for 12 Dutch catchments. Four open-source nowcasting algorithms were tested: Rainymotion Sparse (RM-S), Rainymotion DenseRotation (RM-DR), Pysteps deterministic (PS-D) and probabilistic (PS-P) with 20 ensemble members. As benchmark, Eulerian Persistence (EP) and zero precipitation input (ZP) were used. For every 5-min step in the available nowcasts, a discharge forecast with a 12-hr forecast horizon was constructed. Simulations using the observed radar rainfall were used as reference. Rainfall and discharge forecast errors were found to increase with both increasing rainfall intensity and spatial variability. For the discharge forecasts, this relationship depends on the initial conditions, as the forecast error increases more quickly with rainfall intensity when the groundwater table is shallow. Overall, discharge forecasts using RM-DR, PS-D and PS-P outperform the other methods. Threshold exceedance forecasts were assessed by using the maximum event discharge as threshold. Compared to benchmark ZP, an exceedance is, on average, forecast 223 (EP), 196 (RM-S), 213 (RM-DR), 119 (PS-D) and 143 min (PS-P) in advance. The EP results are counterbalanced by both a high false alarm ratio (FAR) and inconsistent forecasts. Contrarily, PS-D and PS-P produce lower FAR and inconsistency index values than all other methods. All methods advance short-term discharge forecasting compared to no rainfall forecasts at all, though all have shortcomings. As forecast rainfall volumes are a crucial factor in discharge forecasts, a future focus on improving this aspect in nowcasting is recommended.

Radiance measurements from several types of passive microwave (PMW) sensors are combined in the Global Precipitation Measurement mission (GPM) to increase the temporal and spatial coverage of precipitation observations. The measurements of these sensors are converted to precipitation estimates by the GPM Profiling Algorithm (GPROF). High frequency PMW-channels are used to retrieve precipitation estimates over land, as these frequencies can measure the radiance scattered by ice particles in rain clouds. Scattering related to shallow and low-intensity precipitation events, however, is limited. Hence, radiometric signals associated with these events are hard to distinguish from the naturally emitted radiation from the Earth’s surface, especially since this so-called background radiation is dependent on the surface type. A better understanding of the physical processes that occur during precipitation events can help to identify possible weaknesses in the GPROF algorithm. Hence, this study couples overpasses of GPM radiometers over the Netherlands to two dual-polarization radars from the Royal Netherlands Meteorological Institute (KNMI) in 2019. All rainy overpasses (>0.1 mm/hr) within a 75 km radius around one of the radars are selected. This coupling provides the opportunity to relate GPROFs performance to physical characteristics of precipitation events, such as the vertical reflectivity profile and dual-polarization information on the melting layer. Additionally, simultaneous observations from both the PMW sensor and the dual-frequency precipitation radar (DPR, used as a-priori database in GPROF) aboard the GPM core satellite are available. Hence, space-based and ground-based reflectivity profiles can be compared and coupled to discrepancies of the GPROF algorithm.