Study on the existence of the transportation particle tracking model in the interactive medium

This paper assumes that the particle jumps randomly (Guassian jumps) from one point to another along one of the imaginary lines inside the interactive medium. Since this study was done in the space, we consider that the position of the particle at any time [Formula: see text] has a multivariate distribution. The random waiting time of the particle for each Gaussian jump depends on its length. An identical set of programed nanosensors (with unit speed) were used to track this particle. Each line has a sensor that starts the tracking process from the origin. The existence of the necessary conditions which give the optimal search plan and the minimum expected value of the particle detection has been proven. This study is supported by a numerical example.

Quantifying time-variant travel time distribution by multi-fidelity model in hillslope under nonstationary hydrologic conditions

The travel time distribution (TTD) is a lumped representation of groundwater discharge and solute export responding to rainfall. It reflects the mixing process of water parcels and solute particles of different ages and characterizes reactive transport progress in hillslope aquifers. As a result of the mixing process, groundwater leaving the system at a certain time is an integration of multiple water parcels of different ages from different historical rainfall events. Under nonstationary rainfall input condition, the TTD varies with transit groundwater flow, leading to the time-variant TTD. Most methods for estimating time-variant TTD are constrained by requiring either the long-term continuous hydrogeochemical data or the intensive computations. This study introduces a multi-fidelity model to overcome these limitations and evaluate time-variant TTD numerically. In this multi-fidelity model, groundwater age distribution model is taken as the high-fidelity model, and particle tracking model without random walk is taken as the low-fidelity model. Non-parametric regression by non-linear Gaussian process is applied to correlate the two models and then build up the multi-fidelity model. The advantage of the multi-fidelity model is that it combines the accuracy of high-fidelity model and the computational efficiency of low-fidelity model. Moreover, in groundwater and solute transport model with low P\\'eclet number, as the spatial scale of the model increases, the number of particles required for multi-fidelity model is reduced significantly compared to random walk particle tracking model. The correlation between high and low-fidelity models is demonstrated in a one dimensional pulse injection case. In a two dimensional hypothetical model, convergence analysis indicates that the multi-fidelity model converges well when increasing the number of high-fidelity models. Error analysis also confirms the good performance of the multi-fidelity model.

Development of a Backward–Forward Stochastic Particle Tracking Model for Identification of Probable Sedimentation Sources in Open Channel Flow

As reservoirs subject to sedimentation, the dam gradually loses its ability to store water. The identification of the sources of deposited sediments is an effective and efficient means of tackling sedimentation problems. A state-of-the-art Lagrangian stochastic particle tracking model with backward–forward tracking methods is applied to identify the probable source regions of deposited sediments. An influence function is introduced into the models to represent the influence of a particular upstream area on the sediment deposition area. One can then verify if a specific area might be a probable source by cross-checking the values of influence functions calculated backward and forward, respectively. In these models, the probable sources of the deposited sediments are considered to be in a grid instead of at a point for derivation of the values of influence functions. The sediment concentrations in upstream regions must be known a priori to determine the influence functions. In addition, the accuracy of the different types of diffusivity at the water surface is discussed in the study. According to the results of the case study of source identification, the regions with higher sediment concentrations computed by only backward simulations do not necessarily imply a higher likelihood of sources. It is also shown that from the ensemble results when the ensemble mean of the concentration is higher, the ensemble standard deviation of the concentration is also increased.

Using the Red Band Difference Algorithm to Detect and Monitor a Karenia spp. Bloom Off the South Coast of Ireland, June 2019

During the months of May, June, July and August 2019 the Red Band Difference algorithm was tested over Irish waters to assess its suitability for the Irish harmful algal bloom alert system. Over the 4 weeks of June an extensive localised surface phytoplankton bloom formed in the Celtic Sea, south of Ireland. Satellite imagery from the Sentinel-3a’s Ocean and Land Colour Instrument, processed using the Red Band Difference algorithm detected the bloom in surface shelf waters and helped monitor its movement. Daily satellite images indicated that the bloom appeared at the sea surface on the 2nd June 2019 and peaked in size and surface abundance in offshore shelf waters within 4 weeks, remnants remained at the surface into July. A particle tracking approach was used to replicate oceanic circulation patterns in the vicinity of the observed algal bloom and estimate its trajectory. The initial horizontal distribution of particles in the tracking model were based on a satellite imagery polygon of the bloom when it first appeared in surface waters. Good agreement was observed between satellite imagery of the bloom and the particle tracking model. In situ sampling efforts from a research cruise and the national inshore phytoplankton monitoring programme confirmed that Karenia mikimotoi was the causative organism of the bloom. This pilot study shows great potential to use the Red Band Difference algorithm in the existing Irish harmful algal bloom alert system. In addition, satellite ocean colour data combined with particle tracking model estimates can be a useful tool to monitor high biomass harmful algal bloom forming species, such as Karenia mikimotoi, in surface coastal waters around Ireland and elsewhere.

Marine Litter on the Coast of the Algarve: Main Sources and Distribution Using a Modeling Approach

The accumulation of floating marine litter poses a serious threat to the global environment and the economy all over the world, particularly of coastal municipalities that rely on tourism and recreational activities. Data of marine litter is thus crucial, but is usually limited, and can be complemented with modelling results. In this study, the operational modelling system of Algarve (SOMA) was combined with a Lagrangian particle-tracking model and blended with scarce litter monitoring data, to provide first insights into the distribution and accumulation of floating marine litter on the Algarve coast. Different meteo-oceanographic conditions, sources regions and wind drift behaviors were considered. Field data and model results show a considerable concentration of marine litter along the beaches and coastal regions. The model also suggests that oceanographic conditions and wind drift have a great influence on the transport and accumulation rate of the floating marine litter on the coast, with the highest rates of accumulation during the winter and the counter current period, concentrated mostly on the south-western coast of the Algarve.

Identification of coral spawn source areas around Sekisei Lagoon for recovery and poleward habitat migration by using a particle-tracking model

A massive coral bleaching event occurred in 2016 in the interior of Japan’s largest coral lagoon, the Sekisei Lagoon, located in the Kuroshio upstream region in southwestern Japan. Recovery of the coral lagoon will require the influx of coral spawn and larvae; therefore, it is important to identify and conserve source sites. A surface-particle-tracking simulation of coral spawn and larvae was used to identify source areas of coral spawn outside of the Sekisei Lagoon for potential recovery of the interior lagoon. The northern coastal zone of Iriomote Island, including Hatoma Island, was identified as a major source area. Hatoma Island was also identified as a key source for the Kuroshio downstream region and for aiding the poleward migration of coral habitat under ongoing global climate change, making it one of the most important source areas in the Nansei Archipelago.

Modeling Delta Smelt Distribution for Hypothesized Swimming Behaviors

Delta Smelt, Hypomesus transpacificus, is an endangered pelagic fish native to the San Francisco Estuary. The distribution of Delta Smelt in the estuary shifts landward from low-salinity habitat to freshwater habitat before spawning. This spawning migration often coincides with the first substantial freshwater inflow to the estuary during winter. To accomplish this landward shift in distribution, Delta Smelt are believed to use the tides by swimming to faster-moving currents during flood tides and then repositioning themselves to slower-moving currents to reduce seaward movement on ebb tides. Studies have hypothesized that the swimming behavior of Delta Smelt during this period is influenced by environmental conditions such as salinity and turbidity. The details of these swimming behaviors—including the extent to which flows, salinity, and turbidity affect behaviors and distributions—are uncertain. The spawning migration is of management interest because an increase in observed counts of Delta Smelt at the South Delta water-export facilities has coincided roughly with the spawning migration in many years. In this study, we investigated a range of hypothesized swimming behaviors using a three-dimensional particle-tracking model for water year 2002 during the spawning migration, and compared the predicted distributions of Delta Smelt to distributions inferred from catch data. Our goal was to improve understanding of the influence of Delta Smelt swimming on distribution, and, ultimately, to develop a modeling tool to help management agencies identify conditions associated with entrainment losses. Predictions of Delta Smelt distributions and entrainment varied greatly among behaviors. Without swimming, Delta Smelt would be rapidly transported seaward of Suisun Bay, while continuous tidal migration would move them deep into the interior Delta. These behaviors and a simple turbidity-driven behavior model predicted distributions inconsistent with observations, while more complex behavior rules allowed improved predictions.

Modeling Delta Smelt Distribution for Hypothesized Swimming Behaviors

Delta Smelt, Hypomesus transpacificus, is an endangered pelagic fish native to the San Francisco Estuary. The distribution of Delta Smelt in the estuary shifts landward from low-salinity habitat to freshwater habitat before spawning. This spawning migration often coincides with the first substantial freshwater inflow to the estuary during winter. To accomplish this landward shift in distribution, Delta Smelt are believed to use the tides by swimming to faster-moving currents during flood tides and then repositioning themselves to slower-moving currents to reduce seaward movement on ebb tides. Studies have hypothesized that the swimming behavior of Delta Smelt during this period is influenced by environmental conditions such as salinity and turbidity. The details of these swimming behaviors—including the extent to which flows, salinity, and turbidity affect behaviors and distributions—are uncertain. The spawning migration is of management interest because an increase in observed counts of Delta Smelt at the South Delta water-export facilities has coincided roughly with the spawning migration in many years. In this study, we investigated a range of hypothesized swimming behaviors using a three-dimensional particle-tracking model for water year 2002 during the spawning migration, and compared the predicted distributions of Delta Smelt to distributions inferred from catch data. Our goal was to improve understanding of the influence of Delta Smelt swimming on distribution, and, ultimately, to develop a modeling tool to help management agencies identify conditions associated with entrainment losses. Predictions of Delta Smelt distributions and entrainment varied greatly among behaviors. Without swimming, Delta Smelt would be rapidly transported seaward of Suisun Bay, while continuous tidal migration would move them deep into the interior Delta. These behaviors and a simple turbidity-driven behavior model predicted distributions inconsistent with observations, while more complex behavior rules allowed improved predictions.

The wind-induced bias of the Thies Laser Precipitation Monitor obtained using CFD and a Lagrangian particle tracking model

<p>Environmental sources of measurement biases affect the accuracy of non-catching (mostly contact-less) precipitation gauges (Lanza et al., 2021). Wind is among the most significant influencing variables, since instruments exposed to the wind generate strong airflow velocity gradients and turbulence near their sensing volume. Hydrometeor trajectories are diverted by the induced updraft/downdraft and acceleration near the instrument, affecting the measured particle size distribution, and leading to an over- or underestimation of the precipitation intensity. This bias is common to all precipitation measurement instruments, including traditional catching-type gauges, but is amplified in non-catching gauges due to their complex shapes and measuring principles. Wind also changes the velocity of the falling hydrometeors, introducing further potential biases since velocity is explicitly used by disdrometers (in combination with the hydrometeors size) to determine the type of precipitation and to discard outliers.</p><p>The present work focuses on the Thies laser precipitation monitor, which employs a laser beam to detect hydrometeors in fight. It has a complex, non-axisymmetric shape, due to the physical constraints of its measuring principle. To evaluate the effect of wind on liquid precipitation measurements, Computational Fluid Dynamics simulations were run, using OpenFOAM, together with a Lagrangian particle tracking model. The drag coefficient formulation validated by Cauteruccio et al. (2021) was implemented in the OpenFOAM package. Various drop diameters were considered (0.25, 0.5, 0.75 and from 1 to 8 mm in 1 mm increments), and for each drop size, the vertical and horizontal velocity components were set equal to the terminal velocity and the free-stream velocity, respectively. Nine angles of attack were considered, from 0° to 180°, in 22.5° increments. For each angle, five different wind speed values (2, 5, 10, 15 and 20 m/s) were simulated. Each combination was run twice, first using a constant velocity field (as if the instrument were transparent to the wind) to evaluate the sole shielding effect of the instrument body on the measurement section, and then using the effective velocity fields.</p><p>The data were then processed, using a suitable drop size distribution and for each velocity/angle/rainfall intensity combination the collection efficiency of the instrument was calculated. This work is funded as part of the activities of the EURAMET project 18NRM03 – “INCIPIT – Calibration and Accuracy of Non-Catching Instruments to measure liquid/solid atmospheric precipitation”.</p><p><strong>References:</strong></p><p>Lanza L.G., Merlone A., Cauteruccio A., Chinchella E., Stagnaro M., Dobre M., Garcia Izquierdo M.C., Nielsen J., Kjeldsen H., Roulet Y.A., Coppa G., Musacchio C., Bordianu C., 2021: Calibration of non-catching precipitation measurement instruments: a review. J. Meteorological Applications (submitted).</p><p>Cauteruccio A, Brambilla E, Stagnaro M, Lanza LG, Rocchi D, 2021: Wind tunnel validation of a particle tracking model to evaluate the wind-induced bias of precipitation measurements. Water Resour. Res., (conditionally accepted).</p>